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Types of Research – Explained with Examples

DiscoverPhDs

  • By DiscoverPhDs
  • October 2, 2020

Types of Research Design

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

  • Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
  • Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

Methodology Research

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

Types of Research Methodology

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

Descriptive Research Design

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

Action Research Methods

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Research methods--quantitative, qualitative, and more: overview.

  • Quantitative Research
  • Qualitative Research
  • Data Science Methods (Machine Learning, AI, Big Data)
  • Text Mining and Computational Text Analysis
  • Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

  • SAGE Research Methods
  • Little Green Books  (Quantitative Methods)
  • Little Blue Books  (Qualitative Methods)
  • Dictionaries and Encyclopedias  
  • Case studies of real research projects
  • Sample datasets for hands-on practice
  • Streaming video--see methods come to life
  • Methodspace- -a community for researchers
  • SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

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Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

  • Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
  • Wiley Stats Ref for background information on statistics topics
  • Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

  • D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
  • Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
  • Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

  • IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
  • OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
  • Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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  • URL: https://guides.lib.berkeley.edu/researchmethods

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Chapter 3. Psychological Science

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

Learning objectives.

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

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

Descriptive Research: Assessing the Current State of Affairs

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

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

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

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

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

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

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

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

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

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

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

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

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

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

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

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

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

Correlational Research: Seeking Relationships among Variables

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

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

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

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

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

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

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

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

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

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

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

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

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

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

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

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

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

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

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

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

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

Key Takeaways

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

Exercises and Critical Thinking

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

Image Attributions

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

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

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

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

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

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

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

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

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

Long Descriptions

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

Figure 3.10 long description: Types of scatter plots.

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

[Return to Figure 3.10]

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

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three types of research studies

Research Study Types

There are many different types of research studies, and each has distinct strengths and weaknesses. In general, randomized trials and cohort studies provide the best information when looking at the link between a certain factor (like diet) and a health outcome (like heart disease).

Laboratory and Animal Studies

These are studies done in laboratories on cells, tissue, or animals.

  • Strengths: Laboratories provide strictly controlled conditions and are often the genesis of scientific ideas that go on to have a broad impact on human health. They can help understand the mechanisms of disease.
  • Weaknesses: Laboratory and animal studies are only a starting point. Animals or cells are not a substitute for humans.

Cross-Sectional Surveys

These studies examine the incidence of a certain outcome (disease or other health characteristic) in a specific group of people at one point in time. Surveys are often sent to participants to gather data about the outcome of interest.

  • Strengths: Inexpensive and easy to perform.
  • Weaknesses: Can only establish an association in that one specific time period.

Case-Control Studies

These studies look at the characteristics of one group of people who already have a certain health outcome (the cases) and compare them with a similar group of people who do not have the outcome (the controls). An example may be looking at a group of people with heart disease and another group without heart disease who are similar in age, sex, and economic status, and comparing their intakes of fruits and vegetables to see if this exposure could be associated with heart disease risk.

  • Strengths: Case-control studies can be done quickly and relatively cheaply.
  • Weaknesses: Not ideal for studying diet because they gather information from the past, which can be difficult for most people to recall accurately. Furthermore, people with illnesses often recall past behaviors differently from those without illness. This opens such studies to potential inaccuracy and bias in the information they gather.

Cohort Studies

These are observational studies that follow large groups of people over a long period of time, years or even decades, to find associations of an exposure(s) with disease outcomes. Researchers regularly gather information from the people in the study on several variables (like meat intake, physical activity level, and weight). Once a specified amount of time has elapsed, the characteristics of people in the group are compared to test specific hypotheses (such as a link between high versus low intake of carotenoid-rich foods and glaucoma, or high versus low meat intake and prostate cancer).

  • Strengths: Participants are not required to change their diets or lifestyle as may be with randomized controlled studies. Study sizes may be larger than other study types. They generally provide more reliable information than case-control studies because they don’t rely on information from the past. Cohort studies gather information from participants at the beginning and throughout the study, long before they may develop the disease being studied. As a group, many of these types of studies have provided valuable information about the link between lifestyle factors and disease.
  • Weaknesses: A longer duration of following participants make these studies time-consuming and expensive. Results cannot suggest cause-and-effect, only associations. Evaluation of dietary intake is self-reported.

