descriptive correlational research design importance

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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Learning objectives.

Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
Explain the goals of descriptive research and the statistical techniques used to interpret it.
Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 2.2 “Characteristics of the Three Research Designs” , are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Table 2.2 Characteristics of the Three Research Designs

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Three news papers on a table (The Daily Telegraph, The Guardian, and The Times), all predicting Obama has the edge in the early polls.

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample ) are selected to be representative of all the people that the researcher wishes to know about (the population ). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation —is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 2.5 “Height Distribution” , where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 2.6 “Family Income Distribution” ), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 2.6 “Family Income Distribution” that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 2.6 “Family Income Distribution” that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Graph of a tightly clustered central tendency.

Or they may be more spread out away from it, like this:

Graph of a more spread out central tendency.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 2.5 “Height Distribution” is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

Left: Predictor variable, Right: Outcome variable.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 2.10 “Examples of Scatter Plots” , a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure 2.10 “Examples of Scatter Plots” , the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 2.10 “Examples of Scatter Plots” shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 2.10 “Examples of Scatter Plots” show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

Viewing violent TV may lead to aggressive play.

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Or perhaps aggressive play leads to viewing violent TV.

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

One may cause the other, but there could be a common-causal variable.

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

An example: Parents' discipline style may cause viewing violent TV, and it may also cause aggressive play.

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Figure 2.2.3

Viewing violence (independent variable) and aggressive behavior (dependent variable).

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

Descriptive, correlational, and experimental research designs are used to collect and analyze data.
Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
Explain the goals of descriptive research and the statistical techniques used to interpret it.
Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

Descriptive, correlational, and experimental research designs are used to collect and analyze data.
Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
Independent, r=0.00. The plots on the graph are spread out around the centre.
Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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descriptive correlational research design importance

Importance and use of correlational research

Affiliation.

1 School of Nursing and Midwifery, Trinity College Dublin, Dublin, Republic of Ireland.
PMID: 27424963
DOI: 10.7748/nr.2016.e1382

Background: The importance of correlational research has been reported in the literature yet few research texts discuss design in any detail.

Aim: To discuss important issues and considerations in correlational research, and suggest ways to avert potential problems during the preparation and application of the design.

Discussion: This article targets the gap identified in the literature regarding correlational research design. Specifically, it discusses the importance and purpose of correlational research, its application, analysis and interpretation with contextualisations to nursing and health research.

Conclusion: Findings from correlational research can be used to determine prevalence and relationships among variables, and to forecast events from current data and knowledge. In spite of its many uses, prudence is required when using the methodology and analysing data. To assist researchers in reducing mistakes, important issues are singled out for discussion and several options put forward for analysing data.

Implications for practice: Correlational research is widely used and this paper should be particularly useful for novice nurse researchers. Furthermore, findings generated from correlational research can be used, for example, to inform decision-making, and to improve or initiate health-related activities or change.

Keywords: correlation; correlational research; data analysis; measurement tools; nurses; nursing research; quantitative; variables.

Nursing Research*

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Correlational Research | Guide, Design & Examples

Correlational Research | Guide, Design & Examples

Published on 5 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Correlational vs experimental research, when to use correlational research, how to collect correlational data, how to analyse correlational data, correlation and causation, frequently asked questions about correlational research.

Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in how data is collected and the types of conclusions you can draw.

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Correlational research is ideal for gathering data quickly from natural settings. That helps you generalise your findings to real-life situations in an externally valid way.

There are a few situations where correlational research is an appropriate choice.

To investigate non-causal relationships

You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.

Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.

To explore causal relationships between variables

You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.

Correlational research can provide initial indications or additional support for theories about causal relationships.

To test new measurement tools

You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .

Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.

There are many different methods you can use in correlational research. In the social and behavioural sciences, the most common data collection methods for this type of research include surveys, observations, and secondary data.

It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by post, by phone, or in person.

Surveys are a quick, flexible way to collect standardised data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.

Naturalistic observation

Naturalistic observation is a type of field research where you gather data about a behaviour or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorising actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analysed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalise your results to real-world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.

Secondary data

Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.

Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete, or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.

After collecting data, you can statistically analyse the relationship between variables using correlation or regression analyses, or both. You can also visualise the relationships between variables with a scatterplot.

Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .

Correlation analysis

Using a correlation analysis, you can summarise the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.

The Pearson product-moment correlation coefficient, also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.

Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.

Regression analysis

With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.

You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.

It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other, for a few reasons.

Directionality problem

If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.

Third variable problem

A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.

In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.

A correlation reflects the strength and/or direction of the association between two or more variables.

A positive correlation means that both variables change in the same direction.
A negative correlation means that the variables change in opposite directions.
A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

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Indian J Crit Care Med
v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.

How to cite this article

Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.

We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.

Longitudinal vs Transversal Studies

In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.

Forward vs Backward Directed Studies

In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.

Prospective vs Retrospective Studies

In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.

CLASSIFICATION OF STUDY DESIGNS

Broadly, study designs can be classified as descriptive or analytical (inferential) studies.

Descriptive Studies

Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.

Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2

Analytical (Inferential) Studies

Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.

In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.

Cross-sectional Studies

These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3

Case–control Studies

Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.

Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4

Cohort Studies

Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.

Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5

Interventional Studies

In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.

The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.

In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6

Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7

Source of support: Nil

Conflict of interest: None

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12 Quantitative Descriptive and Correlational Research

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This chapter presents research designs for descriptive and correlational quantitative research. Descriptive research designs are used to address the question “What is x?” Correlational research designs are used to address the question “How are things related?” In contrast to some experimental research designs, in these design types the primary area of interest under investigation is not manipulated by the researcher. Researchers investigating descriptive or correlational research questions commonly use surveys or observational methods to gather data. Surveys are an efficient method for gathering large amounts of information about such things as individuals’ experiences, beliefs, and attitudes. When designing a survey, researchers must consider many things, such as how long it will be and what it will cover. Observation is an important means of gathering data, as when researchers observe video recordings of teachers or students in various situations. Another approach to observational research is the experience sampling method (ESM). In ESM, participants are interrupted at random times throughout the day and asked to respond to questions concerning their experiences in real time. In other words, researchers ask participants what they are doing at the moment they are contacted.

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Importance and use of correlational research, elizabeth a curtis assistant professor, school of nursing and midwifery, trinity college dublin, dublin, republic of ireland, catherine comiskey head of school and professor of healthcare statistics, school of nursing and midwifery, trinity college dublin, dublin, republic of ireland, orla dempsey adjunct quantitative healthcare lead, school of nursing and midwifery, trinity college dublin, dublin, republic of ireland.

Background The importance of correlational research has been reported in the literature yet few research texts discuss design in any detail.

Aim To discuss important issues and considerations in correlational research, and suggest ways to avert potential problems during the preparation and application of the design.

Implications for practice Correlational research is widely used and this paper should be particularly useful for novice nurse researchers. Furthermore, findings generated from correlational research can be used, for example, to inform decision-making, and to improve or initiate health-related activities or change.

Nurse Researcher . 23, 6, 20-25. doi: 10.7748/nr.2016.e1382

[email protected]

This article has been subject to double-blind review and has been checked using antiplagiarism software

None declared

Received: 16 March 2015

Accepted: 11 November 2015

nurses - nursing research - correlational research - correlation - variables - quantitative - measurement tools - data analysis

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Descriptive Correlational: Descriptive vs Correlational Research

Descriptive research and Correlational research are two important types of research studies that help researchers make ambitious and measured decisions in their respective fields. Both descriptive research and correlational research are used in descriptive correlational research.

