Statology

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Descriptive vs. Inferential Statistics: What’s the Difference?

There are two main branches in the field of statistics:

  • Descriptive Statistics

Inferential Statistics

This tutorial explains the difference between the two branches and why each one is useful in certain situations.

Descriptive  Statistics

In a nutshell,  descriptive statistics  aims to  describe  a chunk of raw data using summary statistics, graphs, and tables.

Descriptive statistics are useful because they allow you to understand a group of data much more quickly and easily compared to just staring at rows and rows of raw data values.

For example, suppose we have a set of raw data that shows the test scores of 1,000 students at a particular school. We might be interested in the average test score along with the distribution of test scores.

Using descriptive statistics, we could find the average score and create a graph that helps us visualize the distribution of scores.

This allows us to understand the test scores of the students much more easily compared to just staring at the raw data.

Common Forms of Descriptive Statistics

There are three common forms of descriptive statistics:

1. Summary statistics.  These are statistics that  summarize  the data using a single number. There are two popular types of summary statistics:

  • Measures of central tendency : these numbers describe where the center of a dataset is located. Examples include the  mean   and the  median .
  • Measures of dispersion : these numbers describe how spread out the values are in the dataset. Examples include the  range ,  interquartile range ,  standard deviation , and  variance .

2. Graphs . Graphs help us visualize data. Common types of graphs used to visualize data include boxplots , histograms , stem-and-leaf plots , and scatterplots .

3. Tables . Tables can help us understand how data is distributed. One common type of table is a  frequency table , which tells us how many data values fall within certain ranges. 

Example of Using Descriptive Statistics

The following example illustrates how we might use descriptive statistics in the real world.

Suppose 1,000 students at a certain school all take the same test. We are interested in understanding the distribution of test scores, so we use the following descriptive statistics:

1. Summary Statistics

Mean: 82.13 . This tells us that the average test score among all 1,000 students is 82.13.

Median: 84.  This tells us that half of all students scored higher than 84 and half scored lower than 84.

Max: 100. Min: 45.  This tells us the maximum score that any student obtained was 100 and the minimum score was 45. The  range – which tells us the difference between the max and the min – is 55.

To visualize the distribution of test scores, we can create a histogram – a type of chart that uses rectangular bars to represent frequencies.

non descriptive analysis

Based on this histogram, we can see that the distribution of test scores is roughly bell-shaped. Most of the students scored between 70 and 90, while very few scored above 95 and fewer still scored below 50.

Another easy way to gain an understanding of the distribution of scores is to create a frequency table. For example, the following frequency table shows what percentage of students scored between various ranges:

non descriptive analysis

We can see that just 4% of the total students scored above a 95. We can also see that (12% + 9% + 4% = ) 25% of all students scored an 85 or higher.

A frequency table is particularly helpful if we want to know what percentage of the data values fall above or below a certain value. For example, suppose the school considers an “acceptable” test score to be any score above a 75.

By looking at the frequency table, we can easily see that (20% + 22% + 12% + 9% + 4% = ) 67% of the students received an acceptable test score.

In a nutshell,  inferential statistics  uses a small sample of data to draw  inferences  about the larger population that the sample came from.

For example, we might be interested in understanding the political preferences of millions of people in a country.

However, it would take too long and be too expensive to actually survey every individual in the country. Thus, we would instead take a smaller survey of say, 1,000 Americans, and use the results of the survey to draw inferences about the population as a whole.

This is the whole premise behind inferential statistics – we want to answer some question about a population, so we obtain data for a small sample of that population and use the data from the sample to draw inferences about the population.

The Importance of a Representative Sample

In order to be confident in our ability to use a sample to draw inferences about a population, we need to make sure that we have a  representative sample   – that is, a sample in which the characteristics of the individuals in the sample closely match the characteristics of the overall population.

Ideally, we want our sample to be like a “mini version” of our population. So, if we want to draw inferences on a population of students composed of 50% girls and 50% boys, our sample would not be representative if it included 90% boys and only 10% girls.

non descriptive analysis

If our sample is not similar to the overall population, then we cannot generalize the findings from the sample to the overall population with any confidence.

How to Obtain a Representative Sample

To maximize the chances that you obtain a representative sample, you need to focus on two things:

1. Make sure you use a random sampling method.

There are several different random sampling methods that you can use that are likely to produce a representative sample, including:

  • A simple random sample
  • A systematic random sample
  • A cluster random sample
  • A stratified random sample

Random sampling methods tend to produce representative samples because every member of the population has an equal chance of being included in the sample.

2. Make sure your sample size is large enough . 

Along with using an appropriate sampling method, it’s important to ensure that the sample is large enough so that you have enough data to generalize to the larger population.

To determine how large your sample should be, you have to consider the population size you’re studying, the confidence level you’d like to use, and the margin of error you consider to be acceptable.

Fortunately, you can use online calculators to plug in these values and see how large your sample needs to be.

Common Forms of Inferential Statistics

There are three common forms of inferential statistics:

1. Hypothesis Tests.

Often we’re interested in answering questions about a population such as:

  • Is the percentage of people in Ohio in support of candidate A higher than 50%?
  • Is the mean height of a certain plant equal to 14 inches?
  • Is there a difference between the mean height of students at School A compared to School B?

To answer these questions we can perform a hypothesis test , which allows us to use data from a sample to draw conclusions about populations.

2. Confidence Intervals . 

Sometimes we’re interested in estimating some value for a population. For example, we might be interested in the mean height of a certain plant species in Australia.

Instead of going around and measuring every single plant in the country, we might collect a small sample of plants and measure each one. Then, we can use the mean height of the plants in the sample to estimate the mean height for the population.

However, our sample is unlikely to provide a perfect estimate for the population. Fortunately, we can account for this uncertainty by creating a confidence interval , which provides a range of values that we’re confident the true population parameter falls in.

For example, we might produce a 95% confidence interval of [13.2, 14.8], which says we’re 95% confident that the true mean height of this plant species is between 13.2 inches and 14.8 inches.

3. Regression .

Sometimes we’re interested in understanding the relationship between two variables in a population.

For example, suppose we want to know if  hours spent studying per week  is related to  test scores . To answer this question, we could perform a technique known as  regression analysis .

So, we may observe the number of hours studied along with the test scores for 100 students and perform a regression analysis to see if there is a significant relationship between the two variables.

If the p-value of the regression turns out to be significant , then we can conclude that there is a significant relationship between these two variables in the overall population of students.

The Difference Between Descriptive and Inferential Statistics

In summary, the difference between descriptive and inferential statistics can be described as follows:

Descriptive statistics  use summary statistics, graphs, and tables to describe  a data set.

This is useful for helping us gain a quick and easy understanding of a data set without pouring over all of the individual data values.

Inferential statistics  use samples to draw  inferences  about larger populations.

Depending on the question you want to answer about a population, you may decide to use one or more of the following methods: hypothesis tests, confidence intervals, and regression analysis.

If you do choose to use one of these methods, keep in mind that your sample needs to be representative of your population , or the conclusions you draw will be unreliable.

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  • > Statistics

Non-Parametric Statistics: Types, Tests, and Examples

  • Pragya Soni
  • May 12, 2022

Non-Parametric Statistics: Types, Tests, and Examples title banner

Statistics, an essential element of data management and predictive analysis , is classified into two types, parametric and non-parametric. 

Parametric tests are based on the assumptions related to the population or data sources while, non-parametric test is not into assumptions, it's more factual than the parametric tests. Here is a detailed blog about non-parametric statistics.

What is the Meaning of Non-Parametric Statistics ?

Unlike, parametric statistics, non-parametric statistics is a branch of statistics that is not solely based on the parametrized families of assumptions and probability distribution. Non-parametric statistics depend on either being distribution free or having specified distribution, without keeping any parameters into consideration.

Non-parametric statistics are defined by non-parametric tests; these are the experiments that do not require any sample population for assumptions. For this reason, non-parametric tests are also known as distribution free tests as they don’t rely on data related to any particular parametric group of probability distributions.

In other terms, non-parametric statistics is a statistical method where a particular data is not required to fit in a normal distribution. Usually, non-parametric statistics used the ordinal data that doesn’t rely on the numbers, but rather a ranking or order. For consideration, statistical tests, inferences, statistical models, and descriptive statistics.

Non-parametric statistics is thus defined as a statistical method where data doesn’t come from a prescribed model that is determined by a small number of parameters. Unlike normal distribution model,  factorial design and regression modeling, non-parametric statistics is a whole different content.

Unlike parametric models, non-parametric is quite easy to use but it doesn’t offer the exact accuracy like the other statistical models. Therefore, non-parametric statistics is generally preferred for the studies where a net change in input has minute or no effect on the output. Like even if the numerical data changes, the results are likely to stay the same.

Also Read | What is Regression Testing?

How does Non-Parametric Statistics Work ?

Parametric statistics consists of the parameters like mean,  standard deviation , variance, etc. Thus, it uses the observed data to estimate the parameters of the distribution. Data are often assumed to come from a normal distribution with unknown parameters.

While, non-parametric statistics doesn’t assume the fact that the data is taken from a same or normal distribution. In fact, non-parametric statistics assume that the data is estimated under a different measurement. The actual data generating process is quite far from the normally distributed process.

Types of Non-Parametric Statistics

Non-parametric statistics are further classified into two major categories. Here is the brief introduction to both of them:

1. Descriptive Statistics

Descriptive statistics is a type of non-parametric statistics. It represents the entire population or a sample of a population. It breaks down the measure of central tendency and central variability.

2. Statistical Inference

Statistical inference is defined as the process through which inferences about the sample population is made according to the certain statistics calculated from the sample drawn through that population.

Some Examples of Non-Parametric Tests

In the recent research years, non-parametric data has gained appreciation due to their ease of use. Also, non-parametric statistics is applicable to a huge variety of data despite its mean, sample size, or other variation. As non-parametric statistics use fewer assumptions, it has wider scope than parametric statistics.

Here are some common  examples of non-parametric statistics :

Consider the case of a financial analyst who wants to estimate the value of risk of an investment. Now, rather than making the assumption that earnings follow a normal distribution, the analyst uses a histogram to estimate the distribution by applying non-parametric statistics.

Consider another case of a researcher who is researching to find out a relation between the sleep cycle and healthy state in human beings. Taking parametric statistics here will make the process quite complicated. 

So, despite using a method that assumes a normal distribution for illness frequency. The researcher will opt to use any non-parametric method like quantile regression analysis.

Similarly, consider the case of another health researcher, who wants to estimate the number of babies born underweight in India, he will also employ the non-parametric measurement for data testing.

A marketer that is interested in knowing the market growth or success of a company, will surely employ a non-statistical approach.

Any researcher that is testing the market to check the consumer preferences for a product will also employ a non-statistical data test. As different parameters in nutritional value of the product like agree, disagree, strongly agree and slightly agree will make the parametric application hard.

Any other science or social science research which include nominal variables such as age, gender, marital data, employment, or educational qualification is also called as non-parametric statistics. It plays an important role when the source data lacks clear numerical interpretation.

Also Read | Applications of Statistical Techniques

What are Non-Parametric Tests ?

Types of Non-Parametric Tests:1. Wilcoxon test 2. Mann-Whitney test 3. Kruskal Wallis test 4. Friedmann test

Types of Non-Parametric Tests

  Here is the list of non-parametric tests that are conducted on the population for the purpose of statistics tests :

Wilcoxon Rank Sum Test

The Wilcoxon test also known as rank sum test or signed rank test. It is a type of non-parametric test that works on two paired groups. The main focus of this test is comparison between two paired groups. The test helps in calculating the difference between each set of pairs and analyses the differences.

