descriptive correlational research design citation

Descriptive Correlational Design in Research

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

Introduction

Why use descriptive correlational design.

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

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

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

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

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

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

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

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

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

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

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

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

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• Chicago (A-D)
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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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Descriptive Research and Qualitative Research

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• Eunsook T. Koh 2 &
• Willis L. Owen 2  

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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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Achterberg C. Qualitative methods in nutritional education evaluation research. J Nutr Educ 1988; 20: 244

Article   Google Scholar  

Agar, M.H. The Professional Stranger: an Informal Introduction to Ethnography. Academic Press, New York: Academic Press 1980.

Google Scholar  

Amstrong B., Doll R. Environmental factors and cancer incidence and mortality in different countries, with special reference to dietary practices. Int J Cancer, 1975; 15:617–631

Bang H.O., Dyerberg J., Hjorne, N. The composition of food consumed by Greenland Eskimos. Acta Med Scand 1976; 200: 69–75

Article   CAS   Google Scholar  

Bailey, K.D. Methods of Social Research. New York: Free Press, Macmillan Publishing Co, Inc, 1978.

Beal V.A. The nutritional history in longitudinal research. J Am Dietet A 1967; 51: 526–531

Berdie D.R. Questionnaire length and response rate. J Appl Psychol 1973; 58:278–280

Berdie, D.R., Anderson J.F., Niebuhr, M.A. Questionnaires: Design and Use. Metuchen, N.J.: Scarescrow Press, 1986.

Buell P. Changing incidence of breast cancer in Japanese-American women. JNCI 1973; 51:1479–1483

CAS   Google Scholar  

Dyberg J., Bang H.O., Hjorne N. Fatty acid composition of the plasma lipids in Greenland Eskimos. Am J Clin Nutr 1975; 28: 958–961

Frank G. Life history model of adaptation to disability: the case of a congenital amputee. Soc Sci Med 1984; 19: 639–645

Fetterman, D.L. “A Walk Through the Wilderness: Learning to Find Your Way.” In Experiencing Fieldwork: An Inside View of Qualitative Research. Shaffir, W., Stebbins, R. eds. Newbury Park, CA: Sage, 1991.

Fieldhouse, P. Food & Nutrition: Customs & Culture. New York: Croom Helm, 1986

Fielding, N.G., Fielding, J.L. Linking Data. Beverly Hills, CA: Sage, 1986.

Firestone W.A. Meaning in method: The rhetoric of quantitative and qualitative research. Educational Researcher 1987; 16:16–21

Geertz, C. The interpretation of Cultures. New York: Basic Books, 1973.

Glaser B.G., Strauss, A.L. The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine Publishing Co, 1967.

Goldberger, J.E. Goldberger on Pellagra. Baton Rouge, LA: Louisiana State University Press, 1964.

Greer J.G. What do open-ended questions measure? Public Opinion Quart 1988; 52:365–371

Gordis, L. Epidemiology. Philadelphia PA: Saunders, 1996.

Hammersley, M., Atkinson, P. Ethnography: Principles in Practice. London: Tavistock, 1983.

Headland, T.N., Pike, K.L., Harris, M. Emics and Etics: The Insider/Outsider Debate. Newbury Park, CA: Sage, 1990.

Hodge, R., Kress, G. Social Semiotics. Ithaca, NY: Cornell University Press, 1988.

Holbrook J.T., Patterson K.Y., Bodner J.E., Douglas L.W., Veillon C., Kelsey J.L. Mertz W, Smith J.C. Sodium and potassium intake and balance in adults consuming self selected diets. Am J Clin Nutr 1984; 40: 786–793

Human Nutrition Information Service, US Department of Agriculture, Food Consumption: Households in the United States, Spring 1977, Washington, D.E.: Government Printing Office, Publication H-1, 1982.

ICNND (International Committee on Nutrition for National Defense) Manual for Nutrition Surveys. Second edition. Superintendent of Documents. Washington, D.C.: U.S. Government Printing Office, 1963

Jacob E. Qualitative research traditions: A review. Rev Educ Res 1987; 57: 1–4

Jelliffe, D.B. The Assessment of the Nutritional Status of the Community. WHO Monograph 53. Geneva: World Health Organization, 1966.

