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Chapter 3. Psychological Science & Research

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

Charles Stangor and Jennifer Walinga

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 3.3, 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 3.3).

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.

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).

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 3.4.

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 3.4 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 3.4 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 3.5 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).

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 3.5 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 , as seen in Figure 3.6.

Or they may be more spread out away from it, as seen in Figure 3.7.

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 3.4 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 3.8, 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 3.9 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 3.9 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 3.9 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 3.9 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 .

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 3.10 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 3.12):

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 (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 3.14)

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

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 3.15):

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 3.16

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).

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 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 3.3: “ 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.

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. (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 3.5 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.

Figure 3.9 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.

Introduction to Psychology Copyright © 2019 by Charles Stangor and Jennifer Walinga is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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6 Correlational Design and Analysis

• Published: March 2018
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Interests in how variables may relate to each other and how systems of relationships among variables may be at play often underlie the questions music education researchers pose. This chapter describes basic design and analysis considerations in research that involves the systematic investigation of whether and how variables are related; in other words, correlational research. The chapter poses correlational research as an extension of the book’s previous discussion of descriptive research. The chapter briefly describes the role of correlational studies in advancing theory, presents several issues to consider when designing studies, and provides an introduction to correlation as a statistical concept.

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Home Market Research

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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Descriptive Correlational Design in Research

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.

Yin, R. K. (2009). Case Study Research: Design and Methods . New York, NY: SAGE Publications.

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IvyPanda. (2023, October 29). Descriptive Correlational Design in Research. https://ivypanda.com/essays/descriptive-statistics-and-correlational-design/

"Descriptive Correlational Design in Research." IvyPanda , 29 Oct. 2023, ivypanda.com/essays/descriptive-statistics-and-correlational-design/.

IvyPanda . (2023) 'Descriptive Correlational Design in Research'. 29 October.

IvyPanda . 2023. "Descriptive Correlational Design in Research." October 29, 2023. https://ivypanda.com/essays/descriptive-statistics-and-correlational-design/.

1. IvyPanda . "Descriptive Correlational Design in Research." October 29, 2023. https://ivypanda.com/essays/descriptive-statistics-and-correlational-design/.

Bibliography

IvyPanda . "Descriptive Correlational Design in Research." October 29, 2023. https://ivypanda.com/essays/descriptive-statistics-and-correlational-design/.

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• Statistics: "The Median Isn’t the Message" by Stephen Gould

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Observational Study Designs: Synopsis for Selecting an Appropriate Study Design

Assad a rezigalla.

1 Department of Basic Medical Sciences, College of Medicine, University of Bisha, Bisha, SAU

The selection of a study design is the most critical step in the research methodology. Crucial factors should be considered during the selection of the study design, which is the formulated research question, as well as the method of participant selection. Different study designs can be applied to the same research question(s). Research designs are classified as qualitative, quantitative, and mixed design. Observational design occupies the middle and lower parts of the hierarchy of evidence-based pyramid. 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 selection of descriptive study design.

Introduction and background

A research design is defined as the “set up to decide on, among other issues, how to collect further data, analyze and interpret them, and finally, to provide an answer to the question” [ 1 ]. The primary objective of a research design is to guarantee that the collected evidence allows the answering of the initial question(s) as clearly as possible [ 2 ]. Various study designs have been described in the literature [ 1 - 3 ]. Each of them deals with the specific type of research or research questions and has points of strength and weakness. Broadly, research designs are classified into qualitative and quantitative research and mixed methods [ 3 ]. The quantitative study design is subdivided into descriptive versus analytical study designs or as observational versus interventional (Figure ​ (Figure1). 1 ). Descriptive designs occupy the middle and lower parts of the hierarchy of evidence-based medicine pyramid. Study designs are organized in a hierarchy beginning from the basic "case report" to the highly valued "randomised clinical trial" [ 4 - 5 ].

Case report

The case report describes an individual case or cases in their natural settings. Also, it describes unrecognized syndromes or variants, abnormal findings or outcomes, or association between risk factors and disease. It is the lowest level and the first line of evidence and usually deals with the newly emerging issues and ideas (Table ​ (Table1) 1 ) [ 4 , 6 - 10 ].

Case series

A case series is a report on data from a subject group (multiple patients) without control [ 6 , 11 - 12 ]. Commonly, this design is used for the illustration of novel, unusual, or atypical features identified in medical practice [ 6 ]. The investigator is governed by the availability and accuracy of the records, which can cause biases [ 13 - 14 ]. Bias in a case series can be decreased through consecutive patient enrollment and predefined inclusion and exclusion criteria, explicit specification of study duration, and enrollment of participants (Table 2 ) [ 11 - 12 ].

Correlational study design

Correlational studies (ecologic studies) explore the statistical relationships between the outcome of interest in population and estimate the exposures. It deals with the community rather than in individual cases. The correlational study design can compare two or more relevant variables and reports the association between them without controlling the variables. The aim of correlational study design or research is to uncover any types of systematic relationships between the studied variables. Ecological studies are often used to measure the prevalence and incidence of disease, mainly when the disease is rare. The populations compared can be defined in several ways, such as geographical, time trends, migrants, longitudinal, occupation, and social class. It should be considered that in ecological studies, the results are presented at the population (group) level rather than individuals. Ecological studies do not provide information about the degree or extent of exposure or outcome of interest for particular individuals within the study group (Table  3 ) [ 7 ,  15 - 16 ]. For example, we do not know whether those individuals who died in the study group under observation had higher exposure than those remained alive.