The Wilcoxon test is classified as a statistical  hypothesis tes t and is used to compare two related samples, matched samples, or repeated measurements on a single sample to assess whether their population mean rank is different or not.

Mann- Whitney U Test

The Mann-Whitney U test also known as the Mann-Whitney-Wilcoxon test, Wilcoxon rank sum test and Wilcoxon-Mann-Whitney test. It is a non-parametric test based on null hypothesis. It is equally likely that a randomly selected sample from one sample may have higher value than the other selected sample or maybe less.

Mann-Whitney test is usually used to compare the characteristics between two independent groups when the dependent variable is either ordinal or continuous. But these variables shouldn’t be normally distributed. For a Mann-Whitney test, four requirements are must to meet. The first three are related to study designs and the fourth one reflects the nature of data.

Kruskal Wallis Test

Sometimes referred to as a one way ANOVA on ranks, Kruskal Wallis H test is a nonparametric test that is used to determine the statistical differences between the two or more groups of an independent variable. The word ANOVA is expanded as Analysis of variance.

The test is named after the scientists who discovered it, William Kruskal and W. Allen Wallis. The major purpose of the test is to check if the sample is tested if the sample is taken from the same population or not.

Friedman Test

The Friedman test is similar to the Kruskal Wallis test. It is an alternative to the ANOVA test. The only difference between Friedman test and ANOVA test is that Friedman test works on repeated measures basis. Friedman test is used for creating differences between two groups when the dependent variable is measured in the ordinal.

The Friedman test is further divided into two parts, Friedman 1 test and Friedman 2 test. It was developed by sir Milton Friedman and hence is named after him. The test is even applicable to complete block designs and thus is also known as a special case of Durbin test.

Distribution Free Tests

Distribution free tests are defined as the mathematical procedures. These tests are widely used for testing statistical hypotheses. It makes no assumption about the probability distribution of the variables. An important list of distribution free tests is as follows:

  •  Anderson-Darling test: It is done to check if the sample is drawn from a given distribution or not.
  • Statistical bootstrap methods: It is a basic non-statistical test used to estimate the accuracy and sampling distribution of a statistic.
  • Cochran’s Q: Cochran’s Q is used to check constant treatments in block designs with 0/1 outcomes.
  • Cohen’s kappa: Cohen kappa is used to measure the inter-rater agreement for categorical items.
  • Kaplan-Meier test: Kaplan Meier test helps in estimating the survival function from lifetime data, modeling, and censoring.
  • Two-way analysis Friedman test: Also known as ranking test, it is used to randomize different block designs.
  • Kendall’s tau: The test helps in defining the statistical dependency between two different variables.
  • Kolmogorov-Smirnov test: The test draws the inference if a sample is taken from the same distribution or if two or more samples are taken from the same sample.
  • Kendall’s W: The test is used to measure the inference of an inter-rater agreement .
  • Kuiper’s test: The test is done to determine if the sample drawn from a given distribution is sensitive to cyclic variations or not.
  • Log Rank test: This test compares the survival distribution of two right-skewed and censored samples.
  • McNemar’s test: It tests the contingency in the sample and revert when the row and column marginal frequencies are equal to or not.
  • Median tests: As the name suggests, median tests check if the two samples drawn from the similar population have similar median values or not.
  • Pitman’s permutation test: It is a statistical test that yields the value of p variables. This is done by examining all possible rearrangements of labels.
  • Rank products: Rank products are used to detect expressed genes in replicated microarray experiments.
  • Siegel Tukey tests: This test is used for differences in scale between two groups.
  • Sign test: Sign test is used to test whether matched pair samples are drawn from distributions from equal medians.
  • Spearman’s rank: It is used to measure the statistical dependence between two variables using a monotonic function.
  • Squared ranks test: Squared rank test helps in testing the equality of variances between two or more variables.
  • Wald-Wolfowitz runs a test: This test is done to check if the elements of the sequence are mutually independent or random.

Also Read | Factor Analysis

Advantages and Disadvantages of Non-Parametric Tests

The benefits of non-parametric tests are as follows:

It is easy to understand and apply.

It consists of short calculations.

The assumption of the population is not required.

Non-parametric test is applicable to all data kinds

The limitations of non-parametric tests are:

It is less efficient than parametric tests.

Sometimes the result of non-parametric data is insufficient to provide an accurate answer.

Applications of Non-Parametric Tests

Non-parametric tests are quite helpful, in the cases :

Where parametric tests are not giving sufficient results.

When the testing hypothesis is not based on the sample.

For the quicker analysis of the sample.

When the data is unscaled.

The current scenario of research is based on fluctuating inputs, thus, non-parametric statistics and tests become essential for in-depth research and data analysis .

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non descriptive analysis

6.1 Overview of Non-Experimental Research

Learning objectives.

  • Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
  • Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

Non-experimental research  is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).

Most researchers in psychology consider the distinction between experimental and non-experimental research to be an extremely important one. This is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. As we will see, however, this inability to make causal conclusions does not mean that non-experimental research is less important than experimental research.

When to Use Non-Experimental Research

As we saw in the last chapter , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:

  • the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
  • the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
  • the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
  • the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach will suffice. But the two approaches can also be used to address the same research question in complementary ways. For example, Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into three broad categories: cross-sectional research, correlational research, and observational research. 

First, cross-sectional research  involves comparing two or more pre-existing groups of people. What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a cross-sectional study because the researcher did not manipulate the students’ nationalities. As another example, if we wanted to compare the memory test performance of a group of cannabis users with a group of non-users, this would be considered a cross-sectional study because for ethical and practical reasons we would not be able to randomly assign participants to the cannabis user and non-user groups. Rather we would need to compare these pre-existing groups which could introduce a selection bias (the groups may differ in other ways that affect their responses on the dependent variable). For instance, cannabis users are more likely to use more alcohol and other drugs and these differences may account for differences in the dependent variable across groups, rather than cannabis use per se.

Cross-sectional designs are commonly used by developmental psychologists who study aging and by researchers interested in sex differences. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect) rather than a direct effect of age. For this reason, longitudinal studies in which one group of people is followed as they age offer a superior means of studying the effects of aging. Once again, cross-sectional designs are also commonly used to study sex differences. Since researchers cannot practically or ethically manipulate the sex of their participants they must rely on cross-sectional designs to compare groups of men and women on different outcomes (e.g., verbal ability, substance use, depression). Using these designs researchers have discovered that men are more likely than women to suffer from substance abuse problems while women are more likely than men to suffer from depression. But, using this design it is unclear what is causing these differences. So, using this design it is unclear whether these differences are due to environmental factors like socialization or biological factors like hormones?

When researchers use a participant characteristic to create groups (nationality, cannabis use, age, sex), the independent variable is usually referred to as an experimenter-selected independent variable (as opposed to the experimenter-manipulated independent variables used in experimental research). Figure 6.1 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a cross-sectional study because it is unclear whether the independent variable was manipulated by the researcher or simply selected by the researcher. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then the independent variable was experimenter-manipulated and it is a true experiment. If the researcher simply asked participants whether they made daily to-do lists or not, then the independent variable it is experimenter-selected and the study is cross-sectional. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a cross-sectional study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead. Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed. The crucial point is that what defines a study as experimental or cross-sectional l is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 6.1  Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Second, the most common type of non-experimental research conducted in Psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.  More specifically, in correlational research , the researcher measures two continuous variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related. Correlational research is very similar to cross-sectional research, and sometimes these terms are used interchangeably. The distinction that will be made in this book is that, rather than comparing two or more pre-existing groups of people as is done with cross-sectional research, correlational research involves correlating two continuous variables (groups are not formed and compared).

Third,   observational research  is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In  qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable.  Figure 6.2  shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) is in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Figure 7.1 Internal Validity of Correlational, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlational studies lower still.

Figure 6.2 Internal Validity of Correlation, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlation studies lower still.

Notice also in  Figure 6.2  that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Key Takeaways

  • Non-experimental research is research that lacks the manipulation of an independent variable.
  • There are two broad types of non-experimental research. Correlational research that focuses on statistical relationships between variables that are measured but not manipulated, and observational research in which participants are observed and their behavior is recorded without the researcher interfering or manipulating any variables.
  • In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.
  • A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
  • A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
  • A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
  • A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
  • Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
  • Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

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1.6: Non-Experimental Research

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  • Page ID 32918

  • Yang Lydia Yang
  • Kansas State University

What Is Non-Experimental Research?

Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).

Most researchers in social sciences consider the distinction between experimental and non-experimental research to be an extremely important one. This is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. As we will see, however, this inability to make causal conclusions does not mean that non-experimental research is less important than experimental research. It is simply used in cases where experimental research is not able to be carried out.

When to Use Non-Experimental Research

As we saw earlier , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:

  • the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., how accurate are people’s first impressions?).
  • the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
  • the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question(s).

Types of Non-Experimental Research

The most common type of non-experimental research conducted in social sciences is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable. More specifically, in correlational research , the researcher measures two variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related. Another example is a researcher interested in the relationship between education levels and annual income can collect data on individuals highest education levels and their annual income to see if the two variables are statistically related. In the first example, both variables are interval (continuous). In the second example, one variable is categorical (educational levels) and one is continuous. In either case, we are studying the variables as they naturally occur or have occurred.

Cross-Sectional, Longitudinal, and Cross-Sequential Studies

When social scientists wish to study change over time (for example, when developmental psychologists wish to study aging) they usually take one of three non-experimental approaches: cross-sectional, longitudinal, or cross-sequential. Cross-sectional studies involve comparing two or more pre-existing groups of people (e.g., children at different stages of development). What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect ) rather than a direct effect of age. For this reason, longitudinal studies , in which one group of people is followed over time as they age, offer a superior means of studying the effects of aging. However, longitudinal studies are by definition more time consuming and so require a much greater investment on the part of the researcher and the participants. A third approach, known as cross-sequential studies , combines elements of both cross-sectional and longitudinal studies. Rather than measuring differences between people in different age groups or following the same people over a long period of time, researchers adopting this approach choose a smaller period of time during which they follow people in different age groups. For example, they might measure changes over a ten year period among participants who at the start of the study fall into the following age groups: 20 years old, 30 years old, 40 years old, 50 years old, and 60 years old. This design is advantageous because the researcher reaps the immediate benefits of being able to compare the age groups after the first assessment. Further, by following the different age groups over time they can subsequently determine whether the original differences they found across the age groups are due to true age effects or cohort effects.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure \(\PageIndex{1}\) shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research falls in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the inability to randomly assign children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

7.1.png

Notice also in Figure \(\PageIndex{1}\) that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational (non-experimental) studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables.

Grad Coach

Critical Writing 101

Descriptive vs analytical vs critical writing.

By: Derek Jansen (MBA) | Expert Reviewed By: Dr Eunice Rautenbach | April 2017

Across the thousands of students we work with , descriptive writing (as opposed to critical or analytical writing) is an incredibly pervasive problem . In fact, it’s probably the biggest killer of marks in dissertations, theses and research papers . So, in this post, we’ll explain the difference between descriptive and analytical writing in straightforward terms, along with plenty of practical examples.

analytical and descriptive writing

Descriptive vs Analytical Writing

Writing critically is one of the most important skills you’ll need to master for your academic journey, but what exactly does this mean?

Well, when it comes to writing, at least for academic purposes, there are two main types – descriptive writing and critical writing. Critical writing is also sometimes referred to as analytical writing, so we’ll use these two terms interchangeably.

To understand what constitutes critical (or analytical) writing, it’s useful to compare it against its opposite, descriptive writing. At the most basic level, descriptive writing merely communicates the “ what ”, “ where ”, “ when ” or “ who ”. In other words, it describes a thing, place, time or person. It doesn’t consider anything beyond that or explore the situation’s impact, importance or meaning. Here’s an example of a descriptive sentence:

  “Yesterday, the president unexpectedly fired the minister of finance.”