Two of the largest and longest-running cohort studies of diet are the Harvard-based Nurses’ Health Study and the Health Professionals Follow-up Study.

If you follow nutrition news, chances are you have come across findings from a cohort called the Nurses’ Health Study . The Nurses’ Health Study (NHS) began in 1976, spearheaded by researchers from the Channing Laboratory at the Brigham and Women’s Hospital, Harvard Medical School, and the Harvard T.H. Chan School of Public Health, with funding from the National Institutes of Health. It gathered registered nurses ages 30-55 years from across the U.S. to respond to a series of questionnaires. Nurses were specifically chosen because of their ability to complete the health-related, often very technical, questionnaires thoroughly and accurately. They showed motivation to participate in the long-term study that required ongoing questionnaires every two years. Furthermore, the group provided blood, urine, and other samples over the course of the study.

The NHS is a prospective cohort study, meaning a group of people who are followed forward in time to examine lifestyle habits or other characteristics to see if they develop a disease, death, or some other indicated outcome. In comparison, a retrospective cohort study would specify a disease or outcome and look back in time at the group to see if there were common factors leading to the disease or outcome. A benefit of prospective studies over retrospective studies is greater accuracy in reporting details, such as food intake, that is not distorted by the diagnosis of illness.

To date, there are three NHS cohorts: NHS original cohort, NHS II, and NHS 3. Below are some features unique to each cohort.

NHS – Original Cohort

  • Started in 1976 by Frank Speizer, M.D.
  • Participants: 121,700 married women, ages 30 to 55 in 1976.
  • Outcomes studied: Impact of contraceptive methods and smoking on breast cancer; later this was expanded to observe other lifestyle factors and behaviors in relation to 30 diseases.
  • A food frequency questionnaire was added in 1980 to collect information on dietary intake, and continues to be collected every four years.
  • Started in 1989 by Walter Willett, M.D., M.P.H., Dr.P.H., and colleagues.
  • Participants: 116,430 single and married women, ages 25 to 42 in 1989.
  • Outcomes studied: Impact on women’s health of oral contraceptives initiated during adolescence, diet and physical activity in adolescence, and lifestyle risk factors in a younger population than the NHS Original Cohort. The wide range of diseases examined in the original NHS is now also being studied in NHSII.
  • The first food frequency questionnaire was collected in 1991, and is collected every four years.
  • Started in 2010 by Jorge Chavarro, M.D., Sc.M., Sc.D, Walter Willett, M.D., M.P.H., Dr.P.H., Janet Rich-Edwards, Sc.D., M.P.H, and Stacey Missmer, Sc.D.
  • Participants: Expanded to include not just registered nurses but licensed practical nurses (LPN) and licensed vocational nurses (LVN), ages 19 to 46. Enrollment is currently open.
  • Inclusion of more diverse population of nurses, including male nurses and nurses from Canada.
  • Outcomes studied: Dietary patterns, lifestyle, environment, and nursing occupational exposures that may impact men’s and women’s health; the impact of new hormone preparations and fertility/pregnancy on women’s health; relationship of diet in adolescence on breast cancer risk.

From these three cohorts, extensive research has been published regarding the association of diet, smoking, physical activity levels, overweight and obesity, oral contraceptive use, hormone therapy, endogenous hormones, dietary factors, sleep, genetics, and other behaviors and characteristics with various diseases. In 2016, in celebration of the 40 th  Anniversary of NHS, the  American Journal of Public Health’s  September issue  was dedicated to featuring the many contributions of the Nurses’ Health Studies to public health.

Growing Up Today Study (GUTS)

In 1996, recruitment began for a new cross-generational cohort called  GUTS (Growing Up Today Study) —children of nurses from the NHS II. GUTS is composed of 27,802 girls and boys who were between the ages of 9 and 17 at the time of enrollment. As the entire cohort has entered adulthood, they complete annual questionnaires including information on dietary intake, weight changes, exercise level, substance and alcohol use, body image, and environmental factors. Researchers are looking at conditions more common in young adults such as asthma, skin cancer, eating disorders, and sports injuries.