Keys, A. Seven Countries: A Multivariate Analysis of Death and Corornary Heart Disease. Cambridge, MA: Harvard University Press, 1980.

Kittler, P.G., Sucher, K. Food and Culture in America. New York: Reinhold, 1989.

Kirk, J., Miller, M.L. Reliability and Validity in Qualitative Research. Beverly Hills, CA: Sage, 1986.

Koh E.T., Caples V. Nutrient intake of low-income, black families in Southwest Mississippi. J Am Dietet A 1979; 75:665–670

Kolonel, L.N., Hinds, M.W., Hankin, J.H. “Cancer Patterns Among Migrant and Native-Born Japanese in Hawaii in Relation to Smoking, Drinking, and Dietary Habits.” In Genetic and Environmental Factors in Experimental and Human Cancer. Gelboin, H.V. et al. eds. Tokyo, Japan: Science Press, 1980.

Kromann N., Green A. Epidemiological studies in the Upernavik district, Greenland: Incidence of some chronic disease 1950–1974. Acta Med Scand 1980; 401–405

Krueger, R. Focus Groups: A Practical Guide for Applied Rese arch. Newbury Park, CA: Sage, 1988.

Langness, L.L. Frank, G. Lives: An Anthropological Approach to Biography. Novato, CA: Chandler & Sharp, 1981.

Lind, J. (1753) A Treatise on the Scurvy. Reprinted Edinburgh: Edinburgh University Press, 1953.

Lock, L.F. “The Question of Quality in Qualitative Research.” In Proceedings of the 5th Measurement and Evaluation Symposium. Nelson, J.K. ed. Baton Rouge, LA: Louisiana State University Press, 1987.

Lofland, J., Lofland, L. Analyzing Social Settings: A Guide to Qualitative Research. Belmont, CA: Wadsworth, 1984.

Marshall, C; Rossman, G.B. Designing Qualitative Research. Newbury Park, CA: Sage, 1989.

Mauser, J.S., Kramer, S. Epidemiology — An Introductory Text. Philadelphia PA: Saunders, 1985.

McClendon M.J., O’Brien D.J. Question-order effects on the determinants of subjective well-being. Public Opinion Quart 1988; 52:351–364

McCraken, G. The Long Interview. Newbury Park, CA: Sage, 1988.

McMichael A.J., McCall M.G., Hartshorne J.M., Woodings T.L. Patterns of gastro-intestinal cancer in European migrants to Australia: The role of dietary changes. In J Cancer 1980; 25:431–437

Miles, M.B., Huberman, A.M. Qualitative Data Analysis: A Sourcebook of New Methods. (2nd ed.) Thousand Oaks, CA: Sage, 1994.

Montgomery A.C., Crittenden K.S. Improving coding reliability for open-ended questions. Public Opinion Quart 1977; 41: 235–243

National Center for Health Statistics: Plan and Operation of the HANES. Vital and Health Statistics, Series 1, Nos 10a and 10b, DHEW Pub No (HSM) 73–130. Washington, D.C.: U.S. Government Printing Office, 1973.

O’Brien T., Dugdale V. Questionnaire administration by computer. J Market Res Soc, 1978; 20:228–237

Okolo, E.N. Health Research Design and Methodology. Boca Raton, FL: CRC Press 1990.

Parkin, D.M., Muir, C.S., Whelan, S.L., Gao, Y.T., Ferlay, J., Powell, J. Cancer Incidence in Five Continents VI IARC Sci Publ 120, Lyon: International Agency for Research on Cancer, 1992.

Peterkin B.B., Rizek R.L., Tippett K.S. Nationwide food consumption survey, 1987. Nutr Today 1988; 23:18–24

Poe G.S., Seeman I., McLaughlin J., Mehl E., Dietz M. Don’t know boxes in factual questions in a mail questionnaire: Effects on level and quality of response. Public Opinion Quart 1988; 52: 212–222

Poikolainen K., Karkkainen P. Nature of questionnaire options affects estimates of alcohol intake. J Stud Alcohol 1985; 46: 219–222

Potter, J.D. Food, Nutrition and the Prevention of Cancer: A Global Perspective. Washington, D.C.: American Institute for Cancer Research, 1997.

Rose, G.A., Blackburn, H. Cardiovascular Survey Methods. Geneva, Switzerland: World Health Organization 1968.