As you can see, this sentence just states what happened, when it happened and who was involved. Classic descriptive writing.

Contrasted to this, critical writing takes things a step further and unveils the “ so what? ” – in other words, it explains the impact or consequence of a given situation. Let’s stick with the same event and look at an example of analytical writing:

“The president’s unexpected firing of the well-respected finance minister had an immediate negative impact on investor confidence. This led to a sharp decrease in the value of the local currency, especially against the US dollar. This devaluation means that all dollar-based imports are now expected to rise in cost, thereby raising the cost of living for citizens, and reducing disposable income.”

As you can see in this example, the descriptive version only tells us what happened (the president fired the finance minister), whereas the critical version goes on to discuss some of the impacts of the president’s actions.

Analysis

Ideally, critical writing should always link back to the broader objectives of the paper or project, explaining what each thing or event means in relation to those objectives. In a dissertation or thesis, this would involve linking the discussion back to the research aims, objectives and research questions – in other words, the golden thread .

Sounds a bit fluffy and conceptual? Let’s look at an example:

If your research aims involved understanding how the local environment impacts demand for specialty imported vegetables, you would need to explain how the devaluation of the local currency means that the imported vegetables would become more expensive relative to locally farmed options. This in turn would likely have a negative impact on sales, as consumers would turn to cheaper local alternatives.

As you can see, critical (or analytical) writing goes beyond just describing (that’s what descriptive writing covers) and instead focuses on the meaning of things, events or situations, especially in relation to the core research aims and questions.

Need a helping hand?

non descriptive analysis

But wait, there’s more.

This “ what vs so what”  distinction is important in understanding the difference between description and analysis, but it is not the only difference – the differences go deeper than this. The table below explains some other key differences between descriptive and analytical writing.

Should I avoid descriptive writing altogether?

Not quite. For the most part, you’ll need some descriptive writing to lay the foundation for the critical, analytical writing. In other words, you’ll usually need to state the “what” before you can discuss the “so what”. Therefore, description is simply unavoidable and in fact quite essential , but you do want to keep it to a minimum and focus your word count on the analytical side of things.

As you write, a good rule of thumb is to identify every what (in other words, every descriptive point you make) and then check whether it is accompanied by a so what (in other words, a critical conclusion regarding its meaning or impact).

Of course, this won’t always be necessary as some conclusions are fairly obvious and go without saying. But, this basic practice should help you minimise description, maximise analysis, and most importantly, earn you marks!

Let’s recap.

So, the key takeaways for this post are as follows:

  • Descriptive writing focuses on the what , while critical/analytical writing focuses on the so what .
  • Analytical writing should link the discussion back to the research aims, objectives or research questions (the golden thread).
  • Some amount of description will always be needed, but aim to minimise description and maximise analysis to earn higher marks.

non descriptive analysis

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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5 dos & don'ts when writing a research proposal

19 Comments

Sarah

Thank you so much. This was helpful and a switch from the bad writing habits to the good habits.

Derek Jansen

Great to hear that, Sarah. Glad you found it useful!

Anne Marie

I am currently working on my Masters Thesis and found this extremely informative and helpful. Thank you kindly.

Marisa

I’m currently a University student and this is so helpful. Thank you.

Divya Madhuri Nankiya

It really helped me to get the exact meaning of analytical writing. Differences between the two explains it well

Linda Odero

Thank you! this was very useful

Bridget

With much appreciation, I say thank you. Your explanations are down to earth. It has been helpful.

olumide Folahan

Very helpful towards my theses journey! Many thanks 👍

joan

very helpful

very helpful indeed

Felix

Thanks Derek for the useful coaching

Diana Rose Oyula

Thank you for sharing this. I was stuck on descriptive now I can do my corrections. Thank you.

Siu Tang

I was struggling to differentiate between descriptive and analytical writing. I googled and found this as it is so helpful. Thank you for sharing.

Leonard Ngowo

I am glad to see this differences of descriptive against analytical writing. This is going to improve my masters dissertation

Thanks in deed. It was helpful

Abdurrahman Abdullahi Babale

Thank you so much. I’m now better informed

Stew

Busy with MBA in South Africa, this is very helpful as most of the writing requires one to expound on the topics. thanks for this, it’s a salvation from watching the blinking cursor for hours while figuring out what to write to hit the 5000 word target 😂

Ggracious Enwoods Soko

It’s been fantastic and enriching. Thanks a lot, GRAD COACH.

Sunil Pradhan

Wonderful explanation of descriptive vs analytic writing with examples. This is going to be greatly helpful for me as I am writing my thesis at the moment. Thank you Grad Coach. I follow your YouTube videos and subscribed and liked every time I watch one.

Abdulai Gariba Abanga

Very useful piece. thanks

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  • Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

  • The type of knowledge you aim to produce
  • The type of data you will collect and analyze
  • The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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Chapter 7: Nonexperimental Research

Overview of Nonexperimental Research

Learning Objectives

  • Define nonexperimental research, distinguish it clearly from experimental research, and give several examples.
  • Explain when a researcher might choose to conduct nonexperimental research as opposed to experimental research.

What Is Nonexperimental Research?

Nonexperimental research  is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are  not . But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This distinction is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this inability does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense.

When to Use Nonexperimental Research

As we saw in  Chapter 6 , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable and randomly assign participants to conditions or to orders of conditions. It stands to reason, therefore, that nonexperimental research is appropriate—even necessary—when these conditions are not met. There are many ways in which preferring nonexperimental research can be the case.

  • The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
  • The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?).
  • The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?).
  • The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and nonexperimental approaches is generally dictated by the nature of the research question. If it is about a causal relationship and involves an independent variable that can be manipulated, the experimental approach is typically preferred. Otherwise, the nonexperimental approach is preferred. But the two approaches can also be used to address the same research question in complementary ways. For example, nonexperimental studies establishing that there is a relationship between watching violent television and aggressive behaviour have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] . Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [2] .

Types of Nonexperimental Research

Nonexperimental research falls into three broad categories: single-variable research, correlational and quasi-experimental research, and qualitative research. First, research can be nonexperimental because it focuses on a single variable rather than a statistical relationship between two variables. Although there is no widely shared term for this kind of research, we will call it  single-variable research . Milgram’s original obedience study was nonexperimental in this way. He was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of single-variable research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the research asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.)

As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This detail is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their self-esteem. But this design would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied  compares  with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied thereby introducing another variable.

Research can also be nonexperimental because it focuses on a statistical relationship between two variables but does not include the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both. This kind of research takes two basic forms: correlational research and quasi-experimental research. In correlational research , the researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them. A research methods student who finds out whether each of several middle-school students has been bullied and then measures each student’s self-esteem is conducting correlational research. In  quasi-experimental research , the researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions. For example, a researcher might start an antibullying program (a kind of treatment) at one school and compare the incidence of bullying at that school with the incidence at a similar school that has no antibullying program.

The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In  qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256). [3] Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable.  Figure 7.1  shows how experimental, quasi-experimental, and correlational research vary in terms of internal validity. Experimental research tends to be highest because it addresses the directionality and third-variable problems through manipulation and the control of extraneous variables through random assignment. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Correlational research is lowest because it fails to address either problem. If the average score on the dependent variable differs across levels of the independent variable, it  could  be that the independent variable is responsible, but there are other interpretations. In some situations, the direction of causality could be reversed. In others, there could be a third variable that is causing differences in both the independent and dependent variables. Quasi-experimental research is in the middle because the manipulation of the independent variable addresses some problems, but the lack of random assignment and experimental control fails to address others. Imagine, for example, that a researcher finds two similar schools, starts an antibullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” There is no directionality problem because clearly the number of bullying incidents did not determine which school got the program. However, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying.

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Notice also in  Figure 7.1  that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in  Chapter 5.

Key Takeaways

  • Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both.
  • There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables. Correlational and quasi-experimental research focus on a statistical relationship but lack manipulation or random assignment. Qualitative research focuses on broader research questions, typically involves collecting large amounts of data from a small number of participants, and analyses the data nonstatistically.
  • In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.

Discussion: For each of the following studies, decide which type of research design it is and explain why.

  • A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
  • A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
  • A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
  • A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
  • Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
  • Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
  • Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

Research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

Research that focuses on a single variable rather than a statistical relationship between two variables.

The researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them.

The researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions.

Research Methods in Psychology - 2nd Canadian Edition by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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  • v.25(5); 2020 Aug

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An overview of the qualitative descriptive design within nursing research

Louise doyle.

Associate Professor in Mental Health Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Catherine McCabe

Associate Professor in General Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Brian Keogh

Assistant Professor in Mental Health Nursing, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Annemarie Brady

Chair of Nursing and Chronic Illness, School of Nursing and Midwifery, Trinity College Dublin, Ireland

Qualitative descriptive designs are common in nursing and healthcare research due to their inherent simplicity, flexibility and utility in diverse healthcare contexts. However, the application of descriptive research is sometimes critiqued in terms of scientific rigor. Inconsistency in decision making within the research process coupled with a lack of transparency has created issues of credibility for this type of approach. It can be difficult to clearly differentiate what constitutes a descriptive research design from the range of other methodologies at the disposal of qualitative researchers.

This paper provides an overview of qualitative descriptive research, orientates to the underlying philosophical perspectives and key characteristics that define this approach and identifies the implications for healthcare practice and policy.

Methods and results

Using real-world examples from healthcare research, the paper provides insight to the practical application of descriptive research at all stages of the design process and identifies the critical elements that should be explicit when applying this approach.

Conclusions

By adding to the existing knowledge base, this paper enhances the information available to researchers who wish to use the qualitative descriptive approach, influencing the standard of how this approach is employed in healthcare research.

Introduction

Qualitative descriptive approaches to nursing and healthcare research provide a broad insight into particular phenomena and can be used in a variety of ways including as a standalone research design, as a precursor to larger qualitative studies and commonly as the qualitative component in mixed-methods studies. Despite the widespread use of descriptive approaches within nursing research, there is limited methodological guidance about this type of design in research texts or papers. The lack of adequate representation in research texts has at times resulted in novice researchers using other more complex qualitative designs including grounded theory or phenomenology without meeting the requirements of these approaches ( Lambert and Lambert, 2012 ), or having an appropriate rationale for use of these approaches. This suggests there is a need to have more discussion about how and why descriptive approaches to qualitative research are used. This serves to not only provide information and guidance for researchers, but to ensure acceptable standards in how this approach is applied in healthcare research.

Rationale for qualitative descriptive research

The selection of an appropriate approach to answer research questions is one of the most important stages of the research process; consequently, there is a requirement that researchers can clearly articulate and defend their selection. Those who wish to undertake qualitative research have a range of approaches available to them including grounded theory, phenomenology and ethnography. However, these designs may not be the most suitable for studies that do not require a deeply theoretical context and aim to stay close to and describe participants’ experiences. The most frequently proposed rationale for the use of a descriptive approach to is to provide straightforward descriptions of experiences and perceptions ( Sandelowski, 2010 ), particularly in areas where little is known about the topic under investigation. A qualitative descriptive design may be deemed most appropriate as it recognises the subjective nature of the problem, the different experiences participants have and will present the findings in a way that directly reflects or closely resembles the terminology used in the initial research question ( Bradshaw et al., 2017 ). This is particularly relevant in nursing and healthcare research, which is commonly concerned with how patients experience illness and associated healthcare interventions. The utilisation of a qualitative descriptive approach is often encouraged in Master’s level nurse education programmes as it enables novice clinical nurse researchers explore important healthcare questions that have direct implications and impact for their specific healthcare setting (Colorafi and Evans, 2016). As a Master’s level project is often the first piece of primary research undertaken by nurses, the use of a qualitative descriptive design provides an excellent method to address important clinical issues where the focus is not on increasing theoretical or conceptual understanding, but rather contributing to change and quality improvement in the practice setting ( Chafe, 2017 ).