Randomized Trials

Like cohort studies, these studies follow a group of people over time. However, with randomized trials, the researchers intervene with a specific behavior change or treatment (such as following a specific diet or taking a supplement) to see how it affects a health outcome. They are called “randomized trials” because people in the study are randomly assigned to either receive or not receive the intervention. This randomization helps researchers determine the true effect the intervention has on the health outcome. Those who do not receive the intervention or labelled the “control group,” which means these participants do not change their behavior, or if the study is examining the effects of a vitamin supplement, the control group participants receive a placebo supplement that contains no active ingredients.

  • Strengths: Considered the “gold standard” and best for determining the effectiveness of an intervention (e.g., dietary pattern, supplement) on an endpoint such as cancer or heart disease. Conducted in a highly controlled setting with limited variables that could affect the outcome. They determine cause-and-effect relationships.
  • Weaknesses: High cost, potentially low long-term compliance with prescribed diets, and possible ethical issues. Due to expense, the study size may be small.

Meta-Analyses and Systematic Reviews

A meta-analysis collects data from several previous studies on one topic to analyze and combine the results using statistical methods to provide a summary conclusion. Meta-analyses are usually conducted using randomized controlled trials and cohort studies that have higher quality of evidence than other designs. A systematic review also examines past literature related to a specific topic and design, analyzing the quality of studies and results but may not pool the data. Sometimes a systematic review is followed by conducting a meta-analysis if the quality of the studies is good and the data can be combined.

  • Strengths: Inexpensive and provides a general comprehensive summary of existing research on a topic. This can create an explanation or assumption to be used for further investigation.
  • Weaknesses: Prone to selection bias, as the authors can choose or exclude certain studies, which can change the resulting outcome. Combining data that includes lower-quality studies can also skew the results.

A primer on systematic review and meta-analysis in diabetes research

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Types of Research Studies and How To Interpret Them

The field of nutrition is dynamic, and our understanding and practices are always evolving. Nutrition scientists are continuously conducting new research and publishing their findings in peer-reviewed journals. This adds to scientific knowledge, but it’s also of great interest to the public, so nutrition research often shows up in the news and other media sources. You might be interested in nutrition research to inform your own eating habits, or if you work in a health profession, so that you can give evidence-based advice to others. Making sense of science requires that you understand the types of research studies used and their limitations.

The Hierarchy of Nutrition Evidence

Researchers use many different types of study designs depending on the question they are trying to answer, as well as factors such as time, funding, and ethical considerations. The study design affects how we interpret the results and the strength of the evidence as it relates to real-life nutrition decisions. It can be helpful to think about the types of studies within a pyramid representing a hierarchy of evidence, where the studies at the bottom of the pyramid usually give us the weakest evidence with the least relevance to real-life nutrition decisions, and the studies at the top offer the strongest evidence, with the most relevance to real-life nutrition decisions .

The image shows a triangle, divided horizontally into 4 sections, from bottom to top, labeled as follows: non-human studies in red color; observational studies in blue color; intervention studies in green color, and meta-analyses and systematic reviews in yellow color. At left is an arrow pointing diagonally from bottom to top, labeled "LOW--Strength of evidence/Relevance to real-life nutrition decisions--HIGH."

Figure 2.3. The hierarchy of evidence shows types of research studies relative to their strength of evidence and relevance to real-life nutrition decisions, with the strongest studies at the top and the weakest at the bottom.

The pyramid also represents a few other general ideas. There tend to be more studies published using the methods at the bottom of the pyramid, because they require less time, money, and other resources. When researchers want to test a new hypothesis , they often start with the study designs at the bottom of the pyramid , such as in vitro, animal, or observational studies. Intervention studies are more expensive and resource-intensive, so there are fewer of these types of studies conducted. But they also give us higher quality evidence, so they’re an important next step if observational and non-human studies have shown promising results. Meta-analyses and systematic reviews combine the results of many studies already conducted, so they help researchers summarize scientific knowledge on a topic.

