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Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The  aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

  • Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
  • Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

  • Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
  • Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

  • Experimental research is primarily a quantitative method.
  • Interviews , focus groups , and ethnographies are qualitative methods.
  • Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

  • You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
  • You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

  • If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
  • If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
  • You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

  • Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
  • Double-check manual data entry for errors.
  • If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
  • You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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Case Studies

This guide examines case studies, a form of qualitative descriptive research that is used to look at individuals, a small group of participants, or a group as a whole. Researchers collect data about participants using participant and direct observations, interviews, protocols, tests, examinations of records, and collections of writing samples. Starting with a definition of the case study, the guide moves to a brief history of this research method. Using several well documented case studies, the guide then looks at applications and methods including data collection and analysis. A discussion of ways to handle validity, reliability, and generalizability follows, with special attention to case studies as they are applied to composition studies. Finally, this guide examines the strengths and weaknesses of case studies.

Definition and Overview

Case study refers to the collection and presentation of detailed information about a particular participant or small group, frequently including the accounts of subjects themselves. A form of qualitative descriptive research, the case study looks intensely at an individual or small participant pool, drawing conclusions only about that participant or group and only in that specific context. Researchers do not focus on the discovery of a universal, generalizable truth, nor do they typically look for cause-effect relationships; instead, emphasis is placed on exploration and description.

Case studies typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located. Thick description also involves interpreting the meaning of demographic and descriptive data such as cultural norms and mores, community values, ingrained attitudes, and motives.

Unlike quantitative methods of research, like the survey, which focus on the questions of who, what, where, how much, and how many, and archival analysis, which often situates the participant in some form of historical context, case studies are the preferred strategy when how or why questions are asked. Likewise, they are the preferred method when the researcher has little control over the events, and when there is a contemporary focus within a real life context. In addition, unlike more specifically directed experiments, case studies require a problem that seeks a holistic understanding of the event or situation in question using inductive logic--reasoning from specific to more general terms.

In scholarly circles, case studies are frequently discussed within the context of qualitative research and naturalistic inquiry. Case studies are often referred to interchangeably with ethnography, field study, and participant observation. The underlying philosophical assumptions in the case are similar to these types of qualitative research because each takes place in a natural setting (such as a classroom, neighborhood, or private home), and strives for a more holistic interpretation of the event or situation under study.

Unlike more statistically-based studies which search for quantifiable data, the goal of a case study is to offer new variables and questions for further research. F.H. Giddings, a sociologist in the early part of the century, compares statistical methods to the case study on the basis that the former are concerned with the distribution of a particular trait, or a small number of traits, in a population, whereas the case study is concerned with the whole variety of traits to be found in a particular instance" (Hammersley 95).

Case studies are not a new form of research; naturalistic inquiry was the primary research tool until the development of the scientific method. The fields of sociology and anthropology are credited with the primary shaping of the concept as we know it today. However, case study research has drawn from a number of other areas as well: the clinical methods of doctors; the casework technique being developed by social workers; the methods of historians and anthropologists, plus the qualitative descriptions provided by quantitative researchers like LePlay; and, in the case of Robert Park, the techniques of newspaper reporters and novelists.

Park was an ex-newspaper reporter and editor who became very influential in developing sociological case studies at the University of Chicago in the 1920s. As a newspaper professional he coined the term "scientific" or "depth" reporting: the description of local events in a way that pointed to major social trends. Park viewed the sociologist as "merely a more accurate, responsible, and scientific reporter." Park stressed the variety and value of human experience. He believed that sociology sought to arrive at natural, but fluid, laws and generalizations in regard to human nature and society. These laws weren't static laws of the kind sought by many positivists and natural law theorists, but rather, they were laws of becoming--with a constant possibility of change. Park encouraged students to get out of the library, to quit looking at papers and books, and to view the constant experiment of human experience. He writes, "Go and sit in the lounges of the luxury hotels and on the doorsteps of the flophouses; sit on the Gold Coast settees and on the slum shakedowns; sit in the Orchestra Hall and in the Star and Garter Burlesque. In short, gentlemen [sic], go get the seats of your pants dirty in real research."

But over the years, case studies have drawn their share of criticism. In fact, the method had its detractors from the start. In the 1920s, the debate between pro-qualitative and pro-quantitative became quite heated. Case studies, when compared to statistics, were considered by many to be unscientific. From the 1930's on, the rise of positivism had a growing influence on quantitative methods in sociology. People wanted static, generalizable laws in science. The sociological positivists were looking for stable laws of social phenomena. They criticized case study research because it failed to provide evidence of inter subjective agreement. Also, they condemned it because of the few number of cases studied and that the under-standardized character of their descriptions made generalization impossible. By the 1950s, quantitative methods, in the form of survey research, had become the dominant sociological approach and case study had become a minority practice.

Educational Applications

The 1950's marked the dawning of a new era in case study research, namely that of the utilization of the case study as a teaching method. "Instituted at Harvard Business School in the 1950s as a primary method of teaching, cases have since been used in classrooms and lecture halls alike, either as part of a course of study or as the main focus of the course to which other teaching material is added" (Armisted 1984). The basic purpose of instituting the case method as a teaching strategy was "to transfer much of the responsibility for learning from the teacher on to the student, whose role, as a result, shifts away from passive absorption toward active construction" (Boehrer 1990). Through careful examination and discussion of various cases, "students learn to identify actual problems, to recognize key players and their agendas, and to become aware of those aspects of the situation that contribute to the problem" (Merseth 1991). In addition, students are encouraged to "generate their own analysis of the problems under consideration, to develop their own solutions, and to practically apply their own knowledge of theory to these problems" (Boyce 1993). Along the way, students also develop "the power to analyze and to master a tangled circumstance by identifying and delineating important factors; the ability to utilize ideas, to test them against facts, and to throw them into fresh combinations" (Merseth 1991).

In addition to the practical application and testing of scholarly knowledge, case discussions can also help students prepare for real-world problems, situations and crises by providing an approximation of various professional environments (i.e. classroom, board room, courtroom, or hospital). Thus, through the examination of specific cases, students are given the opportunity to work out their own professional issues through the trials, tribulations, experiences, and research findings of others. An obvious advantage to this mode of instruction is that it allows students the exposure to settings and contexts that they might not otherwise experience. For example, a student interested in studying the effects of poverty on minority secondary student's grade point averages and S.A.T. scores could access and analyze information from schools as geographically diverse as Los Angeles, New York City, Miami, and New Mexico without ever having to leave the classroom.

The case study method also incorporates the idea that students can learn from one another "by engaging with each other and with each other's ideas, by asserting something and then having it questioned, challenged and thrown back at them so that they can reflect on what they hear, and then refine what they say" (Boehrer 1990). In summary, students can direct their own learning by formulating questions and taking responsibility for the study.

Types and Design Concerns

Researchers use multiple methods and approaches to conduct case studies.

Types of Case Studies

Under the more generalized category of case study exist several subdivisions, each of which is custom selected for use depending upon the goals and/or objectives of the investigator. These types of case study include the following:

Illustrative Case Studies These are primarily descriptive studies. They typically utilize one or two instances of an event to show what a situation is like. Illustrative case studies serve primarily to make the unfamiliar familiar and to give readers a common language about the topic in question.

Exploratory (or pilot) Case Studies These are condensed case studies performed before implementing a large scale investigation. Their basic function is to help identify questions and select types of measurement prior to the main investigation. The primary pitfall of this type of study is that initial findings may seem convincing enough to be released prematurely as conclusions.

Cumulative Case Studies These serve to aggregate information from several sites collected at different times. The idea behind these studies is the collection of past studies will allow for greater generalization without additional cost or time being expended on new, possibly repetitive studies.

Critical Instance Case Studies These examine one or more sites for either the purpose of examining a situation of unique interest with little to no interest in generalizability, or to call into question or challenge a highly generalized or universal assertion. This method is useful for answering cause and effect questions.

Identifying a Theoretical Perspective

Much of the case study's design is inherently determined for researchers, depending on the field from which they are working. In composition studies, researchers are typically working from a qualitative, descriptive standpoint. In contrast, physicists will approach their research from a more quantitative perspective. Still, in designing the study, researchers need to make explicit the questions to be explored and the theoretical perspective from which they will approach the case. The three most commonly adopted theories are listed below:

Individual Theories These focus primarily on the individual development, cognitive behavior, personality, learning and disability, and interpersonal interactions of a particular subject.

Organizational Theories These focus on bureaucracies, institutions, organizational structure and functions, or excellence in organizational performance.

Social Theories These focus on urban development, group behavior, cultural institutions, or marketplace functions.

Two examples of case studies are used consistently throughout this chapter. The first, a study produced by Berkenkotter, Huckin, and Ackerman (1988), looks at a first year graduate student's initiation into an academic writing program. The study uses participant-observer and linguistic data collecting techniques to assess the student's knowledge of appropriate discourse conventions. Using the pseudonym Nate to refer to the subject, the study sought to illuminate the particular experience rather than to generalize about the experience of fledgling academic writers collectively.

For example, in Berkenkotter, Huckin, and Ackerman's (1988) study we are told that the researchers are interested in disciplinary communities. In the first paragraph, they ask what constitutes membership in a disciplinary community and how achieving membership might affect a writer's understanding and production of texts. In the third paragraph they state that researchers must negotiate their claims "within the context of his sub specialty's accepted knowledge and methodology." In the next paragraph they ask, "How is literacy acquired? What is the process through which novices gain community membership? And what factors either aid or hinder students learning the requisite linguistic behaviors?" This introductory section ends with a paragraph in which the study's authors claim that during the course of the study, the subject, Nate, successfully makes the transition from "skilled novice" to become an initiated member of the academic discourse community and that his texts exhibit linguistic changes which indicate this transition. In the next section the authors make explicit the sociolinguistic theoretical and methodological assumptions on which the study is based (1988). Thus the reader has a good understanding of the authors' theoretical background and purpose in conducting the study even before it is explicitly stated on the fourth page of the study. "Our purpose was to examine the effects of the educational context on one graduate student's production of texts as he wrote in different courses and for different faculty members over the academic year 1984-85." The goal of the study then, was to explore the idea that writers must be initiated into a writing community, and that this initiation will change the way one writes.

The second example is Janet Emig's (1971) study of the composing process of a group of twelfth graders. In this study, Emig seeks to answer the question of what happens to the self as a result educational stimuli in terms of academic writing. The case study used methods such as protocol analysis, tape-recorded interviews, and discourse analysis.

In the case of Janet Emig's (1971) study of the composing process of eight twelfth graders, four specific hypotheses were made:

  • Twelfth grade writers engage in two modes of composing: reflexive and extensive.
  • These differences can be ascertained and characterized through having the writers compose aloud their composition process.
  • A set of implied stylistic principles governs the writing process.
  • For twelfth grade writers, extensive writing occurs chiefly as a school-sponsored activity, or reflexive, as a self-sponsored activity.

In this study, the chief distinction is between the two dominant modes of composing among older, secondary school students. The distinctions are:

  • The reflexive mode, which focuses on the writer's thoughts and feelings.
  • The extensive mode, which focuses on conveying a message.

Emig also outlines the specific questions which guided the research in the opening pages of her Review of Literature , preceding the report.

Designing a Case Study

After considering the different sub categories of case study and identifying a theoretical perspective, researchers can begin to design their study. Research design is the string of logic that ultimately links the data to be collected and the conclusions to be drawn to the initial questions of the study. Typically, research designs deal with at least four problems:

  • What questions to study
  • What data are relevant
  • What data to collect
  • How to analyze that data

In other words, a research design is basically a blueprint for getting from the beginning to the end of a study. The beginning is an initial set of questions to be answered, and the end is some set of conclusions about those questions.

Because case studies are conducted on topics as diverse as Anglo-Saxon Literature (Thrane 1986) and AIDS prevention (Van Vugt 1994), it is virtually impossible to outline any strict or universal method or design for conducting the case study. However, Robert K. Yin (1993) does offer five basic components of a research design:

  • A study's questions.
  • A study's propositions (if any).
  • A study's units of analysis.
  • The logic that links the data to the propositions.
  • The criteria for interpreting the findings.

In addition to these five basic components, Yin also stresses the importance of clearly articulating one's theoretical perspective, determining the goals of the study, selecting one's subject(s), selecting the appropriate method(s) of collecting data, and providing some considerations to the composition of the final report.

Conducting Case Studies

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of approaches and methods. These approaches, methods, and related issues are discussed in depth in this section.

Method: Single or Multi-modal?

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of methods. Some common methods include interviews , protocol analyses, field studies, and participant-observations. Emig (1971) chose to use several methods of data collection. Her sources included conversations with the students, protocol analysis, discrete observations of actual composition, writing samples from each student, and school records (Lauer and Asher 1988).

Berkenkotter, Huckin, and Ackerman (1988) collected data by observing classrooms, conducting faculty and student interviews, collecting self reports from the subject, and by looking at the subject's written work.

A study that was criticized for using a single method model was done by Flower and Hayes (1984). In this study that explores the ways in which writers use different forms of knowing to create space, the authors used only protocol analysis to gather data. The study came under heavy fire because of their decision to use only one method.

Participant Selection

Case studies can use one participant, or a small group of participants. However, it is important that the participant pool remain relatively small. The participants can represent a diverse cross section of society, but this isn't necessary.

For example, the Berkenkotter, Huckin, and Ackerman (1988) study looked at just one participant, Nate. By contrast, in Janet Emig's (1971) study of the composition process of twelfth graders, eight participants were selected representing a diverse cross section of the community, with volunteers from an all-white upper-middle-class suburban school, an all-black inner-city school, a racially mixed lower-middle-class school, an economically and racially mixed school, and a university school.

Often, a brief "case history" is done on the participants of the study in order to provide researchers with a clearer understanding of their participants, as well as some insight as to how their own personal histories might affect the outcome of the study. For instance, in Emig's study, the investigator had access to the school records of five of the participants, and to standardized test scores for the remaining three. Also made available to the researcher was the information that three of the eight students were selected as NCTE Achievement Award winners. These personal histories can be useful in later stages of the study when data are being analyzed and conclusions drawn.

Data Collection

There are six types of data collected in case studies:

  • Archival records.
  • Interviews.
  • Direct observation.
  • Participant observation.

In the field of composition research, these six sources might be:

  • A writer's drafts.
  • School records of student writers.
  • Transcripts of interviews with a writer.
  • Transcripts of conversations between writers (and protocols).
  • Videotapes and notes from direct field observations.
  • Hard copies of a writer's work on computer.

Depending on whether researchers have chosen to use a single or multi-modal approach for the case study, they may choose to collect data from one or any combination of these sources.

Protocols, that is, transcriptions of participants talking aloud about what they are doing as they do it, have been particularly common in composition case studies. For example, in Emig's (1971) study, the students were asked, in four different sessions, to give oral autobiographies of their writing experiences and to compose aloud three themes in the presence of a tape recorder and the investigator.

In some studies, only one method of data collection is conducted. For example, the Flower and Hayes (1981) report on the cognitive process theory of writing depends on protocol analysis alone. However, using multiple sources of evidence to increase the reliability and validity of the data can be advantageous.

Case studies are likely to be much more convincing and accurate if they are based on several different sources of information, following a corroborating mode. This conclusion is echoed among many composition researchers. For example, in her study of predrafting processes of high and low-apprehensive writers, Cynthia Selfe (1985) argues that because "methods of indirect observation provide only an incomplete reflection of the complex set of processes involved in composing, a combination of several such methods should be used to gather data in any one study." Thus, in this study, Selfe collected her data from protocols, observations of students role playing their writing processes, audio taped interviews with the students, and videotaped observations of the students in the process of composing.

It can be said then, that cross checking data from multiple sources can help provide a multidimensional profile of composing activities in a particular setting. Sharan Merriam (1985) suggests "checking, verifying, testing, probing, and confirming collected data as you go, arguing that this process will follow in a funnel-like design resulting in less data gathering in later phases of the study along with a congruent increase in analysis checking, verifying, and confirming."

It is important to note that in case studies, as in any qualitative descriptive research, while researchers begin their studies with one or several questions driving the inquiry (which influence the key factors the researcher will be looking for during data collection), a researcher may find new key factors emerging during data collection. These might be unexpected patterns or linguistic features which become evident only during the course of the research. While not bearing directly on the researcher's guiding questions, these variables may become the basis for new questions asked at the end of the report, thus linking to the possibility of further research.

Data Analysis

As the information is collected, researchers strive to make sense of their data. Generally, researchers interpret their data in one of two ways: holistically or through coding. Holistic analysis does not attempt to break the evidence into parts, but rather to draw conclusions based on the text as a whole. Flower and Hayes (1981), for example, make inferences from entire sections of their students' protocols, rather than searching through the transcripts to look for isolatable characteristics.

However, composition researchers commonly interpret their data by coding, that is by systematically searching data to identify and/or categorize specific observable actions or characteristics. These observable actions then become the key variables in the study. Sharan Merriam (1988) suggests seven analytic frameworks for the organization and presentation of data:

  • The role of participants.
  • The network analysis of formal and informal exchanges among groups.
  • Historical.
  • Thematical.
  • Ritual and symbolism.
  • Critical incidents that challenge or reinforce fundamental beliefs, practices, and values.

There are two purposes of these frameworks: to look for patterns among the data and to look for patterns that give meaning to the case study.

As stated above, while most researchers begin their case studies expecting to look for particular observable characteristics, it is not unusual for key variables to emerge during data collection. Typical variables coded in case studies of writers include pauses writers make in the production of a text, the use of specific linguistic units (such as nouns or verbs), and writing processes (planning, drafting, revising, and editing). In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, researchers coded the participant's texts for use of connectives, discourse demonstratives, average sentence length, off-register words, use of the first person pronoun, and the ratio of definite articles to indefinite articles.

Since coding is inherently subjective, more than one coder is usually employed. In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, three rhetoricians were employed to code the participant's texts for off-register phrases. The researchers established the agreement among the coders before concluding that the participant used fewer off-register words as the graduate program progressed.

Composing the Case Study Report

In the many forms it can take, "a case study is generically a story; it presents the concrete narrative detail of actual, or at least realistic events, it has a plot, exposition, characters, and sometimes even dialogue" (Boehrer 1990). Generally, case study reports are extensively descriptive, with "the most problematic issue often referred to as being the determination of the right combination of description and analysis" (1990). Typically, authors address each step of the research process, and attempt to give the reader as much context as possible for the decisions made in the research design and for the conclusions drawn.

This contextualization usually includes a detailed explanation of the researchers' theoretical positions, of how those theories drove the inquiry or led to the guiding research questions, of the participants' backgrounds, of the processes of data collection, of the training and limitations of the coders, along with a strong attempt to make connections between the data and the conclusions evident.