This design is also frequently used within mixed-methods studies where qualitative data can explain quantitative findings in explanatory studies, be used for questionnaire development in exploratory studies and validate and corroborate findings in convergent studies ( Doyle et al., 2016 ). There has also been an increase in the use of qualitative descriptive research embedded in large-scale healthcare intervention studies, which can serve a number of purposes including identifying participants’ perceptions of why an intervention worked or, just as importantly, did not work and how the intervention might be improved ( Doyle et al., 2016 ). Using qualitative descriptive research in this manner can help to make the findings of intervention studies more clinically meaningful.

Philosophical and theoretical influences

Qualitative descriptive research generates data that describe the ‘who, what, and where of events or experiences’ from a subjective perspective ( Kim et al., 2017 , p. 23). From a philosophical perspective, this approach to research is best aligned with constructionism and critical theories that use interpretative and naturalistic methods ( Lincoln et al., 2017 ). These philosophical perspectives represent the view that reality exists within various contexts that are dynamic and perceived differently depending on the subject, therefore, reality is multiple and subjective ( Lincoln et al., 2017 ). In qualitative descriptive research, this translates into researchers being concerned with understanding the individual human experience in its unique context. This type of inquiry requires flexible research processes that are inductive and dynamic but do not transform the data beyond recognition from the phenomenon being studied ( Ormston et al., 2014 ; Sandelwoski 2010). Descriptive qualitative research has also been aligned with pragmatism ( Neergaard et al., 2009 ) where decisions are made about how the research should be conducted based on the aims or objectives and context of the study ( Ormston et al., 2014 ). The pragmatist researcher is not aligned to one particular view of knowledge generation or one particular methodology. Instead they look to the concepts or phenomena being studied to guide decision making in the research process, facilitating the selection of the most appropriate methods to answer the research question ( Bishop, 2015 ).

Perhaps linked to the practical application of pragmatism to research, that is, applying the best methods to answer the research question, is the classification of qualitative descriptive research by Sandelowski ( 2010 , p. 82) into a ‘distributed residual category’. This recognises and incorporates uncertainty about the phenomena being studied and the research methods used to study them. For researchers, it permits the use of one or more different types of inquiry, which is essential when acknowledging and exploring different realities and subjective experiences in relation to phenomena ( Long et al., 2018 ). Clarity, in terms of the rationale for the phenomenon being studied and the methods used by the researcher, emerges from the qualitative descriptive approach because the data gathered continue to remain close to the phenomenon throughout the study ( Sandelowski, 2010 ). For this to happen a flexible approach is required and this is evident in the practice of ‘borrowing’ elements of other qualitative methodologies such as grounded theory, phenomenology and ethnography ( Vaismoradi et al., 2013 ).

Regarded as a positive aspect by many researchers who are interested in studying human nature and phenomenon, others believe this flexibility leads to inconsistency across studies and in some cases complacency by researchers. This can result in vague or unexplained decision making around the research process and subsequent lack of credibility. Accordingly, nurse researchers need to be reflexive, that is, clear about their role and position in terms of the phenomena being studied, the context, the theoretical framework and all decision-making processes used in a qualitative descriptive study. This adds credibility to both the study and qualitative descriptive research.

Methods in qualitative descriptive research

As with any research study, the application of descriptive methods will emerge in response to the aims and objectives, which will influence the sampling, data collection and analysis phases of the study.

Most qualitative research aligns itself with non-probability sampling and descriptive research is no different. Descriptive research generally uses purposive sampling and a range of purposive sampling techniques have been described ( Palinkas et al., 2015 ). Many researchers use a combination of approaches such as convenience, opportunistic or snowball sampling as part of the sampling framework, which is determined by the desired sample and the phenomena being studied.

Purposive sampling refers to selecting research participants that can speak to the research aims and who have knowledge and experience of the phenomenon under scrutiny ( Ritchie et al., 2014 ). When purposive sampling is used in a study it delimits and narrows the study population; however, researchers need to remember that other characteristics of the sample will also affect the population, such as the location of the researcher and their flexibility to recruit participants from beyond their base. In addition, the heterogeneity of the population will need to be considered and how this might influence sampling and subsequent data collection and analysis ( Palinkas et al ., 2015 ). Take, for example, conducting research on the experience of caring for people with Alzheimer’s disease (AD). For the most part AD is a condition that affects older people and experiences of participants caring for older people will ultimately dominate the sample. However, AD also affects younger people and how this will impact on sampling needs to be considered before recruitment as both groups will have very different experiences, although there will be overlap. Teddlie and Fu (2007) suggest that although some purposive sampling techniques generate representative cases, most result in describing contrasting cases, which they argue are at the heart of qualitative analysis. To achieve this, Sandelowski (2010) suggests that maximum variation sampling is particularly useful in qualitative descriptive research, which may acknowledge the range of experiences that exist especially in healthcare research. Palinkas et al . (2015) describe maximum variation sampling as identifying shared patterns that emerge from heterogeneity. In other words, researchers attempt to include a wide range of participants and experiences when collecting data. This may be more difficult to achieve in areas where little is known about the substantive area and may depend on the researcher’s knowledge and immersion within the subject area.

Sample size will also need to be considered and although small sample sizes are common in qualitative descriptive research, researchers need to be careful they have enough data collected to meet the study aims ( Ritchie et al., 2014 ). Pre-determining the sample size prior to data collection may stifle the analytic process, resulting in too much or too little data. Traditionally, the gold standard for sample size in qualitative research is data saturation, which differs depending on the research design and the size of the population ( Fusch and Ness, 2015 ). Data saturation is reached ‘when there is enough information to replicate the study, when the ability to obtain additional new information has been attained, and when further coding is no longer feasible’ ( Fusch and Ness, 2015 , p. 1408). However, some argue that although saturation is often reported, it is rarely demonstrated in qualitative descriptive research reports ( Caelli et al., 2003 ; Malterud et al., 2016 ). If data saturation is used to determine sample size, it is suggested that greater emphasis be placed on demonstrating how saturation was reached and at what level to provide more credibility to sample sizes ( Caelli et al., 2003 ). Sample size calculation should be an estimate until saturation has been achieved through the concurrent processes of data collection and analysis. Where saturation has not been achieved, or where sample size has been predetermined for resource reasons, this should be clearly acknowledged. However, there is also a movement away from the reliance on data saturation as a measure of sample size in qualitative research ( Malterud et al., 2016 ). O’Reilly and Parker (2012) question the appropriateness of the rigid application of saturation as a sample size measure arguing that outside of Grounded Theory, its use is inconsistent and at times questionable. Malterud et al. (2016) focus instead on the concept of ‘information power’ to determine sample size. Here, they suggest sample size is determined by the amount of information the sample holds relevant to the actual study rather than the number of participants ( Malterud et al., 2016 ). Some guidance on specific sample size depending on research design has been provided in the literature; however, these are sometimes conflicting and in some cases lack evidence to support their claims ( Guest et al., 2006 ). This is further complicated by the range of qualitative designs and data collection approaches available.

Data collection

Data collection methods in qualitative descriptive research are diverse and aim to discover the who, what and where of phenomena ( Sandelowski, 2000 ). Although semi-structured individual face-to-face interviews are the most commonly used data collection approaches ( Kim et al ., 2017 ), focus groups, telephone interviews and online approaches are also used.

Focus groups involve people with similar characteristics coming together in a relaxed and permissive environment to share their thoughts, experiences and insights ( Krueger and Casey, 2009 ). Participants share their own views and experiences, but also listen to and reflect on the experiences of other group members. It is this synergistic process of interacting with other group members that refines individuals’ viewpoints to a deeper and more considered level and produces data and insights that would not be accessible without the interaction found in a group (Finch et al., 2014). Telephone interviews and online approaches are gaining more traction as they offer greater flexibility and reduced costs for researchers and ease of access for participants. In addition, they may help to achieve maximum variation sampling or examine experiences from a national or international perspective. Face-to-face interviews are often perceived as more appropriate than telephone interviews; however, this assumption has been challenged as evidence to support the use of telephone interviews emerges ( Ward et al., 2015 ). Online data collection also offers the opportunity to collect synchronous and asynchronous data using instant messaging and other online media ( Hooley et al., 2011 ). Online interviews or focus groups conducted via Skype or other media may overcome some of the limitations of telephone interviews, although observation of non-verbal communication may be more difficult to achieve ( Janghorban et al., 2014 ). Open-ended free-text responses in surveys have also been identified as useful data sources in qualitative descriptive studies ( Kim et al . , 2017 ) and in particular the use of online open-ended questions, which can have a large geographical reach ( Seixas et al., 2018 ). Observation is also cited as an approach to data collection in qualitative descriptive research ( Sandelowski, 2000 ; Lambert and Lambert, 2012 ); however, in a systematic review examining the characteristics of qualitative research studies, observation was cited as an additional source of data and was not used as a primary source of data collection ( Kim et al. , 2017 ).

Data analysis and interpretation

According to Lambert and Lambert (2012) , data analysis in qualitative descriptive research is data driven and does not use an approach that has emerged from a pre-existing philosophical or epistemological perspective. Within qualitative descriptive research, it is important analysis is kept at a level at which those to whom the research pertains are easily able to understand and so can use the findings in healthcare practice ( Chafe, 2017 ). The approach to analysis is dictated by the aims of the research and as qualitative descriptive research is generally explorative, inductive approaches will commonly need to be applied although deductive approaches can also be used ( Kim et al . , 2017 ).

Content and thematic analyses are the most commonly used data analysis techniques in qualitative descriptive research. Vaismoradi et al . (2013) argue that content and thematic analysis, although poorly understood and unevenly applied, offer legitimate ways of a lower level of interpretation that is often required in qualitative descriptive research. Sandelowski (2000) indicated that qualitative content analysis is the approach of choice in descriptive research; however, confusion exists between content and thematic analysis, which sometimes means researchers use a combination of the two. Vaismoradi et al. (2013) argue there are differences between the two and that content analysis allows the researchers to analyse the data qualitatively as well as being able to quantify the data whereas thematic analysis provides a purely qualitative account of the data that is richer and more detailed. Decisions to use one over the other will depend on the aims of the study, which will dictate the depth of analysis required. Although there is a range of analysis guidelines available, they share some characteristics and an overview of these, derived from some key texts ( Sandleowski, 2010 ; Braun and Clark, 2006 ; Newell and Burnard, 2006), is presented in Table 1 . Central to these guidelines is an attempt by the researcher to immerse themselves in the data and the ability to demonstrate a consistent and systematic approach to the analysis.

Common characteristics of descriptive qualitative analysis.

Coding in qualitative descriptive research can be inductive and emerge from the data, or a priori where they are based on a pre-determined template as in template analysis. Inductive codes can be ‘in vivo’ where the researcher uses the words or concepts as stated by the participants ( Howitt, 2019 ), or can be named by the researcher and grouped together to form emerging themes or categories through an iterative systematic process until the final themes emerge. Template analysis involves designing a coding template, which is designed inductively from a subset of the data and then applied to all the data and refined as appropriate ( King, 2012 ). It offers a standardised approach that may be useful when several researchers are involved in the analysis process.