Runcie, J.F. Experiencing Social Sresearch. Homewood, IL.: Dorsey Press, 1976.

Sandelowski, M. The Problem of Rigor in Qualitative Research. Adv Nurs Sci April 27–37, 1986.

Schutz R.W. Qualitative research: comments and controversies. Research Quarterly Exercise Sport, 1989; 60:30–35

Sheatsley, P.B. “Questionnaire Construction and Item Writing.” In Handbook of Survey Research. Rossi, P.H., Wright, J.D., Anderson, A.B. eds. Orlando, FL: Academic Press, 1983.

Sherry, B. “Epidemiologic Analytical Research.” In Research. Monsen, E.R. Chicago, IL: American Dietetic Association, 1992

Spradley, J. The Ethnographic Interview. Chicago, IL: Holtz, Rinehart & Winston, 1979.

Spradley, J.P. Participant Observation. New York: Holt, Rinehart & Winston, 1980.

Sudman, S., Bradburn, N.M. Asking Questions: A Practical Guide to Questionnaire Design. (2nd ed.) Washington, D.C.: Jossey-Bass Publisher, 1983.

Taylor, S.J., Bogdan, R. Introduction to Qualitative Research Methods. (2nd ed.) New York: Wiley, 1984.

Thomas, J.R. Nelson, J.K. Research Methods in Physical Activity. (3rd ed.), Champaign, IL: Human Kinetics, 1996.

Van Maanen, J. ed. Qualitative Methodology. Newbury Park, CA: Sage, 1983.

Willett, W. Nutritional Epidemiology. (2nd ed.) New York: Oxford University Press, 1998.

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Koh, E.T., Owen, W.L. (2000). Descriptive Research and Qualitative Research. In: Introduction to Nutrition and Health Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1401-5_12

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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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Understanding Descriptive Research Designs and Methods

Siedlecki, Sandra L. PhD, RN, APRN-CNS, FAAN

Author Affiliation: Senior Nurse Scientist and Clinical Nurse Specialist, Office of Nursing Research & Innovation, Nursing Institute, Cleveland Clinic, Ohio.

The author reports no conflicts of interest.

Correspondence: Sandra L. Siedlecki, PhD, RN, APRN-CNS, 3271 Stillwater Dr, Medina, OH 44256 ( [email protected] ).

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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.

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• Descriptive Research Design | Definition, Methods & Examples

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?

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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 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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McCombes, S. (2022, October 10). Descriptive Research Design | Definition, Methods & Examples. Scribbr. Retrieved 30 August 2024, from https://www.scribbr.co.uk/research-methods/descriptive-research-design/

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Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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Researcher, Academic Writer, Web developer

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• Knowledge Base

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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?

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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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McCombes, S. (2023, June 22). Descriptive Research | Definition, Types, Methods & Examples. Scribbr. Retrieved August 29, 2024, from https://www.scribbr.com/methodology/descriptive-research/

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What is descriptive-correlational research design?  

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Descriptive-correlational research design is a type of research design that aims to describe the distribution of variables and examine the relationships between them without manipulating the variables. It is used to answer questions such as "What is x?" and "How are things related?" Researchers commonly use surveys or observational methods to gather data for descriptive-correlational research. Surveys are efficient for gathering information about individuals' experiences, beliefs, and attitudes, while observation involves observing video recordings or using the experience sampling method to gather real-time data. Descriptive-correlational research design is a part of observational study designs and is considered the simplest type of observational design. It allows researchers to study and describe variables without considering causal or other hypotheses. [1] [2] [3] [4]

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Descriptive-correlational research design and the survey method are closely related but distinct concepts. Descriptive research aims to explain what is common or prevalent in a population, focusing on answering questions like who, what, when, where, and how . On the other hand, correlational research investigates how variables are related to each other . Surveys are commonly used in both descriptive and correlational research designs to gather data efficiently, providing information about individuals' experiences, beliefs, and attitudes . Descriptive surveys specifically focus on gathering information about prevailing conditions or situations for interpretation and comparison, employing scientific methods for analysis and generalization . Therefore, while surveys are a common method used in both descriptive-correlational research designs, the former focuses on describing existing phenomena, while the latter explores relationships between variables.