Cross-sectional study design

The cross-sectional study examines the association between exposures and outcomes on a snap of time. The assessed associations are guided by sound hypotheses and seen as hypothesis-generating [ 17 ]. This design can be descriptive (when dealing with prevalence or survey) or analytic (when comparing groups) [ 17 - 18 ]. The selection of participants in a cross-sectional study design depends on the predefined inclusion and exclusion criteria [ 18 - 19 ]. This method of selection limits randomization (Table 4 ).

Case-control study

A case-control study is an observational analytic retrospective study design [ 12 ]. It starts with the outcome of interest (referred to as cases) and looks back in time for exposures that likely caused the outcome of interest [ 13 , 20 ]. This design compares two groups of participants - those with the outcome of interest and the matched control [ 12 ]. The controls should match the group of interest in most of the aspects, except for the outcome of interest [ 18 ]. The controls should be selected from the same localization or setting of the cases [ 13 , 21 - 22 ]. Case-control studies can determine the relative importance of a predictor variable about the presence or absence of the disease (Table ​ (Table5 5 ).

Cohort study design

The cohort study design is classified as an observational analytic study design. This design compares two groups, with exposure of interest and control one [ 12 , 18 , 22 - 24 ].

Cohort design starts with exposure of interest comparing them to non-exposed participants at the time of study initiation [ 18 , 22 , 24 ]. The non-exposed serve as external control. A cohort design can be either prospective [ 18 ] or retrospective [ 12 , 20 , 24 - 25 ]. In prospective cohort studies, the investigator measures a variety of variables that might be a risk factor or relevant to the development of the outcome of interest. Over time, the participants are observed to detect whether they develop the outcome of interest or not. In this case, the participants who do not develop the outcome of interest can act as internal controls. Retrospective cohort studies use data records that were documented for other purposes. The study duration may vary according to the commencement of data recording. Completion of the study is limited to the analysis of the data [ 18 , 22 , 24 ]. In 2016, Setia reported that, in some instances, cohort design could not be well-defined as prospective or retrospective; this happened when retrospective and prospective data were collected from the same participants (Table ​ (Table6) 6 ) [ 24 ].

The selection of the study design is the most critical step in research methodology [ 4 , 26 ]. An appropriate study design guarantees the achievement of the research objectives. The crucial factors that should be considered in the selection of the study design are the formulated research question, as well as the method of sampling [ 4 , 27 ]. The study design determines the way of sampling and data analysis [ 4 ]. The selection of a research study design depends on many factors. Two crucial points that should be noted during the process selection include different study designs that may be applicable for the same research question(s) and researches may have grey areas in which they have different views about the type of study design [ 4 ].

Conclusions

The selection of appropriate study designs for research is critical. Many research designs can apply to the same research. Appropriate selection guarantees that the author will achieve the research objectives and address the research questions.

Acknowledgments

The author would like to acknowledge Dr. M. Abass, Dr. I. Eljack, Dr. K. Salih, Dr. I. Jack, and my colleagues. Special thanks and appreciation to the college dean and administration of the College of Medicine, University of Bisha (Bisha, Saudi Arabia) for help and allowing the use of facilities.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

Descriptive Research and Qualitative Research

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Descriptive research is a study of status and is widely used in education, nutrition, epidemiology, and the behavioral sciences. Its value is based on the premise that problems can be solved and practices improved through observation, analysis, and description. The most common descriptive research method is the survey, which includes questionnaires, personal interviews, phone surveys, and normative surveys. Developmental research is also descriptive. Through cross-sectional and longitudinal studies, researchers investigate the interaction of diet (e.g., fat and its sources, fiber and its sources, etc.) and life styles (e.g., smoking, alcohol drinking, etc.) and of disease (e.g., cancer, coronary heart disease) development. Observational research and correlational studies constitute other forms of descriptive research. Correlational studies determine and analyze relationships between variables as well as generate predictions. Descriptive research generates data, both qualitative and quantitative, that define the state of nature at a point in time. This chapter discusses some characteristics and basic procedures of the various types of descriptive research.

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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.

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).

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.

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).

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).

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 , as seen in Figure 3.7.

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 .

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 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):

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 (Figure 3.14).

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

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 3.16):

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).

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 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.

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. (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 Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . 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 . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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

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

Research bias

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

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Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

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 . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

Prevent plagiarism, run a free check.

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

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

Cite this Scribbr article

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The mediating role of cardiac patients’ perception of nursing care on the relationship between kinesiophobia, anxiety and depression in rural hospitals: a cross-sectional study

• Mohamed Hussein Ramadan Atta 1 ,
• Shimmaa Mohamed Elsayed 2 ,
• Sharaf Omar Al Shurafi 3 &
• Rasha Salah Eweida 1 , 4  

BMC Nursing volume  23 , Article number:  238 ( 2024 ) Cite this article

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Kinesiophobia could act as a significant barrier against physical activity following cardiac procedures worsening cardiovascular health problems and potentially leading to conditions like hospital-acquired anxiety and depression among patients with cardiovascular disease (CVD). Nurses are the vanguard health care team who can aid patients in taking proactive steps to overcome fear of movement following cardiac procedures.

The overarching aim is to investigate the relationship between kinesiophobia, anxiety and depression, and patients’ perception of nursing care.

A descriptive correlational research design in two rural hospitals, conducted at cardiac intensive care units of Kafr Eldawar Hospital and Damanhur Medical National Institute. Data were collected from 265 nurses, using the following patient-reported outcome measures, the Tampa Scale for Kinesiophobia (TSK), the Hospital Anxiety and Depression Scale (HADS), the Person-Centered Critical Care Nursing Questionnaire (PCCNP) and the patients’ demographic and clinical profile.