Within qualitative research studies generally, the analysis of data and subsequent presentation of research findings can range from studies with a relatively minimal amount of interpretation to those with high levels of interpretation ( Sandelowski and Barroso, 2003 ). The degree of interpretation required in qualitative descriptive research is contentious. Sandelowski (2010) argues that although descriptive research produces findings that are ‘data-near’, they are nevertheless interpretative. Sandelowski (2010) reports that a common misconception in qualitative descriptive designs is that researchers do not need to include any level of analysis and interpretation and can rely solely on indiscriminately selecting direct quotations from participants to answer the research question(s). Although it is important to ensure those familiar with the topic under investigation can recognise their experiences in the description of it ( Kim et al . , 2017 ), this is not to say that there should be no transformation of data. Researchers using a qualitative descriptive design need to, through data analysis, move from un-interpreted participant quotations to interpreted research findings, which can still remain ‘data-near’ ( Sandeklwoski, 2010 ). Willis et al. (2016) suggest that researchers using the qualitative descriptive method might report a comprehensive thematic summary as findings, which moves beyond individual participant reports by developing an interpretation of a common theme. The extent of description and/or interpretation in a qualitative descriptive study is ultimately determined by the focus of the study (Neergard et al ., 2009).

As with any research design, ensuring the rigor or trustworthiness of findings from a qualitative descriptive study is crucial. For a more detailed consideration of the quality criteria in qualitative studies, readers are referred to the seminal work of Lincoln and Guba (1985) in which the four key criteria of credibility, dependability, confirmability and transferability are discussed. At the very least, researchers need to be clear about the methodological decisions taken during the study so readers can judge the trustworthiness of the study and ultimately the findings ( Hallberg, 2013 ). Being aware of personal assumptions and the role they play in the research process is also an important quality criterion (Colorafi and Evans, 2016) and these assumptions can be made explicit through the use of researcher reflexivity in the study ( Bradshaw et al., 2017 ).

Challenges in using a qualitative descriptive design

One of the challenges of utilising a qualitative descriptive design is responding to the charge that many qualitative designs have historically encountered, which is that qualitative designs lack the scientific rigor associated with quantitative approaches ( Vaismoradi et al . , 2013 ). The descriptive design faces further critique in this regard as, unlike other qualitative approaches such as phenomenology or grounded theory, it is not theory driven or oriented ( Neergaard et al ., 2009 ). However, it is suggested that this perceived limitation of qualitative descriptive research only holds true if it is used for the wrong purposes and not primarily for describing the phenomenon ( Neergaard et al ., 2009 ). Kahlke (2014) argues that rather than being atheoretical, qualitative descriptive approaches require researchers to consider to what extent theory will inform the study and are sufficiently flexible to leave space for researchers to utilise theoretical frameworks that are relevant and inform individual research studies. Kim et al. (2017) reported that most descriptive studies reviewed did not identify a theoretical or philosophical framework, but those that did used it to inform the development of either the interview guide or the data analysis framework, thereby identifying the potential use of theory in descriptive designs.

Another challenge around the use of qualitative descriptive research is that it can erroneously be seen as a ‘quick fix’ for researchers who want to employ qualitative methods, but perhaps lack the expertise or familiarity with qualitative research ( Sandelowski, 2010 ). Kim et al. (2017) report how in their review fewer than half of qualitative descriptive papers explicitly identified a rationale for choosing this design, suggesting that in some cases the rationale behind its use was ill considered. Providing a justification for choosing a particular research design is an important part of the research process and, in the case of qualitative descriptive research, a clear justification can offset concerns that a descriptive design was an expedient rather than a measured choice. For studies exploring participants’ experiences, which could be addressed using other qualitative designs, it also helps to clearly make a distinction as to why a descriptive design was the best choice for the research study ( Kim et al ., 2017 ). Similarly, there is a perception that the data analysis techniques most commonly associated with descriptive research – thematic and content analysis are the ‘easiest’ approaches to qualitative analysis; however, as Vaismoradi et al . (2013) suggest, this does not mean they produce low-quality research findings.

As previously identified, a further challenge with the use of qualitative descriptive methods is that as a research design it has limited visibility in research texts and methodological papers ( Kim et al ., 2017 ). This means that novice qualitative researchers have little guidance on how to design and implement a descriptive study as there is a lack of a ‘methodological rulebook’ to guide researchers ( Kahlke, 2014 ). It is also suggested that this lack of strict boundaries and rules around qualitative descriptive research also offers researchers flexibility to design a study using a variety of data collection and analysis approaches that best answer the research question ( Kahlke, 2014 ; Kim et al . , 2017 ). However, should researchers choose to integrate methods ‘borrowed’ from other qualitative designs such as phenomenology or grounded theory, they should do so with the caveat that they do not claim they are using designs they are not actually using ( Neergaard et al . , 2009 ).

Examples of the use of qualitative descriptive research in healthcare

Findings from qualitative descriptive studies within healthcare have the potential to describe the experiences of patients, families and health providers, inform the development of health interventions and policy and promote health and quality of life ( Neergaard et al ., 2009 ; Willis et al ., 2016 ). The examples provided here demonstrate different ways qualitative descriptive methods can be used in a range of healthcare settings.

Simon et al. (2015) used a qualitative descriptive design to identify the perspectives of seriously ill, older patients and their families on the barriers and facilitators to advance care planning. The authors provided a rationale for using a descriptive design, which was to gain a deeper understanding of the phenomenon under investigation. Data were gathered through nine open-ended questions on a researcher-administered questionnaire. Responses to all questions were recorded verbatim and transcribed. Using descriptive, interpretative and explanatory coding that transformed raw data recorded from 278 patients and 225 family members to more abstract ideas and concepts ( Simon et al. , 2015 ), a deeper understanding of the barriers and facilitators to advance care planning was developed. Three categories were developed that identified personal beliefs, access to doctors and interaction with doctors as the central barriers and facilitators to advance care planning. The use of a qualitative descriptive design facilitated the development of a schematic based on these three themes, which provides a framework for use by clinicians to guide improvement in advance care planning.

Focus group interviews are a common data collection method in qualitative descriptive studies and were the method of choice in a study by Pelentsov et al. (2015), which sought to identify the supportive care needs of parents whose child has a rare disease. The rationale provided for using a qualitative descriptive design was to obtain a ‘straight description of the phenomena’ and to provide analysis and interpretation of the findings that remained data-near and representative of the responses of participants. In this study, four semi-structured focus group interviews were conducted with 23 parents. The data from these focus groups were then subjected to a form of thematic analysis during which emerging theories and inferences were identified and organised into a series of thematic networks and ultimately into three global themes. These themes identified that a number of factors including social isolation and lack of knowledge on behalf of healthcare professionals significantly affected how supported parents felt. Identifying key areas of the supportive needs of parents using qualitative description provides direction to health professionals on how best to respond to and support parents of children with a rare disease.

The potential for findings from a qualitative descriptive study to impact on policy was identified in a study by Syme et al. (2016) , who noted a lack of guidance and policies around sexual expression management of residents in long-term care settings. In this study, 20 directors of nursing from long-term care settings were interviewed with a view to identifying challenges in addressing sexual expression in these settings and elicit their recommendations for addressing these challenges in practice and policy. Following thematic analysis, findings relating to what directors of nursing believed to be important components of policy to address sexual expression were identified. These included providing educational resources, having a person-centred care delivery model when responding to sexual expression and providing guidance when working with families. Findings from this qualitative descriptive study provide recommendations that can then feed in to a broader policy on sexual expression in long-term care settings.

The final example of the use of a qualitative descriptive study comes from a mixed-methods study comprising a randomised control trial and a qualitative process evaluation. He et al. (2015) sought to determine the effects of a play intervention for children on parental perioperative anxiety and to explore parents’ perceptions of the intervention. Parents who had children going for surgery were assigned to a control group or an intervention group. The intervention group took part in a 1-hour play therapy session with their child whereas the control group received usual care. Quantitative findings identified there was no difference in parents’ anxiety levels between the intervention and control group. However, qualitative findings identified that parents found the intervention helpful in preparing both themselves and their child for surgery and perceived a reduction in their anxiety about the procedure thereby capturing findings that were not captured by the quantitative measures. In addition, in the qualitative interviews, parents made suggestions about how the play group could be improved, which provides important data for the further development of the intervention.

These examples across a range of healthcare settings provide evidence of the way findings from qualitative descriptive research can be directly used to more fully understand the experiences and perspectives of patients, their families and healthcare providers in addition to guiding future healthcare practice and informing further research.

Qualitative research designs have made significant contributions to the development of nursing and healthcare practices and policy. The utilisation of qualitative descriptive research is common within nursing research and is gaining popularity with other healthcare professions. This paper has identified that the utilisation of this design can be particularly relevant to nursing and healthcare professionals undertaking a primary piece of research and provides an excellent method to address issues that are of real clinical significance to them and their practice setting. However, the conundrum facing researchers who wish to use this approach is its lack of visibility and transparency within methodological papers and texts, resulting in a deficit of available information to researchers when designing such studies. By adding to the existing knowledge base, this paper enhances the information available to researchers who wish to use the qualitative descriptive approach, thus influencing the standard in how this approach is employed in healthcare research. We highlight the need for researchers using this research approach to clearly outline the context, theoretical framework and concepts underpinning it and the decision-making process that informed the design of their qualitative descriptive study including chosen research methods, and how these contribute to the achievement of the study’s aims and objectives. Failure to describe these issues may have a negative impact on study credibility. As seen in our paper, qualitative descriptive studies have a role in healthcare research providing insight into service users and providers’ perceptions and experiences of a particular phenomenon, which can inform healthcare service provision.

Key points for policy, practice and/or research

  • Despite its widespread use, there is little methodological guidance to orientate novice nurse researchers when using the qualitative descriptive design. This paper provides this guidance and champions the qualitative descriptive design as appropriate to explore research questions that require accessible and understandable findings directly relevant to healthcare practice and policy.
  • This paper identifies how the use of a qualitative descriptive design gives direct voice to participants including patients and healthcare staff, allowing exploration of issues of real and immediate importance in the practice area.
  • This paper reports how within qualitative descriptive research, the analysis of data and presentation of findings in a way that is easily understood and recognised is important to contribute to the utilisation of research findings in nursing practice.
  • As this design is often overlooked in research texts despite its suitability to exploring many healthcare questions, this paper adds to the limited methodological guidance and has utility for researchers who wish to defend their rationale for the use of the qualitative descriptive design in nursing and healthcare research.

Louise Doyle (PhD, MSc, BNS, RNT, RPN) is an Associate Professor in Mental Health Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Her research interests are in the area of self-harm and suicide and she has a particular interest and expertise in mixed-methods and qualitative research designs.

Catherine McCabe (PhD, MSc, BNS, RNT, RGN) is an Associate Professor in General Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Her research interests and expertise are in the areas of digital health (chronic disease self-management and social/cultural wellbeing), cancer, dementia, arts and health and systematic reviews.

Brian Keogh (PhD, MSc, BNS, RNT, RPN) is an Assistant Professor in Mental Health Nursing at the School of Nursing and Midwifery, Trinity College Dublin. His main area of research interest is mental health recovery and he specialises in qualitative research approaches with a particular emphasis on grounded theory.

Annemarie Brady (PhD, MSc, BNS, RNT, RPN) is Chair of Nursing and Chronic Illness and Head of School of Nursing and Midwifery at Trinity College Dublin. Her research work has focused on the development of healthcare systems and workforce solutions to respond to increased chronic illness demands within healthcare. She has conducted a range of mixed-method research studies in collaboration with health service providers to examine issues around patient-related outcomes measures, workload measurement, work conditions, practice development, patient safety and competency among healthcare workers.