Descriptive research designs aim to answer the question "What is x?" by portraying existing conditions without manipulation by the researcher, often utilizing surveys or observational methods for data collection . On the other hand, correlational research designs address the question "How are things related?" by examining relationships between variables without intervention . Correlational studies focus on determining the degree of association between variables and do not imply causation. While descriptive research provides a snapshot of a situation, correlational research explores connections between different factors. Both designs play crucial roles in research, with descriptive designs focusing on portraying reality and correlational designs emphasizing relationships between variables without manipulation .

Descriptive correlational research utilizes surveys and observational methods for data collection. Surveys are efficient in gathering extensive information on individuals' experiences, beliefs, and attitudes, requiring careful design considerations. Observational methods involve direct observation of behaviors in natural settings without experimental manipulation, aiding in identifying patterns and relationships between variables. Techniques like scatter plot analysis are commonly used for direct observation, providing insights into behavior patterns over time and in different contexts. Data analysis in descriptive correlational research involves organizing data into patterns and categories to identify themes and formulate hypotheses. The primary aim is to describe relationships between variables without manipulating them, emphasizing the importance of naturalistic data collection and analysis.

Descriptive research in research design refers to a type of research that aims to answer the question "What is x?" It involves gathering data to describe a particular phenomenon or situation without manipulating any variables. Descriptive research designs commonly use surveys or observational methods to collect data. Surveys are efficient for gathering large amounts of information about individuals' experiences, beliefs, and attitudes. Observation, including methods like video recordings or experience sampling, is also used to gather data. Descriptive research is often qualitative in nature, utilizing non-numerical data and describing findings in words and language . It is a part of observational study designs in biomedical research, which focus on describing or comparing natural phenomena under given environmental conditions .

A descriptive correlational design is a research design used to investigate the relationship between variables without manipulating them. It aims to answer the question "How are things related?" Researchers commonly use surveys or observational methods to gather data for descriptive and correlational research. Surveys are efficient for gathering information about individuals' experiences, beliefs, and attitudes, while observation involves observing participants in various situations, such as video recordings or experience sampling method (ESM). Descriptive studies focus on describing the characteristics of a sample without a comparative group, generating hypotheses, and cannot determine associations or test hypotheses. Correlational studies, on the other hand, examine the relationship between variables.

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Barangay Health Workers (BHWs) are essential community health providers in the Philippines, primarily responsible for delivering primary health care services and health education at the grassroots level. They are typically trained volunteers who play a crucial role in enhancing healthcare accessibility and promoting health within their communities. ## Demographics and Characteristics - Most BHWs are female, aged between 31 to 35 years, and often have a high school education. Many have served in their roles for over a decade, demonstrating strong commitment and job satisfaction despite limited financial incentives[[4]]. - A significant portion of BHWs has been reported to have "very good" work performance, with their age and tenure influencing their effectiveness. ## Roles and Responsibilities - BHWs engage in various health-promoting activities, including home visits, health education, and reproductive health promotion, utilizing communication strategies like SMS and public meetings[[4]]. - They are pivotal in implementing health programs, especially during crises like the COVID-19 pandemic, although they face challenges due to inadequate policy support. ## Challenges Faced - BHWs encounter numerous barriers, including insufficient training, lack of resources, and inadequate community support, which hinder their ability to provide effective healthcare. While BHWs are vital to community health, their effectiveness is often compromised by systemic challenges that require attention from policymakers and healthcare administrators.