A significant negative correlation was found between HADS and PCCNP (r: -0.510, p  < 0.001) however, Kinesiophobia was significantly and positively correlated (r: 0.271, p  < 0.001). A direct effect of PCCNP in the presence of the mediator was found to be not statistically significant (-0.015, CR = 0.302, p  = 0.763). Nonetheless, PCCNP indirectly affects kinesiophobia through HADS ( p =-0.099).

Implication for nursing practice

Customizing individualized cardiac rehabilitation (CR) programs based on the emotional experience of cardiac patients will be conducive to rehabilitation and prognosis for patients, thereby lessening the physical burden and improving their quality of life.

Peer Review reports

According to World Heart Federation statistics, 20.5 million people died from cardiovascular disorders (CVD) in 2021, while over 500 million individuals globally still struggle with these conditions as of 2023 [ 1 , 2 ]. More specifically, 6 out of 21 nations in the Middle East and North Africa had higher than average death rates from cardiovascular disease. Egypt is among the Middle Eastern countries with a high incidence of cardiovascular mortality, in which 600.0 women and 491.6 men per 100,000 inhabitants died from CVD. According to the World Health Organization (WHO), the increased incidence of risk factors like obesity, hypertension, and diabetes is among the most prevalent determinants associated with the prevalence of heart-related illnesses in Egypt [ 2 , 3 ].

The most recent guidelines from the European Society of Cardiology recommend the significance of exercise and physical activity (PA) in enhancing lifestyle and preventing cardiovascular disease (CVD) [ 4 ]. Nonetheless, the WHO projects that between 60% and 85% of the global population have sedentary lives, and inadequate physical activity accounts for over 3.5% of annual fatalities [ 5 , 6 ]. Emerging evidence suggests that kinesiophobia, or fear of movement, could act as a significant barrier against physical activity following cardiac procedures. kinesiophobia may potentially hinder rehabilitation efforts and affect the willingness of post-CVD patients to engage in physical activity. Cardiac pain could lead to various negative psychological ramifications, such as increased restrictive behaviors [ 7 ]. Kinesiophobia is defined as “an extreme, illogical, and crippling fear of physical exercise and movement caused by a perception of vulnerability to painful injury” [ 8 ]. Bäck et al. noted that a significant proportion of cardiac patients experience high levels of kinesiophobia, with approximately 20% reporting this fear [ 9 ]. However, Nair et al. found that 86.7% of patients undergoing cardiac surgery procedures experienced preoperative kinesiophobia [ 10 ].

There is a devoid of information related to the causes of kinesiophobia or fear of movement in patients with CVD. It is most probably related to the experienced physical manifestations including shortness of breath, chest pain, or an increased chance of another cardiac episode [ 7 , 11 ]. Unfortunately, avoiding physical activity can feed a vicious cycle of aggravating cardiac disease and raise the risk of cardiovascular complications by causing deconditioning, decreased cardiovascular fitness, and thereby undermining their overall quality of life [ 12 , 13 , 14 ].

Equally important, kinesiophobia can be exacerbated by co-occurring mental health problems such as sadness, anxiety and depression [ 15 , 16 , 17 , 18 ]. In more recent studies, mental health issues are quite prevalent in cardiac patients; estimated up to one-third of people with CVD are suffering from anxiety and depression [ 15 , 19 ]. Bahall et al., reported that comorbid depression and anxiety have significant negative effects on patient’s health, which further discourages patients from engaging in physical activities. Paradoxically, management and rehabilitation of CVD depend heavily on regular exercise and physical activity. In this sense, we believe that addressing cardiac patients’ perception of nursing care would help to overcome feelings of kinesiophobia and other hospital acquired anxiety and depression [ 16 ].

Cardiac patients’ perception of nursing care can impact how open they are in receiving medical advice, and how they interact with healthcare providers including nurses [ 20 ]. Nurses are the vanguard health care team who ought to take a patient-centered approach and attend to both psychological and physical requirements [ 21 ]. They also play a crucial role in providing psychological care tailored to cardiac patients to manage pain, engage in physical activity, and prevent complications that may arise from inactivity [ 22 , 23 ]. Nurses can help patients take more proactive steps to boost their stress tolerance and adaptive coping with illness. In this regard, if the patient positively appraises the nursing care accorded to him, he/ she would be able to curb feelings of fears and limits related to kinesiophobia as well as the associated feelings of emotional discomfort [ 7 , 22 , 24 , 25 ].

Patients who suffer from depression and kinesiophobia frequently find it difficult to control their conditions. Indeed, improving clinical outcomes of cardiac patients can be greatly aided by the nursing care [ 22 , 23 , 26 , 27 ] Nurses may lessen the obstacles caused by kinesiophobia and comorbid illnesses by giving patients compassionate, patient-centered care that makes patients feel heard, supported and understood. Besides, adopting competent nursing care to these situations can enable patients to actively participate in their care more, improving their quality of life and thereby their clinical outcomes [ 24 , 25 , 26 ].

Based on the findings from studies such as Wang et al. (2023) [ 31 ], three types of kinesiophobia were identified in patients with coronary heart disease: low fear, intermediate fear, and high fear. Keessen et al. (2022) [ 28 ] found that moderate and severe levels of kinesiophobia were associated with cardiac anxiety. Additionally, Yükselmiş Ö [ 29 ]observed that individuals with increased kinesiophobia experienced more anxiety/fear of falling and higher levels of depression. Ratnoo et al. (2023) [ 30 ] reported that patients following Coronary Artery Bypass Grafting exhibited moderate levels of anxiety and depression, along with a high level of kinesiophobia.