Margaret McCann (PhD, MSc, BNS, RNT, RGN) is an Assistant Professor in General Nursing at the School of Nursing and Midwifery, Trinity College Dublin. Research interests are focused on chronic illness management, the use of digital health and smart technology in supporting patient/client education, self-management and independence. Other research interests include conducting systematic reviews, infection prevention and control and exploring patient outcomes linked to chronic kidney disease.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical approval was not required for this paper as it is a methodological paper and does not report on participant data.

The author(s) received no financial support for the research, authorship and/or publication of this article.

Louise Doyle https://orcid.org/0000-0002-0153-8326

Margaret McCann https://orcid.org/0000-0002-7925-6396

  • Bishop FL. (2015) Using mixed methods in health research: Benefits and challenges . British Journal of Health Psychology 20 : 1–4. [ PubMed ] [ Google Scholar ]
  • Bradshaw C, Atkinson S, Doody O. (2017) Employing a qualitative description approach in health care research . Global Qualitative Nursing Research 4 : 1–8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Braun V, Clarke V. (2006) Using thematic analysis in psychology . Qualitative Research in Psychology 3 : 77–101. [ Google Scholar ]
  • Caelli K, Ray L, Mill J. (2003) ‘Clear as mud’: Toward greater clarity in generic qualitative research . International Journal of Qualitative Methods 2 : 1–13. [ Google Scholar ]
  • Chafe R. (2017) The Value of Qualitative Description in Health Services and Policy Research . Healthcare Policy 12 : 12–18. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Doyle L, Brady AM, Byrne G. (2016) An overview of mixed methods research–revisited . Journal of Research in Nursing 21 : 623–635. [ Google Scholar ]
  • Finch H, Lewis J, Turley C. Ritchie J, Lewis J, McNaughton Nicholls C, Ormston R. Focus Groups . Qualitative Research Practice. A Guide for Social Science Students and Researchers , London: Sage, pp. 211–242. [ Google Scholar ]
  • Fusch PI, Ness LR. (2015) Are we there yet? Data saturation in qualitative research . The Qualitative Report 20 : 1408–1416. [ Google Scholar ]
  • Guest G, Bunce A, Johnson L. (2006) How many interviews are enough? An experiment with data saturation and variability . Field Methods 18 : 59–82. [ Google Scholar ]
  • Hallberg L. (2013) Quality criteria and generalization of results from qualitative studies . International Journal of Qualitative Studies in Health and Well-being 8 : 1. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • He HG, Zhu LX, Chan WCS, et al. (2015) A mixed-method study of effects of a therapeutic play intervention for children on parental anxiety and parents’ perceptions of the intervention . Journal of Advanced Nursing 71 ( 7 ): 1539–1551. [ PubMed ] [ Google Scholar ]
  • Hooley T, Wellens J, Marriott J. (2011) What is Online research? Using the Internet for Social Science Research , London: Bloomsbury Academic. [ Google Scholar ]
  • Howitt D. (2019) Introduction to Qualitative Methods in Psychology: Putting Theory into Practice , (4th edition). Harlow: Pearson Education Limited. [ Google Scholar ]
  • Janghorban R, Roudsari RL, Taghipour A. (2014) Skype interviewing: The new generation of online synchronous interview in qualitative research . International Journal of Qualitative Studies on Health and Wellbeing 9 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Kahlke RM. (2014) Generic qualitative approaches: Pitfalls and benefits of methodological mixology . International Journal of Qualitative Methods 13 : 37–52. [ Google Scholar ]
  • Kim H, Sefcik JS, Bradway C. (2017) Characteristics of qualitative descriptive studies: A systematic review . Research in Nursing & Health 40 : 23–42. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • King N. (2012) Doing Template Analysis . In: Symon G, Cassell C. (eds) Qualitative Organizational Research: Core Methods and Current Challenges , Los Angeles, CA: Sage. [ Google Scholar ]
  • Krueger RA, Casey MA. (2009) Focus Groups: A Practical Guide for Applied Research , 4th ed. Thousand Oaks, CA: Sage. [ Google Scholar ]
  • Lambert VA, Lambert CE. (2012) Qualitative descriptive research: An acceptable design . Pacific Rim International Journal of Nursing Research 16 : 255–256. [ Google Scholar ]
  • Lincoln YS, Guba EG. (1985) Naturalistic Inquiry , Newbury Park, CA: Sage. [ Google Scholar ]
  • Lincoln YS, Lynham SA, Guba EG. (2017) Paradigmatic Controversies, Contradictions and Emerging Confluences . In: NK Denzin, YS Guba (ed) The Sage Handbook of Qualitative Research , (5th edition). Thousand Oaks, CA: Sage. [ Google Scholar ]
  • Long KM, McDermott F, Meadows GN. (2018) Being pragmatic about healthcare complexity: Our experiences applying complexity theory and pragmatism to health services research . BMC Medicine 16 : 94. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Malterud K, Siersma VD, Guassora AD. (2016) Sample size in qualitative interview studies: Guided by information power . Qualitative Health Research 26 ( 13 ): 1753–1760. [ PubMed ] [ Google Scholar ]
  • Neergaard MA, Olesen F, Andersen RS, et al. (2009) Qualitative description – the poor cousin of health research? BMC Medical Research Methodology 9 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Newell R, Burnard P. (2011) Research for Evidence Based Practice , Oxford: Blackwell Publishing. [ Google Scholar ]
  • O’Reilly M, Parker N. (2012) ‘Unsatisfactory Saturation’: A critical exploration of the notion of saturated sample sizes in qualitative research . Qualitative Research 13 ( 2 ): 190–197. [ Google Scholar ]
  • Ormston R, Spencer L, Barnard M, et al. (2014) The foundations of qualitative research . In: Ritchie J, Lewis J, McNaughton Nicholls C, Ormston R. (eds) Qualitative Research Practice. A Guide for Social Science Students and Researchers , London: Sage, pp. 1–25. [ Google Scholar ]
  • Palinkas LA, Horwitz SM, Green CA, et al. (2015) Purposeful sampling for qualitative data collection and analysis in mixed method implementation research . Administration and Policy in Mental Health and Mental Health Services Research 42 : 533–544. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Pelentsov LL, Fielder AL, Esterman AJ. (2016) The supportive care needs of parents with a child with a rare disease: A qualitative descriptive study . Journal of Pediatric Nursing 31 ( 3 ): e207–e218. [ PubMed ] [ Google Scholar ]
  • Ritchie J, Lewis J, Elam G, et al. (2014) Designing and selecting samples . In: Ritchie J, Lewis J, McNaughton Nicholls C, Ormston R. (eds) Qualitative Research Practice. A Guide for Social Science Students and Researchers , London: Sage, pp. 111–145. [ Google Scholar ]
  • Sandelowski M. (2000) Whatever happened to qualitative description? Research in Nursing & Health 23 : 334–340. [ PubMed ] [ Google Scholar ]
  • Sandelowski M. (2010) What’s in a name? Qualitative description revisited . Research in Nursing & Health 33 : 77–84. [ PubMed ] [ Google Scholar ]
  • Sandelowski M, Barroso J. (2003) Classifying the findings in qualitative studies . Qualitative Health Research 13 : 905–923. [ PubMed ] [ Google Scholar ]
  • Seixas BV, Smith N, Mitton C. (2018) The qualitative descriptive approach in international comparative studies: Using online qualitative surveys . International Journal of Health Policy Management 7 ( 9 ): 778–781. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Simon J, Porterfield P, Bouchal SR, et al. (2015) ‘Not yet’ and ‘just ask’: Barriers and facilitators to advance care planning – a qualitative descriptive study of the perspectives of seriously ill, older patients and their families . BMJ Supportive & Palliative Care 5 : 54–62. [ PubMed ] [ Google Scholar ]
  • Syme ML, Lichtenberg P, Moye J. (2016) Recommendations for sexual expression management in long-term care: A qualitative needs assessment . Journal of Advanced Nursing 72 ( 10 ): 2457–2467. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Teddlie C, Yu F. (2007) Mixed methods sampling: A typology with examples . Journal of Mixed Methods Research 1 : 77–100. [ Google Scholar ]
  • Vaismoradi M, Turunen H, Bondas T. (2013) Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study . Nursing & Health Sciences 15 : 398–405. [ PubMed ] [ Google Scholar ]
  • Ward K, Gott M, Hoare K. (2015) Participants’ views of telephone interviews within a grounded theory study . Journal of Advanced Nursing 71 : 2775–2785. [ PubMed ] [ Google Scholar ]
  • Willis DG, Sullivan-Bolyai S, Knafl K, et al. (2016) Distinguishing features and similarities between descriptive phenomenological and qualitative descriptive research . Western Journal of Nursing Research 38 : 1185–1204. [ PubMed ] [ Google Scholar ]
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Non-experimental research: What it is, overview & advantages

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Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information.

Unlike experimental research , where the variables are held constant, non-experimental research happens during the study when the researcher cannot control, manipulate or alter the subjects but relies on interpretation or observations to conclude.

This means that the method must not rely on correlations, surveys , or case studies and cannot demonstrate an actual cause and effect relationship.

Characteristics of non-experimental research

Some of the essential characteristics of non-experimental research are necessary for the final results. Let’s talk about them to identify the most critical parts of them.

characteristics of non-experimental research

  • Most studies are based on events that occurred previously and are analyzed later.
  • In this method, controlled experiments are not performed for reasons such as ethics or morality.
  • No study samples are created; on the contrary, the samples or participants already exist and develop in their environment.
  • The researcher does not intervene directly in the environment of the sample.
  • This method studies the phenomena exactly as they occurred.

Types of non-experimental research

Non-experimental research can take the following forms:

Cross-sectional research : Cross-sectional research is used to observe and analyze the exact time of the research to cover various study groups or samples. This type of research is divided into:

  • Descriptive: When values are observed where one or more variables are presented.
  • Causal: It is responsible for explaining the reasons and relationship that exists between variables in a given time.

Longitudinal research: In a longitudinal study , researchers aim to analyze the changes and development of the relationships between variables over time. Longitudinal research can be divided into:

  • Trend: When they study the changes faced by the study group in general.
  • Group evolution: When the study group is a smaller sample.
  • Panel: It is in charge of analyzing individual and group changes to discover the factor that produces them.

LEARN ABOUT: Quasi-experimental Research

When to use non-experimental research

Non-experimental research can be applied in the following ways:

  • When the research question may be about one variable rather than a statistical relationship about two variables.
  • There is a non-causal statistical relationship between variables in the research question.
  • The research question has a causal research relationship, but the independent variable cannot be manipulated.
  • In exploratory or broad research where a particular experience is confronted.

Advantages and disadvantages

Some advantages of non-experimental research are:

  • It is very flexible during the research process
  • The cause of the phenomenon is known, and the effect it has is investigated.
  • The researcher can define the characteristics of the study group.

Among the disadvantages of non-experimental research are:

  • The groups are not representative of the entire population.
  • Errors in the methodology may occur, leading to research biases .

Non-experimental research is based on the observation of phenomena in their natural environment. In this way, they can be studied later to reach a conclusion.

Difference between experimental and non-experimental research

Experimental research involves changing variables and randomly assigning conditions to participants. As it can determine the cause, experimental research designs are used for research in medicine, biology, and social science. 

Experimental research designs have strict standards for control and establishing validity. Although they may need many resources, they can lead to very interesting results.

Non-experimental research, on the other hand, is usually descriptive or correlational without any explicit changes done by the researcher. You simply describe the situation as it is, or describe a relationship between variables. Without any control, it is difficult to determine causal effects. The validity remains a concern in this type of research. However, it’s’ more regarding the measurements instead of the effects.