Although the Berkenkotter, Huckin, and Ackerman (1988) study does not, case study reports often include the reactions of the participants to the study or to the researchers' conclusions. Because case studies tend to be exploratory, most end with implications for further study. Here researchers may identify significant variables that emerged during the research and suggest studies related to these, or the authors may suggest further general questions that their case study generated.

For example, Emig's (1971) study concludes with a section dedicated solely to the topic of implications for further research, in which she suggests several means by which this particular study could have been improved, as well as questions and ideas raised by this study which other researchers might like to address, such as: is there a correlation between a certain personality and a certain composing process profile (e.g. is there a positive correlation between ego strength and persistence in revising)?

Also included in Emig's study is a section dedicated to implications for teaching, which outlines the pedagogical ramifications of the study's findings for teachers currently involved in high school writing programs.

Sharan Merriam (1985) also offers several suggestions for alternative presentations of data:

  • Prepare specialized condensations for appropriate groups.
  • Replace narrative sections with a series of answers to open-ended questions.
  • Present "skimmer's" summaries at beginning of each section.
  • Incorporate headlines that encapsulate information from text.
  • Prepare analytic summaries with supporting data appendixes.
  • Present data in colorful and/or unique graphic representations.

Issues of Validity and Reliability

Once key variables have been identified, they can be analyzed. Reliability becomes a key concern at this stage, and many case study researchers go to great lengths to ensure that their interpretations of the data will be both reliable and valid. Because issues of validity and reliability are an important part of any study in the social sciences, it is important to identify some ways of dealing with results.

Multi-modal case study researchers often balance the results of their coding with data from interviews or writer's reflections upon their own work. Consequently, the researchers' conclusions become highly contextualized. For example, in a case study which looked at the time spent in different stages of the writing process, Berkenkotter concluded that her participant, Donald Murray, spent more time planning his essays than in other writing stages. The report of this case study is followed by Murray's reply, wherein he agrees with some of Berkenkotter's conclusions and disagrees with others.

As is the case with other research methodologies, issues of external validity, construct validity, and reliability need to be carefully considered.

Commentary on Case Studies

Researchers often debate the relative merits of particular methods, among them case study. In this section, we comment on two key issues. To read the commentaries, choose any of the items below:

Strengths and Weaknesses of Case Studies

Most case study advocates point out that case studies produce much more detailed information than what is available through a statistical analysis. Advocates will also hold that while statistical methods might be able to deal with situations where behavior is homogeneous and routine, case studies are needed to deal with creativity, innovation, and context. Detractors argue that case studies are difficult to generalize because of inherent subjectivity and because they are based on qualitative subjective data, generalizable only to a particular context.

Flexibility

The case study approach is a comparatively flexible method of scientific research. Because its project designs seem to emphasize exploration rather than prescription or prediction, researchers are comparatively freer to discover and address issues as they arise in their experiments. In addition, the looser format of case studies allows researchers to begin with broad questions and narrow their focus as their experiment progresses rather than attempt to predict every possible outcome before the experiment is conducted.

Emphasis on Context

By seeking to understand as much as possible about a single subject or small group of subjects, case studies specialize in "deep data," or "thick description"--information based on particular contexts that can give research results a more human face. This emphasis can help bridge the gap between abstract research and concrete practice by allowing researchers to compare their firsthand observations with the quantitative results obtained through other methods of research.

Inherent Subjectivity

"The case study has long been stereotyped as the weak sibling among social science methods," and is often criticized as being too subjective and even pseudo-scientific. Likewise, "investigators who do case studies are often regarded as having deviated from their academic disciplines, and their investigations as having insufficient precision (that is, quantification), objectivity and rigor" (Yin 1989). Opponents cite opportunities for subjectivity in the implementation, presentation, and evaluation of case study research. The approach relies on personal interpretation of data and inferences. Results may not be generalizable, are difficult to test for validity, and rarely offer a problem-solving prescription. Simply put, relying on one or a few subjects as a basis for cognitive extrapolations runs the risk of inferring too much from what might be circumstance.

High Investment

Case studies can involve learning more about the subjects being tested than most researchers would care to know--their educational background, emotional background, perceptions of themselves and their surroundings, their likes, dislikes, and so on. Because of its emphasis on "deep data," the case study is out of reach for many large-scale research projects which look at a subject pool in the tens of thousands. A budget request of $10,000 to examine 200 subjects sounds more efficient than a similar request to examine four subjects.

Ethical Considerations

Researchers conducting case studies should consider certain ethical issues. For example, many educational case studies are often financed by people who have, either directly or indirectly, power over both those being studied and those conducting the investigation (1985). This conflict of interests can hinder the credibility of the study.

The personal integrity, sensitivity, and possible prejudices and/or biases of the investigators need to be taken into consideration as well. Personal biases can creep into how the research is conducted, alternative research methods used, and the preparation of surveys and questionnaires.

A common complaint in case study research is that investigators change direction during the course of the study unaware that their original research design was inadequate for the revised investigation. Thus, the researchers leave unknown gaps and biases in the study. To avoid this, researchers should report preliminary findings so that the likelihood of bias will be reduced.

Concerns about Reliability, Validity, and Generalizability

Merriam (1985) offers several suggestions for how case study researchers might actively combat the popular attacks on the validity, reliability, and generalizability of case studies:

  • Prolong the Processes of Data Gathering on Site: This will help to insure the accuracy of the findings by providing the researcher with more concrete information upon which to formulate interpretations.
  • Employ the Process of "Triangulation": Use a variety of data sources as opposed to relying solely upon one avenue of observation. One example of such a data check would be what McClintock, Brannon, and Maynard (1985) refer to as a "case cluster method," that is, when a single unit within a larger case is randomly sampled, and that data treated quantitatively." For instance, in Emig's (1971) study, the case cluster method was employed, singling out the productivity of a single student named Lynn. This cluster profile included an advanced case history of the subject, specific examination and analysis of individual compositions and protocols, and extensive interview sessions. The seven remaining students were then compared with the case of Lynn, to ascertain if there are any shared, or unique dimensions to the composing process engaged in by these eight students.
  • Conduct Member Checks: Initiate and maintain an active corroboration on the interpretation of data between the researcher and those who provided the data. In other words, talk to your subjects.
  • Collect Referential Materials: Complement the file of materials from the actual site with additional document support. For example, Emig (1971) supports her initial propositions with historical accounts by writers such as T.S. Eliot, James Joyce, and D.H. Lawrence. Emig also cites examples of theoretical research done with regards to the creative process, as well as examples of empirical research dealing with the writing of adolescents. Specific attention is then given to the four stages description of the composing process delineated by Helmoltz, Wallas, and Cowley, as it serves as the focal point in this study.
  • Engage in Peer Consultation: Prior to composing the final draft of the report, researchers should consult with colleagues in order to establish validity through pooled judgment.

Although little can be done to combat challenges concerning the generalizability of case studies, "most writers suggest that qualitative research should be judged as credible and confirmable as opposed to valid and reliable" (Merriam 1985). Likewise, it has been argued that "rather than transplanting statistical, quantitative notions of generalizability and thus finding qualitative research inadequate, it makes more sense to develop an understanding of generalization that is congruent with the basic characteristics of qualitative inquiry" (1985). After all, criticizing the case study method for being ungeneralizable is comparable to criticizing a washing machine for not being able to tell the correct time. In other words, it is unjust to criticize a method for not being able to do something which it was never originally designed to do in the first place.

Annotated Bibliography

Armisted, C. (1984). How Useful are Case Studies. Training and Development Journal, 38 (2), 75-77.

This article looks at eight types of case studies, offers pros and cons of using case studies in the classroom, and gives suggestions for successfully writing and using case studies.

Bardovi-Harlig, K. (1997). Beyond Methods: Components of Second Language Teacher Education . New York: McGraw-Hill.

A compilation of various research essays which address issues of language teacher education. Essays included are: "Non-native reading research and theory" by Lee, "The case for Psycholinguistics" by VanPatten, and "Assessment and Second Language Teaching" by Gradman and Reed.

Bartlett, L. (1989). A Question of Good Judgment; Interpretation Theory and Qualitative Enquiry Address. 70th Annual Meeting of the American Educational Research Association. San Francisco.

Bartlett selected "quasi-historical" methodology, which focuses on the "truth" found in case records, as one that will provide "good judgments" in educational inquiry. He argues that although the method is not comprehensive, it can try to connect theory with practice.

Baydere, S. et. al. (1993). Multimedia conferencing as a tool for collaborative writing: a case study in Computer Supported Collaborative Writing. New York: Springer-Verlag.

The case study by Baydere et. al. is just one of the many essays in this book found in the series "Computer Supported Cooperative Work." Denley, Witefield and May explore similar issues in their essay, "A case study in task analysis for the design of a collaborative document production system."

Berkenkotter, C., Huckin, T., N., & Ackerman J. (1988). Conventions, Conversations, and the Writer: Case Study of a Student in a Rhetoric Ph.D. Program. Research in the Teaching of English, 22, 9-44.

The authors focused on how the writing of their subject, Nate or Ackerman, changed as he became more acquainted or familiar with his field's discourse community.

Berninger, V., W., and Gans, B., M. (1986). Language Profiles in Nonspeaking Individuals of Normal Intelligence with Severe Cerebral Palsy. Augmentative and Alternative Communication, 2, 45-50.

Argues that generalizations about language abilities in patients with severe cerebral palsy (CP) should be avoided. Standardized tests of different levels of processing oral language, of processing written language, and of producing written language were administered to 3 male participants (aged 9, 16, and 40 yrs).

Bockman, J., R., and Couture, B. (1984). The Case Method in Technical Communication: Theory and Models. Texas: Association of Teachers of Technical Writing.

Examines the study and teaching of technical writing, communication of technical information, and the case method in terms of those applications.

Boehrer, J. (1990). Teaching With Cases: Learning to Question. New Directions for Teaching and Learning, 42 41-57.

This article discusses the origins of the case method, looks at the question of what is a case, gives ideas about learning in case teaching, the purposes it can serve in the classroom, the ground rules for the case discussion, including the role of the question, and new directions for case teaching.

Bowman, W. R. (1993). Evaluating JTPA Programs for Economically Disadvantaged Adults: A Case Study of Utah and General Findings . Washington: National Commission for Employment Policy.

"To encourage state-level evaluations of JTPA, the Commission and the State of Utah co-sponsored this report on the effectiveness of JTPA Title II programs for adults in Utah. The technique used is non-experimental and the comparison group was selected from registrants with Utah's Employment Security. In a step-by-step approach, the report documents how non-experimental techniques can be applied and several specific technical issues can be addressed."

Boyce, A. (1993) The Case Study Approach for Pedagogists. Annual Meeting of the American Alliance for Health, Physical Education, Recreation and Dance. (Address). Washington DC.

This paper addresses how case studies 1) bridge the gap between teaching theory and application, 2) enable students to analyze problems and develop solutions for situations that will be encountered in the real world of teaching, and 3) helps students to evaluate the feasibility of alternatives and to understand the ramifications of a particular course of action.

Carson, J. (1993) The Case Study: Ideal Home of WAC Quantitative and Qualitative Data. Annual Meeting of the Conference on College Composition and Communication. (Address). San Diego.

"Increasingly, one of the most pressing questions for WAC advocates is how to keep [WAC] programs going in the face of numerous difficulties. Case histories offer the best chance for fashioning rhetorical arguments to keep WAC programs going because they offer the opportunity to provide a coherent narrative that contextualizes all documents and data, including what is generally considered scientific data. A case study of the WAC program, . . . at Robert Morris College in Pittsburgh demonstrates the advantages of this research method. Such studies are ideal homes for both naturalistic and positivistic data as well as both quantitative and qualitative information."

---. (1991). A Cognitive Process Theory of Writing. College Composition and Communication. 32. 365-87.

No abstract available.

Cromer, R. (1994) A Case Study of Dissociations Between Language and Cognition. Constraints on Language Acquisition: Studies of Atypical Children . Hillsdale: Lawrence Erlbaum Associates, 141-153.

Crossley, M. (1983) Case Study in Comparative and International Education: An Approach to Bridging the Theory-Practice Gap. Proceedings of the 11th Annual Conference of the Australian Comparative and International Education Society. Hamilton, NZ.

Case study research, as presented here, helps bridge the theory-practice gap in comparative and international research studies of education because it focuses on the practical, day-to-day context rather than on the national arena. The paper asserts that the case study method can be valuable at all levels of research, formation, and verification of theories in education.

Daillak, R., H., and Alkin, M., C. (1982). Qualitative Studies in Context: Reflections on the CSE Studies of Evaluation Use . California: EDRS

The report shows how the Center of the Study of Evaluation (CSE) applied qualitative techniques to a study of evaluation information use in local, Los Angeles schools. It critiques the effectiveness and the limitations of using case study, evaluation, field study, and user interview survey methodologies.

Davey, L. (1991). The Application of Case Study Evaluations. ERIC/TM Digest.

This article examines six types of case studies, the type of evaluation questions that can be answered, the functions served, some design features, and some pitfalls of the method.

Deutch, C. E. (1996). A course in research ethics for graduate students. College Teaching, 44, 2, 56-60.

This article describes a one-credit discussion course in research ethics for graduate students in biology. Case studies are focused on within the four parts of the course: 1) major issues, 2 )practical issues in scholarly work, 3) ownership of research results, and 4) training and personal decisions.

DeVoss, G. (1981). Ethics in Fieldwork Research. RIE 27p. (ERIC)

This article examines four of the ethical problems that can happen when conducting case study research: acquiring permission to do research, knowing when to stop digging, the pitfalls of doing collaborative research, and preserving the integrity of the participants.

Driscoll, A. (1985). Case Study of a Research Intervention: the University of Utah’s Collaborative Approach . San Francisco: Far West Library for Educational Research Development.

Paper presented at the annual meeting of the American Association of Colleges of Teacher Education, Denver, CO, March 1985. Offers information of in-service training, specifically case studies application.

Ellram, L. M. (1996). The Use of the Case Study Method in Logistics Research. Journal of Business Logistics, 17, 2, 93.

This article discusses the increased use of case study in business research, and the lack of understanding of when and how to use case study methodology in business.

Emig, J. (1971) The Composing Processes of Twelfth Graders . Urbana: NTCE.

This case study uses observation, tape recordings, writing samples, and school records to show that writing in reflexive and extensive situations caused different lengths of discourse and different clusterings of the components of the writing process.

Feagin, J. R. (1991). A Case For the Case Study . Chapel Hill: The University of North Carolina Press.

This book discusses the nature, characteristics, and basic methodological issues of the case study as a research method.

Feldman, H., Holland, A., & Keefe, K. (1989) Language Abilities after Left Hemisphere Brain Injury: A Case Study of Twins. Topics in Early Childhood Special Education, 9, 32-47.

"Describes the language abilities of 2 twin pairs in which 1 twin (the experimental) suffered brain injury to the left cerebral hemisphere around the time of birth and1 twin (the control) did not. One pair of twins was initially assessed at age 23 mo. and the other at about 30 mo.; they were subsequently evaluated in their homes 3 times at about 6-mo intervals."

Fidel, R. (1984). The Case Study Method: A Case Study. Library and Information Science Research, 6.

The article describes the use of case study methodology to systematically develop a model of online searching behavior in which study design is flexible, subject manner determines data gathering and analyses, and procedures adapt to the study's progressive change.

Flower, L., & Hayes, J. R. (1984). Images, Plans and Prose: The Representation of Meaning in Writing. Written Communication, 1, 120-160.

Explores the ways in which writers actually use different forms of knowing to create prose.

Frey, L. R. (1992). Interpreting Communication Research: A Case Study Approach Englewood Cliffs, N.J.: Prentice Hall.

The book discusses research methodologies in the Communication field. It focuses on how case studies bridge the gap between communication research, theory, and practice.

Gilbert, V. K. (1981). The Case Study as a Research Methodology: Difficulties and Advantages of Integrating the Positivistic, Phenomenological and Grounded Theory Approaches . The Annual Meeting of the Canadian Association for the Study of Educational Administration. (Address) Halifax, NS, Can.

This study on an innovative secondary school in England shows how a "low-profile" participant-observer case study was crucial to the initial observation, the testing of hypotheses, the interpretive approach, and the grounded theory.

Gilgun, J. F. (1994). A Case for Case Studies in Social Work Research. Social Work, 39, 4, 371-381.

This article defines case study research, presents guidelines for evaluation of case studies, and shows the relevance of case studies to social work research. It also looks at issues such as evaluation and interpretations of case studies.

Glennan, S. L., Sharp-Bittner, M. A. & Tullos, D. C. (1991). Augmentative and Alternative Communication Training with a Nonspeaking Adult: Lessons from MH. Augmentative and Alternative Communication, 7, 240-7.

"A response-guided case study documented changes in a nonspeaking 36-yr-old man's ability to communicate using 3 trained augmentative communication modes. . . . Data were collected in videotaped interaction sessions between the nonspeaking adult and a series of adult speaking."

Graves, D. (1981). An Examination of the Writing Processes of Seven Year Old Children. Research in the Teaching of English, 15, 113-134.

Hamel, J. (1993). Case Study Methods . Newbury Park: Sage. .

"In a most economical fashion, Hamel provides a practical guide for producing theoretically sharp and empirically sound sociological case studies. A central idea put forth by Hamel is that case studies must "locate the global in the local" thus making the careful selection of the research site the most critical decision in the analytic process."

Karthigesu, R. (1986, July). Television as a Tool for Nation-Building in the Third World: A Post-Colonial Pattern, Using Malaysia as a Case-Study. International Television Studies Conference. (Address). London, 10-12.

"The extent to which Television Malaysia, as a national mass media organization, has been able to play a role in nation building in the post-colonial period is . . . studied in two parts: how the choice of a model of nation building determines the character of the organization; and how the character of the organization influences the output of the organization."

Kenny, R. (1984). Making the Case for the Case Study. Journal of Curriculum Studies, 16, (1), 37-51.

The article looks at how and why the case study is justified as a viable and valuable approach to educational research and program evaluation.

Knirk, F. (1991). Case Materials: Research and Practice. Performance Improvement Quarterly, 4 (1 ), 73-81.

The article addresses the effectiveness of case studies, subject areas where case studies are commonly used, recent examples of their use, and case study design considerations.

Klos, D. (1976). Students as Case Writers. Teaching of Psychology, 3.2, 63-66.

This article reviews a course in which students gather data for an original case study of another person. The task requires the students to design the study, collect the data, write the narrative, and interpret the findings.

Leftwich, A. (1981). The Politics of Case Study: Problems of Innovation in University Education. Higher Education Review, 13.2, 38-64.

The article discusses the use of case studies as a teaching method. Emphasis is on the instructional materials, interdisciplinarity, and the complex relationships within the university that help or hinder the method.