Regarding nursing care practice and kinesiophobia, Wang et al. (2023) [ 31 ], delineated the perceptions and practices of cardiac surgery nurses regarding kinesiophobia management. The study highlighted a scenario characterized by a high level of recognition but limited engagement among nurses, coupled with deficits in knowledge retention and a lack of willingness to address kinesiophobia. The authors underscored the necessity of advancing kinesiophobia management through the implementation of key strategies, including the adoption of an effective health education model, fostering stable collaboration between medical staff and family caregivers, streamlining clinical protocols, establishing specialized nursing teams, and delineating clear lines of multidisciplinary responsibilities. In addition, Bastani, et al. in 2022 [ 32 ], focused on examining how the quality of nursing care relates to anxiety and depression in patients with CVD. The findings from this research affirm the significant impact of care quality on anxiety and depression levels among patients with CVD.

To our knowledge, this is the first study examining the correlation between kinesiophobia, emotional state, and perception of nursing care among cardiac patients at both national and international levels. Therefore, this study provides fertile ground for mapping the factors correlated with kinesiophobia and how kinesiophobia impacts mental and physical health outcomes in patients with CVD. This would ultimately aid in adequate support for these patients as well as improving their functional capacity. Moreover, our work addresses a significant gap by calling for prioritizing this pressing issue on the nurses’ agenda, as they typically engage in direct patient’ care. Consequently, it can offer valuable insights into the clinical application perspective for the proper management of kinesiophobia. This study will consider perception of nursing care as a feasible mediator in the relationship between kinesiophobia, and anxiety and depression among cardiac patients in rural hospitals. Given the foregoing literature, we hypothesize that:

Hypothesis 1

Perception of nursing care is negatively correlated with kinesiophobia.

Hypothesis 2

Kinesiophobia is positively correlated with anxiety and depression.

Hypothesis 3

Perception of nursing care plays a mediating role between kinesiophobia, anxiety and depression.

Aim of the study

The overarching aim is to investigate the relationship between kinesiophobia, anxiety, and depression and patients’ perceptions of nursing care among patients with cardiovascular disease. Further objectives are to predict factors that affect feelings of anxiety, depression, and kinesiophobia and to analyze the mediation role of patients’ perceptions of nursing care on the associations between kinesiophobia, anxiety, and depression.

Methodology and materials

A descriptive correlational research design was adopted.

The research was conducted at the cardiac care units of two rural hospitals, namely Damanhur Hospital and Kafr Eldawar. Each hospital’s cardiac intensive care unit (ICU) has a total capacity of 50 beds.

The study protocol received approval from the Research Ethics Committee of the Faculty of Nursing, Damanhur University ( RES: 65-b ). Before their involvement in the study, all participants provided informed consent or appropriate representative (relative), with full knowledge that their participation was voluntary and they had the right to withdraw without facing any consequences. Throughout the study, strict measures were taken to ensure the confidentiality of the participants.

Participants & sampling

The study employed systematic randomized techniques to select participants in the total number of cardiac patients. This data collection followed rigorous guidelines to ensure the validity and reliability of the study’s results.

The sample size was calculated using the G*Power Windows 3.1.9.7 program, with a power of 0.95, an effect size of 0.15, an alpha error probability of 0.05, and several predictors = 2. Using Based on the calculation, this study required an a priori sample size of 215 patients randomly, the researcher decided to recruit 270 patients after considering a 20% loss ratio of follow-up. The statistician tried to match the eligible criteria, to be eligible, participants were diagnosed as cardiac ill patients, did not have any musculoskeletal problems (e.g.; handicapped, osteoporosis) and were less than 60 years old to control for the covariates of osteoarthritis and depression-related medical conditions as a confounding factor [ 33 ].

To ensure that all eligible patients were properly represented in the study sample, the systematic randomization sampling technique was used through the steps mentioned. Initially, the list of patient’s names who were admitted was considered. Then, systematic randomization sampling was conducted based on the systematic rule of using a fixed interval. In the selection process, the researchers include the last patient from every 3 patients (i.e., 3, 6, 9, etc.). The total sample size consisted of 270 patients, including cardiac patients. Five of those patients refused to participate in the study. The final sample size was divided into an equal sample size (265) that was selected from Kafr Eldawar Hospital and Damanhur Medical National Institute (Fig.  1 ). Each randomly selected patient was screened to identify those who met the predetermined inclusion criteria. Steps were repeated until the number of decided-upon subjects was reached. Each recruited subject was interviewed individually to establish rapport and apply tool I, followed by II, III, and IV.

Each patient was interviewed individually to establish rapport and collect data related to the measured outcomes. The researchers provide patients with clear, structured, and standardized tools that are relevant to the topic being assessed to mitigate any potential biases and ensure objectivity throughout the data collection. Data collection was conducted over two months between the beginning of July 2023 and the end of August 2023.

Measured outcomes

This study employed four different instruments to gather data:

Tool I: a structured form related to the social and clinical profile of patients was divided into two parts: Part 1: collected socio-demographic data, such as the patient’s gender, age, place of residence, and marital status. Part 2: collected clinical data, including diagnosis period, history of illness.