Descriptive research is defined as a research method that involves observing behavior to describe attributes objectively and systematically. A descriptive research project seeks to comprehend phenomena or groups in depth.

Correlational research , on the other hand, is a method that describes and predicts how variables are naturally related in the real world without the researcher attempting to alter them or assign causation between them.

The main objective of descriptive research is to create a snapshot of the current state of affairs, whereas correlational research helps in comparing two or more entities or variables.

What is descriptive correlational research?

Descriptive correlational research is a type of research design that tries to explain the relationship between two or more variables without making any claims about cause and effect. It includes collecting and analyzing data on at least two variables to see if there is a link between them.

In descriptive correlational research, researchers collect data to explain the variables of interest and figure out how they relate. The main goal is to give a full account of the variables and how they are related without changing them or assuming that one thing causes another.

In descriptive correlational research, researchers do not change any variables or try to find cause-and-effect connections. Instead, they just watch and measure the variables of interest and then look at the patterns and relationships that emerge from the data.

Experimental research involves the independent variable to see how it affects the dependent variable, while descriptive correlational research just describes the relationship between variables.

In descriptive correlational research, correlational research designs measure the magnitude and direction of the relationship between two or more variables, revealing their associations. At the outset creating initial equivalence between the groups or variables being compared is essential in descriptive correlational research

The independent variable occurs prior to the measurement of the measured dependent variable in descriptive correlational research. Its goal is to explain the traits or actions of a certain population or group and look at the connections between independent and dependent variables.

How are descriptive research and correlational research carried out?

Descriptive research is carried out using three methods, namely:

Case studies – Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they don’t have the capacity to make accurate predictions.
Surveys – A survey is a set of questions that is administered to a population, also known as respondents. Surveys are a popular market research tool that helps collect meaningful insights from the respondents. To gather good quality data, a survey should have good survey questions, which should be a balanced mix of open-ended and close-ended questions .
Naturalistic Observation – Naturalistic observations are carried out in the natural environment without disturbing the person/ object in observation. It is much like taking notes about people in a supermarket without letting them know. This leads to a greater validity of collected data because people are unaware they are being observed here. This tends to bring out their natural characteristics.

Correlational research also uses naturalistic observation to collect data. However, in addition, it uses archival data to gather information. Archival data is collected from previously conducted research of a similar nature. Archival data is collected through primary research.

In contrast to naturalistic observation, information collected through archived is straightforward. For example, counting the number of people named Jacinda in the United States using their social security number.

Descriptive Research vs Correlational Research

Features of Descriptive Correlational Research

The key features of descriptive correlational research include the following:

01. Description

The main goal, just like with descriptive research, is to describe the variables of interest thoroughly. Researchers aim to explain a certain group or event’s traits, behaviors, or attitudes.

02. Relationships

Like correlational research, descriptive correlational research looks at how two or more factors are related. It looks at how variables are connected to each other, such as how they change over time or how they are linked.

03. Quantitative analysis

Most methods for analyzing quantitative analysis data are used in descriptive correlational research. Researchers use statistical methods to study and measure the size and direction of relationships between variables.

04. No manipulation

As with correlational research, the researcher does not change or control the variables. The data is taken in its natural environment without any changes or interference.

05. Cross-sectional or longitudinal

Cross-sectional or longitudinal designs can be used for descriptive correlational research. It collects data at one point in time, while longitudinal research collects data over a long period of time to look at changes and relationships over time.

Examples of descriptive correlational research

For example, descriptive correlational research could look at the link between a person’s age and how much money they make. The researcher would take a sample of people’s ages and incomes and then look at the data to see if there is a link between the two factors.

Example 1 : A research project is done to find out if there is a link between how long college students sleep and how well they do in school. They keep track of how many hours kids sleep each night and what their GPAs are. By studying the data, the researcher can describe how the students sleep and find out if there is a link between how long they sleep and how well they do in school.
Example 2 : A researcher wants to know how people’s exercise habits affect their physical health if they are between the ages of 40 and 60. They take notes on things like how often and how hard you work out, your body mass index (BMI), blood pressure, and cholesterol numbers. By analyzing the data, the researcher can describe the participants’ exercise habits and physical health and look for any links between these factors.
Example 3 : Let’s say a researcher wants to find out if college students who work out feel less stressed. Using a poll, the researcher finds out how many hours students spend exercising each week and how stressed they feel. By looking at the data, the researcher may find that there is a moderate negative correlation between exercise and stress levels. This means that as exercise grows, stress levels tend to go down.

Descriptive correlational research is a good way to learn about the characteristics of a population or group and the relationships between its different parts. It lets researchers describe variables in detail and look into their relationships without suggesting that one variable caused another.

Descriptive correlational research gives useful insights and can be used as a starting point for more research or to come up with hypotheses. It’s important to be aware of the problems with this type of study, such as the fact that it can’t show cause and effect and relies on cross-sectional data.

Still, descriptive correlational research helps us understand things and makes making decisions in many areas easier.

QuestionPro is a very useful tool for descriptive correlational research. Its many features and easy-to-use interface help researchers collect and study data quickly, giving them a better understanding of the characteristics and relationships between variables in a certain population or group.

The different kinds of questions, analytical research tools, and reporting features on the software improve the research process and help researchers come up with useful results. QuestionPro makes it easier to do descriptive correlational research, which makes it a useful tool for learning important things and making decisions in many fields.

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Designing and Carrying Out Correlational Studies Using Real-World Data

First Online: 10 February 2022

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Charles P. Friedman 4 ,
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Charles P. Friedman

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Answers to Self-Tests

Self-test 13.1.

Spurious correlation

The key observation of concern here is that the 3500 app users form only 1.4% of the overall population (3500/247,000) eligible to use the app. So, a study team needs to ask themselves what factors caused those relatively few individuals to use the app, and might these factors also have led to the observed weight loss ? Some possible factors could include:

Pressure from family and friends (and support) to lose weight

A forthcoming significant future event, e.g. a wedding or graduation

A health scare, e.g. a heart attack, suffered by the patients themselves or a close family member

Developing a new relationship that caused them to “turn over a new leaf” in their lives

Any of these factors are plausible explanations for why weight loss app users might start and continue to use the app, but are also reasons why that person might lose weight even without the app. So, use of the app might be a marker of the small proportion of people in the population who really intend to lose weight, and are being supported by others close to them to lose weight—rather than the cause. To investigate this possibility, a random subset of app users and non-users could be surveyed, asking them about any of the above factors. A qualitative investigation (using interviews or focus groups) of factors associated with success at weight loss (both promoters and inhibitors) would be useful to carry out before finalizing the survey questions to ensure that factors identified as important by responders are included.

Selection bias

Generally, consistent app users are younger than non-users and more likely to own and use a smartphone, which puts them in a income group than non-app users or people who do not own a smart phone. So, what we could be seeing here is bigger weight loss in the subset of people who own and use a smart phone compared to those who do not. This selection bias could be investigated by comparing data on the educational level and household income of app users and non-users, and adding these variable into a regression model to predict weight loss based on a number of factors that are known to be associated with success at losing weight. The person’s use or not of the smart phone app could then be added to this model to investigate if this new model explains more of the observed variation in weight loss, and if the coefficient for this variable is significant.

Information bias

If the person’s weight at the start and the end of the study period is recorded by the app in app users but is recorded informally (in a notebook, diary or vague remembrance of weight a year ago) in the non-users, this could lead to an information bias. Assume that the weight loss based on data recorded in the app is correct. However, it is possible that the weight loss for non-users is underestimated because the non-app users have forgotten or misplaced their weight recording from a year ago, and innocently substitute a figure that is closer to their current weight than it was in reality. If this is the case, the lack of an objective record in non-app users of their weight a year ago would tend to reduce their apparent weights loss, and thus make it appear less than the measured weight loss in app users. To eliminate this bias, we would need an objective record of the baseline weight for the entire population.