Creating a bibliography is a crucial step in the research process, serving as a comprehensive list of sources that supports the research work. It involves several essential steps, from formulating a research question to compiling and formatting the references. This process not only aids in organizing research but also enhances the credibility and authority of the work. Below are the key steps involved in creating a bibliography, as informed by the relevant research papers. ## Formulating the Research Question - The first step in creating a bibliography is to clearly define the research question. This involves translating the question into a well-documented language that can be understood and searched within bibliographic databases . - Defining the subject closely helps in avoiding distractions from unrelated information and ensures that the bibliography remains focused and relevant . ## Selecting and Accessing Resources - Choose appropriate data resources, which may include databases, libraries, and online repositories. This step often involves a combination of computer-based searches and manual research . - Utilize software tools like PASSPORT, OCLC, and Pro-cite to simplify the task of compiling and formatting bibliographic data . ## Compiling and Organizing References - Collect references systematically, ensuring that all relevant sources are included. This stage involves planning, reference collection, and editing . - Annotated bibliographies can be particularly useful as they not only list sources but also provide summaries and critical analyses, helping researchers evaluate the relevance and quality of each source . ## Formatting the Bibliography - Use bibliographic management software, such as the personal bibliographic managers in Microsoft Word, to format the bibliography according to the required style (e.g., APA, MLA) . - Ensure that the bibliography is correctly formatted and free of errors, as this contributes to the overall professionalism and accuracy of the research work . ## Reviewing and Finalizing - Review the bibliography to ensure completeness and accuracy. This may involve correcting the strategy by reformulating if the precision of the search was not high . - Consider using intelligent systems that assist in creating annotated bibliographies and enable autonomous citation indexing, which can enhance the quality and interoperability of the bibliography . While these steps provide a structured approach to creating a bibliography, it is important to remain flexible and adapt the process to the specific requirements of the research project. Additionally, the use of digital tools and software can significantly streamline the process, making it less tedious and more efficient. However, researchers should be aware of the limitations of these tools and ensure that they complement rather than replace critical thinking and manual verification.

Study skills and time management are critical components of academic success, influencing students' ability to organize their learning effectively. Research indicates that effective time management not only enhances academic performance but also contributes to students' overall well-being and life satisfaction. ## Importance of Time Management - Effective time management is linked to improved academic outcomes, as it allows students to prioritize tasks and allocate resources efficiently. - Studies show that students who employ structured time management strategies report higher levels of motivation and engagement in their studies. ## Socio-Cultural and Emotional Factors - The development of time management skills is influenced by socio-cultural and pedagogical factors, highlighting the need for a holistic approach to teaching these skills. - Emotional impacts, such as stress from multitasking and work overload, can hinder effective time management, suggesting that emotional well-being is integral to academic success. ## Gender Differences in Time Management - Research indicates that female students tend to manage their time more effectively than male students, particularly in planning. While the literature emphasizes the positive correlation between time management and academic achievement, some studies suggest that the relationship is not always straightforward, indicating a need for further exploration of contextual factors that may influence these dynamics.

The strategic management approach plays a crucial role in enhancing the performance of community-based projects by fostering effective governance, resource mobilization, and stakeholder engagement. This multifaceted approach ensures sustainability and success in various community initiatives. ## Key Aspects of Strategic Management in Community Projects - **Project Governance**: Effective governance structures significantly influence project sustainability. Strong leadership and clear decision-making processes enhance accountability and community trust, leading to better project outcomes. - **Resource Mobilization**: Strategic management emphasizes the importance of financial planning and resource allocation. Studies show that organizations with robust financial management strategies achieve higher performance levels, as they can effectively utilize available resources. - **Community Participation**: Engaging local stakeholders in decision-making processes is vital. Strategic management frameworks encourage participatory approaches, which enhance project relevance and community ownership. - **Monitoring and Evaluation**: Continuous assessment of project performance through strategic management practices ensures that projects adapt to changing circumstances, thereby improving sustainability. While strategic management approaches are essential for the success of community-based projects, challenges such as limited resources and varying levels of community engagement can hinder their effectiveness. Addressing these challenges is crucial for maximizing the potential benefits of strategic management in community initiatives.

The development of an effective instrumento de validación for assessing digital competencies is crucial for educational contexts. Key characteristics of such instruments include validity, reliability, and comprehensive coverage of digital skills. ## Validity and Dimensionality - Instruments must demonstrate construct validity through exploratory and confirmatory factor analyses, ensuring they accurately measure the intended competencies. - For instance, a validated instrument in Mexico identified five dimensions of digital literacy, confirming its construct validity. ## Reliability - High internal consistency is essential, often measured using Cronbach’s Alpha. Instruments should achieve values above 0.70, indicating robust reliability. - The COMDID A instrument, for example, showed high reliability with a calculated Cronbach’s Alpha, affirming its effectiveness in assessing teaching digital competence. ## Comprehensive Skill Coverage - Effective instruments should encompass a range of digital skills, including data management, software proficiency, and multimedia presentation capabilities. While these characteristics are vital for a robust instrumento de validación, it is also important to consider the evolving nature of digital competencies, which may require ongoing updates to assessment tools to remain relevant in rapidly changing technological landscapes.