Tool II: The Tampa Scale for Kinesiophobia (TSK)

The TSK questionnaire, which was developed in 1991 by Miller R., and Kori S [ 34 ], is a tool used to measure fear of movement. It aims to assess a patient’s excessive, irrational, and debilitating fear of physical activity, which stems from a perceived vulnerability to painful injury. The questionnaire consists of 17 items, and respondents rate their agreement on a 4-point Likert scale ranging from “Strongly Disagree” to “Strongly Agree.” Total scores on the TSK range from 17 to 68, with lower scores indicating minimal or no fear of movement and higher scores indicating a greater degree of kinesiophobia. The TSK has been found to have strong internal consistency across all items [ 35 ]. The reliability of the Finnish version of the TSK, as measured by test-retest reliability, was found to be 0.887 [ 36 ]. In the current study, the Arabic translation of the TSK demonstrated good internal consistency, as indicated by Cronbach’s α value of 0.88.

Tool III: Hospital Anxiety and Depression Scale (HADS)

The Hospital Anxiety and Depression Scale (HADS) is a questionnaire consisting of 14 items, with seven questions dedicated to measuring anxiety and seven questions for measuring depression. Each question is scored on a scale from zero (indicating no impairment) to three (indicating severe impairment), resulting in a maximum score of 21 for both anxiety and depression [ 37 ]. The HADS has been widely used to assess anxiety and depression in cardiac patients, and a study by Amin et al. (2022) [ 38 ] reported a Cronbach’s alpha value of 0.70, indicating good internal consistency. To aid in the interpretation of scores, a classification scheme can be applied: scores ranging from 0 to 7 suggest the absence of clinical symptoms, scores between 8 and 10 indicate moderate levels of depression or anxiety, and scores from 11 to 21 indicate the presence of clinically significant depression or anxiety. The authors of the study also translated the HADS into Arabic and found it to have good internal consistency, as indicated by a Cronbach’s alpha value of 0.87.

Tool IV: Patient version of Person-centered Critical Care Nursing Questionnaire (PCCNPq)

The PCCNPq (Person-Centered Critical Care Nursing Perception Questionnaire) is a 20-item questionnaire developed by Hong and Kang (2020) to assess person-centered critical care nursing from the perspective of patients [ 39 ]. The questionnaire consists of five factors: compassion, expertise, communication, comfort, and respect. Each item is rated on a 4-point Likert-type scale, with response options ranging from 1 (strongly disagree) to 4 (strongly agree). Higher scores indicate a greater perception of individualized care. Concerning reliability, the questionnaire had acceptable internal consistency as Cronbach’s α of 0.89 to 0.91 [ 39 ] and had 0.91, in the present study.

Statistical analysis

Data were fed to the computer and analyzed using IBM SPSS software package version 23.0. A one-way ANOVA test was used to compare more than two categories. Student t-test was used to compare two categories for normally distributed quantitative variables. Pearson coefficient was used to correlate between normally distributed quantitative variables. Linear regression was assessed to detect factors that affect HADS and Kinesiophobia. Path analysis was assessed using AMOS 23. 0 software to detect the Direct and Indirect Effect of Person-Centered Critical Care Nursing on Kinesiophobia mediating by (HADS). s ignificance of the obtained results was judged at the 5% level.

Concerning participant characteristics, 68.3% were female and 80 patients aged between 40 and 50 years old accounted for the largest proportion (30.2%) in Table  1 . Regarding the level of education, illiterate was the largest proportion in the education level ( n  = 114,43%). More than half (52.5%) of the studied patients had working categorical was craft. Participants had congestive heart failure and rheumatic heart disease (23.0%, and 26.0% respectively), less than half of them (46.4%) experienced cardiac disease from 5 to 10 years, and 88.3% of them reported no other disease history. A statistical significance relation was found between all demographic characteristics and the HAD score. Moreover, age, gender, level of education, family history, diagnosis, and onset of disease were significantly correlated with the total score of the PCCNP questionnaire. Also, a statistical correlation was found between the Tampa Scale for Kinesiophobia, and demographic data including age ( p  = 0.024), sex (0.034), diagnosis (0.054), and level of education (0.047) (see Table  1 ) .

Two models were generated to explore the relationship between the studied variables. Model 1 denotes the effect of PCCNP q on HADS. Model 2 represents the effect of PCCNPq on the TSK. Being female (B=-9.149, Beta= -0.412, t=-6.993, p  < 0.001), and having enough income (B=-3.383, Beta= -0.163, t=-2.884, p  = 0.004) were negatively associated with greater feelings of anxiety and depression in the studied cardiac patients. While statistically significance positive associated found with being married (B = 1.210, Beta = 0.125, t = 2.223, p  = 0.027), onset (B = 0.585, Beta = 0.198, t = 3.474, p  = 0.001), presence of additionally diseases (B = 12.491, Beta = 0.388, t = 6.098, p  = 0.001) and Family history (B = 4.068, Beta = 0.161, t = 3.234, p  = 0.001). To validate the relationship between the study variables, a regression analysis was performed, with the HAD scale as the mediator variable, PCCNP as the independent variable, and Kinesiophobia as the dependent variable. Model 1 shows that there is a moderate negative correlation (B=-0.295, Beta= -0.409, t=-8.061, p  < 0.001) between the PCCNP questionnaire on HAD (R2 = 0.470, Adjusted R2 = 0.449, F = 22.512, p  < 0.001). This means that the majority of being caring toward cardiac patients, the minor the feeling of anxiety and depression. Model 2 illumines that there is a high positive correlation (B = 0.377, Beta = 0.366, t = 0.762, p  = 0.447) between the PCCNP questionnaire on Kinesiophobia (R2 = 0.080, Adjusted R2 = 0.073, F = 11.375, p  < 0.001). In addition to being statistically significant, the beta coefficient for the Model2 effect of PCCNP q in the model is -0.295. This indicates that a lower Tampa Scale for Kinesiophobia score is linked to a stronger Model2 effect of PCCNP. Regression analysis results indicate that PCCNP is associated with decreased anxiety, despair, and mobility fear, suggesting that it is a helpful intervention for ICU cardiac patients (see Table  2 ).