Confounding

It is possible that the app was provided to a subset of the population specifically selected by the health care delivery network as part of a risk management exercise to reduce their chances of developing a weight-related illness (such as diabetes, stroke, heart attacks). In addition, other actions may also have formed part of this program, for example regular contacts with a dietician, feedback on their weight and exercise regime compared to that of others in the program, or incentives such as a lower renewal fee if they were successful at reaching their weight loss goal at year end. All of these would be confounders in any study that claimed to measure the impact of the app on weight loss. A searching question directed back to the health care delivery network about potential reasons for app use would help the study team uncover whether the app was targeted to a specific population, and if the users also received other weight loss support.

Self-Test 13.2

The histogram shows how the percent online time increased dramatically in 2020, then reduced slightly in 2021 and 2022, but still remained higher than for the earlier years. Two of the added value scores (physiology and pathology) remained roughly the same, but the anatomy added value score appeared to drop in 2020 and the following years.

The X-Y plot of mean added value score against percent online time shows a negative correlation, with a slight drop in the mean score with increasing online time. This is confirmed by the regression line, showing all data points close to the line, and R squared of 0.97. From this analysis, one would be inclined to conclude that online learning does not promote student learning.

The histogram clearly indicates that the effects differ across the subjects, and the averaging across the subjects conceals this effect. It appears that the increased time spent online had little effect on the added value scores for two subjects, but was associated with a large drop in the anatomy program added value scores. This might be because anatomy is better taught face to face, or it might be another factor, such as a change in program admission criteria, in teaching staff or the materials used. For example, an increase in the baseline anatomy skills of the students in 2020 and onwards could explain the decrease in added value. Equally, the explanation for reduced anatomy added value scores may be an effect of the COVID pandemic which started in 2020, but this would be expected to affect all three subjects.

To investigate if the “online effect” is larger for anatomy than for the two other subjects, the slopes of the regression lines for anatomy alone versus the mean of all three subjects could be compared. The final graph below shows that the reduction in added value with online teaching is greater for anatomy with a regression coefficient of 0.29, almost double the coefficient of 0.17 for the mean across all three subjects.

Further data that could be sought to understand the reason for the observed changes includes:

The numbers of students in the program each year, as some of these changes could simply be due to small numbers in certain years.

The actual entering scores of students for each subject and year, as interpretation of the added value score varies depending on the starting point.

Data about teaching staff numbers and skills and student participation rates and satisfaction year by year and across the three subjects, to help understand why online learning may have led to lower added value for anatomy, if this is true.

Further information about the types (and quality) of online learning experience offered across the three subjects. It is possible, for example, that the online offerings for anatomy differed from those in physiology and pathology.

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Looking for descriptive correlational design definition and meaning? This research paper example explains all the details of this quantitative research method.

Introduction

Why use descriptive correlational design.

Descriptive statistics refers to information that has been analyzed in order to reveal the basic features of data collected or used in a study (Fowler, 2013). They provide researchers with summaries and other critical information regarding study samples and measures. The two main types include measures of central tendency and the measure of spread (Kothari, 2004). A common occurrence when using descriptive data is the emergence of certain patterns that make it easy for researchers to understand and make sense of data. The statistical data can either be used for further research studies or as an independent entity that can be used to make conclusions (Fowler, 2013). Certain research situations involve the use of only descriptive statistics because of the large sample sizes and complexity of data. A study that involves the computation of mean, median, and mode would require descriptive statistics (Yin, 2009).

For instance, they would be sued in a study that aims to find the media score in a class of 100 students with different test results. On the other hand, surveys, case studies, and naturalistic observations can only be successfully conducted using descriptive statistics. An example of research that involved descriptive statistics only is a research study conducted by Andreyeva, Michaud, and Soest (2007) to investigate obesity and health in Europeans aged 50 years and older. The study aimed to study the prevalence of obesity and related health complications among Europeans aged 50 years and above (Andreyeva, Michaud & Soest, 2007). The study involved the collection of data from participants without altering any environmental factors. It was published in the Journal of Public Health in 2007.

Descriptive correlational design is used in research studies that aim to provide static pictures of situations as well as establish the relationship between different variables (McBurney & White, 2009). In correlational research, two variables, such as the height and weight of individuals, are studied to establish their relationship. One of the research topics that can be studied using a descriptive correctional design is the height and weight of college students between the ages of 18 and 25. This study can be tied to their nutrition or frequency of taking meals in a day. The design is appropriate for the aforementioned topic because in conducting the study, the researcher will be required to collect data based on the behavior or attitudes of the participants.

For instance, the number of times the participants eat a certain meal or take a certain beverage. On the other hand, the researcher will be required to establish the relationship between the frequency of taking certain meals or beverages and gains in weight. The researcher could also establish the relationship between the weight and height of the participants. The study design would also enable the researcher to determine changes in the participants’ behaviors or attitudes over time in order to determine how these changes affect the outcomes or possible trends that could emerge in the future (Monsen & Horn, 2007).

Do SAT scores determine the GPA achieved by college students? This research question has both predictor and criterion variables. In this research question, SAT scores represent the predictor variable, and college GPA represents the criterion variable. College GPA is the criterion variable because it is the component being predicted using students’ SAT scores. On the other hand, SAT scores are the predictor variable because they determine the GPA attained in college. The research question seeks to determine whether students’ SAT scores predict the GPA scores they attain in college.

This research paper focused on descriptive correlation design definition and goals. This quantitative research method aims to describe two or more variables and their relationships. Descriptive correlation design can provide a picture of the current state of affairs. For instance, in psychology, it can be a picture of a given group of individuals, their thoughts, behaviors, or feelings.

Andreyeva, T., Michaud, P. C., & Soest, A. (2007). Obesity and Health in Europeans Aged 50 Years and Older. Public Health 121 (1), 497-509.

Fowler, F. J. (2013). Survey Research Methods . New York, NY: SAGE Publications.

Kothari, C. R. (2004). Research Methodology: Methods and Techniques . New York, NY: New Age International.

McBurney, D. & White, T. (2009). Research Methods . New York, NY: Cengage Learning.

Monsen, E. R & Horn, L. V. (2007). Research: Successful Approaches . New York: American Dietetic Association.

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Published: 19 October 2021

Description, prediction, explanation

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Description, prediction and explanation are all important in science. We welcome descriptive, predictive and explanatory studies, so long as the work is clear about its aims and uses appropriate methods to achieve its goals.

Traditionally, the social and life sciences have emphasized explanation: the identification of causal relationships between phenomena, such that intervening to change the cause would necessarily change the outcome. Furthermore, social and life scientists prioritize mechanistic evidence that can explain causal relationships between events or traits.

Description and prediction have traditionally had a secondary role in the natural and social sciences. Description has largely been considered valuable only insofar as it provides the starting point for causal inference. Prediction can be useful, but in and of itself has little to do with the pursuit of ‘scientific truth’ or the identification of laws that govern natural and social phenomena.

These historical emphases and preferences have also determined what is considered a ‘significant scientific advance’ in these fields — that is, the identification and accurate estimation of causal effects.

The past few years have seen a change in this attitude, partly because of the rise of big data and partly because of the life and social sciences gaining increased interaction with computer science and machine learning, where prediction is the central goal.