Mabrito, M. (1991, Oct.). Electronic Mail as a Vehicle for Peer Response: Conversations of High and Low Apprehensive Writers. Written Communication, 509-32.

McCarthy, S., J. (1955). The Influence of Classroom Discourse on Student Texts: The Case of Ella . East Lansing: Institute for Research on Teaching.

A look at how students of color become marginalized within traditional classroom discourse. The essay follows the struggles of one black student: Ella.

Matsuhashi, A., ed. (1987). Writing in Real Time: Modeling Production Processes Norwood, NJ: Ablex Publishing Corporation.

Investigates how writers plan to produce discourse for different purposes to report, to generalize, and to persuade, as well as how writers plan for sentence level units of language. To learn about planning, an observational measure of pause time was used" (ERIC).

Merriam, S. B. (1985). The Case Study in Educational Research: A Review of Selected Literature. Journal of Educational Thought, 19.3, 204-17.

The article examines the characteristics of, philosophical assumptions underlying the case study, the mechanics of conducting a case study, and the concerns about the reliability, validity, and generalizability of the method.

---. (1988). Case Study Research in Education: A Qualitative Approach San Francisco: Jossey Bass.

Merry, S. E., & Milner, N. eds. (1993). The Possibility of Popular Justice: A Case Study of Community Mediation in the United States . Ann Arbor: U of Michigan.

". . . this volume presents a case study of one experiment in popular justice, the San Francisco Community Boards. This program has made an explicit claim to create an alternative justice, or new justice, in the midst of a society ordered by state law. The contributors to this volume explore the history and experience of the program and compare it to other versions of popular justice in the United States, Europe, and the Third World."

Merseth, K. K. (1991). The Case for Cases in Teacher Education. RIE. 42p. (ERIC).

This monograph argues that the case method of instruction offers unique potential for revitalizing the field of teacher education.

Michaels, S. (1987). Text and Context: A New Approach to the Study of Classroom Writing. Discourse Processes, 10, 321-346.

"This paper argues for and illustrates an approach to the study of writing that integrates ethnographic analysis of classroom interaction with linguistic analysis of written texts and teacher/student conversational exchanges. The approach is illustrated through a case study of writing in a single sixth grade classroom during a single writing assignment."

Milburn, G. (1995). Deciphering a Code or Unraveling a Riddle: A Case Study in the Application of a Humanistic Metaphor to the Reporting of Social Studies Teaching. Theory and Research in Education, 13.

This citation serves as an example of how case studies document learning procedures in a senior-level economics course.

Milley, J. E. (1979). An Investigation of Case Study as an Approach to Program Evaluation. 19th Annual Forum of the Association for Institutional Research. (Address). San Diego.

The case study method merged a narrative report focusing on the evaluator as participant-observer with document review, interview, content analysis, attitude questionnaire survey, and sociogram analysis. Milley argues that case study program evaluation has great potential for widespread use.

Minnis, J. R. (1985, Sept.). Ethnography, Case Study, Grounded Theory, and Distance Education Research. Distance Education, 6.2.

This article describes and defines the strengths and weaknesses of ethnography, case study, and grounded theory.

Nunan, D. (1992). Collaborative language learning and teaching . New York: Cambridge University Press.

Included in this series of essays is Peter Sturman’s "Team Teaching: a case study from Japan" and David Nunan’s own "Toward a collaborative approach to curriculum development: a case study."

Nystrand, M., ed. (1982). What Writers Know: The Language, Process, and Structure of Written Discourse . New York: Academic Press.

Owenby, P. H. (1992). Making Case Studies Come Alive. Training, 29, (1), 43-46. (ERIC)

This article provides tips for writing more effective case studies.

---. (1981). Pausing and Planning: The Tempo of Writer Discourse Production. Research in the Teaching of English, 15 (2),113-34.

Perl, S. (1979). The Composing Processes of Unskilled College Writers. Research in the Teaching of English, 13, 317-336.

"Summarizes a study of five unskilled college writers, focusing especially on one of the five, and discusses the findings in light of current pedagogical practice and research design."

Pilcher J. and A. Coffey. eds. (1996). Gender and Qualitative Research . Brookfield: Aldershot, Hants, England.

This book provides a series of essays which look at gender identity research, qualitative research and applications of case study to questions of gendered pedagogy.

Pirie, B. S. (1993). The Case of Morty: A Four Year Study. Gifted Education International, 9 (2), 105-109.

This case study describes a boy from kindergarten through third grade with above average intelligence but difficulty in learning to read, write, and spell.

Popkewitz, T. (1993). Changing Patterns of Power: Social Regulation and Teacher Education Reform. Albany: SUNY Press.

Popkewitz edits this series of essays that address case studies on educational change and the training of teachers. The essays vary in terms of discipline and scope. Also, several authors include case studies of educational practices in countries other than the United States.

---. (1984). The Predrafting Processes of Four High- and Four Low Apprehensive Writers. Research in the Teaching of English, 18, (1), 45-64.

Rasmussen, P. (1985, March) A Case Study on the Evaluation of Research at the Technical University of Denmark. International Journal of Institutional Management in Higher Education, 9 (1).

This is an example of a case study methodology used to evaluate the chemistry and chemical engineering departments at the University of Denmark.

Roth, K. J. (1986). Curriculum Materials, Teacher Talk, and Student Learning: Case Studies in Fifth-Grade Science Teaching . East Lansing: Institute for Research on Teaching.

Roth offers case studies on elementary teachers, elementary school teaching, science studies and teaching, and verbal learning.

Selfe, C. L. (1985). An Apprehensive Writer Composes. When a Writer Can't Write: Studies in Writer's Block and Other Composing-Process Problems . (pp. 83-95). Ed. Mike Rose. NMY: Guilford.

Smith-Lewis, M., R. and Ford, A. (1987). A User's Perspective on Augmentative Communication. Augmentative and Alternative Communication, 3, 12-7.

"During a series of in-depth interviews, a 25-yr-old woman with cerebral palsy who utilized augmentative communication reflected on the effectiveness of the devices designed for her during her school career."

St. Pierre, R., G. (1980, April). Follow Through: A Case Study in Metaevaluation Research . 64th Annual Meeting of the American Educational Research Association. (Address).

The three approaches to metaevaluation are evaluation of primary evaluations, integrative meta-analysis with combined primary evaluation results, and re-analysis of the raw data from a primary evaluation.

Stahler, T., M. (1996, Feb.) Early Field Experiences: A Model That Worked. ERIC.

"This case study of a field and theory class examines a model designed to provide meaningful field experiences for preservice teachers while remaining consistent with the instructor's beliefs about the role of teacher education in preparing teachers for the classroom."

Stake, R. E. (1995). The Art of Case Study Research. Thousand Oaks: Sage Publications.

This book examines case study research in education and case study methodology.

Stiegelbauer, S. (1984) Community, Context, and Co-curriculum: Situational Factors Influencing School Improvements in a Study of High Schools. Presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Discussion of several case studies: one looking at high school environments, another examining educational innovations.

Stolovitch, H. (1990). Case Study Method. Performance And Instruction, 29, (9), 35-37.

This article describes the case study method as a form of simulation and presents guidelines for their use in professional training situations.

Thaller, E. (1994). Bibliography for the Case Method: Using Case Studies in Teacher Education. RIE. 37 p.

This bibliography presents approximately 450 citations on the use of case studies in teacher education from 1921-1993.

Thrane, T. (1986). On Delimiting the Senses of Near-Synonyms in Historical Semantics: A Case Study of Adjectives of 'Moral Sufficiency' in the Old English Andreas. Linguistics Across Historical and Geographical Boundaries: In Honor of Jacek Fisiak on the Occasion of his Fiftieth Birthday . Berlin: Mouton de Gruyter.

United Nations. (1975). Food and Agriculture Organization. Report on the FAO/UNFPA Seminar on Methodology, Research and Country: Case Studies on Population, Employment and Productivity . Rome: United Nations.

This example case study shows how the methodology can be used in a demographic and psychographic evaluation. At the same time, it discusses the formation and instigation of the case study methodology itself.

Van Vugt, J. P., ed. (1994). Aids Prevention and Services: Community Based Research . Westport: Bergin and Garvey.

"This volume has been five years in the making. In the process, some of the policy applications called for have met with limited success, such as free needle exchange programs in a limited number of American cities, providing condoms to prison inmates, and advertisements that depict same-sex couples. Rather than dating our chapters that deal with such subjects, such policy applications are verifications of the type of research demonstrated here. Furthermore, they indicate the critical need to continue community based research in the various communities threatened by acquired immuno-deficiency syndrome (AIDS) . . . "

Welch, W., ed. (1981, May). Case Study Methodology in Educational Evaluation. Proceedings of the Minnesota Evaluation Conference. Minnesota. (Address).

The four papers in these proceedings provide a comprehensive picture of the rationale, methodology, strengths, and limitations of case studies.

Williams, G. (1987). The Case Method: An Approach to Teaching and Learning in Educational Administration. RIE, 31p.

This paper examines the viability of the case method as a teaching and learning strategy in instructional systems geared toward the training of personnel of the administration of various aspects of educational systems.

Yin, R. K. (1993). Advancing Rigorous Methodologies: A Review of 'Towards Rigor in Reviews of Multivocal Literatures.' Review of Educational Research, 61, (3).

"R. T. Ogawa and B. Malen's article does not meet its own recommended standards for rigorous testing and presentation of its own conclusions. Use of the exploratory case study to analyze multivocal literatures is not supported, and the claim of grounded theory to analyze multivocal literatures may be stronger."

---. (1989). Case Study Research: Design and Methods. London: Sage Publications Inc.

This book discusses in great detail, the entire design process of the case study, including entire chapters on collecting evidence, analyzing evidence, composing the case study report, and designing single and multiple case studies.

Related Links

Consider the following list of related Web sites for more information on the topic of case study research. Note: although many of the links cover the general category of qualitative research, all have sections that address issues of case studies.

  • Sage Publications on Qualitative Methodology: Search here for a comprehensive list of new books being published about "Qualitative Methodology" http://www.sagepub.co.uk/
  • The International Journal of Qualitative Studies in Education: An on-line journal "to enhance the theory and practice of qualitative research in education." On-line submissions are welcome. http://www.tandf.co.uk/journals/tf/09518398.html
  • Qualitative Research Resources on the Internet: From syllabi to home pages to bibliographies. All links relate somehow to qualitative research. http://www.nova.edu/ssss/QR/qualres.html

Citation Information

Bronwyn Becker, Patrick Dawson, Karen Devine, Carla Hannum, Steve Hill, Jon Leydens, Debbie Matuskevich, Carol Traver, and Mike Palmquist. (1994-2024). Case Studies. The WAC Clearinghouse. Colorado State University. Available at https://wac.colostate.edu/repository/writing/guides/.

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

  • What was treatment like 30 years ago?
  • Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
  • endless section threes.
  • Really…
  • But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
  • that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
  • run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
  • ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
  • all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
  • when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
  • actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
  • ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
  • having dreadful side effects the next day I woke up [pause]
  • . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
  • Oh how awful.
  • And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
  • [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
  • nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
  • and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
  • nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
  • know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

  • Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
  • Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
  • Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

  • Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
  • Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
  • Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
  • Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
  • Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

  • Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

  • Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

  • Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

  • Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
  • Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

  • Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

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  • Published: 22 March 2008

Methods of data collection in qualitative research: interviews and focus groups

  • P. Gill 1 ,
  • K. Stewart 2 ,
  • E. Treasure 3 &
  • B. Chadwick 4  

British Dental Journal volume  204 ,  pages 291–295 ( 2008 ) Cite this article

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Interviews and focus groups are the most common methods of data collection used in qualitative healthcare research

Interviews can be used to explore the views, experiences, beliefs and motivations of individual participants

Focus group use group dynamics to generate qualitative data

Qualitative research in dentistry

Conducting qualitative interviews with school children in dental research

Analysing and presenting qualitative data

This paper explores the most common methods of data collection used in qualitative research: interviews and focus groups. The paper examines each method in detail, focusing on how they work in practice, when their use is appropriate and what they can offer dentistry. Examples of empirical studies that have used interviews or focus groups are also provided.

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Introduction

Having explored the nature and purpose of qualitative research in the previous paper, this paper explores methods of data collection used in qualitative research. There are a variety of methods of data collection in qualitative research, including observations, textual or visual analysis (eg from books or videos) and interviews (individual or group). 1 However, the most common methods used, particularly in healthcare research, are interviews and focus groups. 2 , 3

The purpose of this paper is to explore these two methods in more detail, in particular how they work in practice, the purpose of each, when their use is appropriate and what they can offer dental research.

Qualitative research interviews

There are three fundamental types of research interviews: structured, semi-structured and unstructured. Structured interviews are, essentially, verbally administered questionnaires, in which a list of predetermined questions are asked, with little or no variation and with no scope for follow-up questions to responses that warrant further elaboration. Consequently, they are relatively quick and easy to administer and may be of particular use if clarification of certain questions are required or if there are likely to be literacy or numeracy problems with the respondents. However, by their very nature, they only allow for limited participant responses and are, therefore, of little use if 'depth' is required.

Conversely, unstructured interviews do not reflect any preconceived theories or ideas and are performed with little or no organisation. 4 Such an interview may simply start with an opening question such as 'Can you tell me about your experience of visiting the dentist?' and will then progress based, primarily, upon the initial response. Unstructured interviews are usually very time-consuming (often lasting several hours) and can be difficult to manage, and to participate in, as the lack of predetermined interview questions provides little guidance on what to talk about (which many participants find confusing and unhelpful). Their use is, therefore, generally only considered where significant 'depth' is required, or where virtually nothing is known about the subject area (or a different perspective of a known subject area is required).

Semi-structured interviews consist of several key questions that help to define the areas to be explored, but also allows the interviewer or interviewee to diverge in order to pursue an idea or response in more detail. 2 This interview format is used most frequently in healthcare, as it provides participants with some guidance on what to talk about, which many find helpful. The flexibility of this approach, particularly compared to structured interviews, also allows for the discovery or elaboration of information that is important to participants but may not have previously been thought of as pertinent by the research team.

For example, in a recent dental public heath study, 5 school children in Cardiff, UK were interviewed about their food choices and preferences. A key finding that emerged from semi-structured interviews, which was not previously thought to be as highly influential as the data subsequently confirmed, was the significance of peer-pressure in influencing children's food choices and preferences. This finding was also established primarily through follow-up questioning (eg probing interesting responses with follow-up questions, such as 'Can you tell me a bit more about that?') and, therefore, may not have emerged in the same way, if at all, if asked as a predetermined question.

The purpose of research interviews

The purpose of the research interview is to explore the views, experiences, beliefs and/or motivations of individuals on specific matters (eg factors that influence their attendance at the dentist). Qualitative methods, such as interviews, are believed to provide a 'deeper' understanding of social phenomena than would be obtained from purely quantitative methods, such as questionnaires. 1 Interviews are, therefore, most appropriate where little is already known about the study phenomenon or where detailed insights are required from individual participants. They are also particularly appropriate for exploring sensitive topics, where participants may not want to talk about such issues in a group environment.

Examples of dental studies that have collected data using interviews are 'Examining the psychosocial process involved in regular dental attendance' 6 and 'Exploring factors governing dentists' treatment philosophies'. 7 Gibson et al . 6 provided an improved understanding of factors that influenced people's regular attendance with their dentist. The study by Kay and Blinkhorn 7 provided a detailed insight into factors that influenced GDPs' decision making in relation to treatment choices. The study found that dentists' clinical decisions about treatments were not necessarily related to pathology or treatment options, as was perhaps initially thought, but also involved discussions with patients, patients' values and dentists' feelings of self esteem and conscience.

There are many similarities between clinical encounters and research interviews, in that both employ similar interpersonal skills, such as questioning, conversing and listening. However, there are also some fundamental differences between the two, such as the purpose of the encounter, reasons for participating, roles of the people involved and how the interview is conducted and recorded. 8

The primary purpose of clinical encounters is for the dentist to ask the patient questions in order to acquire sufficient information to inform decision making and treatment options. However, the constraints of most consultations are such that any open-ended questioning needs to be brought to a conclusion within a fairly short time. 2 In contrast, the fundamental purpose of the research interview is to listen attentively to what respondents have to say, in order to acquire more knowledge about the study topic. 9 Unlike the clinical encounter, it is not to intentionally offer any form of help or advice, which many researchers have neither the training nor the time for. Research interviewing therefore requires a different approach and a different range of skills.

The interview

When designing an interview schedule it is imperative to ask questions that are likely to yield as much information about the study phenomenon as possible and also be able to address the aims and objectives of the research. In a qualitative interview, good questions should be open-ended (ie, require more than a yes/no answer), neutral, sensitive and understandable. 2 It is usually best to start with questions that participants can answer easily and then proceed to more difficult or sensitive topics. 2 This can help put respondents at ease, build up confidence and rapport and often generates rich data that subsequently develops the interview further.

As in any research, it is often wise to first pilot the interview schedule on several respondents prior to data collection proper. 8 This allows the research team to establish if the schedule is clear, understandable and capable of answering the research questions, and if, therefore, any changes to the interview schedule are required.

The length of interviews varies depending on the topic, researcher and participant. However, on average, healthcare interviews last 20-60 minutes. Interviews can be performed on a one-off or, if change over time is of interest, repeated basis, 4 for example exploring the psychosocial impact of oral trauma on participants and their subsequent experiences of cosmetic dental surgery.

Developing the interview

Before an interview takes place, respondents should be informed about the study details and given assurance about ethical principles, such as anonymity and confidentiality. 2 This gives respondents some idea of what to expect from the interview, increases the likelihood of honesty and is also a fundamental aspect of the informed consent process.

Wherever possible, interviews should be conducted in areas free from distractions and at times and locations that are most suitable for participants. For many this may be at their own home in the evenings. Whilst researchers may have less control over the home environment, familiarity may help the respondent to relax and result in a more productive interview. 9 Establishing rapport with participants prior to the interview is also important as this can also have a positive effect on the subsequent development of the interview.

When conducting the actual interview it is prudent for the interviewer to familiarise themselves with the interview schedule, so that the process appears more natural and less rehearsed. However, to ensure that the interview is as productive as possible, researchers must possess a repertoire of skills and techniques to ensure that comprehensive and representative data are collected during the interview. 10 One of the most important skills is the ability to listen attentively to what is being said, so that participants are able to recount their experiences as fully as possible, without unnecessary interruptions.