Table  3 illustrates the correlation the relationship between anxiety, depression, nurse-patient care, and fear of movement in cardiac patients. The mean scores of HADS, PCCNP q, and TSK of 265 cardiac patients were 21.92± (10.36), 43.83± (14.39), and 50.54± (10.67), respectively. HADS had a strong correlation with the PCCNP q ( r  = 0.968), and the TSK ( r  = 0.992). This suggests that cardiac patients with higher anxiety and depression scores were also more likely to report symptoms of kinesiophobia and to experience PCCNP deficits. Pearson’s correlation analysis exposed that HADS were significantly negatively correlated between the scale PCCNP (r:-0.510, p  < 0.001) while significantly positively correlated Tampa Scale for Kinesiophobia (r: 0.271, p  < 0.001) correspondingly. Also, a significant positive correlation between the PCCNP questionnaire and the Tampa Scale for Kinesiophobia was found ( r  = 0.154, p  = 0.012) (see Table  3 ).

The study assessed the mediating role of HAD in the relationship between nursing care and kinesiophobia ( see Table  4 & Fig.  2 ). The results revealed a statistically significant direct effect (-0.367, CR= -9.628, p  < 0.001)) of the effect of PCCNP on HADS. This means that cardiac patients have less anxiety and depression when receiving Person-Centered Critical Care Nursing. Furthermore, the direct effect of Person-Centered Critical Care Nursing (-0.015, CR = 0.302, p  = 0.763) on Kinesiophobia in the presence of the mediator was also found to be not statistically significant. This indicates that there is no association between cardiac patients’ kinesiophobia and PCCNP upon ICU admission. Nonetheless, PCCNP indirectly affects kinesiophobia through HADS. With an indirect effect of -0.099, statistical significance is achieved. This indicates that by lowering anxiety and despair, PCCNP helps cardiac patients who are afraid to move. Hence, HADS partially mediated the relationship between Person-Centered Critical Care Nursing and Kinesiophobia. This means that in the model (Fig.  2 ), there is a significant negative correlation of 33.248 (< 0.001) in Path analysis.

Path analysis to detect the Direct and Indirect Effect of Person-Centered Critical Care Nursing on Kinesiophobia mediating by Hospital Anxiety and Depression Scale (HADS). Model fit parameters CFI; IFI; RMSEA (1.000; 1.000; 0.350). CFI = Comparative fit index; IFI = incremental fit index; and RMSEA = Root Mean Square Error of Approximation. Model χ 2 ; significance 33.248 * (< 0.001 * )

The overarching aim of the current study was to examine the intricate associations between cardiac patients’ perceptions of nursing care and variables such as Kinesiophobia, depression, and anxiety. The empirical findings unveiled both direct and indirect impacts of person-centered critical care nursing on kinesiophobia. The mediation role played by anxiety and depression in this relationship provides a nuanced understanding of the multifaceted dynamics influencing patient outcomes within critical care settings. The direct effect implies that the implementation of person-centered care practices independently contributes to the amelioration of kinesiophobia among cardiac patients. This discernment underscores the intrinsic value of personalized and empathetic approaches inherent in person-centered care, engendering a heightened sense of control and comprehension for patients.

Bäck et al. emphasized that cardiac patients exhibit high levels of kinesiophobia, with a prevalence rate of 20% [ 9 ]. However, there is a lack of studies investigating kinesiophobia specifically in Egypt.

The indirect effect, mediated by anxiety and depression, underscores the intricate interplay between psychological factors and kinesiophobia in the context of critical nursing care. Anxiety and depression can heighten the perception of the threat associated with physical activity, leading to an exaggerated fear of movement or re-injury. These psychological states can impair coping mechanisms, reducing patients’ ability to manage and tolerate discomfort or perceived risk during physical activity, further reinforcing kinesiophobia. Additionally, anxiety and depression can contribute to a negative cycle of avoidance behavior, where patients withdraw from physical activities that they perceive as threatening, leading to deconditioning and increased kinesiophobia [ 40 ].

The correlation between the PCCNQ and the Kinesiophobia, suggests that patients who experience person-centered critical care nursing deficits are also more likely to report symptoms of kinesiophobia. This makes sense, as person-centered critical care nursing is designed to promote patients’ autonomy, control, and decision. Nursing care plays a crucial role in addressing these conditions, and significantly impacts the effectiveness of interventions. Patients who do not feel supported or understood by their caregivers may develop a sense of mistrust or fear, leading to increased anxiety about engaging in physical activities that could exacerbate their condition. Feeling neglected or misunderstood by healthcare providers may lead to a sense of vulnerability or lack of control, contributing to fear of movement. Furthermore, patients who perceive deficits in person-centered care may also be more likely to experience higher levels of overall distress, which can manifest as kinesiophobia [ 22 ].