Editorially, we do not believe that descriptive studies are inherently of lower value or interest, especially when they uncover previously unknown phenomena or describe phenomena at scale through new measures and tools. Similarly, forecasting studies are invaluable in predicting future outcomes — for instance, predicting risk of disease, identifying young people at risk of poorer educational outcomes or predicting the impacts of anthropogenic climate change — when causal relationships are poorly understood or even when the relationships between effects cannot plausibly be causal.

If your manuscript asks a directional question (does x cause/impact/affect y ?), but reports only correlational evidence, it will most probably be returned to you without review, explaining the reason for our decision. Experiments are the key tool for causal inference. However, for several of the key questions regarding human behaviour, manipulating the independent variables of interest experimentally may be unethical, illegal or unfeasible. In those cases, we will expect that authors make use of identification strategies developed for observational data — for example, difference-in-difference designs, regression discontinuity or instrumental variables 1 .

If your manuscript aims to forecast future outcomes in a domain of broad interest and significance, we will expect that it includes out-of-sample validation of your predictions in an independent dataset if a suitable test dataset exists. In cases where no other suitable dataset exists, work can rely on cross-validation using the same dataset and partitioning the dataset into training and test components.

We value descriptive studies, especially when robust descriptions of specific phenomena are lacking or new phenomena of broad significance are discovered and the dataset is large and sufficiently diverse or representative. Mechanistic evidence is not a requirement for publication in those cases, nor is forecasting: although descriptive studies may form the starting point for causal inference or prediction in the future, this isn’t a requirement for their publication. However, if the phenomenon in question has been well described in the past and the specific field expects mechanistic evidence as the next step, we will expect that the work goes beyond description.

Researchers have argued that the boundaries between prediction and explanation are far less sharp than traditionally conceived: identifying causal effects provides a basis for prediction of future outcomes in the same contexts. However, explanatory models are almost invariably built without consideration of predictive accuracy, especially beyond the specific context. Recent proposals have made a case for ‘integrative empirical modelling’ that combines causal inference and prediction of future outcomes 2 . We find these proposals valuable and strongly encourage the submission of research that makes use of integrative empirical modelling.

Marinescu, I. E., Lawlor, P. N. & Kording, K. P. Nat. Hum. Behav. 2 , 891–898 (2018).

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Hofman, J. M. et al. Nature 595 , 181–188 (2021).

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What is Correlational Research? (+ Design, Examples)

Appinio Research · 04.03.2024 · 30min read

Ever wondered how researchers explore connections between different factors without manipulating them? Correlational research offers a window into understanding the relationships between variables in the world around us. From examining the link between exercise habits and mental well-being to exploring patterns in consumer behavior, correlational studies help us uncover insights that shape our understanding of human behavior, inform decision-making, and drive innovation. In this guide, we'll dive into the fundamentals of correlational research, exploring its definition, importance, ethical considerations, and practical applications across various fields. Whether you're a student delving into research methods or a seasoned researcher seeking to expand your methodological toolkit, this guide will equip you with the knowledge and skills to conduct and interpret correlational studies effectively.

What is Correlational Research?

Correlational research is a methodological approach used in scientific inquiry to examine the relationship between two or more variables. Unlike experimental research, which seeks to establish cause-and-effect relationships through manipulation and control of variables, correlational research focuses on identifying and quantifying the degree to which variables are related to one another. This method allows researchers to investigate associations, patterns, and trends in naturalistic settings without imposing experimental manipulations.

Importance of Correlational Research

Correlational research plays a crucial role in advancing scientific knowledge across various disciplines. Its importance stems from several key factors:

Exploratory Analysis : Correlational studies provide a starting point for exploring potential relationships between variables. By identifying correlations, researchers can generate hypotheses and guide further investigation into causal mechanisms and underlying processes.
Predictive Modeling : Correlation coefficients can be used to predict the behavior or outcomes of one variable based on the values of another variable. This predictive ability has practical applications in fields such as economics, psychology, and epidemiology, where forecasting future trends or outcomes is essential.
Diagnostic Purposes: Correlational analyses can help identify patterns or associations that may indicate the presence of underlying conditions or risk factors. For example, correlations between certain biomarkers and disease outcomes can inform diagnostic criteria and screening protocols in healthcare.
Theory Development: Correlational research contributes to theory development by providing empirical evidence for proposed relationships between variables. Researchers can refine and validate theoretical models in their respective fields by systematically examining correlations across different contexts and populations.
Ethical Considerations: In situations where experimental manipulation is not feasible or ethical, correlational research offers an alternative approach to studying naturally occurring phenomena. This allows researchers to address research questions that may otherwise be inaccessible or impractical to investigate.

Correlational vs. Causation in Research

It's important to distinguish between correlation and causation in research. While correlational studies can identify relationships between variables, they cannot establish causal relationships on their own. Several factors contribute to this distinction:

Directionality: Correlation does not imply the direction of causation. A correlation between two variables does not indicate which variable is causing the other; it merely suggests that they are related in some way. Additional evidence, such as experimental manipulation or longitudinal studies , is needed to establish causality.
Third Variables: Correlations may be influenced by third variables, also known as confounding variables, that are not directly measured or controlled in the study. These third variables can create spurious correlations or obscure true causal relationships between the variables of interest.
Temporal Sequence: Causation requires a temporal sequence, with the cause preceding the effect in time. Correlational studies alone cannot establish the temporal order of events, making it difficult to determine whether one variable causes changes in another or vice versa.

Understanding the distinction between correlation and causation is critical for interpreting research findings accurately and drawing valid conclusions about the relationships between variables. While correlational research provides valuable insights into associations and patterns, establishing causation typically requires additional evidence from experimental studies or other research designs.

Key Concepts in Correlation

Understanding key concepts in correlation is essential for conducting meaningful research and interpreting results accurately.

Correlation Coefficient

The correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. It's denoted by the symbol r and ranges from -1 to +1.

A correlation coefficient of -1 indicates a perfect negative correlation, meaning that as one variable increases, the other decreases in a perfectly predictable manner.
A coefficient of +1 signifies a perfect positive correlation, where both variables increase or decrease together in perfect sync.
A coefficient of 0 implies no correlation, indicating no systematic relationship between the variables.

Strength and Direction of Correlation

The strength of correlation refers to how closely the data points cluster around a straight line on the scatterplot. A correlation coefficient close to -1 or +1 indicates a strong relationship between the variables, while a coefficient close to 0 suggests a weak relationship.

Strong correlation: When the correlation coefficient approaches -1 or +1, it indicates a strong relationship between the variables. For example, a correlation coefficient of -0.9 suggests a strong negative relationship, while a coefficient of +0.8 indicates a strong positive relationship.
Weak correlation: A correlation coefficient close to 0 indicates a weak or negligible relationship between the variables. For instance, a coefficient of -0.1 or +0.1 suggests a weak correlation where the variables are minimally related.

The direction of correlation determines how the variables change relative to each other.

Positive correlation: When one variable increases, the other variable also tends to increase. Conversely, when one variable decreases, the other variable tends to decrease. This is represented by a positive correlation coefficient.
Negative correlation: In a negative correlation, as one variable increases, the other variable tends to decrease. Similarly, when one variable decreases, the other variable tends to increase. This relationship is indicated by a negative correlation coefficient.

Scatterplots

A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents the values of both variables for a single observation. By plotting the data points on a Cartesian plane, you can visualize patterns and trends in the relationship between the variables.