Non-Human Studies: In Vitro & Animal Studies

The simplest form of nutrition research is an in vitro study . In vitro means “within glass,” (although plastic is used more commonly today) and these experiments are conducted within flasks, dishes, plates, and test tubes. These studies are performed on isolated cells or tissue samples, so they’re less expensive and time-intensive than animal or human studies. In vitro studies are vital for zooming in on biological mechanisms, to see how things work at the cellular or molecular level. However, these studies shouldn’t be used to draw conclusions about how things work in humans (or even animals), because we can’t assume that the results will apply to a whole, living organism.

Two photos representing lab research. At left, a person appearing to be a woman with long dark hair and dark skin handles tiny tubes in a black bucket of ice. More tubes surround the bucket on the table. At right, a white mouse with red eyes peers out of an opening of a cage.

Animal studies are one form of  in vivo research, which translates to “within the living.” Rats and mice are the most common animals used in nutrition research. Animals are often used in research that would be unethical to conduct in humans. Another advantage of animal dietary studies is that researchers can control exactly what the animals eat. In human studies, researchers can tell subjects what to eat and even provide them with the food, but they may not stick to the planned diet. People are also not very good at estimating, recording, or reporting what they eat and in what quantities. In addition, animal studies typically do not cost as much as human studies.

There are some important limitations of animal research. First, an animal’s metabolism and physiology are different from humans. Plus, animal models of disease (cancer, cardiovascular disease, etc.), although similar, are different from human diseases. Animal research is considered preliminary, and while it can be very important to the process of building scientific understanding and informing the types of studies that should be conducted in humans, animal studies shouldn’t be considered relevant to real-life decisions about how people eat.

Observational Studies

Observational studies in human nutrition collect information on people’s dietary patterns or nutrient intake and look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health. These types of study designs can only identify correlations (relationships) between nutrition and health; they can’t show that one factor causes another. (For that, we need intervention studies, which we’ll discuss in a moment.) Observational studies that describe factors correlated with human health are also called epidemiological studies . 1

One example of a nutrition hypothesis that has been investigated using observational studies is that eating a Mediterranean diet reduces the risk of developing cardiovascular disease. (A Mediterranean diet focuses on whole grains, fruits and vegetables, beans and other legumes, nuts, olive oil, herbs, and spices. It includes small amounts of animal protein (mostly fish), dairy, and red wine. 2 ) There are three main types of observational studies, all of which could be used to test hypotheses about the Mediterranean diet:

  • Cohort studies follow a group of people (a cohort) over time, measuring factors such as diet and health outcomes. A cohort study of the Mediterranean diet would ask a group of people to describe their diet, and then researchers would track them over time to see if those eating a Mediterranean diet had a lower incidence of cardiovascular disease.
  • Case-control studies compare a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes. For example, researchers might compare a group of people with cardiovascular disease with a group of healthy controls to see whether there were more controls or cases that followed a Mediterranean diet.
  • Cross-sectional studies collect information about a population of people at one point in time. For example, a cross-sectional study might compare the dietary patterns of people from different countries to see if diet correlates with the prevalence of cardiovascular disease in the different countries.

Prospective cohort studies, which enroll a cohort and follow them into the future, are usually considered the strongest type of observational study design. Retrospective studies look at what happened in the past, and they’re considered weaker because they rely on people’s memory of what they ate or how they felt in the past. There are several well-known examples of prospective cohort studies that have described important correlations between diet and disease:

  • Framingham Heart Study : Beginning in 1948, this study has followed the residents of Framingham, Massachusetts to identify risk factors for heart disease.
  • Health Professionals Follow-Up Study : This study started in 1986 and enrolled 51,529 male health professionals (dentists, pharmacists, optometrists, osteopathic physicians, podiatrists, and veterinarians), who complete diet questionnaires every 2 years.
  • Nurses Health Studies : Beginning in 1976, these studies have enrolled three large cohorts of nurses with a total of 280,000 participants. Participants have completed detailed questionnaires about diet, other lifestyle factors (smoking and exercise, for example), and health outcomes.

Observational studies have the advantage of allowing researchers to study large groups of people in the real world, looking at the frequency and pattern of health outcomes and identifying factors that correlate with them. But even very large observational studies may not apply to the population as a whole. For example, the Health Professionals Follow-Up Study and the Nurses Health Studies include people with above-average knowledge of health. In many ways, this makes them ideal study subjects, because they may be more motivated to be part of the study and to fill out detailed questionnaires for years. However, the findings of these studies may not apply to people with less baseline knowledge of health.