LEARN MORE: Descriptive Research vs Correlational Research

Whether you should choose experimental research or non-experimental research design depends on your goals and resources. If you need any help with how to conduct research and collect relevant data, or have queries regarding the best approach for your research goals, contact us today! You can create an account with our survey software and avail of 88+ features including dashboard and reporting for free.

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What Is Descriptive Analytics? 5 Examples

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  • 09 Nov 2021

Data analytics is a valuable tool for businesses aiming to increase revenue, improve products, and retain customers. According to research by global management consulting firm McKinsey & Company, companies that use data analytics are 23 times more likely to outperform competitors in terms of new customer acquisition than non-data-driven companies. They were also nine times more likely to surpass them in measures of customer loyalty and 19 times more likely to achieve above-average profitability.

Data analytics can be broken into four key types :

  • Descriptive, which answers the question, “What happened?”
  • Diagnostic , which answers the question, “Why did this happen?”
  • Predictive , which answers the question, “What might happen in the future?”
  • Prescriptive , which answers the question, “What should we do next?”

Each type of data analysis can help you reach specific goals and be used in tandem to create a full picture of data that informs your organization’s strategy formulation and decision-making.

Descriptive analytics can be leveraged on its own or act as a foundation for the other three analytics types. If you’re new to the field of business analytics, descriptive analytics is an accessible and rewarding place to start.

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What Is Descriptive Analytics?

Descriptive analytics is the process of using current and historical data to identify trends and relationships. It’s sometimes called the simplest form of data analysis because it describes trends and relationships but doesn’t dig deeper.

Descriptive analytics is relatively accessible and likely something your organization uses daily. Basic statistical software, such as Microsoft Excel or data visualization tools , such as Google Charts and Tableau, can help parse data, identify trends and relationships between variables, and visually display information.

Descriptive analytics is especially useful for communicating change over time and uses trends as a springboard for further analysis to drive decision-making .

Here are five examples of descriptive analytics in action to apply at your organization.

Related: 5 Business Analytics Skills for Professionals

5 Examples of Descriptive Analytics

1. traffic and engagement reports.

One example of descriptive analytics is reporting. If your organization tracks engagement in the form of social media analytics or web traffic, you’re already using descriptive analytics.

These reports are created by taking raw data—generated when users interact with your website, advertisements, or social media content—and using it to compare current metrics to historical metrics and visualize trends.

For example, you may be responsible for reporting on which media channels drive the most traffic to the product page of your company’s website. Using descriptive analytics, you can analyze the page’s traffic data to determine the number of users from each source. You may decide to take it one step further and compare traffic source data to historical data from the same sources. This can enable you to update your team on movement; for instance, highlighting that traffic from paid advertisements increased 20 percent year over year.

The three other analytics types can then be used to determine why traffic from each source increased or decreased over time, if trends are predicted to continue, and what your team’s best course of action is moving forward.

2. Financial Statement Analysis

Another example of descriptive analytics that may be familiar to you is financial statement analysis. Financial statements are periodic reports that detail financial information about a business and, together, give a holistic view of a company’s financial health.

There are several types of financial statements, including the balance sheet , income statement , cash flow statement , and statement of shareholders’ equity. Each caters to a specific audience and conveys different information about a company’s finances.

Financial statement analysis can be done in three primary ways: vertical, horizontal, and ratio.

Vertical analysis involves reading a statement from top to bottom and comparing each item to those above and below it. This helps determine relationships between variables. For instance, if each line item is a percentage of the total, comparing them can provide insight into which are taking up larger and smaller percentages of the whole.

Horizontal analysis involves reading a statement from left to right and comparing each item to itself from a previous period. This type of analysis determines change over time.

Finally, ratio analysis involves comparing one section of a report to another based on their relationships to the whole. This directly compares items across periods, as well as your company’s ratios to the industry’s to gauge whether yours is over- or underperforming.

Each of these financial statement analysis methods are examples of descriptive analytics, as they provide information about trends and relationships between variables based on current and historical data.

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3. Demand Trends

Descriptive analytics can also be used to identify trends in customer preference and behavior and make assumptions about the demand for specific products or services.

Streaming provider Netflix’s trend identification provides an excellent use case for descriptive analytics. Netflix’s team—which has a track record of being heavily data-driven—gathers data on users’ in-platform behavior. They analyze this data to determine which TV series and movies are trending at any given time and list trending titles in a section of the platform’s home screen.

Not only does this data allow Netflix users to see what’s popular—and thus, what they might enjoy watching—but it allows the Netflix team to know which types of media, themes, and actors are especially favored at a certain time. This can drive decision-making about future original content creation, contracts with existing production companies, marketing, and retargeting campaigns.

4. Aggregated Survey Results

Descriptive analytics is also useful in market research. When it comes time to glean insights from survey and focus group data, descriptive analytics can help identify relationships between variables and trends.

For instance, you may conduct a survey and identify that as respondents’ age increases, so does their likelihood to purchase your product. If you’ve conducted this survey multiple times over several years, descriptive analytics can tell you if this age-purchase correlation has always existed or if it was something that only occurred this year.

Insights like this can pave the way for diagnostic analytics to explain why certain factors are correlated. You can then leverage predictive and prescriptive analytics to plan future product improvements or marketing campaigns based on those trends.

Related: What Is Marketing Analytics?

5. Progress to Goals

Finally, descriptive analytics can be applied to track progress to goals. Reporting on progress toward key performance indicators (KPIs) can help your team understand if efforts are on track or if adjustments need to be made.

For example, if your organization aims to reach 500,000 monthly unique page views, you can use traffic data to communicate how you’re tracking toward it. Perhaps halfway through the month, you’re at 200,000 unique page views. This would be underperforming because you’d like to be halfway to your goal at that point—at 250,000 unique page views. This descriptive analysis of your team’s progress can allow further analysis to examine what can be done differently to improve traffic numbers and get back on track to hit your KPI.

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Using Data to Identify Relationships and Trends

“Never before has so much data about so many different things been collected and stored every second of every day,” says Harvard Business School Professor Jan Hammond in the online course Business Analytics . “In this world of big data, data literacy —the ability to analyze, interpret, and even question data—is an increasingly valuable skill.”

Leveraging descriptive analytics to communicate change based on current and historical data and as a foundation for diagnostic, predictive, and prescriptive analytics has the potential to take you and your organization far.

Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems.

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  • What is Descriptive Analytics?
  • Knowledge Hub

Descriptive Analytics Definition

How does descriptive analytics work, what can descriptive analytics tell us, examples of descriptive analytics.

  • What are the advantages of descriptive analytics?

Descriptive vs Predictive vs Prescriptive Analytics

Learning Analytics is not simply about collecting data from learners, but about finding meaning in the data in order to improve future learning.

To do this, learning analytics relies on a number of analytical methods: descriptive analytics, diagnostic analytics, predictive analytics , and prescriptive analytics .

Descriptive analytics is a statistical method that is used to search and summarize historical data in order to identify patterns or meaning.

For learning analytics, this is a reflective analysis of learner data and is meant to provide insight into historical patterns of behaviors and performance in online learning environments.

For example, in an online learning course with a discussion board, descriptive analytics could determine how many students participated in the discussion, or how many times a particular student posted in the discussion forum.

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Data aggregation and data mining are two techniques used in descriptive analytics to discover historical data. Data is first gathered and sorted by data aggregation in order to make the datasets more manageable by analysts.

Data mining describes the next step of the analysis and involves a search of the data to identify patterns and meaning. Identified patterns are analyzed to discover the specific ways that learners interacted with the learning content and within the learning environment.

What can descriptive analytics tell us?

The kind of information that descriptive analytics can provide depends on the learning analytic capability of the learning management system (LMS) being used and what the system is reporting on specifically.

Some common indicators that can be identified include learner engagement and learner performance. With learner engagement, analysts can detect the participation level of learners in the course and how and when course resources were accessed.

Performance data provides analysts with insight into how well learners succeeded on the course; this information could come from data taken from assessments or assignments. It’s important to note that insights learned from descriptive analysis are not used for making inferences or predictions about a learner’s future performance.

The analytical method is meant to provide strategic insight into where learners, or a specific learner, may have needed more support. It can also help course designers improve the design of learning by providing insight into what went well and what did not go well on the course.

Many LMS platforms and learning systems offer descriptive analytical reporting with the aim of help businesses and institutions measure learner performance to ensure that training goals and targets are met.

The findings from descriptive analytics can quickly identify areas that require improvement – whether that be improving learner engagement or the effectiveness of course delivery.

Here are some examples of how descriptive analytics is being used in the field of learning analytics:

  • Tracking course enrollments, course compliance rates,
  • Recording which learning resources are accessed and how often
  • Summarizing the number of times a learner posts in a discussion board
  • Tracking assignment and assessment grades
  • Comparing pre-test and post-test assessments
  • Analyzing course completion rates by learner or by course
  • Collating course survey results
  • Identifying length of time that learners took to complete a course

Advantages of descriptive analytics

When learners engage in online learning, they leave a digital trace behind with every interaction they have in the learning environment.

This means that descriptive analytics in online learning can gain insight into behaviours and performance indicators that would otherwise not be known.

Here are some advantages to utilizing this information:

  • Quickly and easily report on the Return on Investment (ROI) by showing how performance achieved business or target goals.
  • Identify gaps and performance issues early – before they become problems.
  • Identify specific learners who require additional support, regardless of how many students or employees there are.
  • Identify successful learners in order to offer positive feedback or additional resources.
  • Analyze the value and impact of course design and learning resources.

How does Descriptive Analytics differ from Predictive Analytics and Prescriptive Analytics?

Descriptive Analytics is focused solely on historical data.

You can think of Predictive Analytics as then using this historical data to develop statistical models that will then forecast about future possibilities.

Prescriptive Analytics takes Predictive Analytics a step further and takes the possible forecasted outcomes and predicts consequences for these outcomes.

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Ivan Andreev

Demand Generation & Capture Strategist

Ivan is a dedicated and versatile professional with over 12 years of experience in online marketing and a proven track record of turning challenges into opportunities. Ivan works diligently to improve internal processes and explore new possibilities for the company.

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Descriptive Analysis: How-To, Types, Examples

Jun 22, 2020 by Thomas Bush

From diagnostic to predictive, there are many different types of data analysis . Perhaps the most straightforward of them is descriptive analysis, which seeks to describe or summarize past and present data, helping to create accessible data insights. In this short guide, we’ll review the basics of descriptive analysis, including what exactly it is, what benefits it has, how to do it, as well as some types and examples.

What Is Descriptive Analysis?

Descriptive analysis, also known as descriptive analytics or descriptive statistics , is the process of using statistical techniques to describe or summarize a set of data. As one of the major types of data analysis, descriptive analysis is popular for its ability to generate accessible insights from otherwise uninterpreted data.

Unlike other types of data analysis, the descriptive analysis does not attempt to make predictions about the future. Instead, it draws insights solely from past data, by manipulating in ways that make it more meaningful.

Benefits of Descriptive Analysis

Descriptive analysis is all about trying to describe or summarize data. Although it doesn’t make predictions about the future, it can still be extremely valuable in business environments . This is chiefly because descriptive analysis makes it easier to consume data, which can make it easier for analysts to act on.

Another benefit of descriptive analysis is that it can help to filter out less meaningful data. This is because the statistical techniques used within this type of analysis usually focus on the patterns in data, and not the outliers.

Types of Descriptive Analysis

According to CampusLabs.com , descriptive analysis can be categorized as one of four types. They are measures of frequency, central tendency, dispersion or variation, and position.

Measures of Frequency

In descriptive analysis, it’s essential to know how frequently a certain event or response occurs. This is the purpose of measures of frequency, like a count or percent. For example, consider a survey where 1,000 participants are asked about their favourite ice cream flavor. A list of 1,000 responses would be difficult to consume, but the data can be made much more accessible by measuring how many times a certain flavor was selected.