Interpretation: When examining a scatterplot, observe the pattern of data points. If the points cluster around a straight line, it indicates a strong correlation. However, if the points are scattered randomly, it suggests a weak or no correlation.
Outliers: Identify any outliers or data points that deviate significantly from the overall pattern. Outliers can influence the correlation coefficient and may warrant further investigation to determine their impact on the relationship between variables.
Line of Best Fit: In some cases, you may draw a line of best fit through the data points to visually represent the overall trend in the relationship. This line can help illustrate the direction and strength of the correlation between the variables.

Understanding these key concepts will enable you to interpret correlation coefficients accurately and draw meaningful conclusions from your data.

How to Design a Correlational Study?

When embarking on a correlational study, careful planning and consideration are crucial to ensure the validity and reliability of your research findings.

Research Question Formulation

Formulating clear and focused research questions is the cornerstone of any successful correlational study. Your research questions should articulate the variables you intend to investigate and the nature of the relationship you seek to explore. When formulating your research questions:

Be Specific: Clearly define the variables you are interested in studying and the population to which your findings will apply.
Be Testable: Ensure that your research questions are empirically testable using correlational methods. Avoid vague or overly broad questions that are difficult to operationalize.
Consider Prior Research: Review existing literature to identify gaps or unanswered questions in your area of interest. Your research questions should build upon prior knowledge and contribute to advancing the field.

For example, if you're interested in examining the relationship between sleep duration and academic performance among college students, your research question might be: "Is there a significant correlation between the number of hours of sleep per night and GPA among undergraduate students?"

Participant Selection

Selecting an appropriate sample of participants is critical to ensuring the generalizability and validity of your findings. Consider the following factors when selecting participants for your correlational study:

Population Characteristics: Identify the population of interest for your study and ensure that your sample reflects the demographics and characteristics of this population.
Sampling Method: Choose a sampling method that is appropriate for your research question and accessible, given your resources and constraints. Standard sampling methods include random sampling, stratified sampling, and convenience sampling.
Sample Size: Determine the appropriate sample size based on factors such as the effect size you expect to detect, the desired level of statistical power, and practical considerations such as time and budget constraints.

For example, suppose you're studying the relationship between exercise habits and mental health outcomes in adults aged 18-65. In that case, you might use stratified random sampling to ensure representation from different age groups within the population.

Variables Identification

Identifying and operationalizing the variables of interest is essential for conducting a rigorous correlational study. When identifying variables for your research:

Independent and Dependent Variables: Clearly distinguish between independent variables (factors that are hypothesized to influence the outcome) and dependent variables (the outcomes or behaviors of interest).
Control Variables: Identify any potential confounding variables or extraneous factors that may influence the relationship between your independent and dependent variables. These variables should be controlled for in your analysis.
Measurement Scales: Determine the appropriate measurement scales for your variables (e.g., nominal, ordinal, interval, or ratio) and select valid and reliable measures for assessing each construct.

For instance, if you're investigating the relationship between socioeconomic status (SES) and academic achievement, SES would be your independent variable, while academic achievement would be your dependent variable. You might measure SES using a composite index based on factors such as income, education level, and occupation.

Data Collection Methods

Selecting appropriate data collection methods is essential for obtaining reliable and valid data for your correlational study. When choosing data collection methods:

Quantitative vs. Qualitative : Determine whether quantitative or qualitative methods are best suited to your research question and objectives. Correlational studies typically involve quantitative data collection methods like surveys, questionnaires, or archival data analysis.
Instrument Selection: Choose measurement instruments that are valid, reliable, and appropriate for your variables of interest. Pilot test your instruments to ensure clarity and comprehension among your target population.
Data Collection Procedures : Develop clear and standardized procedures for data collection to minimize bias and ensure consistency across participants and time points.

For example, if you're examining the relationship between smartphone use and sleep quality among adolescents, you might administer a self-report questionnaire assessing smartphone usage patterns and sleep quality indicators such as sleep duration and sleep disturbances.

Crafting a well-designed correlational study is essential for yielding meaningful insights into the relationships between variables. By meticulously formulating research questions , selecting appropriate participants, identifying relevant variables, and employing effective data collection methods, researchers can ensure the validity and reliability of their findings.

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How to Analyze Correlational Data?

Once you have collected your data in a correlational study, the next crucial step is to analyze it effectively to draw meaningful conclusions about the relationship between variables.

How to Calculate Correlation Coefficients?

The correlation coefficient is a numerical measure that quantifies the strength and direction of the relationship between two variables. There are different types of correlation coefficients, including Pearson's correlation coefficient (for linear relationships), Spearman's rank correlation coefficient (for ordinal data ), and Kendall's tau (for non-parametric data). Here, we'll focus on calculating Pearson's correlation coefficient (r), which is commonly used for interval or ratio-level data.

To calculate Pearson's correlation coefficient (r), you can use statistical software such as SPSS, R, or Excel. However, if you prefer to calculate it manually, you can use the following formula:

r = Σ((X - X̄)(Y - Ȳ)) / ((n - 1) * (s_X * s_Y))

X and Y are the scores of the two variables,
X̄ and Ȳ are the means of X and Y, respectively,
n is the number of data points,
s_X and s_Y are the standard deviations of X and Y, respectively.

Interpreting Correlation Results

Once you have calculated the correlation coefficient (r), it's essential to interpret the results correctly. When interpreting correlation results:

Magnitude: The absolute value of the correlation coefficient (r) indicates the strength of the relationship between the variables. A coefficient close to 1 or -1 suggests a strong correlation, while a coefficient close to 0 indicates a weak or no correlation.
Direction: The sign of the correlation coefficient (positive or negative) indicates the direction of the relationship between the variables. A positive correlation coefficient indicates a positive relationship (as one variable increases, the other tends to increase), while a negative correlation coefficient indicates a negative relationship (as one variable increases, the other tends to decrease).
Statistical Significance : Assess the statistical significance of the correlation coefficient to determine whether the observed relationship is likely to be due to chance. This is typically done using hypothesis testing, where you compare the calculated correlation coefficient to a critical value based on the sample size and desired level of significance (e.g., α =0.05).

Statistical Significance

Determining the statistical significance of the correlation coefficient involves conducting hypothesis testing to assess whether the observed correlation is likely to occur by chance. The most common approach is to use a significance level (alpha, α ) of 0.05, which corresponds to a 5% chance of obtaining the observed correlation coefficient if there is no true relationship between the variables.

To test the null hypothesis that the correlation coefficient is zero (i.e., no correlation), you can use inferential statistics such as the t-test or z-test. If the calculated p-value is less than the chosen significance level (e.g., p <0.05), you can reject the null hypothesis and conclude that the correlation coefficient is statistically significant.

Remember that statistical significance does not necessarily imply practical significance or the strength of the relationship. Even a statistically significant correlation with a small effect size may not be meaningful in practical terms.

By understanding how to calculate correlation coefficients, interpret correlation results, and assess statistical significance, you can effectively analyze correlational data and draw accurate conclusions about the relationships between variables in your study.

Correlational Research Limitations

As with any research methodology, correlational studies have inherent considerations and limitations that researchers must acknowledge and address to ensure the validity and reliability of their findings.

Third Variables

One of the primary considerations in correlational research is the presence of third variables, also known as confounding variables. These are extraneous factors that may influence or confound the observed relationship between the variables under study. Failing to account for third variables can lead to spurious correlations or erroneous conclusions about causality.

For example, consider a correlational study examining the relationship between ice cream consumption and drowning incidents. While these variables may exhibit a positive correlation during the summer months, the true causal factor is likely to be a third variable—such as hot weather—that influences both ice cream consumption and swimming activities, thereby increasing the risk of drowning.

To address the influence of third variables, researchers can employ various strategies, such as statistical control techniques, experimental designs (when feasible), and careful operationalization of variables.