We’ve already mentioned another important limitation of observational studies—that they can only determine correlation, not causation. A prospective cohort study that finds that people eating a Mediterranean diet have a lower incidence of heart disease can only show that the Mediterranean diet is correlated with lowered risk of heart disease. It can’t show that the Mediterranean diet directly prevents heart disease. Why? There are a huge number of factors that determine health outcomes such as heart disease, and other factors might explain a correlation found in an observational study. For example, people who eat a Mediterranean diet might also be the same kind of people who exercise more, sleep more, have higher income (fish and nuts can be expensive!), or be less stressed. These are called confounding factors ; they’re factors that can affect the outcome in question (i.e., heart disease) and also vary with the factor being studied (i.e., Mediterranean diet).

Intervention Studies

Intervention studies , also sometimes called experimental studies or clinical trials, include some type of treatment or change imposed by the researcher. Examples of interventions in nutrition research include asking participants to change their diet, take a supplement, or change the time of day that they eat. Unlike observational studies, intervention studies can provide evidence of cause and effect , so they are higher in the hierarchy of evidence pyramid.

The gold standard for intervention studies is the randomized controlled trial (RCT) . In an RCT, study subjects are recruited to participate in the study. They are then randomly assigned into one of at least two groups, one of which is a control group (this is what makes the study controlled ). In an RCT to study the effects of the Mediterranean diet on cardiovascular disease development, researchers might ask the control group to follow a low-fat diet (typically recommended for heart disease prevention) and the intervention group to eat a Mediterrean diet. The study would continue for a defined period of time (usually years to study an outcome like heart disease), at which point the researchers would analyze their data to see if more people in the control or Mediterranean diet had heart attacks or strokes. Because the treatment and control groups were randomly assigned, they should be alike in every other way except for diet, so differences in heart disease could be attributed to the diet. This eliminates the problem of confounding factors found in observational research, and it’s why RCTs can provide evidence of causation, not just correlation.

Imagine for a moment what would happen if the two groups weren’t randomly assigned. What if the researchers let study participants choose which diet they’d like to adopt for the study? They might, for whatever reason, end up with more overweight people who smoke and have high blood pressure in the low-fat diet group, and more people who exercised regularly and had already been eating lots of olive oil and nuts for years in the Mediterranean diet group. If they found that the Mediterranean diet group had fewer heart attacks by the end of the study, they would have no way of knowing if this was because of the diet or because of the underlying differences in the groups. In other words, without randomization, their results would be compromised by confounding factors, with many of the same limitations as observational studies.

In an RCT of a supplement, the control group would receive a placebo —a “fake” treatment that contains no active ingredients, such as a sugar pill. The use of a placebo is necessary in medical research because of a phenomenon known as the placebo effect. The placebo effect results in a beneficial effect because of a subject’s belief in the treatment, even though there is no treatment actually being administered.

For example, imagine an athlete who consumes a sports drink and then runs 100 meters in 11.0 seconds. On a different day, under the exact same conditions, the athlete is given a Super Duper Sports Drink and again runs 100 meters, this time in 10.5 seconds. But what the athlete didn’t know was that the Super Duper Sports Drink was the same as the regular sports drink—it just had a bit of food coloring added. There was nothing different between the drinks, but the athlete believed that the Super Duper Sports Drink was going to help him run faster, so he did. This improvement is due to the placebo effect. Ironically, a study similar to this example was published in 2015, demonstrating the power of the placebo effect on athletic performance. 3

A cartoon depicts the study described in the text. At left is shown the "super duper sports drink" (sports drink plus food coloring) in orange. At right is the regular sports drink in green. A cartoon guy with yellow hair is pictured sprinting. The time with the super duper sports drink is 10.50 seconds, and the time with the regular sports drink is 11.00 seconds. The image reads "the improvement is the placebo effect."