Other important skills include adopting open and emotionally neutral body language, nodding, smiling, looking interested and making encouraging noises (eg, 'Mmmm') during the interview. 2 The strategic use of silence, if used appropriately, can also be highly effective at getting respondents to contemplate their responses, talk more, elaborate or clarify particular issues. Other techniques that can be used to develop the interview further include reflecting on remarks made by participants (eg, 'Pain?') and probing remarks ('When you said you were afraid of going to the dentist what did you mean?'). 9 Where appropriate, it is also wise to seek clarification from respondents if it is unclear what they mean. The use of 'leading' or 'loaded' questions that may unduly influence responses should always be avoided (eg, 'So you think dental surgery waiting rooms are frightening?' rather than 'How do you find the waiting room at the dentists?').

At the end of the interview it is important to thank participants for their time and ask them if there is anything they would like to add. This gives respondents an opportunity to deal with issues that they have thought about, or think are important but have not been dealt with by the interviewer. 9 This can often lead to the discovery of new, unanticipated information. Respondents should also be debriefed about the study after the interview has finished.

All interviews should be tape recorded and transcribed verbatim afterwards, as this protects against bias and provides a permanent record of what was and was not said. 8 It is often also helpful to make 'field notes' during and immediately after each interview about observations, thoughts and ideas about the interview, as this can help in data analysis process. 4 , 8

Focus groups

Focus groups share many common features with less structured interviews, but there is more to them than merely collecting similar data from many participants at once. A focus group is a group discussion on a particular topic organised for research purposes. This discussion is guided, monitored and recorded by a researcher (sometimes called a moderator or facilitator). 11 , 12

Focus groups were first used as a research method in market research, originating in the 1940s in the work of the Bureau of Applied Social Research at Columbia University. Eventually the success of focus groups as a marketing tool in the private sector resulted in its use in public sector marketing, such as the assessment of the impact of health education campaigns. 13 However, focus group techniques, as used in public and private sectors, have diverged over time. Therefore, in this paper, we seek to describe focus groups as they are used in academic research.

When focus groups are used

Focus groups are used for generating information on collective views, and the meanings that lie behind those views. They are also useful in generating a rich understanding of participants' experiences and beliefs. 12 Suggested criteria for using focus groups include: 13

As a standalone method, for research relating to group norms, meanings and processes

In a multi-method design, to explore a topic or collect group language or narratives to be used in later stages

To clarify, extend, qualify or challenge data collected through other methods

To feedback results to research participants.

Morgan 12 suggests that focus groups should be avoided according to the following criteria:

If listening to participants' views generates expectations for the outcome of the research that can not be fulfilled

If participants are uneasy with each other, and will therefore not discuss their feelings and opinions openly

If the topic of interest to the researcher is not a topic the participants can or wish to discuss

If statistical data is required. Focus groups give depth and insight, but cannot produce useful numerical results.

Conducting focus groups: group composition and size

The composition of a focus group needs great care to get the best quality of discussion. There is no 'best' solution to group composition, and group mix will always impact on the data, according to things such as the mix of ages, sexes and social professional statuses of the participants. What is important is that the researcher gives due consideration to the impact of group mix (eg, how the group may interact with each other) before the focus group proceeds. 14

Interaction is key to a successful focus group. Sometimes this means a pre-existing group interacts best for research purposes, and sometimes stranger groups. Pre-existing groups may be easier to recruit, have shared experiences and enjoy a comfort and familiarity which facilitates discussion or the ability to challenge each other comfortably. In health settings, pre-existing groups can overcome issues relating to disclosure of potentially stigmatising status which people may find uncomfortable in stranger groups (conversely there may be situations where disclosure is more comfortable in stranger groups). In other research projects it may be decided that stranger groups will be able to speak more freely without fear of repercussion, and challenges to other participants may be more challenging and probing, leading to richer data. 13

Group size is an important consideration in focus group research. Stewart and Shamdasani 14 suggest that it is better to slightly over-recruit for a focus group and potentially manage a slightly larger group, than under-recruit and risk having to cancel the session or having an unsatisfactory discussion. They advise that each group will probably have two non-attenders. The optimum size for a focus group is six to eight participants (excluding researchers), but focus groups can work successfully with as few as three and as many as 14 participants. Small groups risk limited discussion occurring, while large groups can be chaotic, hard to manage for the moderator and frustrating for participants who feel they get insufficient opportunities to speak. 13

Preparing an interview schedule

Like research interviews, the interview schedule for focus groups is often no more structured than a loose schedule of topics to be discussed. However, in preparing an interview schedule for focus groups, Stewart and Shamdasani 14 suggest two general principles:

Questions should move from general to more specific questions

Question order should be relative to importance of issues in the research agenda.

There can, however, be some conflict between these two principles, and trade offs are often needed, although often discussions will take on a life of their own, which will influence or determine the order in which issues are covered. Usually, less than a dozen predetermined questions are needed and, as with research interviews, the researcher will also probe and expand on issues according to the discussion.

Moderating a focus group looks easy when done well, but requires a complex set of skills, which are related to the following principles: 15

Participants have valuable views and the ability to respond actively, positively and respectfully. Such an approach is not simply a courtesy, but will encourage fruitful discussions

Moderating without participating: a moderator must guide a discussion rather than join in with it. Expressing one's own views tends to give participants cues as to what to say (introducing bias), rather than the confidence to be open and honest about their own views

Be prepared for views that may be unpalatably critical of a topic which may be important to you

It is important to recognise that researchers' individual characteristics mean that no one person will always be suitable to moderate any kind of group. Sometimes the characteristics that suit a moderator for one group will inhibit discussion in another

Be yourself. If the moderator is comfortable and natural, participants will feel relaxed.

The moderator should facilitate group discussion, keeping it focussed without leading it. They should also be able to prevent the discussion being dominated by one member (for example, by emphasising at the outset the importance of hearing a range of views), ensure that all participants have ample opportunity to contribute, allow differences of opinions to be discussed fairly and, if required, encourage reticent participants. 13

Other relevant factors

The venue for a focus group is important and should, ideally, be accessible, comfortable, private, quiet and free from distractions. 13 However, while a central location, such as the participants' workplace or school, may encourage attendance, the venue may affect participants' behaviour. For example, in a school setting, pupils may behave like pupils, and in clinical settings, participants may be affected by any anxieties that affect them when they attend in a patient role.

Focus groups are usually recorded, often observed (by a researcher other than the moderator, whose role is to observe the interaction of the group to enhance analysis) and sometimes videotaped. At the start of a focus group, a moderator should acknowledge the presence of the audio recording equipment, assure participants of confidentiality and give people the opportunity to withdraw if they are uncomfortable with being taped. 14

A good quality multi-directional external microphone is recommended for the recording of focus groups, as internal microphones are rarely good enough to cope with the variation in volume of different speakers. 13 If observers are present, they should be introduced to participants as someone who is just there to observe, and sit away from the discussion. 14 Videotaping will require more than one camera to capture the whole group, as well as additional operational personnel in the room. This is, therefore, very obtrusive, which can affect the spontaneity of the group and in a focus group does not usually yield enough additional information that could not be captured by an observer to make videotaping worthwhile. 15

The systematic analysis of focus group transcripts is crucial. However, the transcription of focus groups is more complex and time consuming than in one-to-one interviews, and each hour of audio can take up to eight hours to transcribe and generate approximately 100 pages of text. Recordings should be transcribed verbatim and also speakers should be identified in a way that makes it possible to follow the contributions of each individual. Sometimes observational notes also need to be described in the transcripts in order for them to make sense.

The analysis of qualitative data is explored in the final paper of this series. However, it is important to note that the analysis of focus group data is different from other qualitative data because of their interactive nature, and this needs to be taken into consideration during analysis. The importance of the context of other speakers is essential to the understanding of individual contributions. 13 For example, in a group situation, participants will often challenge each other and justify their remarks because of the group setting, in a way that perhaps they would not in a one-to-one interview. The analysis of focus group data must therefore take account of the group dynamics that have generated remarks.

Focus groups in dental research

Focus groups are used increasingly in dental research, on a diverse range of topics, 16 illuminating a number of areas relating to patients, dental services and the dental profession. Addressing a special needs population difficult to access and sample through quantitative measures, Robinson et al . 17 used focus groups to investigate the oral health-related attitudes of drug users, exploring the priorities, understandings and barriers to care they encounter. Newton et al . 18 used focus groups to explore barriers to services among minority ethnic groups, highlighting for the first time differences between minority ethnic groups. Demonstrating the use of the method with professional groups as subjects in dental research, Gussy et al . 19 explored the barriers to and possible strategies for developing a shared approach in prevention of caries among pre-schoolers. This mixed method study was very important as the qualitative element was able to explain why the clinical trial failed, and this understanding may help researchers improve on the quantitative aspect of future studies, as well as making a valuable academic contribution in its own right.

Interviews and focus groups remain the most common methods of data collection in qualitative research, and are now being used with increasing frequency in dental research, particularly to access areas not amendable to quantitative methods and/or where depth, insight and understanding of particular phenomena are required. The examples of dental studies that have employed these methods also help to demonstrate the range of research contexts to which interview and focus group research can make a useful contribution. The continued employment of these methods can further strengthen many areas of dentally related work.

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Gill, P., Stewart, K., Treasure, E. et al. Methods of data collection in qualitative research: interviews and focus groups. Br Dent J 204 , 291–295 (2008). https://doi.org/10.1038/bdj.2008.192

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case study method of collecting data

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The case study approach

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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case study method of collecting data

8 Essential Qualitative Data Collection Methods

Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement. 

British mathematician and marketing mastermind Clive Humby once famously stated that “Data is the new oil.”  He has a point. Without data, nonprofit organizations are left second-guessing what their clients and supporters think, how their brand compares to others in the market, whether their messaging is on-point, how their campaigns are performing, where improvements can be made, and how overall results can be optimized. 

There are two primary data collection methodologies: qualitative data collection and quantitative data collection. At UpMetrics, we believe that relying on quantitative, static data is no longer an option to drive effective impact. In the nonprofit sector, where financial gain is not the sole purpose of your organization’s existence. In this guide, we’ll focus on qualitative data collection methods and how they can help you gather, analyze, and collate information that can help drive your organization forward. 

What is Qualitative Data? 

Data collection in qualitative research focuses on gathering contextual information. Unlike quantitative data, which focuses primarily on numbers to establish ‘how many’ or ‘how much,’ qualitative data collection tools allow you to assess the ‘why’s’ and ‘how’s’ behind those statistics. This is vital for nonprofits as it enables organizations to determine:

  • Existing knowledge surrounding a particular issue.
  • How social norms and cultural practices impact a cause.
  • What kind of experiences and interactions people have with your brand.
  • Trends in the way people change their opinions.
  • Whether meaningful relationships are being established between all parties.

In short, qualitative data collection methods collect perceptual and descriptive information that helps you understand the reasoning and motivation behind particular reactions and behaviors. For that reason, qualitative data methods are usually non-numerical and center around spoken and written words rather than data extrapolated from a spreadsheet or report. 

Qualitative vs. Quantitative Data 

Quantitative and qualitative data represent both sides of the same coin. There will always be some degree of debate over the importance of quantitative vs. qualitative research, data, and collection. However, successful organizations should strive to achieve a balance between the two. 

Organizations can track their performance by collecting quantitative data based on metrics including dollars raised, membership growth, number of people served, overhead costs, etc. This is all essential information to have. However, the data lacks value without the additional details provided by qualitative research because it doesn’t tell you anything about how your target audience thinks, feels, and acts. 

Qualitative data collection is particularly relevant in the nonprofit sector as the relationships people have with the causes they support are fundamentally personal and cannot be expressed numerically. Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement. 

8 Types of Qualitative Data Collection Methods  

As we have firmly established the need for qualitative data, it’s time to answer the next big question: how to collect qualitative data. 

Here is a list of the most common qualitative data collection methods. You don’t need to use them all in your quest for gathering information. However, a foundational understanding of each will help you refine your research strategy and select the methods that are likely to provide the highest quality business intelligence for your organization. 

1. Interviews

One-on-one interviews are one of the most commonly used data collection methods in qualitative research because they allow you to collect highly personalized information directly from the source. Interviews explore participants' opinions, motivations, beliefs, and experiences and are particularly beneficial in gathering data on sensitive topics because respondents are more likely to open up in a one-on-one setting than in a group environment. 

Interviews can be conducted in person or by online video call. Typically, they are separated into three main categories:

  • Structured Interviews - Structured interviews consist of predetermined (and usually closed) questions with little or no variation between interviewees. There is generally no scope for elaboration or follow-up questions, making them better suited to researching specific topics. 
  • Unstructured Interviews – Conversely, unstructured interviews have little to no organization or preconceived topics and include predominantly open questions. As a result, the discussion will flow in completely different directions for each participant and can be very time-consuming. For this reason, unstructured interviews are generally only used when little is known about the subject area or when in-depth responses are required on a particular subject.
  • Semi-Structured Interviews – A combination of the two interviews mentioned above, semi-structured interviews comprise several scripted questions but allow both interviewers and interviewees the opportunity to diverge and elaborate so more in-depth reasoning can be explored. 

While each approach has its merits, semi-structured interviews are typically favored as a way to uncover detailed information in a timely manner while highlighting areas that may not have been considered relevant in previous research efforts. Whichever type of interview you utilize, participants must be fully briefed on the format, purpose, and what you hope to achieve. With that in mind, here are a few tips to follow: 

  • Give them an idea of how long the interview will last
  • If you plan to record the conversation, ask permission beforehand
  • Provide the opportunity to ask questions before you begin and again at the end. 

2. Focus Groups

Focus groups share much in common with less structured interviews, the key difference being that the goal is to collect data from several participants simultaneously. Focus groups are effective in gathering information based on collective views and are one of the most popular data collection instruments in qualitative research when a series of one-on-one interviews proves too time-consuming or difficult to schedule. 

Focus groups are most helpful in gathering data from a specific group of people, such as donors or clients from a particular demographic. The discussion should be focused on a specific topic and carefully guided and moderated by the researcher to determine participant views and the reasoning behind them. 

Feedback in a group setting often provides richer data than one-on-one interviews, as participants are generally more open to sharing when others are sharing too. Plus, input from one participant may spark insight from another that would not have come to light otherwise. However, here are a couple of potential downsides:

  • If participants are uneasy with each other, they may not be at ease openly discussing their feelings or opinions.
  • If the topic is not of interest or does not focus on something participants are willing to discuss, data will lack value. 

The size of the group should be carefully considered. Research suggests over-recruiting to avoid risking cancellation, even if that means moderators have to manage more participants than anticipated. The optimum group size is generally between six and eight for all participants to be granted ample opportunity to speak. However, focus groups can still be successful with as few as three or as many as fourteen participants. 

3. Observation

Observation is one of the ultimate data collection tools in qualitative research for gathering information through subjective methods. A technique used frequently by modern-day marketers, qualitative observation is also favored by psychologists, sociologists, behavior specialists, and product developers. 

The primary purpose is to gather information that cannot be measured or easily quantified. It involves virtually no cognitive input from the participants themselves. Researchers simply observe subjects and their reactions during the course of their regular routines and take detailed field notes from which to draw information. 

Observational techniques vary in terms of contact with participants. Some qualitative observations involve the complete immersion of the researcher over a period of time. For example, attending the same church, clinic, society meetings, or volunteer organizations as the participants. Under these circumstances, researchers will likely witness the most natural responses rather than relying on behaviors elicited in a simulated environment. Depending on the study and intended purpose, they may or may not choose to identify themselves as a researcher during the process. 

Regardless of whether you take a covert or overt approach, remember that because each researcher is as unique as every participant, they will have their own inherent biases. Therefore, observational studies are prone to a high degree of subjectivity. For example, one researcher’s notes on the behavior of donors at a society event may vary wildly from the next. So, each qualitative observational study is unique in its own right. 

4. Open-Ended Surveys and Questionnaires

Open-ended surveys and questionnaires allow organizations to collect views and opinions from respondents without meeting in person. They can be sent electronically and are considered one of the most cost-effective qualitative data collection tools. Unlike closed question surveys and questionnaires that limit responses, open-ended questions allow participants to provide lengthy and in-depth answers from which you can extrapolate large amounts of data. 

The findings of open-ended surveys and questionnaires can be challenging to analyze because there are no uniform answers. A popular approach is to record sentiments as positive, negative, and neutral and further dissect the data from there. To gather the best business intelligence, carefully consider the presentation and length of your survey or questionnaire. Here is a list of essential considerations:

  • Number of questions : Too many can feel intimidating, and you’ll experience low response rates. Too few can feel like it’s not worth the effort. Plus, the data you collect will have limited actionability. The consensus on how many questions to include varies depending on which sources you consult. However, 5-10 is a good benchmark for shorter surveys that take around 10 minutes and 15-20 for longer surveys that take approximately 20 minutes to complete. 
  • Personalization: Your response rate will be higher if you greet patients by name and demonstrate a historical knowledge of their interactions with your brand. 
  • Visual elements : Recipients can be easily turned off by poorly designed questionnaires. Besides, it’s a good idea to customize your survey template to include brand assets like colors, logos, and fonts to increase brand loyalty and recognition.
  • Reminders : Sending survey reminders is the best way to improve your response rate. You don’t want to hassle respondents too soon, nor do you want to wait too long. Sending a follow-up at around the 3-7 mark is usually the most effective. 
  • Building a feedback loop : Adding a tick-box requesting permission for further follow-ups is a proven way to elicit more in-depth feedback. Plus, it gives respondents a voice and makes their opinion feel valued.

5. Case Studies

Case studies are often a preferred method of qualitative research data collection for organizations looking to generate incredibly detailed and in-depth information on a specific topic. Case studies are usually a deep dive into one specific case or a small number of related cases. As a result, they work well for organizations that operate in niche markets.

Case studies typically involve several qualitative data collection methods, including interviews, focus groups, surveys, and observation. The idea is to cast a wide net to obtain a rich picture comprising multiple views and responses. When conducted correctly, case studies can generate vast bodies of data that can be used to improve processes at every client and donor touchpoint. 

The best way to demonstrate the purpose and value of a case study is with an example: A Longitudinal Qualitative Case Study of Change in Nonprofits – Suggesting A New Approach to the Management of Change . 

The researchers established that while change management had already been widely researched in commercial and for-profit settings, little reference had been made to the unique challenges in the nonprofit sector. The case study examined change and change management at a single nonprofit hospital from the viewpoint of all those who witnessed and experienced it. To gain a holistic view of the entire process, research included interviews with employees at every level, from nursing staff to CEOs, to identify the direct and indirect impacts of change. Results were collated based on detailed responses to questions about preparing for change, experiencing change, and reflecting on change.

6. Text Analysis

Text analysis has long been used in political and social science spheres to gain a deeper understanding of behaviors and motivations by gathering insights from human-written texts. By analyzing the flow of text and word choices, relationships between other texts written by the same participant can be identified so that researchers can draw conclusions about the mindset of their target audience. Though technically a qualitative data collection method, the process can involve some quantitative elements, as often, computer systems are used to scan, extract, and categorize information to identify patterns, sentiments, and other actionable information. 