The perception of nursing care among cardiac patients can significantly influence their levels of anxiety, depression, and subsequently, their experience of kinesiophobia. A positive perception of nursing care, characterized by empathy, attentiveness, and effective communication, can help alleviate anxiety and depression by fostering a sense of security and support. Patients who feel well-cared for may be more likely to engage in physical activities without excessive fear, reducing kinesiophobia. Conversely, a negative perception of nursing care, marked by perceived neglect, inadequate communication, or lack of support, can contribute to heightened anxiety and depression levels among patients. This negative experience may reinforce kinesiophobia as patients may feel less confident in their ability to safely engage in physical activities. Therefore, the perception of nursing care plays a crucial role in shaping the psychological well-being of cardiac patients and their ability to overcome kinesiophobia [ 41 , 42 ]. This result emphasizes the interconnectedness of physical and psychological well-being, suggesting that improvements in mental health may play a pivotal role in alleviating kinesiophobia.

Consistent with this result, the investigation conducted by Bastani, et al. in 2022 [ 32 ], suggests that streamlining the admission and hospitalization processes for elderly patients in age-friendly medical facilities could potentially lead to a reduction in stress, anxiety, and depression among this demographic. Notably, hospitals with a clinical emphasis demonstrated high scores in care quality, corresponding to lower scores in anxiety and depression.

Furthermore, in their 2021 study, Westas and colleagues [ 27 ] found that patients with cardiovascular disease (CVD) often felt neglected in terms of their psychological needs, with healthcare professionals in cardiac care frequently overlooking depressive symptoms. The study emphasizes the importance of healthcare providers considering the overall well-being of CVD patients to identify and address depressive symptoms, fostering trust and preventing worsening health trajectories. Empowered CVD patients who can express their needs are more likely to receive assistance for depressive symptoms. To strengthen patient-provider relationships and support patients’ ability to address their needs, healthcare professionals should actively discuss and assess depressive symptoms, encouraging patients to express emotional challenges.

The intricate relationship between anxiety and cardiac issues creates a cycle wherein patients may exhibit altered movement patterns and behaviors. Heightened hypervigilance stemming from anxiety can make individuals acutely aware of bodily sensations associated with their cardiac condition, leading to a reluctance to engage in physical activities. This avoidance may extend to situations or activities perceived as potential triggers for discomfort or cardiac events, resulting in a sedentary lifestyle that exacerbates physical deconditioning. Patients may perceive exercise as a potential stressor, amplifying their anxiety and reinforcing kinesiophobia. Addressing anxiety in cardiac patients is vital not only for their mental well-being but also for breaking the cycle of kinesiophobia. Negative interpretations of symptoms influenced by anxiety further discourage participation in exercise, impacting adherence to cardiac rehabilitation programs. Social and cognitive factors, such as catastrophic thinking and social isolation, contribute to the development of kinesiophobia [ 43 ]. This is consistent with the study conducted by Fan et al. [ 44 ]., who concluded that individuals with coronary heart disease who undergo a specialized nursing intervention see improvements in various aspects, such as decreased anxiety and depression, enhanced quality of life related to angina, and better physiological outcomes.

The physical symptoms associated with cardiac conditions, such as chest pain or shortness of breath, can further contribute to a fear of movement. Additionally, cardiac rehabilitation programs especially in the acute stage, while essential for recovery, may inadvertently reinforce kinesiophobia by pushing patients to confront physical activities that trigger anxiety or discomfort. The fear of pain, injury, or exacerbating their cardiac condition can create a psychological barrier, preventing cardiac patients from engaging in regular physical activity [ 7 ]. Additionally, the current cardiac patients who have serious cardiac illnesses such as aortic aneurysm, congestive heart failure, supraventricular tachycardia or any other disease have higher levels of kinesiophobia.

The pervasive feelings of sadness and fatigue linked to depression can reduce motivation to participate in physical activities. As depression sets in, patients may lose interest in sustaining an active lifestyle, resulting in a more sedentary way of living. This decreased physical activity can lead to restricted movement, as individuals may steer clear of regular tasks or exercises that are vital for maintaining cardiovascular health [ 45 , 46 ]. The current participants revealed a higher rate of depression which is correlated positively with kinesiophobia.

Depression typically has detrimental effects on individuals, and there is no scientific basis to suggest that it positively influences fear of movement among cardiac patients. Depression, as a mental health condition, tends to exert negative impacts on various aspects of a person’s life, including physical health. In the specific context of cardiac patients, depression is associated with reduced motivation, physical symptoms such as fatigue, and cognitive impairments. These factors contribute to a heightened fear of movement among cardiac patients, as they may perceive exercise as challenging or uncomfortable [ 47 ]. Additionally, negative perceptions and beliefs about their abilities, coupled with social withdrawal, can further reinforce kinesiophobia. Inconsistent with this point, kinesiophobia is positively correlated with depression in the current study.

A cardiac diagnosis often brings about significant lifestyle changes, such as dietary restrictions, medication regimens, and the necessity for regular medical monitoring. These adjustments can lead to feelings of loss, frustration, and a sense of diminished control over one’s life, contributing to the development of depression. The physical symptoms associated with cardiac conditions, including fatigue and shortness of breath, can further exacerbate feelings of helplessness and despair [ 48 ]. The fear of mortality and the potential limitations on daily activities can instill a persistent sense of anxiety and sadness. Social isolation, common among cardiac patients due to lifestyle modifications or perceived fragility, can also contribute to the prevalence of depression [ 49 ].

Moreover, the physiological impact of cardiovascular issues on the brain, through mechanisms such as reduced blood flow or inflammation, can directly contribute to depressive symptoms. Dhar and Barton (2016) [ 50 ] concluded that the intricate relationship between Major Depressive Disorder (MDD) and Coronary Heart Disease (CHD) involves complex and multifactorial mechanisms, including the sympathetic nervous system, platelet hyperactivity, inflammation, and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, among others. Conducting a definitive mortality study is challenging due to the complexities and costs associated. However, the current evidence underscores the importance of optimizing efficacy and minimizing potential harm when selecting treatments for individuals with MDD and comorbid CHD. It is suggested that MDD should be regarded as a common and modifiable risk factor for CHD, similar to established factors like smoking, hypertension, and hyperlipidemia. The detrimental combination of MDD and CHD results in adverse health outcomes for both conditions, contributing to escalating movement restrictions [ 11 , 51 , 52 ].