Measures of Central Tendency

In descriptive analysis, it’s also worth knowing the central (or average) event or response. Common measures of central tendency include the three averages — mean, median, and mode. As an example, consider a survey in which the height of 1,000 people is measured. In this case, the mean average would be a very helpful descriptive metric.

Measures of Dispersion

Sometimes, it may be worth knowing how data is distributed across a range. To illustrate this, consider the average height in a sample of two people. If both individuals are six feet tall, the average height is six feet. However, if one individual is five feet tall and the other is seven feet tall, the average height is still six feet. In order to measure this kind of distribution, measures of dispersion like range or standard deviation can be employed.

Measures of Position

Last of all, descriptive analysis can involve identifying the position of one event or response in relation to others. This is where measures like percentiles and quartiles can be used.

How to Do Descriptive Analysis

Like many types of data analysis, descriptive analysis can be quite open-ended. In other words, it’s up to you what you want to look for in your analysis. With that said, the process of descriptive analysis usually consists of the same few steps.

  • Collect data

The first step in any type of data analysis is to collect the data. This can be done in a variety of ways, but surveys and good old fashioned measurements are often used.

Another important step in descriptive and other types of data analysis is to clean the data. This is because data may be formatted in inaccessible ways, which will make it difficult to manipulate with statistics. Cleaning data may involve changing its textual format, categorizing it, and/or removing outliers.

  • Apply methods

Finally, descriptive analysis involves applying the chosen statistical methods so as to draw the desired conclusions. What methods you choose will depend on the data you are dealing with and what you are looking to determine. If in doubt, review the four types of descriptive analysis methods explained above.

When to Do Descriptive Analysis

Descriptive analysis is often used when reviewing any past or present data. This is because raw data is difficult to consume and interpret, while the metrics offered by descriptive analysis are much more focused.

Descriptive analysis can also be conducted as the precursor to diagnostic or predictive analysis , providing insights into what has happened in the past before attempting to explain why it happened or predicting what will happen in the future.

Descriptive Analysis Example

As an example of descriptive analysis, consider an insurance company analyzing its customer base.

The insurance company may know certain traits about its customers, such as their gender, age, and nationality. To gain a better profile of their customers, the insurance company can apply descriptive analysis.

Measures of frequency can be used to identify how many customers are under a certain age; measures of central tendency can be used to identify who most of their customers are; measures of dispersion can be used to identify the variation in, for example, the age of their customers; finally, measures of position can be used to compare segments of customers based on specific traits.

Final Thoughts

Descriptive analysis is a popular type of data analysis. It’s often conducted before diagnostic or predictive analysis, as it simply aims to describe and summarize past data.

To do so, descriptive analysis uses a variety of statistical techniques, including measures of frequency, central tendency, dispersion, and position. How exactly you conduct descriptive analysis will depend on what you are looking to find out, but the steps usually involve collecting, cleaning, and finally analyzing data.

In any case, this business analysis process is invaluable when working with data.

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Descriptive Assessments

Once challenging behavior is operationally defined and a measurement system is selected, descriptive assessments (Castillo et al., 2018; Lerman & Iwata, 1993; Mace & Lalli, 1991; Martens et al., 2008) may ensue. Descriptive assessments utilize direct observation and data collection of the challenging behavior under naturalistic environmental conditions, thereby addressing the concerns with informant reports of past events.

One assessment tool, the scatterplot (Touchette et al., 1985), can be used to visually depict the occurrence and temporal relationship of the response(s) to various environmental events, illuminating possible patterns not readily observed in a graph or extrapolated from indirect assessments. Additionally, descriptive assessments can be helpful in generating hypotheses regarding the function of challenging behavior (Lerman & Iwata, 1993).

Descriptive assessments provide information about the environmental situations under which challenging behavior does and does not occur, and most importantly, provides crucial information needed to develop socially valid experimental analyses of the putative variables influencing challenging behavior.

Descriptive Assessment Strategies

Within the context of a FBAs, descriptive assessment procedures typically begin by observing problem behavior across different antecedent conditions (Erchul & Martens, 2010) referred to as scatterplot recording (Touchette et al., 1985). Scatterplot recording examines under what conditions challenging behavior is most likely to occur but does not measure the consequences that follow challenging behavior. Thus, hypotheses regarding behavioral function based on scatterplot recordings are limited. Scatterplots may still be informative, however, in determining the optimal time to engage in recording of behavior and its consequences (Eckert et al., 2005).

A-B-C Recording Another way to determine under what conditions challenging behavior is most likely to occur while simultaneously examining consequences is to engage in Antecedent-Behavior-Consequence (A-B-C) recording (Bijou et al., 1968). This type of assessment involves recording the occurrence of problem behavior, under what conditions it occurred (antecedents), and what consequence(s) were provided. This process continues until a clear pattern of antecedents and consequences associated with problem behavior emerges (Lee & Miltenberger, 1997).

Sequential Recordings An alternative and more informative strategy for examining the relationship between behavior and its consequences is to conduct sequential recordings and examine the conditional probability of a consequence given behavior. Prior to collecting these data, specific problem behavior(s) and consequences are defined so that behavior categories (e.g., problem behavior and all other behavior) are mutually exclusive and consequences represent the four broad categories of reinforcement (i.e., social-positive, social-negative, automatic positive or negative). Following data collection, conditional probabilities are calculated and those consequences that have a high probability of following challenging behavior indicate potential functions.

non descriptive analysis

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Meaning of non-descriptive in English

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  • Often, apparently non-descriptive terms were originally descriptive in character , but over time , they have lost their transparency .
  • The problem with using a non-descriptive host name is that the name itself provides very little information on the server's purpose .
  • account (to someone ) for something
  • accountability
  • adumbration
  • demythologize
  • inexpressibly
  • justificatory
  • lay something out
  • mansplaining
  • undefinable

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nondescriptive

Definition of nondescriptive

Examples of nondescriptive in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'nondescriptive.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1881, in the meaning defined above

Dictionary Entries Near nondescriptive

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Cite this Entry

“Nondescriptive.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/nondescriptive. Accessed 25 Feb. 2024.

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COMMENTS

  1. Descriptive vs. Inferential Statistics: What's the Difference?

    Descriptive statistics are useful because they allow you to understand a group of data much more quickly and easily compared to just staring at rows and rows of raw data values. For example, suppose we have a set of raw data that shows the test scores of 1,000 students at a particular school. We might be interested in the average test score ...

  2. Descriptive Statistics

    In quantitative research, after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

  3. An Introduction to Statistics: Choosing the Correct Statistical Test

    W hat is the R esearch H ypothesis?. Sometimes, a study may just describe the characteristics of the sample, e.g., a prevalence study. Here, the statistical analysis involves only descriptive statistics.For example, Sridharan et al. aimed to analyze the clinical profile, species distribution, and susceptibility pattern of patients with invasive candidiasis. 3 They used descriptive statistics ...

  4. Qualitative and descriptive research: Data type versus data analysis

    Qualitative research collects data qualitatively, and the method of analysis is also primarily qualitative. This often involves an inductive exploration of the data to identify recurring themes, patterns, or concepts and then describing and interpreting those categories. Of course, in qualitative research, the data collected qualitatively can ...

  5. Non-Parametric Statistics: Types, Tests, and Examples

    Types of Non-Parametric Statistics . Non-parametric statistics are further classified into two major categories. Here is the brief introduction to both of them: 1. Descriptive Statistics . Descriptive statistics is a type of non-parametric statistics. It represents the entire population or a sample of a population.

  6. Descriptive statistics

    A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics. Descriptive statistics is distinguished from inferential statistics (or inductive statistics) by its aim to summarize a sample ...

  7. 6.1 Overview of Non-Experimental Research

    Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).

  8. Descriptive Research

    Grammar Style consistency See an example Descriptive research methods Descriptive research is usually defined as a type of quantitative research, though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable. Surveys

  9. 1.6: Non-Experimental Research

    When to Use Non-Experimental Research. As we saw earlier, experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable.It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when ...

  10. What does non-destructive analysis mean?

    Activation analysis techniques provide a unique lens through which the idea of non-destructive can be defined. In many fields, activation analysis is considered to be non-destructive. At times this includes some of the fields where no damage to the sample is highly sought after: archeology and forensics.

  11. Descriptive vs Analytical/Critical Writing (+ Examples)

    Descriptive writing focuses on the what, while critical/analytical writing focuses on the so what. Analytical writing should link the discussion back to the research aims, objectives or research questions (the golden thread). Some amount of description will always be needed, but aim to minimise description and maximise analysis to earn higher ...

  12. Types of Research Designs Compared

    Other interesting articles. 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. Statistics. Normal distribution. Skewness. Kurtosis. Degrees of freedom. Variance. Null hypothesis.

  13. Quantitative Research Designs: Non-Experimental vs. Experimental

    Non-experimental research, on the other hand, can be just as interesting, but you cannot draw the same conclusions from it as you can with experimental research. Non-experimental research is usually descriptive or correlational, which means that you are either describing a situation or phenomenon simply as it stands, or you are describing a ...

  14. Overview of Nonexperimental Research

    Key Takeaways. Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both. There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables.

  15. An overview of the qualitative descriptive design within nursing

    Go to: Abstract Background Qualitative descriptive designs are common in nursing and healthcare research due to their inherent simplicity, flexibility and utility in diverse healthcare contexts. However, the application of descriptive research is sometimes critiqued in terms of scientific rigor.

  16. Non-experimental research: What it is, Types & Tips

    Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information.

  17. What Is Descriptive Analytics? 5 Examples

    Descriptive analytics is the process of using current and historical data to identify trends and relationships. It's sometimes called the simplest form of data analysis because it describes trends and relationships but doesn't dig deeper. Descriptive analytics is relatively accessible and likely something your organization uses daily.

  18. What descriptive statistics to report for non-normal data

    Consistent with @whuber, the data are not usually capable of telling you how Gaussian a variable's distribution is. So it makes sense to employ statistics that are always descriptive, such as the empirical cumulative distribution function, extended box plots (showing more quantiles than the 3 quartiles) and quantiles. The median is always descriptive and interpretable for a continuous variable ...

  19. What is Descriptive Analytics? Definition & Examples

    Descriptive analytics is a statistical method that is used to search and summarize historical data in order to identify patterns or meaning. For learning analytics, this is a reflective analysis of learner data and is meant to provide insight into historical patterns of behaviors and performance in online learning environments.

  20. Descriptive Analysis: How-To, Types, Examples

    Descriptive analysis, also known as descriptive analytics or descriptive statistics, is the process of using statistical techniques to describe or summarize a set of data. As one of the major types of data analysis, descriptive analysis is popular for its ability to generate accessible insights from otherwise uninterpreted data.

  21. Descriptive Assessments

    Descriptive Assessments. Once challenging behavior is operationally defined and a measurement system is selected, descriptive assessments (Castillo et al., 2018; Lerman & Iwata, 1993; Mace & Lalli, 1991; Martens et al., 2008) may ensue. Descriptive assessments utilize direct observation and data collection of the challenging behavior under ...

  22. NON-DESCRIPTIVE

    adjective (also nondescriptive) uk / ˌnɒn.dɪˈskrɪp.tɪv / us / ˌnɑːn.dɪˈskrɪp.t̬ɪv / Add to word list not clearly describing or explaining something, or telling you what it is: The menu was non-descriptive: there was a list of options, but no information on the items. The work that I'm doing now is abstract, non-descriptive, and highly symbolic.

  23. Nondescriptive Definition & Meaning

    nondescriptive: [adjective] not serving to describe : not descriptive.