You might be wondering how to collect written information from your research subjects. There are many different options, and approaches can be overt or covert. 

Examples include:

  • Investigating how often certain cause-related words and phrases are used in client and donor social media posts.
  • Asking participants to keep a journal or diary.
  • Analyzing existing interview transcripts and survey responses.

By conducting a detailed analysis, you can connect elements of written text to specific issues, causes, and cultural perspectives, allowing you to draw empirical conclusions about personal views, behaviors, and social relations. With small studies focusing on participants' subjective experience on a specific theme or topic, diaries and journals can be particularly effective in building an understanding of underlying thought processes and beliefs. 

7. Audio and Video Recordings

Similarly to how data is collected from a person’s writing, you can draw valuable conclusions by observing someone’s speech patterns, intonation, and body language when you watch or listen to them interact in a particular environment or within specific surroundings. 

Video and audio recordings are helpful in circumstances where researchers predict better results by having participants be in the moment rather than having them think about what to write down or how to formulate an answer to an email survey. 

You can collect audio and video materials for analysis from multiple sources, including:

  • Previously filmed records of events
  • Interview recordings
  • Video diaries

Utilizing audio and video footage allows researchers to revisit key themes, and it's possible to use the same analytical sources in multiple studies – providing that the scope of the original recording is comprehensive enough to cover the intended theme in adequate depth. 

It can be challenging to present the results of audio and video analysis in a quantifiable form that helps you gauge campaign and market performance. However, results can be used to effectively design concept maps that extrapolate central themes that arise consistently. Concept Mapping offers organizations a visual representation of thought patterns and how ideas link together between different demographics. This data can prove invaluable in identifying areas for improvement and change across entire projects and organizational processes. 

8. Hybrid Methodologies

It is often possible to utilize data collection methods in qualitative research that provide quantitative facts and figures. So if you’re struggling to settle on an approach, a hybrid methodology may be a good starting point. For instance, a survey format that asks closed and open questions can collect and collate quantitative and qualitative data. 

A Net Promoter Score (NPS) survey is a great example. The primary goal of an NPS survey is to collect quantitative ratings of various factors on a score of 1-10. However, they also utilize open-ended follow-up questions to collect qualitative data that helps identify insights into the trends, thought processes, reasoning, and behaviors behind the initial scoring. 

Collect and Collate Actionable Data with UpMetrics

Most nonprofits believe data is strategically important. It has been statistically proven that organizations with advanced data insights achieve their missions more efficiently. Yet, studies show that despite 90% of organizations collecting data, only 5% believe internal decision-making is data-driven. At UpMetrics, we’re here to help you change that. 

UpMetrics specializes in bringing technology and humanity together to serve social good. Our unique  social impact software  combines quantitative and qualitative data collection methods and analysis techniques, enabling social impact organizations to gain insights, drive action, and inspire change. By reporting and analyzing quantitative and qualitative data in one intuitive platform, your impact organization gains the understanding it needs to identify the drivers of positive outcomes, achieve transparency, and increase knowledge sharing across stakeholders.

Contact us today  to learn more about our  nonprofit impact measurement  solutions and discover the power of a partnership with UpMetrics. 

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Using In-Depth Interviews as a Primary Source of Data for Developing Case Studies

  • By: Elsa Q. Villa
  • Product: Sage Research Methods Cases Part 2
  • Publisher: SAGE Publications Ltd
  • Publication year: 2016
  • Online pub date: October 27, 2016
  • Discipline: Education
  • Methods: Case study research , In-depth interviews , Empirical data
  • DOI: https:// doi. org/10.4135/9781473958043
  • Keywords: engineering , NASA , primary sources , probing , profiling , teaching , teams Show all Show less
  • Online ISBN: 9781473958043 Copyright: © SAGE Publications Ltd 2017 More information Less information

The case study presented here highlights a unique method of collecting data for phenomenological qualitative studies. Drawing from many scholars in qualitative research, Irving Seidman developed this method of in-depth interviewing with attributes of validity and reliability. It is a time-intensive method in which three interviews are conducted, each 1 week apart, to allow participants to reflect on what they have shared in constructing meaning of their lived experiences. The first interview seeks understanding of the experiences and/or events leading to the phenomenon under study, such as the complex lives of critical care nurses or special needs teachers. The second interview explores their lives in the moment, while the third and last interview seeks participants’ meaning of these experiences. Once these data are transcribed, a profile is developed using participants’ exact words to develop a chronological story of their lives in the phenomenon under study in order to extract the essence of their contextualized lives for data analysis. For the case presented here, the phenomenon of investigation was to understand the lived experiences of Latinas who chose to study engineering at university.

Learning Outcomes

By the end of this case study, students should be able to

  • Use the Seidman method of interviewing
  • Create a profile of each research participant
  • Develop a case study

Project Overview and Context

In 2012, I submitted a proposal, with colleagues from other disciplines, to the National Science Foundation under their Gender in Science and Engineering program; the proposal was funded. Our aim was to examine the identities of Latinas in engineering undergraduate studies to understand their persistence, resilience, and agency for studying these disciplines given the underrepresentation of women in these fields.

This case study discusses the use of in-depth interviewing method to collect rich data on the lived experiences of these women in engineering. Our research question driving the qualitative investigation was as follows: What is the relationship among identity, resilience, and persistence of Latinas in engineering? Our study of Latinas took place at a public, 4-year institution on the US–Mexico border where almost 80% of the undergraduate students are Hispanic or Latino/a. The undergraduate enrollment of women is slightly higher than the national average at 20% and 19%, respectively. A total of 26 Latinas participated in our study.

As the lead investigator in this study, I selected in-depth interviewing as our data collection method, as the aim of our phenomenological study was to understand how engineer identity is developed in and through Latinas’ pre- and in-college experiences. I learned this qualitative research method in one of my doctoral courses at New Mexico State University under the direction of James O’Donnell, who was a protégé of Irving Seidman, developer of this particular method. Thus, I learned the method firsthand from someone with expertise; this contributed to my deep understanding of the method and its benefits, particularly for case study method, as the data are rich. In this method, participants share their life experiences leading to the phenomenon under study resulting in participants reflecting on meaningful experiences through sequenced phases in telling their story.

While Seidman refers to this method as ethnographic, traditionalists in ethnography argue the need for participant observation to label this method as such. Susan E. Chase argues the case for this method of data collection as ‘contemporary narrative inquiry’ with roots in ethnographic methods: Early 20th-century ethnographers used life histories, or oral histories, to understand extinct, elusive, or otherwise inaccessible cultures, such as the lives of American slaves in the 19th century. Chase states, ‘[c]ontemporary narrative inquiry can be characterized as an amalgram of interdisciplinary analytic lenses, diverse disciplinary approaches, and both traditional and innovative methods—all revolving around an interest in biographical particulars as narrated by the one who lives them’ (p. 651). Thus, the Seidman method can be characterized as contemporary narrative inquiry.

Research Practicalities

Drawing from numerous ethnographic and other qualitative methods, Irving Seidman developed this data collection method for phenomenological research investigations. In what I call the ‘Seidman method’, data are collected to obtain ‘thick descriptions’ of a participant’s life experiences in order to understand the essence of their experience in a particular phenomenon. Thick descriptions emerge from the data as complete and contextualized descriptions of a participant’s experiences, including conversations and other details to make the experience ‘come alive’ for the reader. Susan Chase describes these data as narrative that

is retrospective meaning making—the shaping and ordering of past experience. Narrative is a way of understanding one’s own and others’ actions, of organizing events and objects into meaningful whole, and of connecting and seeing the consequences of actions and events over time. (p. 656)

Three interviews are conducted a week apart to allow time for participants to reflect on what has been shared upon completion of each interview. The foci of the three interviews are (1) past experiences leading to the phenomenon under study, (2) current experiences in the phenomenon, and (3) perceived meaning of the phenomenon. With each interview lasting approximately 90 min, the result is 4-6 h of digitally recorded data that are transcribed verbatim. In our case study, 26 participants generated nearly 100 h of transcribed data.

The Seidman method uses these focused interviews as the sole source of data as participants share their experiences and/or events perceived as important. These narratives are extended, retrospective stories about participants’ experiences leading to and during their study of engineering. To obtain these extended stories, the interviewer seeks to ‘probe’ deeper into these experiences or events to elicit details, which result in thick descriptions of their lived experiences. I use quotes on the word ‘probe’ because Seidman cautions researchers that this is exploration, not probing. However, to me the word ‘probe’ indicates a way to obtain details about what is being shared in order to more fully understand the interviewee’s perception of the experience. The probes are simply to garner more detail without influencing the participant’s responses, such as introducing leading questions or visibly reacting to their responses.

Once interviews are transcribed and coded, the researcher extrapolates themes from patterns emerging from the data. In our research, we used the qualitative software NVivo 10 to facilitate this analytical process.

Research Design

Our study, The Fence Builders: Case Studies of Latinas’ Constructing and Authoring Agentive Selfs toward Studying Engineering , sought to understand the essence of participants’ experiences in navigating career choice and persistence in engineering studies. We wanted to explore the fundamental meanings of these experiences in forming engineer identities and developing agency for any barriers they faced. That is, the participants, or their ‘agentive selfs’, performed actions to overcome barriers and persist in their studies. For example, one participant shared her desire to join a particular study group; however, members of that group would not extend an invitation to her. In order to gain their trust, the participant earned the highest grade on a test in a course in which they were all enrolled. This action (i.e., her agentive self) enabled her in receiving an invitation to join the group.

We drew on a qualitative approach to investigate the various factors mediating Latinas’ decision-making and persistence in their engineering studies in order to understand the complex gender issues among Latinos, in general, and Latinas, in particular. As noted by Yvonna Lincoln and Egon Guba, qualitative methods use naturalistic approaches to understand, illuminate, and interpret the multiple realities of individuals in particular contexts. Thus, the Seidman method proved to be an appropriate method to extract a rich source of data of participants’ perceptions of their lived experiences toward and in engineering studies.

As expected for a study of this nature, we used purposeful sampling to identify research participants. In this type of sampling, as noted by Sharan Merriam, selection reflects the phenomenon of interest. Merriam suggests the researcher consider the phenomenon under study and then make a list of the criteria needed to understand the phenomenon. Once the criteria are identified, the researcher identifies suitable sites or locations to choose participants meeting the criteria. In our case, the criteria were Latinas, who were undergraduate students in engineering, at a public university located on the US–Mexico border. These criteria directly reflect the purpose of the study to enable capture of rich data.

Barney Glaser and Anselm Strauss emphasize the need to use maximum variation sampling in order to create diversity across a sample. For our study, we needed to identify Latinas from different majors at different times during their studies to create this variation across the purposeful sample of Latinas in engineering. In using this variation in sample, researchers should find shared patterns emerging from the data, which contributes to validating the research. After interviewing 26 participants and having their interviews transcribed and coded, patterns began to emerge, and the team made the decision that we had enough participants. In other words, saturation had been reached.

In-Depth Interviews as a Data Collection Method

Because the first interview focuses on past experiences leading to the phenomenon under study, we asked, ‘What life experiences led you to study engineering?’ Participants typically provided a brief description of what they felt was important in their lives, as indicated in two samples that follow later. The interviewer then took each of these details and probed deeper to obtain fuller descriptions asking, in some cases, for participants to reconstruct past experiences. The second interview explored their experiences as an engineering student. At this point, our team wanted to understand their particular trajectory in college studies. The opening question of the second interview was as follows: ‘What experiences have you had in your engineering studies?’ Again, significant events were briefly mentioned, and these events were noted (written) in order to further probe into these events. The third interview delved into meaning for the participant. Thus, the opening question of the third interview was as follows: ‘What does being an engineer mean to you?’ In essence, the three interviews focused on past and present experiences and, finally, participants’ perceived meaning in light of what they shared with the interviewer.

No other questions outside of the perceived events and/or experiences are asked: The interviewer simply probed into the experiences that the participant shared as meaningful to the phenomenon under study. These probes continued in order to create thick descriptions of these introduced experiences and any other experiences that the participant shares. For example, Amber, an electrical engineering student in her final semester, stated the following in her opening response in the first interview:

Actually, when I first started high school, I went to an engineering magnet program; and so, through my four years, I actually started taking engineering classes. And the whole reason I actually took it was so that I could get better in math.

She continued with sharing her struggles with math, how supportive her parents had been in her choice to study engineering, and how so many others had been supportive of her.

While Amber had identified several key events, the interviewer initially chose the one about math. Rather than asking a question, the interviewer probed as follows: ‘You said, you wanted to get better in math. So tell me about that’. Her response was: ‘Actually, that one came from my dad because … I grew up with all my cousins , they’re really, really smart. None of them did engineering actually . They should have. But they all were really, really math savvy ’. In bold emerged new elements significant to Amber; these were later probed, which eventually revealed her experiences with her father in fixing an air conditioner and building a fence. Thus, events were co-constructed between listener and interviewer as the narrative further developed.

Analytical Method

All interviews were digitally recorded, transcribed verbatim, and uploaded into NVivo 10, a qualitative software program. The Seidman method includes an analytical method once interviews are completed and transcribed: Create a vignette or profile of each participant using their exact words and place them chronologically to create their ‘story’ in a flowing manner. These profiles were uploaded into NVivo 10 to reference during coding using our theoretical framework and research questions as guides.

For deeper analysis, our team identified a variety of methods to extrapolate the essence of our participants’ experiences, such as narrative analysis and constant comparative. I chose case study method. Sharan Merriam describes case study as a holistic description of a particular phenomenon investigated within a bounded system—‘a unit around which there are boundaries’ (p. 178). In our case study, the particular phenomenon of interest is the participation of Latinas in an ‘instance of concern’ (p. 28) where both Amber and Autumn encountered apprentice-like experiences with more knowledgeable other(s)—their fathers. Specifically, each had built fences with their fathers in their youth.

This ‘instance of concern’ aligned to one of the major themes emerging from our study: the role of significant others in engineer identity development. Case study method allowed me to focus on one particular aspect of that major finding: detailed descriptions of each of them in this ‘building experience’. While many of the participants in our larger study identified meaningful, problem-solving activities using engineering-like artifacts, Amber and Autumn had similar features in the instance of concern—that of building fences with their fathers.

Method in Action

While I have briefly mentioned how Seidman interviews unfold, the following further illustrates the method in action, as I asked the other ‘fence builder’ Autumn the opening question in her first interview:

Interviewer: What life experiences led you to study engineering?

Autumn: Well, there were a couple of things: One of the biggest that always comes out in my mind is , I really like looking at the stars at night , and one day I just asked my parents , who are the people who go to space? And they said aeronautical engineers that work at NASA. So, I mean, at nine or ten, that became my life goal . I wanted to be an engineer that worked at NASA . And that drove a lot of what I did . Even in some classes, at least now I have difficulties and that’s always something that I keep in the back of my mind, that I want to be an engineer. I want to work at NASA. That keeps me going. And when I was a kid, I did play with Legos . I did the standard engineering thing. We always want to build things . We want to destroy things. We want to make things that you know we use our imagination to create …

In bold are items I wrote in my notes as I listened to all she had to say in response to my question, which continued beyond what I have written here. Then, I took each of these in turn to further probe for elaboration and detail. Of course, the elaboration led to more experiences and events, as did Amber’s, eventually revealing her experiences with building a fence with her father. No other questions were asked except for those eliciting detail.

In describing this method, I use the following metaphor: It is like a barren tree with major branches. The interviewee initially describes these major branches. Your job as an interviewer is to add more branches and leaves until the tree is full. It is important to note the interviewer does not create new branches. Thus, other questions I asked Autumn following her opening statement were as follows:

  • 1. ‘Reconstruct for me the time your parents told you that aeronautical engineers go into space’.
  • 2. ‘How did you know engineers build and design?’
  • 3. ‘You mentioned you had difficulties. Could you share what you meant by that?’

The probes continued with whatever explanation Autumn offered until no other information was new. At this point, 90 min had passed, just as Seidman suggested would be the length of each interview. The length of the interview is dependent on the participant’s trust of the interviewer. When trust exists, details of participants’ experiences emerge.

Participant Profiles

Once interviews are completed and transcribed, profiles were created using the participants’ exact words and placing events and experiences in chronological order. These profiles proved to be powerful because participants’ stories came to life; and our research team could see each story as unique, yet revealing patterns, or themes, that emerged. Thus, the profiles provided the context of each of their lived experiences and meaning making, which facilitated deeper analysis of data.

Autumn’s Profile

The following are opening few paragraphs of Autumn’s profile illustrating how details are chronologically placed and in her own words:

There were a couple of things [that led to my engineering studies]. One of the biggest that always comes out in my mind is [the fact that] I really liked looking at the stars at night. I would stare for hours, just looking at the stars trying to figure out constellations and all that. And my dad, [a science teacher who grew up during the time when we landed on the moon during the heyday of NASA—I asked him, ‘Who goes up into space?’]. He just said, ‘Oh, well, aeronautical engineers do that; the people who work at NASA. You know, the same ones that [sic] went to the moon. They’re the ones who get to do all that cool stuff’. That’s what it really boiled down to is just, me one day asking, ‘How do I get to do that?’ So, [at the age of] nine or ten, that became my life goal. I wanted to be an engineer who worked at NASA. And that drove a lot of what I did. I want to work at NASA. That keeps me going.

And [second], when I was a kid, I did play with Legos. I did the standard engineering thing. We always want to build things. We want to destroy things. We want to make things that you know we use our imagination to create. And so that’s always something that keeps me going in engineering is just, I want to do something that creates. I want to create things. I want to use my imagination to solve problems. And you know, underneath all of that, I want to work at NASA. And I’ve been really lucky in that aspect actually. I have a job with them that starts in July. So I’m really excited. Yeah. It’s been a long dream coming, and I finally get to work there. So, yeah, I mean it’s, I’ve been really lucky just with all of my circumstances, and just the opportunities that I’ve been given.

Every time someone asks what I wanted to do—I had no idea what an aeronautical engineer was at the time—but I knew I wanted to do it. I knew that engineer[s] dealt with math, they dealt with science, they built things, they solved problems. I kind of knew what engineering was, but I mean aeronautics, I don’t think I’d even heard the word before, before my parents mentioned it. And after that, you know, I learned that they’re related into space, they are engineers, they do aerodynamic type things and that kept me interested, so yeah. It’s funny just going from something that you have no idea what it really means. It took me, you know, a couple years to really understand. I even think until high school I didn’t realize how in-depth some of the subject matter actually was [although I did] start seeing it. And I mean, the math that’s related, the science. All the physical or the physics-based learning that’s involved with it. At the time, I didn’t realize how in-depth that would have been. You know that they are the people who go to space; they are the ones that create things that get there. And that’s all I needed at the time. And it worked. I don’t know. It’s really weird. It’s a weird story. But. It keeps me going. So, it’s always interesting to tell people that. I like it though.