Men in the current study revealed higher kinesiophobia, depression and anxiety. Socialization norms that dictate traditional masculine roles may lead men to suppress emotions and resist seeking mental health support. Men with cardiac disorders also may experience higher levels of stress due to concerns about their health, financial burdens, or the impact of the condition on their ability to fulfil societal roles [ 53 ]. If they lack adaptive coping mechanisms or perceive seeking help as a sign of weakness, they may be more prone to developing symptoms of depression and anxiety. Moreover, cardiac disorders can lead to physical limitations and lifestyle changes, affecting an individual’s sense of identity, self-esteem, and independence [ 54 ]. For males who traditionally associate their self-worth with physical prowess and independence, these changes may be particularly challenging to navigate, contributing to feelings of depression and anxiety. The fear of exercise, particularly in the context of cardiac disorders, may further contribute to psychological challenges [ 41 , 55 ].

Cardiac patients engaged in craft work who also experience financial constraints may exhibit heightened levels of kinesiophobia individuals with limited financial resources may face challenges accessing appropriate healthcare and rehabilitation services, hindering their ability to receive tailored guidance on safe and gradual physical activity [ 7 , 56 ]. The fear of exacerbating their cardiac condition without proper supervision could intensify their aversion to movement. Furthermore, the economic strain itself may contribute to heightened stress and anxiety, as financial worries are known stressors [ 57 ]. This additional psychological burden can magnify concerns about the potential risks associated with physical exertion, reinforcing kinesiophobia. Moreover, engaging in craft work may involve prolonged periods of sedentary behavior, which can contribute to deconditioning and a heightened sense of vulnerability during physical activity [ 11 ]. The intersection of financial constraints, limited access to healthcare resources, and the sedentary nature of certain occupations can thus create a complex interplay that fosters kinesiophobia among cardiac patients involved in craft work with insufficient income.

A notable correlation was observed between the perception of nursing care and kinesiophobia anxiety and depression in the cardiac participants. Patient’s demographic and clinical characteristics such as being female, married, having sufficient income, experiencing the onset of cardiac disease, having comorbid health conditions, and having a family history are associated with the reduced likelihood of heightened feelings of anxiety and depression among participants. The regression analysis revealed that the perception of nursing care is negatively linked to anxiety, depression, and mobility-related kinesiophobia among the studied rural cohorts.

Implication

The study’s outcomes are of vital necessity in customizing an individualized cardiac rehabilitation program (CR) based on the emotional experience of cardiac patients, which will be conducive to rehabilitation and prognosis for patients, thereby lessening the physical burden and improving their quality of life. Additionally, it grants the interdisciplinary collaboration of the nursing staff, physicians, and psychologists to lay out psychoprophylactic programs and take precautions against kinesiophobia by reducing feelings of fear and anxiety linked to it among post-CVD patients. The existing findings also have implications for holistic nursing care in terms of early identification of barriers to physical activity, improved effectiveness of the recovery process, and averting recurrent hospital stays. Considering the relationship of kinesiophobia with mild to moderate physical activity, clinicians may have taken precautions against encouraging individuals with MI to engage in physical activity. Further studies should detail the relationship between physical activity and kinesiophobia in more comprehensive physical activity monitoring from MI patients with a pedometer or sensor-based devices.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We express our heartfelt gratitude to all individuals who took part in the study.

The present study did not get any dedicated financial funding from public, commercial, or not-for-profit funding organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Psychiatric and Mental Health Nursing Department, Faculty of Nursing, Alexandria University, Alexandria City, Egypt

Mohamed Hussein Ramadan Atta & Rasha Salah Eweida

Lecturer of Critical Care and Emergency Nursing, Faculty of Nursing, Damnhour University, Damnhour City, Egypt

Shimmaa Mohamed Elsayed

Al Aqsa University, Gaza City, Palestine

Sharaf Omar Al Shurafi

Psychiatric and Mental Health Specialty, Nursing Department, College of Health and Sport Sciences, University of Bahrain, Manama City, Bahrain

Rasha Salah Eweida

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Mohamed Hussein Ramadan Atta: find the research problem, revise of translation, data collection, conceptualize the discussion, and write and edit the first draft. Shimmaa Mohamed Elsayed: theoretical framework, write protocol, data collection, statistical analysis, tabular& and graphical presentation data. Sharaf Omar Al Shurafi: data collection, methodology, paraphrasing and language checking. Rasha Salah Eweida: translation of questionnaire, data collection, methodology, write&edit final draft.All authors have agreed on the final version and meet at least one of the following criteria (recommended by the ICMJE [http://www.ic mje.org/recommendations/]:

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Correspondence to Mohamed Hussein Ramadan Atta .

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Atta, M.H.R., Elsayed, S.M., Shurafi, S. et al. The mediating role of cardiac patients’ perception of nursing care on the relationship between kinesiophobia, anxiety and depression in rural hospitals: a cross-sectional study. BMC Nurs 23 , 238 (2024). https://doi.org/10.1186/s12912-024-01875-3

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Received : 11 January 2024

Accepted : 17 March 2024

Published : 10 April 2024

DOI : https://doi.org/10.1186/s12912-024-01875-3

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