Causal Inferences

Correlation does not imply causation—a fundamental principle in correlational research. While correlational studies can identify relationships between variables, they cannot determine causality. This is because correlation merely describes the degree to which two variables co-vary; it does not establish a cause-and-effect relationship between them.

For example, consider a correlational study that finds a positive relationship between the frequency of exercise and self-reported happiness. While it may be tempting to conclude that exercise causes happiness, it's equally plausible that happier individuals are more likely to exercise regularly. Without experimental manipulation and control over potential confounding variables, causal inferences cannot be made.

To strengthen causal inferences in correlational research, researchers can employ longitudinal designs, experimental methods (when ethical and feasible), and theoretical frameworks to guide their interpretations.

Sample Size and Representativeness

The size and representativeness of the sample are critical considerations in correlational research. A small or non-representative sample may limit the generalizability of findings and increase the risk of sampling bias .

For example, if a correlational study examines the relationship between socioeconomic status (SES) and educational attainment using a sample composed primarily of high-income individuals, the findings may not accurately reflect the broader population's experiences. Similarly, an undersized sample may lack the statistical power to detect meaningful correlations or relationships.

To mitigate these issues, researchers should aim for adequate sample sizes based on power analyses, employ random or stratified sampling techniques to enhance representativeness and consider the demographic characteristics of the target population when interpreting findings.

Ensure your survey delivers accurate insights by using our Sample Size Calculator . With customizable options for margin of error, confidence level, and standard deviation, you can determine the optimal sample size to ensure representative results. Make confident decisions backed by robust data.

Reliability and Validity

Ensuring the reliability and validity of measures is paramount in correlational research. Reliability refers to the consistency and stability of measurement over time, whereas validity pertains to the accuracy and appropriateness of measurement in capturing the intended constructs.

For example, suppose a correlational study utilizes self-report measures of depression and anxiety. In that case, it's essential to assess the measures' reliability (e.g., internal consistency, test-retest reliability) and validity (e.g., content validity, criterion validity) to ensure that they accurately reflect participants' mental health status.

To enhance reliability and validity in correlational research, researchers can employ established measurement scales, pilot-test instruments, use multiple measures of the same construct, and assess convergent and discriminant validity.

By addressing these considerations and limitations, researchers can enhance the robustness and credibility of their correlational studies and make more informed interpretations of their findings.

Correlational Research Examples and Applications

Correlational research is widely used across various disciplines to explore relationships between variables and gain insights into complex phenomena. We'll examine examples and applications of correlational studies, highlighting their practical significance and impact on understanding human behavior and societal trends across various industries and use cases.

Psychological Correlational Studies

In psychology, correlational studies play a crucial role in understanding various aspects of human behavior, cognition, and mental health. Researchers use correlational methods to investigate relationships between psychological variables and identify factors that may contribute to or predict specific outcomes.

For example, a psychological correlational study might examine the relationship between self-esteem and depression symptoms among adolescents. By administering self-report measures of self-esteem and depression to a sample of teenagers and calculating the correlation coefficient between the two variables, researchers can assess whether lower self-esteem is associated with higher levels of depression symptoms.

Other examples of psychological correlational studies include investigating the relationship between:

Parenting styles and academic achievement in children
Personality traits and job performance in the workplace
Stress levels and coping strategies among college students

These studies provide valuable insights into the factors influencing human behavior and mental well-being, informing interventions and treatment approaches in clinical and counseling settings.

Business Correlational Studies

Correlational research is also widely utilized in the business and management fields to explore relationships between organizational variables and outcomes. By examining correlations between different factors within an organization, researchers can identify patterns and trends that may impact performance, productivity, and profitability.

For example, a business correlational study might investigate the relationship between employee satisfaction and customer loyalty in a retail setting. By surveying employees to assess their job satisfaction levels and analyzing customer feedback and purchase behavior, researchers can determine whether higher employee satisfaction is correlated with increased customer loyalty and retention.

Other examples of business correlational studies include examining the relationship between:

Leadership styles and employee motivation
Organizational culture and innovation
Marketing strategies and brand perception

These studies provide valuable insights for organizations seeking to optimize their operations, improve employee engagement, and enhance customer satisfaction.

Marketing Correlational Studies

In marketing, correlational studies are instrumental in understanding consumer behavior, identifying market trends, and optimizing marketing strategies. By examining correlations between various marketing variables, researchers can uncover insights that drive effective advertising campaigns, product development, and brand management.

For example, a marketing correlational study might explore the relationship between social media engagement and brand loyalty among millennials. By collecting data on millennials' social media usage, brand interactions, and purchase behaviors, researchers can analyze whether higher levels of social media engagement correlate with increased brand loyalty and advocacy.

Another example of a marketing correlational study could focus on investigating the relationship between pricing strategies and customer satisfaction in the retail sector. By analyzing data on pricing fluctuations, customer feedback , and sales performance, researchers can assess whether pricing strategies such as discounts or promotions impact customer satisfaction and repeat purchase behavior.

Other potential areas of inquiry in marketing correlational studies include examining the relationship between:

Product features and consumer preferences
Advertising expenditures and brand awareness
Online reviews and purchase intent

These studies provide valuable insights for marketers seeking to optimize their strategies, allocate resources effectively, and build strong relationships with consumers in an increasingly competitive marketplace. By leveraging correlational methods, marketers can make data-driven decisions that drive business growth and enhance customer satisfaction.

Correlational Research Ethical Considerations

Ethical considerations are paramount in all stages of the research process, including correlational studies. Researchers must adhere to ethical guidelines to ensure the rights, well-being, and privacy of participants are protected. Key ethical considerations to keep in mind include:

Informed Consent: Obtain informed consent from participants before collecting any data. Clearly explain the purpose of the study, the procedures involved, and any potential risks or benefits. Participants should have the right to withdraw from the study at any time without consequence.
Confidentiality: Safeguard the confidentiality of participants' data. Ensure that any personal or sensitive information collected during the study is kept confidential and is only accessible to authorized individuals. Use anonymization techniques when reporting findings to protect participants' privacy.
Voluntary Participation: Ensure that participation in the study is voluntary and not coerced. Participants should not feel pressured to take part in the study or feel that they will suffer negative consequences for declining to participate.
Avoiding Harm: Take measures to minimize any potential physical, psychological, or emotional harm to participants. This includes avoiding deceptive practices, providing appropriate debriefing procedures (if necessary), and offering access to support services if participants experience distress.
Deception: If deception is necessary for the study, it must be justified and minimized. Deception should be disclosed to participants as soon as possible after data collection, and any potential risks associated with the deception should be mitigated.
Researcher Integrity: Maintain integrity and honesty throughout the research process. Avoid falsifying data, manipulating results, or engaging in any other unethical practices that could compromise the integrity of the study.
Respect for Diversity: Respect participants' cultural, social, and individual differences. Ensure that research protocols are culturally sensitive and inclusive, and that participants from diverse backgrounds are represented and treated with respect.
Institutional Review: Obtain ethical approval from institutional review boards or ethics committees before commencing the study. Adhere to the guidelines and regulations set forth by the relevant governing bodies and professional organizations.

Adhering to these ethical considerations ensures that correlational research is conducted responsibly and ethically, promoting trust and integrity in the scientific community.