Returning to Autumn’s opening response to the first question of the first interview, you can see how different the opening paragraphs of the profile are from her first response because of the detail she provided later in the interview. Thus, the generation of profiles created rich ‘stories’ of participants’ lived experiences in seeking engineering as a career choice.

Practical Lessons Learned

Several lessons have been learned in this process: (1) be mindful in selecting your participants, (2) suppress the urge to ask questions beyond what they have shared and/or leading questions, (3) be patient in interviewing only a few participants at a time and/or developing profiles, (4) the challenge in preparing others to use the Seidman method, (5) using qualitative software to manage data analysis, and (6) the importance of profiles to facilitate data analysis.

Carefully Select Your Participants

Seidman warns researchers of identifying participants who you supervise, teach, or have other relational power with potential participants. I made that mistake: I had a participant who was working with me on another project and asked her if she wanted to be a participant. While she agreed, she was not as open as I felt she could have been if she was not in that vulnerable position. After that incident, I made sure I did not know the participants.

Suppress the Urge to Be Ambitious

At one point, I felt ambitious and had several participants I was interviewing during the same time frame who were in different stages of the interview process. For example, during 1 week, I had two participants in their first interview and another in her second interview. I became confused during the first interview with the new participant, and when she mentioned an event in her university experience, I began probing her to further elaborate on her experience. This is the focus of the second interview, not the first. Instead, I should have gently reminded her that we would be discussing these events the following week in the second interview. Realizing my confusion, I self-corrected and re-directed the interview to events leading to the phenomenon. So, I urge patience and mindfulness in arranging the interviews.

Be Mindful of Your Reaction to Participants’ Experiences

During the interviews, I sometimes felt the need to ask questions not suggested by the participant and had to suppress that urge, such as ‘You felt isolated as a woman, right?’ This is a leading question and should not be asked.

Sometimes during the first interviews, participants would deviate and start talking about their experiences as an undergraduate engineering student. I would not interrupt, and then remind them that we will explore that experience, or set of experiences, during the second interview the following week.

Learning the Method

On our research team, three of us conducted interviews over an academic year, which was a great benefit given its time intensiveness. To prepare our team for the interviews and to introduce the method to others, I planned a 2-h workshop where I distributed Seidman’s book and discussed the major features of the method. I had already conducted one interview and used parts of the audio recording to demonstrate major points of the Seidman method, such as asking for more detail rather than asking questions not related to what was shared by the interviewee.

In retrospect, I would have extended the workshop by asking potential interviewers to interview each other using a digital recorder asking each other: ‘What led you to becoming a research scientist?’ I would ask them to transcribe their interviews for self- and peer-critique. Then, I would have followed with reflection on the method and how they could have improved the method.

Using Qualitative Software

Our team uploaded all transcriptions into NVivo 10 for coding and analysis. This qualitative software has been extremely beneficial given the large corpus of data—over 70 interviews lasting anywhere from 45 min to 1.5 h. With this much data, analysis is facilitated and allows for viewing the data in a variety of ways, such as a graph with related themes.

Building Participant Profiles

As we began our analysis, we became a bit confused at times with coded passages without a context. We realized that the corpus of data was overwhelming with 26 participants totaling 78 interviews. It was at this time, we re-visited the Seidman method where he recommends the development of profiles or vignettes for contextualizing participants’ stories. As a result, we suspended data analysis and developed profiles for each participant.

Developing these profiles proved to be an arduous process because participants would give long descriptions with varying events or experiences mentioned, which were typically not in chronological order. Thus, in creating profiles, you have to carefully arrange their sentences to make it seem as if the participant had shared the details of their experiences from the beginning to the present time. This is challenging because you are moving back and forth between the transcriptions and the profile document. It becomes tiresome. Nonetheless, the outcome is rewarding in understanding each participant’s story, which allows you to avoid making assumptions during coding as you interpret the data.

Using the Seidman method is an open-ended process of data collection. This ethnographic method generates rich and thick descriptions of participants’ lived experiences. The disadvantage of the method is its time intensiveness; however, the time invested in using the method results in rich data to support your findings and provide the evidence of the validity and reliability of the method. It is time intensive in the following ways: (1) the interviews of each participant occur over a 3-week period; (2) you cannot interview too many during any one time period; thus, making the time for data collection lengthier; (3) the digitally recorded data must be transcribed; and (4) profiles need to be done. Once these are done, you can then work on coding and analysis.

The richness of the data is the payoff in demonstrating ‘trustworthiness’ of the study, a term coined by Yvonna Lincoln and Egon Guba to demonstrate validity and reliability in qualitative studies—terms typically used in quantitative studies.

Exercise and Discussion Questions

  • 1. What types of phenomena are suitable for using the Seidman method for data collection?
  • 2. What advantages does this method have over other types of ethnographic methods? Disadvantages?
  • 3. How does this method demonstrate reliability? Validity?
  • 4. What advantages does this method have over structured interviews, that is, interview protocols created from the research questions? Disadvantages?

Further Readings

Web resources.

Link to abstract of the article cited above: ‘Creating an equitable classroom environment: A case study of a preservice teacher learning what it means to “do inquiry”’. http://digitalcommons.utep.edu/teacher_papers/126/

Link to the article cited above: ‘Creating an equitable classroom environment: A case study of a preservice teacher learning what it means to “do inquiry”’. http://www.highbeam.com/doc/1G1-390091867.html

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Methodology

  • Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The  aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

  • Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
  • Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

  • Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
  • Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

  • Experimental research is primarily a quantitative method.
  • Interviews , focus groups , and ethnographies are qualitative methods.
  • Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

  • You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
  • You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

  • If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
  • If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
  • You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

  • Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
  • Double-check manual data entry for errors.
  • If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

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case study method of collecting data

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
  • You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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Qualitative Data Collection and Analysis Methods – PSY

Qualitative data collection methods in each design or approach.

The department of psychology approves six approaches or designs within qualitative methodology.  Each of these designs uses its own kind of data sources.  Table 1 outlines the main primary and secondary sources of data in each design. 

  • Primary sources are data from actual participants.
  • Secondary data sources are from others.
  • The researcher’s notes describing observations of participants or behaviors in their natural environments.  This is the more common usage, and is most common in ethnographic studies.
  • The researcher’s notes to self about themes noticed while collecting data, possibly important points in the data, ideas to come back to, and so on.
  • Another related term is memos , although memos in grounded theory tend to be brief or extended essays charting the development of theory, rather than simple notes.  Strictly speaking, these notes or memos are not data in themselves, but point to data in another source.

Table 1. The Fit of Method and Type of Data

Data Collection in Ethnography

Typically, ethnographers collect data while in the field. Their data collection methods can include

  • Participant observation.
  • Naturalistic observation.
  • Writing field notes.
  • Conducting unstructured or structured interviews (sometimes audiotaped or videotaped).
  • Reviewing documents, records, photographs, videotapes, maps, genograms, and sociograms.
  • Interviewing focus groups (although the department of psychology does not approve focus group research for dissertations).
  • Any accessible and dependable source of information about the behaviors, interactions, customs, values, beliefs, attitudes, and practices of the members of that culture can be a source of data.

It is worth remembering that the time-world of cultural groups is longer than it is for individual persons, and so:

  • Data collection may need to cover a longer time in order to capture the true flavor of the culture.
  • Field research methods need to adapt to the demands of the field; ethnography allows for flexibility in the design of its methods to accommodate the challenges of the field.

However, for both of these reasons—the longer time-world of the culture or group and the occasional need to change data collection methods to meet challenges in the field—Institutional Review Board (IRB) complications can be introduced and must be addressed, further lengthening the time of the ethnographic study.

Data Collection in Case Studies

Case studies always include multiple sources of information because the case includes multiple kinds of issues. For example, a case study of a training program would obtain and analyze information about

  • The participants.
  • The nature of the organizational issues calling for the training.
  • The kinds of training provided.
  • The outcomes of the program.
  • The background and training of the staff, and so on.

In addition to multiple information sources, every case study provides an in-depth description of the contexts of the case:

  • Its setting (for example, the kind of business structure and office complex set-up where the training program takes place).
  • Its contexts (social contexts, political contexts, affiliations affecting outcomes, and so on).

The setting and context are an intrinsic part of the case.

Consequently, because cases contain many kinds of information and contexts, case studies use many different methods of data collection. These can include the full range of qualitative methods such as:

  • Open-ended surveys.
  • Interviews.
  • Field observations. Reviews of documents, records, and other materials.
  • Evaluation of audiovisual materials.
  • Descriptions of contexts and collateral materials; and so on.

A well-designed case study does not rely on a single method and source of data because any true case (bounded system) will have many characteristics and it is not known ahead of time which characteristics are important. Determining that is the work of the case study.

Data Collection in Grounded Theory

The dominant methods of data collection in grounded theory research are:

  • Interviews (usually audiotaped).
  • Participant and nonparticipant observations.
  • Conversations. Recorded diaries.
  • Field notes.
  • Descriptions of comparative instances.
  • Personal narratives of experiences.

The participants in a grounded theory study often will be interviewed more than once and asked to reflect on and refine the preliminary conclusions drawn by the researcher.

  • Reinterviewing participants about them, asking for their feedback, or;
  • Interviewing a new round of participants about how well the hypothesized elements of the new theory actually explain their experiences.

The methods of doing these forms of data collection do not differ markedly from similar methods across all qualitative approaches. However, grounded theorists sometimes avoid too much study of the extant literature on their topic before going into the field, in hopes that they will not be biased by previous conjectures and data about the topic. It is their aim to allow the data to teach them and guide their analyses into rich explanations.

Data Collection in Phenomenology

There are two descriptive levels of the empirical phenomenological model that arise from the data collected:

  • Level 1: The original data are comprised of naïve descriptions obtained from participants through open-ended questions and dialogue. Naïve means simply, “in their own words, without reflection.”
  • Level 2: The researcher describes the structures of the experiences based on reflective analysis and interpretation of the research participant’s account or story.

To collect data for these levels of analysis, the primary tool is the in-depth personal interview.

  • Interviews typically are open (meaning, no forced answers), with three main kinds of questions:
  • An opening or initial question .  Usually this is only pre-written question, designed carefully to inquire into the participant’s lived (everyday) experience of the phenomenon under investigation.
  • Follow-up questions are asked to tease out deeper or more detailed elaborations of the earlier answers or to clarify unclear statements or ask about non-verbal gestures.
  • Guiding questions are asked to help the respondents return to the topic of the interview when they stray or digress.
  • The goal of the opening question (and all other questions) is to allow the respondent the maximum freedom to respond from within his or her lived (everyday, non-reflective) experience.

Because the objective is to collect data that are profoundly descriptive (rich in detail) and introspective, these interviews often can be lengthy, sometimes lasting as long as an hour or more.

Sometimes other sources of data are used in phenomenological studies, when those sources are equivalent in some way to the in-depth interview. For example:

  • In a study of the lived experience of grief, poems or other writings by the participants (or other people) about personal grief experiences might be collected in the same way as the in-depth interviews.
  • Audiovisual materials having a direct bearing on the lived experience of grief might be included as data (for example, photos of the participant with the deceased person).

Although other less personal data sources (such as letters, official documents, and news accounts) are seldom used as direct information about the lived experience, the researcher may find in a particular case that these are useful either in illuminating the participant’s story itself or in creating a rich and textured background description of the contexts and settings in which the participant experienced the phenomenon.

Data Collection in Heuristic Inquiry

The data collection methods of heuristic research in general are similar to those in phenomenological research, including primarily intensive and in-depth (audiotaped and transcribed) interviews with all participants (co-researchers). However, because the researcher is also a participant, any activities that allow the participants to describe their experiences of the phenomenon can be considered acceptable data collection procedures.

For instance:

  • Journaling.
  • Writing letters, prose, or poetry about the phenomenon.
  • Composing and reflecting on music related to the phenomenon.
  • Creating art or film about one’s experience.
  • Any other reasonable method of articulating one’s experience of a phenomenon can become useful data for heuristic reflection.

In many heuristic studies, the researcher (co-researcher) collects and records his or her own data regarding the experience under inquiry by using intensive journaling. Intensive journaling provides a means not only for collecting and recoding data but also a vehicle for self-reflection.

Data collection, in a heuristic inquiry, often is integrated with data analysis, because personal reflection is an integral part of every stage or phase of the processes.

  • While there will be activities that clearly are data collection versus data analysis, at times the distinction is not so clear.
  • Furthermore, analysis of earlier data may change the focus and intensity of later data collection, as when one’s insight into the meaning of the phenomenon for oneself forces new questions to be asked of one’s co-researchers or when new insights gleaned from the co-researchers force the researcher to go back to her own experience and reevaluate it for new meaning.

Data Collection in Generic Qualitative Inquiry

Data collection in this approach typically uses data collection methods that elicit people’s verbal reports on their ideas about things that are outside themselves. However, its focus on real events and issues means it seldom uses unstructured data collection methods (such as open-ended conversational interviewing from phenomenology, participant and nonparticipant field observation from ethnography, and the like).

Instead, generic qualitative inquiry requires:

  • Semi- or fully structured interviews.
  • Qualitative questionnaires.
  • Qualitative surveys.
  • Content- or activity-specific observations, and the like.

The core focus is external and real-world as opposed to internal, psychological, and subjective. (The attitudes and opinions in opinion polling, for example, are valued for their reflection on the external issues.)  Here are some characteristics of generic qualitative data collection:

  • Generic qualitative data collection seeks qualitative information from representative samples of people about:
  • Real-world events.
  • Observable and experienced situations or conditions.
  • Attitudes, opinions, or beliefs about external situations or conditions.
  • Their experiences.
  • Researchers want less to “go deep” and more to get a broad range of opinions, ideas, or reflections:
  • Occasionally, a small, non-representative but highly informed sample can provide rich information about the topic. For instance, a few experienced nurses can often provide rich, accurate, and helpful information about common patient reactions to certain procedures, because part of a nurse’s role is to observe patients’ experiences and reactions carefully.
  • More often, however, the sampling in this approach aims for larger representation of the population in mind. Although this is not a hard-and-fast rule, generic qualitative data collection typically uses larger samples than other qualitative approaches use because larger samples tend to be more widely representative.
  • As with all qualitative inquiry, if the sample is transparently and fairly representative of the target population or is clearly rich in information about the topic, readers may be persuaded to apply the findings to similar people or situations outside the sample itself.

Most generic qualitative studies rely on the following data collection methods:

  • Semi- or fully structured (pre-written questions) interviews, either oral (the most common method) or written (uncommon). In these qualitative interviews, the questions are structured based on the knowledge of the researcher, although there may be opportunities for “tell me more” kinds of questions. In other words, the data collected in this approach can be obtained from questions based on theoretical constructs in the existing literature, unlike other forms of qualitative data collection.
  • Questionnaires . Usually these are mix-scaled or quantitative items (for example, Likert-type scales asking preferences or degrees of agreement) with opportunities for qualitative comments; this approach requires mixed-method designs. Again, the researcher will build these questionnaires and their items from preknowledge about the topic.
  • Written or oral surveys . The standard opinion or voter poll is a good example, but survey research has its own rather deep literature and can be much more sophisticated that simple opinion or voter surveying. Once again, the items in the survey will be constructed on the basis of previous knowledge about the topic.

This concludes the discussion of qualitative data collection methods.  Please review the Presentation on “Quantitative Data Analysis Methods” in Unit 4, if you have not done so already.

(For a more thorough discussion of data collection, see the guide Qualitative Research Approaches in Psychology and Human Services .)

Consider this quotation from Charmaz (2006), “Simply thinking through how to word open-ended questions averts forcing responses into narrow categories” (p. 18).

Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis . Thousand Oaks, CA: SAGE. ISBN: 9780761973522.

Doc. reference: phd_t3_psy_u04s3_h04_qualcoll.html

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Statistics - Data collection - Case Study Method

Case study research is a qualitative research method that is used to examine contemporary real-life situations and apply the findings of the case to the problem under study. Case studies involve a detailed contextual analysis of a limited number of events or conditions and their relationships. It provides the basis for the application of ideas and extension of methods. It helps a researcher to understand a complex issue or object and add strength to what is already known through previous research.

STEPS OF CASE STUDY METHOD

In order to ensure objectivity and clarity, a researcher should adopt a methodical approach to case studies research. The following steps can be followed:

Identify and define the research questions - The researcher starts with establishing the focus of the study by identifying the research object and the problem surrounding it. The research object would be a person, a program, an event or an entity.

Select the cases - In this step the researcher decides on the number of cases to choose (single or multiple), the type of cases to choose (unique or typical) and the approach to collect, store and analyze the data. This is the design phase of the case study method.

Collect the data - The researcher now collects the data with the objective of gathering multiple sources of evidence with reference to the problem under study. This evidence is stored comprehensively and systematically in a format that can be referenced and sorted easily so that converging lines of inquiry and patterns can be uncovered.

Evaluate and analyze the data - In this step the researcher makes use of varied methods to analyze qualitative as well as quantitative data. The data is categorized, tabulated and cross checked to address the initial propositions or purpose of the study. Graphic techniques like placing information into arrays, creating matrices of categories, creating flow charts etc. are used to help the investigators to approach the data from different ways and thus avoid making premature conclusions. Multiple investigators may also be used to examine the data so that a wide variety of insights to the available data can be developed.

Presentation of Results - The results are presented in a manner that allows the reader to evaluate the findings in the light of the evidence presented in the report. The results are corroborated with sufficient evidence showing that all aspects of the problem have been adequately explored. The newer insights gained and the conflicting propositions that have emerged are suitably highlighted in the report.

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Home » Data Collection – Methods Types and Examples

Data Collection – Methods Types and Examples

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Data collection

Data Collection

Definition:

Data collection is the process of gathering and collecting information from various sources to analyze and make informed decisions based on the data collected. This can involve various methods, such as surveys, interviews, experiments, and observation.

In order for data collection to be effective, it is important to have a clear understanding of what data is needed and what the purpose of the data collection is. This can involve identifying the population or sample being studied, determining the variables to be measured, and selecting appropriate methods for collecting and recording data.

Types of Data Collection

Types of Data Collection are as follows:

Primary Data Collection

Primary data collection is the process of gathering original and firsthand information directly from the source or target population. This type of data collection involves collecting data that has not been previously gathered, recorded, or published. Primary data can be collected through various methods such as surveys, interviews, observations, experiments, and focus groups. The data collected is usually specific to the research question or objective and can provide valuable insights that cannot be obtained from secondary data sources. Primary data collection is often used in market research, social research, and scientific research.

Secondary Data Collection

Secondary data collection is the process of gathering information from existing sources that have already been collected and analyzed by someone else, rather than conducting new research to collect primary data. Secondary data can be collected from various sources, such as published reports, books, journals, newspapers, websites, government publications, and other documents.

Qualitative Data Collection

Qualitative data collection is used to gather non-numerical data such as opinions, experiences, perceptions, and feelings, through techniques such as interviews, focus groups, observations, and document analysis. It seeks to understand the deeper meaning and context of a phenomenon or situation and is often used in social sciences, psychology, and humanities. Qualitative data collection methods allow for a more in-depth and holistic exploration of research questions and can provide rich and nuanced insights into human behavior and experiences.

Quantitative Data Collection

Quantitative data collection is a used to gather numerical data that can be analyzed using statistical methods. This data is typically collected through surveys, experiments, and other structured data collection methods. Quantitative data collection seeks to quantify and measure variables, such as behaviors, attitudes, and opinions, in a systematic and objective way. This data is often used to test hypotheses, identify patterns, and establish correlations between variables. Quantitative data collection methods allow for precise measurement and generalization of findings to a larger population. It is commonly used in fields such as economics, psychology, and natural sciences.

Data Collection Methods

Data Collection Methods are as follows:

Surveys involve asking questions to a sample of individuals or organizations to collect data. Surveys can be conducted in person, over the phone, or online.

Interviews involve a one-on-one conversation between the interviewer and the respondent. Interviews can be structured or unstructured and can be conducted in person or over the phone.

Focus Groups

Focus groups are group discussions that are moderated by a facilitator. Focus groups are used to collect qualitative data on a specific topic.

Observation

Observation involves watching and recording the behavior of people, objects, or events in their natural setting. Observation can be done overtly or covertly, depending on the research question.

Experiments

Experiments involve manipulating one or more variables and observing the effect on another variable. Experiments are commonly used in scientific research.

Case Studies

Case studies involve in-depth analysis of a single individual, organization, or event. Case studies are used to gain detailed information about a specific phenomenon.

Secondary Data Analysis

Secondary data analysis involves using existing data that was collected for another purpose. Secondary data can come from various sources, such as government agencies, academic institutions, or private companies.

How to Collect Data

The following are some steps to consider when collecting data:

  • Define the objective : Before you start collecting data, you need to define the objective of the study. This will help you determine what data you need to collect and how to collect it.
  • Identify the data sources : Identify the sources of data that will help you achieve your objective. These sources can be primary sources, such as surveys, interviews, and observations, or secondary sources, such as books, articles, and databases.
  • Determine the data collection method : Once you have identified the data sources, you need to determine the data collection method. This could be through online surveys, phone interviews, or face-to-face meetings.
  • Develop a data collection plan : Develop a plan that outlines the steps you will take to collect the data. This plan should include the timeline, the tools and equipment needed, and the personnel involved.
  • Test the data collection process: Before you start collecting data, test the data collection process to ensure that it is effective and efficient.
  • Collect the data: Collect the data according to the plan you developed in step 4. Make sure you record the data accurately and consistently.
  • Analyze the data: Once you have collected the data, analyze it to draw conclusions and make recommendations.
  • Report the findings: Report the findings of your data analysis to the relevant stakeholders. This could be in the form of a report, a presentation, or a publication.
  • Monitor and evaluate the data collection process: After the data collection process is complete, monitor and evaluate the process to identify areas for improvement in future data collection efforts.
  • Ensure data quality: Ensure that the collected data is of high quality and free from errors. This can be achieved by validating the data for accuracy, completeness, and consistency.
  • Maintain data security: Ensure that the collected data is secure and protected from unauthorized access or disclosure. This can be achieved by implementing data security protocols and using secure storage and transmission methods.
  • Follow ethical considerations: Follow ethical considerations when collecting data, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring that the research does not cause harm to participants.
  • Use appropriate data analysis methods : Use appropriate data analysis methods based on the type of data collected and the research objectives. This could include statistical analysis, qualitative analysis, or a combination of both.
  • Record and store data properly: Record and store the collected data properly, in a structured and organized format. This will make it easier to retrieve and use the data in future research or analysis.
  • Collaborate with other stakeholders : Collaborate with other stakeholders, such as colleagues, experts, or community members, to ensure that the data collected is relevant and useful for the intended purpose.

Applications of Data Collection

Data collection methods are widely used in different fields, including social sciences, healthcare, business, education, and more. Here are some examples of how data collection methods are used in different fields:

  • Social sciences : Social scientists often use surveys, questionnaires, and interviews to collect data from individuals or groups. They may also use observation to collect data on social behaviors and interactions. This data is often used to study topics such as human behavior, attitudes, and beliefs.
  • Healthcare : Data collection methods are used in healthcare to monitor patient health and track treatment outcomes. Electronic health records and medical charts are commonly used to collect data on patients’ medical history, diagnoses, and treatments. Researchers may also use clinical trials and surveys to collect data on the effectiveness of different treatments.
  • Business : Businesses use data collection methods to gather information on consumer behavior, market trends, and competitor activity. They may collect data through customer surveys, sales reports, and market research studies. This data is used to inform business decisions, develop marketing strategies, and improve products and services.
  • Education : In education, data collection methods are used to assess student performance and measure the effectiveness of teaching methods. Standardized tests, quizzes, and exams are commonly used to collect data on student learning outcomes. Teachers may also use classroom observation and student feedback to gather data on teaching effectiveness.
  • Agriculture : Farmers use data collection methods to monitor crop growth and health. Sensors and remote sensing technology can be used to collect data on soil moisture, temperature, and nutrient levels. This data is used to optimize crop yields and minimize waste.
  • Environmental sciences : Environmental scientists use data collection methods to monitor air and water quality, track climate patterns, and measure the impact of human activity on the environment. They may use sensors, satellite imagery, and laboratory analysis to collect data on environmental factors.
  • Transportation : Transportation companies use data collection methods to track vehicle performance, optimize routes, and improve safety. GPS systems, on-board sensors, and other tracking technologies are used to collect data on vehicle speed, fuel consumption, and driver behavior.

Examples of Data Collection

Examples of Data Collection are as follows:

  • Traffic Monitoring: Cities collect real-time data on traffic patterns and congestion through sensors on roads and cameras at intersections. This information can be used to optimize traffic flow and improve safety.
  • Social Media Monitoring : Companies can collect real-time data on social media platforms such as Twitter and Facebook to monitor their brand reputation, track customer sentiment, and respond to customer inquiries and complaints in real-time.
  • Weather Monitoring: Weather agencies collect real-time data on temperature, humidity, air pressure, and precipitation through weather stations and satellites. This information is used to provide accurate weather forecasts and warnings.
  • Stock Market Monitoring : Financial institutions collect real-time data on stock prices, trading volumes, and other market indicators to make informed investment decisions and respond to market fluctuations in real-time.
  • Health Monitoring : Medical devices such as wearable fitness trackers and smartwatches can collect real-time data on a person’s heart rate, blood pressure, and other vital signs. This information can be used to monitor health conditions and detect early warning signs of health issues.

Purpose of Data Collection

The purpose of data collection can vary depending on the context and goals of the study, but generally, it serves to:

  • Provide information: Data collection provides information about a particular phenomenon or behavior that can be used to better understand it.
  • Measure progress : Data collection can be used to measure the effectiveness of interventions or programs designed to address a particular issue or problem.
  • Support decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions.
  • Identify trends : Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
  • Monitor and evaluate : Data collection can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.

When to use Data Collection

Data collection is used when there is a need to gather information or data on a specific topic or phenomenon. It is typically used in research, evaluation, and monitoring and is important for making informed decisions and improving outcomes.

Data collection is particularly useful in the following scenarios:

  • Research : When conducting research, data collection is used to gather information on variables of interest to answer research questions and test hypotheses.
  • Evaluation : Data collection is used in program evaluation to assess the effectiveness of programs or interventions, and to identify areas for improvement.
  • Monitoring : Data collection is used in monitoring to track progress towards achieving goals or targets, and to identify any areas that require attention.
  • Decision-making: Data collection is used to provide decision-makers with information that can be used to inform policies, strategies, and actions.
  • Quality improvement : Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Characteristics of Data Collection

Data collection can be characterized by several important characteristics that help to ensure the quality and accuracy of the data gathered. These characteristics include:

  • Validity : Validity refers to the accuracy and relevance of the data collected in relation to the research question or objective.
  • Reliability : Reliability refers to the consistency and stability of the data collection process, ensuring that the results obtained are consistent over time and across different contexts.
  • Objectivity : Objectivity refers to the impartiality of the data collection process, ensuring that the data collected is not influenced by the biases or personal opinions of the data collector.
  • Precision : Precision refers to the degree of accuracy and detail in the data collected, ensuring that the data is specific and accurate enough to answer the research question or objective.
  • Timeliness : Timeliness refers to the efficiency and speed with which the data is collected, ensuring that the data is collected in a timely manner to meet the needs of the research or evaluation.
  • Ethical considerations : Ethical considerations refer to the ethical principles that must be followed when collecting data, such as ensuring confidentiality and obtaining informed consent from participants.

Advantages of Data Collection

There are several advantages of data collection that make it an important process in research, evaluation, and monitoring. These advantages include:

  • Better decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions, leading to better decision-making.
  • Improved understanding: Data collection helps to improve our understanding of a particular phenomenon or behavior by providing empirical evidence that can be analyzed and interpreted.
  • Evaluation of interventions: Data collection is essential in evaluating the effectiveness of interventions or programs designed to address a particular issue or problem.
  • Identifying trends and patterns: Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
  • Increased accountability: Data collection increases accountability by providing evidence that can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.
  • Validation of theories: Data collection can be used to test hypotheses and validate theories, leading to a better understanding of the phenomenon being studied.
  • Improved quality: Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Limitations of Data Collection

While data collection has several advantages, it also has some limitations that must be considered. These limitations include:

  • Bias : Data collection can be influenced by the biases and personal opinions of the data collector, which can lead to inaccurate or misleading results.
  • Sampling bias : Data collection may not be representative of the entire population, resulting in sampling bias and inaccurate results.
  • Cost : Data collection can be expensive and time-consuming, particularly for large-scale studies.
  • Limited scope: Data collection is limited to the variables being measured, which may not capture the entire picture or context of the phenomenon being studied.
  • Ethical considerations : Data collection must follow ethical principles to protect the rights and confidentiality of the participants, which can limit the type of data that can be collected.
  • Data quality issues: Data collection may result in data quality issues such as missing or incomplete data, measurement errors, and inconsistencies.
  • Limited generalizability : Data collection may not be generalizable to other contexts or populations, limiting the generalizability of the findings.

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Curriculum, instruction, and pedagogy article, behind the screen: drug discovery using the big data of phenotypic analysis.

case study method of collecting data

  • 1 Department of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
  • 2 Department of Biology, Hastings College, Hastings, NE, United States
  • 3 Department of Biology, Allen University, Columbia, SC, United States
  • 4 Department of Chemistry and Life Science, United States Military Academy, West Point, NY, United States
  • 5 Department of Biological and Health Sciences, Crown College, St. Bonifacius, MN, United States
  • 6 Department of Biology, University of North Carolina at Pembroke, Pembroke, NC, United States

Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.

Introduction

Constant innovation in drug discovery makes it difficult for undergraduate courses to access up-to-date technology for teaching current methods in pharmaceutical research. Exposing students to large data sets collected or modeled by data generated in real laboratories can help increase engagement ( Freeman et al., 2014 ; Kontra et al., 2015 ) and allows universities to provide students with hands-on activities without having to budget for expensive lab equipment. This type of pedagogical tool would be especially helpful for teaching current methods in pharmaceutical science as lab equipment for these methods are expensive, hard to maintain, and are sometimes not very accessible due to privacy within industry and academia.

With high throughput screening and big data analysis becoming more vital in scientific fields, it is important for students to be trained in these methods to make them more prepared for future careers in STEM ( Miller, 2014 ; Stephens et al., 2015 ; Barone et al., 2017 ; Howe et al., 2017 ; Williams and Teal, 2017 ). A High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address how faculty can introduce advancements in STEM fields. The HITS network was motivated by the slow progress undergraduate programs had made toward updating curricula to more modern quantitative standards ( Robertson et al., 2021 ). The goal of HITS was to develop innovative curriculum materials in the form of case-based studies that involve hands-on activities with large high throughput datasets. The HITS initiative has built an interactive network that has successfully circulated high throughput case-based datasets across the country while also generating tools to help instructors develop their own case-based lesson plans ( Bixler et al., 2021 ; Robertson et al., 2021 ). High throughput cases developed by the HITS network directly address common barriers to incorporating big data into curricula by using publicly available datasets, well detailed teaching notes, and highly adaptable cases ( Williams et al., 2019 ). Case studies are a great way for students to learn high throughput methodology in tandem with high throughput quantitative skills ( Samsa et al., 2021 ). Problem-based learning tools, such as case studies, urge students to solve real world problems which improves student motivation to learn and understand key topics ( Gallagher et al., 1995 ; Lombardi and Oblinger, 2007 ). Case studies are interactive and faculty that have implemented case studies in their curriculum have observed an increase in student critical thinking and understanding of scientific concepts ( Yadav et al., 2007 ).

High throughput screening is necessary for drug development with screens developed and optimized for a large variety of target pathways. Providing students with case studies and real-world datasets can teach students how to analyze high throughput screening data in addition to interactive teaching of current methods in drug discovery. In drug discovery there are two types of screens: target-based and phenotypic-based ( Swinney, 2013 ). Target-based screens are used when a cellular target is known to be involved in disease progression and are based on change in activity of a specific protein with a known role in the cellular pathway of interest ( Croston, 2017 ). Target-based screens are ideal for diseases with known cellular targets but are not applicable for drug discovery for diseases with no known cellular targets. Phenotypic screening is based on a cellular biomarker and is often target agnostic. Phenotypic screens are useful for discovering new therapeutic targets but are harder to optimize for high throughput use and may need more customized statistical metrics compared to target-based screens ( Moffat et al., 2017 ). Phenotypic screens have been successful in drug discovery campaigns for a variety of diseases such as bacterial and parasitic infection ( Battah et al., 2019 ; Saccoccia et al., 2020 ). “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” describes the development and use of a high throughput screen for detecting compounds that interfere with a cancer-specific pathway from the perspective of a graduate student. Students learn the difference between target-based and phenotypic-based screens ( Swinney, 2013 ) and how experimental design and statistical analysis differs depending on assay readout ( Markossian et al., 2004 ; Zhang, 2011 ). Target-based and phenotypic screening methods are very different, especially in the type of samples used in screening and assay readout ( Strovel et al., 2016 ) (see supplemental teaching notes). These differences lead to variation in how datasets from each type of screen are analyzed. Customizing statistical analysis to best match the scientific protocol is very important and must be done without compromising a researcher’s ethical responsibility in data reporting. It has been reported that a large percentage of published research articles do not report statistical analysis properly or responsibly ( Chiu et al., 2017 ; Diong et al., 2018 ). Incorrect data analysis and interpretation can have drastic effects on the development of future studies in all fields by inaccurately informing researchers. It is important for authors to understand how to properly choose statistical methods and report their results responsibly. In high throughput screening campaigns, methods of statistical analysis should be chosen based on experimental design and parameters of the data rather than which method gives desired results ( Markossian et al., 2004 ; Zhang, 2011 ; Lindner et al., 2018 ). This case study introduces students to drug discovery screening techniques while also prompting them to think critically about the ethics of statistical analysis and data reporting.

This case is customizable, making it applicable to a wide variety of curriculums. The case discusses cancer biology ( Griffith et al., 1999 ; Henson et al., 2009 ; Cesare and Reddel, 2010 ), high throughput screening, statistics, and ethics in science (see supplemental teaching notes). Any combination of these topics can be emphasized for a particular course. To demonstrate the adaptability of the case, we implemented it in 4 undergraduate courses (BIOL 459: Molecular Biology, SCI 458: Scientific Research and Analysis, BIO 4610: Animal Physiology, and PHRS 500: Innovations and Transformations in Pharmacy and Pharmaceutical Sciences) and a graduate level course (PHRS 802: Introduction to Drug Development). Each implementation was catered toward course curriculum while also meeting the case learning objectives. We found that our implementations in undergraduate and graduate courses met the learning objectives and improved student comfort in discussing the case material.

Pedagogical framework(s)

It is hard to give students hands-on experience with experimental methods in high throughput screening as the equipment needed to run experiments is costly, hard to maintain, and often hard to access. Case studies can be a valuable interactive tool for teaching topics involving high throughput screening and statistical analysis of large datasets ( Mahdi et al., 2020 ). Here, we describe the implementations of “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” in 4 undergraduate courses and 1 graduate course. The case study was taught to over 70 students across 3 universities in the United States and students were surveyed to assess the case’s ability to meet the learning objectives.

The lesson plan included a pre-class reading assignment and question set, an in-class lecture and data analysis activity, and students were sent home with a post-class homework assignment involving a data analysis activity and a question set. Students who consented to being evaluated were given paper surveys at the beginning and end of the in-class session to measure improvement of student understanding after the in-class portion of the lesson. The pre-class reading, teaching notes, in-class activity dataset, step-by-step instructions for data analysis, and homework dataset and questions are provided ( Supplementary material ).

Methods: learning environment; learning objectives; pedagogical format

Learning environment, phrs 802 graduate level drug development and professional skills development.

PHRS 802 was an introductory course for first year graduate students in the Pharmaceutical Sciences PhD program at the University of North Carolina at Chapel Hill. All 19 students in this course had at least a bachelor’s degree and the student age range was 22–35. The class met once a week for a 60 min in-person class session. One of the main purposes of this course was to expose students to methods commonly used in each stage of drug development ( Sun et al., 2022 ). Since “Behind the Screen” describes high throughput screen development in the context of drug discovery, this case fit well into the course curriculum. For PHRS 802, we emphasized the case themes in high throughput drug discovery methods and customization of statistical analysis for phenotypic screening. The implementation of “Behind the Screen” was done in one 60-min class session of PHRS 802. The session included a 30-min lecture and a 30-min in-class activity (supplemental in class dataset). The in-class activity was done together as a whole class.