Correlational Research Best Practices and Tips

Conducting a successful correlational study requires careful planning, attention to detail, and adherence to best practices in research methodology. Here are some tips and best practices to help you conduct your correlational research effectively:

Clearly Define Variables: Clearly define the variables you are studying and operationalize them into measurable constructs. Ensure that your variables are accurately and consistently measured to avoid ambiguity and ensure reliability.
Use Valid and Reliable Measures: Select measurement instruments that are valid and reliable for assessing your variables of interest. Pilot test your measures to ensure clarity, comprehension, and appropriateness for your target population.
Consider Potential Confounding Variables: Identify and control for potential confounding variables that could influence the relationship between your variables of interest. Consider including control variables in your analysis to isolate the effects of interest.
Ensure Adequate Sample Size: Determine the appropriate sample size based on power analyses and considerations of statistical power. Larger sample sizes increase the reliability and generalizability of your findings.
Random Sampling: Whenever possible, use random sampling techniques to ensure that your sample is representative of the population you are studying. If random sampling is not feasible, carefully consider the characteristics of your sample and the extent to which findings can be generalized.
Statistical Analysis : Choose appropriate statistical techniques for analyzing your data, taking into account the nature of your variables and research questions. Consult with a statistician if necessary to ensure the validity and accuracy of your analyses.
Transparent Reporting: Transparently report your methods, procedures, and findings in accordance with best practices in research reporting. Clearly articulate your research questions, methods, results, and interpretations to facilitate reproducibility and transparency.
Peer Review: Seek feedback from colleagues, mentors, or peer reviewers throughout the research process. Peer review helps identify potential flaws or biases in your study design, analysis, and interpretation, improving your research's overall quality and credibility.

By following these best practices and tips, you can conduct your correlational research with rigor, integrity, and confidence, leading to valuable insights and contributions to your field.

Conclusion for Correlational Research

Correlational research serves as a powerful tool for uncovering connections between variables in the world around us. By examining the relationships between different factors, researchers can gain valuable insights into human behavior, health outcomes, market trends, and more. While correlational studies cannot establish causation on their own, they provide a crucial foundation for generating hypotheses, predicting outcomes, and informing decision-making in various fields. Understanding the principles and practices of correlational research empowers researchers to explore complex phenomena, advance scientific knowledge, and address real-world challenges. Moreover, embracing ethical considerations and best practices in correlational research ensures the integrity, validity, and reliability of study findings. By prioritizing informed consent, confidentiality, and participant well-being, researchers can conduct studies that uphold ethical standards and contribute meaningfully to the body of knowledge. Incorporating transparent reporting, peer review, and continuous learning further enhances the quality and credibility of correlational research. Ultimately, by leveraging correlational methods responsibly and ethically, researchers can unlock new insights, drive innovation, and make a positive impact on society.

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Descriptive Research

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Descriptive research: what it is and how to use it.

8 min read Understanding the who, what and where of a situation or target group is an essential part of effective research and making informed business decisions.

For example you might want to understand what percentage of CEOs have a bachelor’s degree or higher. Or you might want to understand what percentage of low income families receive government support – or what kind of support they receive.

Descriptive research is what will be used in these types of studies.

In this guide we’ll look through the main issues relating to descriptive research to give you a better understanding of what it is, and how and why you can use it.

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What is descriptive research?

Descriptive research is a research method used to try and determine the characteristics of a population or particular phenomenon.

Using descriptive research you can identify patterns in the characteristics of a group to essentially establish everything you need to understand apart from why something has happened.

Market researchers use descriptive research for a range of commercial purposes to guide key decisions.

For example you could use descriptive research to understand fashion trends in a given city when planning your clothing collection for the year. Using descriptive research you can conduct in depth analysis on the demographic makeup of your target area and use the data analysis to establish buying patterns.

Conducting descriptive research wouldn’t, however, tell you why shoppers are buying a particular type of fashion item.

Descriptive research design

Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis.

As a survey method, descriptive research designs will help researchers identify characteristics in their target market or particular population.

These characteristics in the population sample can be identified, observed and measured to guide decisions.

Descriptive research characteristics

While there are a number of descriptive research methods you can deploy for data collection, descriptive research does have a number of predictable characteristics.

Here are a few of the things to consider:

Measure data trends with statistical outcomes

Descriptive research is often popular for survey research because it generates answers in a statistical form, which makes it easy for researchers to carry out a simple statistical analysis to interpret what the data is saying.

Descriptive research design is ideal for further research

Because the data collection for descriptive research produces statistical outcomes, it can also be used as secondary data for another research study.

Plus, the data collected from descriptive research can be subjected to other types of data analysis .

Uncontrolled variables

A key component of the descriptive research method is that it uses random variables that are not controlled by the researchers. This is because descriptive research aims to understand the natural behavior of the research subject.

It’s carried out in a natural environment

Descriptive research is often carried out in a natural environment. This is because researchers aim to gather data in a natural setting to avoid swaying respondents.

Data can be gathered using survey questions or online surveys.

For example, if you want to understand the fashion trends we mentioned earlier, you would set up a study in which a researcher observes people in the respondent’s natural environment to understand their habits and preferences.

Descriptive research allows for cross sectional study

Because of the nature of descriptive research design and the randomness of the sample group being observed, descriptive research is ideal for cross sectional studies – essentially the demographics of the group can vary widely and your aim is to gain insights from within the group.

This can be highly beneficial when you’re looking to understand the behaviors or preferences of a wider population.

Descriptive research advantages

There are many advantages to using descriptive research, some of them include:

Cost effectiveness

Because the elements needed for descriptive research design are not specific or highly targeted (and occur within the respondent’s natural environment) this type of study is relatively cheap to carry out.

Multiple types of data can be collected

A big advantage of this research type, is that you can use it to collect both quantitative and qualitative data. This means you can use the stats gathered to easily identify underlying patterns in your respondents’ behavior.

Descriptive research disadvantages

Potential reliability issues.

When conducting descriptive research it’s important that the initial survey questions are properly formulated.

If not, it could make the answers unreliable and risk the credibility of your study.

Potential limitations

As we’ve mentioned, descriptive research design is ideal for understanding the what, who or where of a situation or phenomenon.

However, it can’t help you understand the cause or effect of the behavior. This means you’ll need to conduct further research to get a more complete picture of a situation.

Descriptive research methods

Because descriptive research methods include a range of quantitative and qualitative research, there are several research methods you can use.

Use case studies

Case studies in descriptive research involve conducting in-depth and detailed studies in which researchers get a specific person or case to answer questions.

Case studies shouldn’t be used to generate results, rather it should be used to build or establish hypothesis that you can expand into further market research .

For example you could gather detailed data about a specific business phenomenon, and then use this deeper understanding of that specific case.

Use observational methods

This type of study uses qualitative observations to understand human behavior within a particular group.

By understanding how the different demographics respond within your sample you can identify patterns and trends.

As an observational method, descriptive research will not tell you the cause of any particular behaviors, but that could be established with further research.

Use survey research

Surveys are one of the most cost effective ways to gather descriptive data.

An online survey or questionnaire can be used in descriptive studies to gather quantitative information about a particular problem.

Survey research is ideal if you’re using descriptive research as your primary research.

Descriptive research examples

Descriptive research is used for a number of commercial purposes or when organizations need to understand the behaviors or opinions of a population.

One of the biggest examples of descriptive research that is used in every democratic country, is during elections.

Using descriptive research, researchers will use surveys to understand who voters are more likely to choose out of the parties or candidates available.

Using the data provided, researchers can analyze the data to understand what the election result will be.

In a commercial setting, retailers often use descriptive research to figure out trends in shopping and buying decisions.

By gathering information on the habits of shoppers, retailers can get a better understanding of the purchases being made.

Another example that is widely used around the world, is the national census that takes place to understand the population.

The research will provide a more accurate picture of a population’s demographic makeup and help to understand changes over time in areas like population age, health and education level.

Where Qualtrics helps with descriptive research

Whatever type of research you want to carry out, there’s a survey type that will work.

Qualtrics can help you determine the appropriate method and ensure you design a study that will deliver the insights you need.

Our experts can help you with your market research needs , ensuring you get the most out of Qualtrics market research software to design, launch and analyze your data to guide better, more accurate decisions for your organization.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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What Is a Correlational Study And Examples of correlational research
Descriptive Correlational: Descriptive vs correlational research
Chapter 9 Correlational Research Designs
Differences Between Descriptive Research and Correlational Research
Descriptive Correlational Research Examples
descriptive correlational research design

VIDEO

Unit 1: Correlational Research (AP Psychology)
Descriptive, inferential, & correlational statistics, PART
CORRELATIONAL DESIGN (chapter 11)
REVISION LESSON 5 ON CORRELATION: || DOES CORRELATION DETERMINE CAUSALITY? WHAT IS THE DIFFERENCE?
The three types of research methods #reseach #study
CORRELATIONAL RESEARCH

COMMENTS

2.2 Psychologists Use Descriptive, Correlational, and Experimental
2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior ... The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables: ... The use of multiple regression analysis shows an important advantage of correlational research ...
Correlational Research
Revised on June 22, 2023. A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them. A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.
3.2 Psychologists Use Descriptive, Correlational, and Experimental
Descriptive research is research designed to provide a snapshot ... The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables. ... (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs ...
Study designs: Part 2
INTRODUCTION. In our previous article in this series, [ 1] we introduced the concept of "study designs"- as "the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.". Study designs are primarily of two types - observational and interventional, with the former being ...
Importance and use of correlational research
Background: The importance of correlational research has been reported in the literature yet few research texts discuss design in any detail. Aim: To discuss important issues and considerations in correlational research, and suggest ways to avert potential problems during the preparation and application of the design. Discussion: This article targets the gap identified in the literature ...
Observational Study Designs: Synopsis for Selecting an Appropriate
The observational design is subdivided into descriptive, including cross-sectional, case report or case series, and correlational, and analytic which includes cross-section, case-control, and cohort studies. Each research design has its uses and points of strength and limitations. The aim of this article to provide a simplified approach for the ...
Correlational Research
A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them. A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative. Positive correlation.
Understanding Research Study Designs
Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23 (Suppl 4):S305-S307. Keywords: Clinical trials as topic, Observational studies as topic, Research designs. We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized. Go to:
Descriptive Research
Revised on June 22, 2023. Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when and how questions, but not why questions. A descriptive research design can use a wide variety of research methods to investigate one or more variables.
12 Quantitative Descriptive and Correlational Research
Important Terms for Review Important Terms for Review. References ... In contrast to some experimental research designs, in these design types the primary area of interest under investigation is not manipulated by the researcher. Researchers investigating descriptive or correlational research questions commonly use surveys or observational ...
What Is a Research Design
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
Importance and use of correlational research
Background The importance of correlational research has been reported in the literature yet few research texts discuss design in any detail.. Aim To discuss important issues and considerations in correlational research, and suggest ways to avert potential problems during the preparation and application of the design.. Discussion: This article targets the gap identified in the literature ...
Descriptive Correlational: Descriptive vs Correlational Research
In descriptive correlational research, it's important to make sure that the groups or variables being compared are the same at the start. ... Descriptive correlational research is a type of research design that tries to explain the relationship between two or more variables without making any claims about cause and effect. ... In descriptive ...
(PDF) Descriptive Research Designs
This study employs a quantitative descriptive correlational research design, combining two research strategies-descriptive design and correlational research. Descriptive design involves observing ...
Designing and Carrying Out Correlational Studies Using Real ...
Recall from Sects. 6.9.2 and 10.2 that the purpose of correlational studies is to explore the relationships between one or more independent variables and one or more dependent variables. These variables are recorded in one or more datasets, routinely generated as a by-product of ongoing life experience, health care, research, educational, or public health practice.
PDF SURVEY AND CORRELATIONAL RESEARCH DESIGNS
Open-ended items are most often used with the qualitative research design because the responses in the survey are purely descriptive. Indeed, the Jha et al. (2009) focus group study was a qualitative research study. For all other research designs—those that are quantitative—the challenge is in coding the open-ended responses of participants.
Understanding Descriptive Research Designs and Methods
The researchers used the descriptive-correlational research design. The researchers utilized three questionnaires to assess factors affecting students' performance: the Teachers' Classroom ...
Descriptive Research Design
Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied. ... Correlational Research. This involves examining the relationships between two or more variables to describe their patterns or associations ...
Importance and use of correlational research
Research Methodology: To sample 115 employees, a quantitative methodology and correlational descriptive design, as well as a census technique, were used. The data were collected using structured ...
Descriptive Correlational Design in Research
This research paper focused on descriptive correlation design definition and goals. This quantitative research method aims to describe two or more variables and their relationships. Descriptive correlation design can provide a picture of the current state of affairs. For instance, in psychology, it can be a picture of a given group of ...
Description, prediction, explanation
Description, prediction and explanation are all important in science. We welcome descriptive, predictive and explanatory studies, so long as the work is clear about its aims and uses appropriate ...
What is Correlational Research? (+ Design, Examples)
Conclusion for Correlational Research. Correlational research serves as a powerful tool for uncovering connections between variables in the world around us. By examining the relationships between different factors, researchers can gain valuable insights into human behavior, health outcomes, market trends, and more.
Descriptive Research Design: What It Is and How to Use It
Descriptive research design. Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis. As a survey method, descriptive research designs will help ...
Effects of Descriptive Peer Assessment on the Learning of Middle and
To address the first research question, a correlation analysis of the students' peer assessment scores and the scores assigned by the teacher was conducted. ... There are two possible explanations for the high correlation between online descriptive peer assessment and teacher outcomes. ... The challenge of design. Assessment & Evaluation in ...

2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Descriptive Research: Assessing the Current State of Affairs

Correlational Research: Seeking Relationships Among Variables

Experimental Research: Understanding the Causes of Behavior

Research Focus: Video Games and Aggression

Key Takeaways

Exercises and Critical Thinking

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Descriptive Research: Assessing the Current State of Affairs

Correlational Research: Seeking Relationships among Variables

Experimental Research: Understanding the Causes of Behaviour

Research Focus: Video Games and Aggression

Key Takeaways

Exercises and Critical Thinking

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Importance and use of correlational research

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Correlational Research | Guide, Design & Examples

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To investigate non-causal relationships

To explore causal relationships between variables

To test new measurement tools

Naturalistic observation

Secondary data

Correlation analysis

Regression analysis

Directionality problem

Third variable problem

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Pritha Bhandari

Understanding Research Study Designs

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TERMS USED IN RESEARCH DESIGNS

Longitudinal vs Transversal Studies

Forward vs Backward Directed Studies

Prospective vs Retrospective Studies

CLASSIFICATION OF STUDY DESIGNS

Descriptive Studies

Analytical (Inferential) Studies

Cross-sectional Studies

Case–control Studies

Cohort Studies

Interventional Studies

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Descriptive Correlational: Descriptive vs Correlational Research

What is descriptive correlational research?

How are descriptive research and correlational research carried out?

Descriptive Research vs Correlational Research

Features of Descriptive Correlational Research

01. Description

02. Relationships

03. Quantitative analysis

04. No manipulation

05. Cross-sectional or longitudinal

Examples of descriptive correlational research

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Designing and Carrying Out Correlational Studies Using Real-World Data