PHRS 500 innovations and transformations in pharmacy and pharmaceutical sciences

PHRS 500 was a summer course held at the University of North Carolina at Chapel Hill for undergraduate students interested in pursuing careers in pharmaceutical science. A majority of the 15 students in this course were visiting from out of the country and the age range of the class was 18–25. The goal of this course was to expose students to methods commonly used in each stage of drug development as well as give students an idea of what a graduate career in pharmaceutical sciences looks like. “Behind the Screen” fit very nicely into the PHRS 500 curriculum as it describes high throughput screen development in the context of drug discovery. For PHRS 500, we highlighted high throughput drug discovery methods and customization of statistical analysis for phenotypic screening. This case was especially applicable to PHRS 500 because it is written from the perspective of a graduate student. Since the participants in this course were interested in attending graduate school, this narrative gave them some insight into what graduate education might look like. The implementation of “Behind the Screen” was done in one 90-min class session of PHRS 500. The lecture took 30 min, leaving 60 min for the in-class activity. The in-class activity was done together as a whole class.

SCI 458 scientific research and analysis

Scientific Research and Analysis SCI 458 is an upper-level class for undergraduates at Crown College. There were 6 students in the class and the prerequisites were Applied Statistics and at least one science course. This case study was relevant to this course since it exposed students to data analysis methods and decision-making. While the biology content was less relevant, students from a range of majors can understand the drug discovery process more broadly and appreciate the value. The pre-work (supplemental case study for students) was given and then the slides presented in class as the instructor walked through the case study with the students over about 3 50-min class periods. This implementation was the pilot run of the case study, and some changes were made which were then used in the remaining classes. Since changes were made to the lesson materials after this implementation, the data collected from this course was not included in the data analysis for this study.

Bio 4610 animal physiology

Animal Physiology BIO 4610 is an upper-level class for undergraduate students at the University of North Carolina at Pembroke. The course functions as an advanced physiology course and is required for biomedical majors. There were 24 students in the class and the prerequisites were Anatomy and Physiology I and II. The class meets three times a week for 50 min for lecture and once a week for 90 min for lab. We used the case in our unit on data analysis and did not emphasize the cancer biology aspect. This case was especially relevant for the course, as most of the students were seniors applying to graduate school or medical school. The implementation of “Behind the Screen” was done with pre-work before class and one 90-min lab block of in-class time. The class block included a 30-min lecture and 60 min of in-class activity. The in-class activity was done in pairs.

BIOL 459 molecular biology

Molecular Biology BIOL 459 is an upper-level course for undergraduate students at Hastings College. The course comprised of 6-students and was an elective for Biology and Biochemistry majors, with Introduction to Genetics and Cell Biology as prerequisites. It met three times a week for 80 min and twice a week for 130 min. Unlike most courses at the college, this course focuses on lab work and reading literature with a small amount of lecture material and class activities. This case was relevant to the course in understanding aspects of experimental design and data analysis. Students were introduced to the case study and worked through most of the pre-work in one 80-min period and then worked on the in-class portion the next week in an 80-min period. Students worked together in class and finished the remaining homework on their own.

Pedagogical format

This pedagogical tool has 3 components: pre-class reading and questions (supplemental case study for students), in class lecture and activity (supplemental teaching notes and in class dataset), and post-class homework activity and questions (supplemental homework dataset and homework). The pre-class reading is a story-like description of a graduate student, Merry, joining a lab and being introduced to her first project. The narrative includes dialog between the graduate student and a senior member of the lab which explains the key topics of the case. The pre-class reading has questions embedded throughout the narrative as well as some at the end to help gage if the student is understanding the key takeaways of the reading assignment.

The in-class portion of the case study includes a lecture and in-class activity. The lecture is very customizable so lecturers could focus on the elements of the case that are most suited toward the course curriculum (supplemental teaching notes). For the implementations, lecture times typically ranged from approximately 15–30 min. Lectures were focused on what the instructor found most important for students to understand from the pre-class reading. The lecture portion included a power point that reviewed the main topics of the case (drug screening methods, cancer biology, statistical analysis methods, how quantitative polymerase chain reaction (qPCR) works, etc.) as well as time for students to ask questions about the pre-class assignment and the lecture content. The in-class activity followed the lecture and lasted anywhere between 30 min to about 2 h (over multiple class sessions). The in-class activity involved the class following the instructor through analyzing a data set using a target-based statistical metric and a metric more conducive to phenotypic screening (supplemental in class dataset). The in-class activity demonstrates what happens when you try to use a target-based metric to analyze a phenotypic screening dataset. Students used what they learned in the pre-class reading and lecture to explain which analysis made the most sense in these experiments and why. Some course instructors (PHRS 802 and 500) took time at the end of the in-class activity to discuss the biological significance of the data the class analyzed and helped students understand what the next steps would be in a drug screening campaign.

The homework assignment for the students included a second data set (supplemental homework dataset) which students were expected to analyze with both metrics to confirm the phenotypic screening metric was most appropriate. The homework assignment also included a few questions for the students to complete, to make sure they understood what their data meant in a biological context. Students were also given written step-by-step instructions on how to do the data analysis to help if they got stuck doing the homework (supplemental case study for students—last section). The homework assignment is intended to take 45–60 min to complete.

Learning objectives

The course learning outcomes relevant to the case study state that on successful completion of the course students should be able to Table 1 :

• Define phenotypic cell-based screening and identify appropriate screening controls.

• Apply statistical modeling to a phenotypic screen to identify biologically meaningful results.

• Interpret the biological significance of a Z’-value and a Z*-value.

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Table 1 . Breakdown of where each learning objective is addressed in the case materials.

Data collection

Consent forms were handed out to students in the class session before the implementation to ensure they had ample time to read over the form. Study participants handed signed consent forms in to the instructor before the implementation session started.

Students were expected to have completed the pre-class reading and questions prior to the lecture to give students a foundational understanding of screening types and why they differ in the way they are statistically analyzed. Pre class questions from the reading were expected to be completed as homework before class and were turned in except for in BIOL 459 and SCI 458. Consenting participants were asked to fill out a paper survey before the lecture. The survey included questions about participant demographic and asked students to rank their level of agreement with a list of statements. The statements were focused on how comfortable the student was in describing themselves as a scientist as well as how familiar they were with the case study topics.

After the in-class activity was completed, consenting students were given a second paper survey to fill out that asked the same questions as the pre-class survey. Student pre-and post-class responses were compiled and analyzed by Wilcoxon test to determine if student understanding of the key topics improved after the lecture and in-class activity. A small multiple choice question set is provided to help further assess students pre-and post-implementation (supplemental class quizzes).

In all courses the homework dataset and questions were turned in for a grade. The homework questions were focused on assessing the students’ ability to understand the biological significance of their statistical analysis.

Results (to date)

Study demographics.

Overall, we collected data from 21 undergraduate students and 12 graduate students. The undergraduate participants were aged 18–35 with a majority (57%) of students falling in the 18–21 age range ( Figure 1 ). Of the students surveyed, 52% had no previous lab experience and 40% were first generation college students. The majority (71%) of undergraduate participants were female, with less than 5% of students preferring not to report their gender. The graduate level participants were either in the 22–25 age range or 30–35, with most of the students (83%) aged 22–25 ( Figure 2 ). At least 58% of the graduate students were female, with 8% preferring not to disclose their gender. Most of the graduate students (75%) were not first-generation college students. Unsurprisingly, 100% of the graduate students had previous lab experience.

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Figure 1 . Undergraduate student demographics. Demographic breakdown of undergraduate participants in all implementations. Data was collected by survey questions given to consenting students and included multiple choice questions about the individual’s race, ethnicity, gender, and age. Students were also asked if they had previous experience in a lab setting and if they were first generation (gen) college students. Results were compiled and depicted as pie charts.

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Figure 2 . Graduate student demographics. Demographic breakdown of graduate participants from the implementation done in PHRS 802. Data was collected by survey questions given to consenting students and included multiple choice questions about the individual’s race, ethnicity, gender, and age. Students were also asked if they had previous experience in a lab setting and if they were first generation (gen) college students. Results were compiled and depicted as pie charts.

Analysis of in-class data analysis activity

Most of the undergraduate students were interested in biology and enjoyed the course they were participating in as most students agreed or strongly agreed with the statements “biology excites me” (81%), “I am engaged in this class” (71%), and “I like to participate in this class” (67%) in the pre-class survey ( Figure 3 ). Before the in-class lecture and statistics activity, a majority of the undergraduate students felt neutral, disagreed, or strongly disagreed with the statements “I know what a phenotypic screen is”(71.4%), “I can define Z’ and Z*”(81%), I feel comfortable performing statistical analysis”(81%), and “I can determine when a statistical method is appropriate”(66.6%). After the in-class section of the lesson plan, student responses for the statements “I know what a phenotypic screen is” and “I can define Z’ and Z*” skewed significantly more toward agree and strongly agree ( p < 0.0001). Students also had an increased comfortability in performing statistical analysis ( p < 0.01) as well as determining which statistical method to choose for a screening project ( p < 0.05). We also saw a significant increase in student confidence in sharing ideas in a group setting ( p < 0.05) as well as explaining quantitative topics to peers ( p < 0.05).

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Figure 3 . Pre-and Post-class survey questions demonstrate improvement in undergraduate student understanding of key topics. Distribution of student responses to survey questions before (pre) and after (post) the in-class portion of implementation. Students were asked to select which response (strongly agree, agree, neutral, disagree, or strongly disagree) best described their sentiment toward the statements listed. Pre-and post-class responses were analyzed via Wilcoxon test to determine if there was a significant increase in “agree” or “strongly agree” responses to any of the statements. Results suggested the case improved student comfortability sharing ideas in large groups, explaining quantitative topics, and determining appropriate statistical methods (* p  < 0.05). There also was significant improvement in student comfortability in statistical analysis (** p  < 0.01) as well as understanding of phenotypic screens, Z’ analysis, and Z* analysis (**** p  < 0.0001).

The graduate students were very comfortable with biology and most identified as scientists with most of the participants agreeing or strongly agreeing with the statements “I am a scientist” (92%), “I am a researcher” (100%), and “biology excites me” (91%) in the pre-class survey ( Figure 4 ). The graduate students displayed a slight increase in their confidence in determining what a phenotypic screen is ( p < 0.05) and defining Z’ and Z* ( p < 0.05). It should be noted that the graduate student participant group was at a higher education level than the undergraduate participants. The graduate students likely had more experience in statistical analysis and quantitative topics compared to the undergraduate student population.

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Figure 4 . Pre-and Post-class survey questions demonstrate moderate improvement in graduate student understanding of key topics. Distribution of student responses to survey questions before (pre) and after (post) the in-class portion of implementation. Students were asked to select which response (strongly agree, agree, neutral, disagree, or strongly disagree) best described their sentiment toward the statements listed. Pre-and post-class responses were analyzed via Wilcoxon test to determine if there was a significant increase in “agree” or “strongly agree” responses to any of the statements. Results indicate the case moderately improved student understanding of phenotypic screens, Z’ analysis, and Z* analysis (* p  < 0.05).

Student feedback

In the post-class survey, students were asked two open-ended questions. The first question was: compared to a traditional lecture, how did the format affect your experience? The second question asked for feedback on what worked well in the in-class activity and what aspects needed to be adjusted to improve student experience. Based on student responses to the first question, it seemed that some students found the content a little hard to understand at first, but overall felt more comfortable with their quantitative skills after the activity. Many students found the lesson format more engaging and preferred the hands-on activity to traditional lectures. A selection of undergraduate and graduate student responses to question one that represent the main points are shown below:

• “I really enjoyed the flipped classroom style. I felt like I came into class with all the pieces of the puzzle but the lecturer and activity put the pieces together into a picture.”

• “It allows for more engagement with the material and gave hands-on experience in analyzing data.”

• “This was hard to understand, but it did help with computational skills. I feel more comfortable with Excel now.”

• “I enjoyed the interactive nature of examining the data. It made it more hands-on and tangible.”

Student responses to the second question mostly mentioned the length of the pre-class reading assignment and the amount of time spent on the lecture. A few students found the pre-class reading to be a little long and took a long time to read. One graduate student felt that the dialog aspect of the pre-class reading was distracting and did not contribute to their understanding. Students also suggested a shorter lecture would allow for more time to be spent on the hands-on data activity, which they felt they got more benefit from compared to the lecture. A selection of student responses that represent the main improvement suggestions have been listed below:

• “The class can be a little more interactive. Let the students do [the analysis] themselves first and then give the answer.”

• “I like this module so much, but maybe the pre-class part can be written in an easier way to [understand] because it’s a little bit difficult to understand for students [new to this topic].”

• “Maybe a bit more time build into [class] for excel because some [parts] move too fast.”

• “More time on hands-on example and shorter lecture.”

The implementations of this case demonstrate that it can be used in a wide variety of undergraduate and graduate courses to teach students topics in drug discovery research. Student survey data showed that the case was effective in improving student confidence in ability to discuss the key topics in undergraduate and graduate level courses. We had a relatively small population of participants (21 undergraduate and 12 graduate students) and we would likely get a better idea of the effectiveness of the course with a larger survey group. The undergraduate classes were small (less than 25 students), so while we were able to reach a wide range of courses, the survey data was limited. We implemented a single graduate level course, which resulted in very limited survey data for students at this level. Implementing in other graduate courses would certainly provide a better view of how the case improves graduate student understanding of drug discovery methods. We did not compare the graduate student responses to the undergraduate student responses as our sample size was too small. Understanding if there is a difference in efficacy between these two student populations would be great to assess with additional implementation data. It is also worth noting that the graduate student assessment was identical to the undergraduate student assessment, and it is possible that the case may need to be adjusted for graduate courses to improve the efficacy of the case study.

There was also some variation in how the case was implemented in each course. Most courses allotted one 50–60-min class period for the lesson, however, one implementation was in a 90-min session and one was implemented over multiple 50 min class sessions. There was also some variety in data software used for the in-class activity. Some students preferred to use google sheets, while others used Microsoft Excel. These slight variations between implementations also factor into the limitations of this study, and more survey data may be informative for the best way to teach the case in the future.

Our implementation data shows that this in-class activity is an engaging and accessible way to teach students about drug discovery research methods. We observed throughout our implementations that many students preferred to use Google Sheets (a free resource) as they were most familiar with this software. The use of the free-ware, Google Sheets, allows students to get hands-on experience with real-world datasets at no cost to the university. The case study also includes dialog between a new graduate student and their mentors, which may be of interest to undergraduate students who are considering pursuing a graduate degree. “Behind the Screen” discusses a variety of scientific topics, making it easy for instructors to customize the case for their course. The main themes in this case are high throughput drug discovery methods, cancer biology, statistical analysis of large datasets, and ethics of data analysis and reporting.

We hope that “Behind the Screen” will be customized and implemented in multiple undergraduate courses. Each instructor that participated in this study was able to successfully tailor the case to fit into the curriculum of their course. For example, in the pharmaceutical science course implementations (PHRS 802 and PHRS 500), the lecture and in-class discussions were focused mostly on how target-based and phenotypic-based screens differ in drug discovery and when to use each type. The lecture was also dedicated to discussing the two types of statistical analyses described in the case and where and when each method would be applied. In addition to emphasizing these scientific points, the undergraduate pharmaceutical course implementation also had some discussion about graduate school, as these students were all interested in pursuing graduate careers. These classes focused less on the biology of the assay. This class also did not discuss the ethical implications that may occur when choosing statistical analysis methods.

For implementation in biology courses, it may be beneficial to focus more on the biological significance of the screen (supplemental teaching notes). This aspect will highlight the importance of understanding disease-specific cellular pathways in the design and implementation of high throughput screens. The instructor could then discuss the benefits and drawbacks of implementing phenotypic or target-based screens. Once the high throughput screening process has been introduced, methods for statistical analysis of screening data can be discussed. If ethics is part of the course curriculum, this is a great place to emphasize proper statistical procedures and discuss responsible data reporting. Following these discussions, the data from the phenotypic screen can be introduced. The instructor can explain that the assay uses qPCR to detect changes in a DNA biomarker, and “hits” (drugs that detectably change biomarker levels) are samples that fall above or below the statistical cutoff described in the case study [3 times the median of absolute deviation (MAD)] ( Zhang, 2011 ). Lastly, we recommend making sure all students in the class understand the basics of how qPCR analysis works. In-depth methodology knowledge is not necessary, but since the in-class dataset involves working with cycle threshold (C T ) values, it is important to ensure that students grasp the origin of these data values to gain a better understanding of statistical significance in data analysis. After the lecture, the instructor can answer any student questions and proceed to the in-class activity.

High throughput screening commonly used in drug discovery campaigns, and while it is essential that students are taught how to analyze large datasets, it is difficult for undergraduate institutions to provide hands-on experience with these methods. “Behind the Screen” is an interactive and highly versatile case study that provides students the opportunity to work with large datasets modeled from a real-world first-in-class screen. The case aims to increase students’ confidence in their ability to define phenotypic screening and proper controls, apply statical modeling to a phenotypic screen, and interpret the biological significance of a Z’- and Z*- value. Implementations of this case proved it to be successful in significantly improving undergraduate and graduate confidence in ability to confidently discuss the learning objectives. While the case was effective in these student populations, our sample sizes were small. Further implementation will allow us to evaluate if the case performs differently between undergraduate and graduate students.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Institution Review Boards at University of North Carolina at Pembroke, University of North Carolina at Chapel Hill, Crown College, and Hastings College. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

MF: Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. MJ: Funding acquisition, Resources, Supervision, Writing – review & editing. SP: Conceptualization, Funding acquisition, Resources, Supervision, Writing – review & editing. AS: Conceptualization, Data curation, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing. OA: Conceptualization, Writing – review & editing. ME: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing. AT: Conceptualization, Data curation, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing. CA: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Funding for high throughput data generation/modeling was provided by NIH/NCI R03CA252796 (MJ) and a UNC Lineberger Comprehensive Cancer Center Developmental Award which is supported in part by P30 CA016086 Cancer Center Core Support Grant (SP and MJ).

Acknowledgments

We recognize Dr. Sabrina Robertson and Dr. Carlos Goller, lead coordinators of the HITS Network under NSF Award 173031. Their efforts were instrumental in bringing the members of this team together to implement this work. We also thank all students who participated in our implementations and allowed us to collect survey data. Thank you to Amy Pomeroy for providing insightful discussion at the HITS meeting in May 2022.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2024.1342378/full#supplementary-material

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Keywords: high throughput screening, drug discovery, active learning, education, case study, big data

Citation: Froney MM, Jarstfer MB, Pattenden SG, Solem AC, Aina OO, Eslinger MR, Thomas A and Alexander CM (2024) Behind the screen: drug discovery using the big data of phenotypic analysis. Front. Educ . 9:1342378. doi: 10.3389/feduc.2024.1342378

Received: 21 November 2023; Accepted: 30 January 2024; Published: 14 February 2024.

Reviewed by:

Copyright © 2024 Froney, Jarstfer, Pattenden, Solem, Aina, Eslinger, Thomas and Alexander. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Courtney M. Alexander, [email protected]

This article is part of the Research Topic

Using Case Study and Narrative Pedagogy to Guide Students Through the Process of Science

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