Figure 2.4. An example of the placebo effect

Blinding is a technique to prevent bias in intervention studies. In a study without blinding, the subject and the researchers both know what treatment the subject is receiving. This can lead to bias if the subject or researcher have expectations about the treatment working, so these types of trials are used less frequently. It’s best if a study is double-blind , meaning that neither the researcher nor the subject know what treatment the subject is receiving. It’s relatively simple to double-blind a study where subjects are receiving a placebo or treatment pill, because they could be formulated to look and taste the same. In a single-blind study , either the researcher or the subject knows what treatment they’re receiving, but not both. Studies of diets—such as the Mediterranean diet example—often can’t be double-blinded because the study subjects know whether or not they’re eating a lot of olive oil and nuts. However, the researchers who are checking participants’ blood pressure or evaluating their medical records could be blinded to their treatment group, reducing the chance of bias.

Like all studies, RCTs and other intervention studies do have some limitations. They can be difficult to carry on for long periods of time and require that participants remain compliant with the intervention. They’re also costly and often have smaller sample sizes. Furthermore, it is unethical to study certain interventions. (An example of an unethical intervention would be to advise one group of pregnant mothers to drink alcohol to determine its effects on pregnancy outcomes, because we know that alcohol consumption during pregnancy damages the developing fetus.)

VIDEO: “ Not all scientific studies are created equal ” by David H. Schwartz, YouTube (April 28, 2014), 4:26.

Meta-Analyses and Systematic Reviews

At the top of the hierarchy of evidence pyramid are systematic reviews and meta-analyses . You can think of these as “studies of studies.” They attempt to combine all of the relevant studies that have been conducted on a research question and summarize their overall conclusions. Researchers conducting a systematic review formulate a research question and then systematically and independently identify, select, evaluate, and synthesize all high-quality evidence that relates to the research question. Since systematic reviews combine the results of many studies, they help researchers produce more reliable findings. A meta-analysis is a type of systematic review that goes one step further, combining the data from multiple studies and using statistics to summarize it, as if creating a mega-study from many smaller studies . 4

However, even systematic reviews and meta-analyses aren’t the final word on scientific questions. For one thing, they’re only as good as the studies that they include. The Cochrane Collaboration is an international consortium of researchers who conduct systematic reviews in order to inform evidence-based healthcare, including nutrition, and their reviews are among the most well-regarded and rigorous in science. For the most recent Cochrane review of the Mediterranean diet and cardiovascular disease, two authors independently reviewed studies published on this question. Based on their inclusion criteria, 30 RCTs with a total of 12,461 participants were included in the final analysis. However, after evaluating and combining the data, the authors concluded that “despite the large number of included trials, there is still uncertainty regarding the effects of a Mediterranean‐style diet on cardiovascular disease occurrence and risk factors in people both with and without cardiovascular disease already.” Part of the reason for this uncertainty is that different trials found different results, and the quality of the studies was low to moderate. Some had problems with their randomization procedures, for example, and others were judged to have unreliable data. That doesn’t make them useless, but it adds to the uncertainty about this question, and uncertainty pushes the field forward towards more and better studies. The Cochrane review authors noted that they found seven ongoing trials of the Mediterranean diet, so we can hope that they’ll add more clarity to this question in the future. 5

Science is an ongoing process. It’s often a slow process, and it contains a lot of uncertainty, but it’s our best method of building knowledge of how the world and human life works. Many different types of studies can contribute to scientific knowledge. None are perfect—all have limitations—and a single study is never the final word on a scientific question. Part of what advances science is that researchers are constantly checking each other’s work, asking how it can be improved and what new questions it raises.

Self-Check:

Attributions:

  • “Chapter 1: The Basics” from Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR , CC BY-NC-SA 4.0
  • “ The Broad Role of Nutritional Science ,” section 1.3 from the book An Introduction to Nutrition (v. 1.0), CC BY-NC-SA 3.0

References:

  • 1 Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica , 24 (2), 199–210. https://doi.org/10.11613/BM.2014.022
  • 2 Harvard T.H. Chan School of Public Health. (2018, January 16). Diet Review: Mediterranean Diet . The Nutrition Source. https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/mediterranean-diet/
  • 3 Ross, R., Gray, C. M., & Gill, J. M. R. (2015). Effects of an Injected Placebo on Endurance Running Performance. Medicine and Science in Sports and Exercise , 47 (8), 1672–1681. https://doi.org/10.1249/MSS.0000000000000584
  • 4 Hooper, A. (n.d.). LibGuides: Systematic Review Resources: Systematic Reviews vs Other Types of Reviews . Retrieved February 7, 2020, from //libguides.sph.uth.tmc.edu/c.php?g=543382&p=5370369
  • 5 Rees, K., Takeda, A., Martin, N., Ellis, L., Wijesekara, D., Vepa, A., Das, A., Hartley, L., & Stranges, S. (2019). Mediterranean‐style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews , 3 . https://doi.org/10.1002/14651858.CD009825.pub3
  • Figure 2.3. The hierarchy of evidence by Alice Callahan, is licensed under CC BY 4.0
  • Research lab photo by National Cancer Institute on Unsplas h ; mouse photo by vaun0815 on Unsplash
  • Figure 2.4. “Placebo effect example” by Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR

Experiments that are conducted outside of living organisms, within flasks, dishes, plates, or test tubes.

Research that is conducted in living organisms, such as rats and mice.

In nutrition, research that is conducted by collecting information on people’s dietary patterns or nutrient intake to look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health.

Relationships between two factors (e.g., nutrition and health).

Research that follows a group of people (a cohort) over time, measuring factors such as diet and health outcomes.

Research that compares a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes.

Research that collects information about a population of people at one point in time.

Looking into the future.

Looking at what happened in the past.

Factors that can affect the outcome in question.

Research that includes some type of treatment or change imposed by the researchers; sometimes called experimental studies or clinical trials.

The gold standard for intervention studies, because the research involves a control group and participants are randomized.

A “fake” treatment that contains no active ingredients, such as a sugar pill.

The beneficial effect that results from a subject's belief in a treatment, not from the treatment itself.

technique to prevent bias in intervention studies, where either the research team, the subject, or both don’t know what treatment the subject is receiving.

Neither the research team nor the subject know what treatment the subject is receiving.

Either the research team or the subject know what treatment is being given, but not both.

Researchers formulate a research question and then systematically and independently identify, select, evaluate, and synthesize all high-quality evidence from previous research that relates to the research question.

A type of systematic review that combines data from multiple studies and uses statistical methods to summarize it, as if creating a mega-study from many smaller studies.

Nutrition: Science and Everyday Application, v. 1.0 Copyright © 2020 by Alice Callahan, PhD; Heather Leonard, MEd, RDN; and Tamberly Powell, MS, RDN is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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What are some different types of research studies?

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There are many different types of research studies. Generally, there are two major types of studies available on Research for Me @UNC: research studies and clinical trials . When a research study is about disease or human health, it is called a clinical research study. When a research study involves drugs or other therapies that aim to slow or stop a disease, then it is called a clinical trial. Volunteers are an important part of all of these research studies! Explore other types of research studies below. 

Survey - Survey studies ask people questions about their knowledge, attitudes, and feelings about a wide range of topics. You can complete these surveys online, over the phone, or by mail. Sometimes, these studies might also be in-person interviews or group discussions.

Lifestyle - Lifestyle studies look at what happens when people participate in different types of activities over a set period of time. You may attend activity sessions in a center or clinic or be asked to change the way that you do something in your daily activities. Often, these studies are interested in how changes in behavior can affect our health or other parts of our lives.

Drug - Drug studies are heavily regulated by the US Government. Studies often involve medications that are not currently available to the general public. They are called “investigational” drugs and have not yet been approved by the FDA (US Food and Drug Administration) for your normal health care provider to prescribe. Other drug studies may involve comparisons between two or more FDA-approved medications.

Device - Device studies are done to learn if a new medical device helps relieve a certain medical condition. Devices you may be familiar with are pacemakers, diabetes testing meters, and hearing aids. These studies usually involve devices that are not currently available to the general public and have not been approved for use by the FDA. Sometimes, they may be studying an FDA-approved device, but for use in treating a new condition. 

Procedure - Procedure studies learn about the safety and effectiveness of certain medical procedures. Sometimes they compare a new medical procedure to one already in use. Procedures might include things like imaging (x-rays), stitches, blood tests, and surgeries.

Medical Outcomes - Outcomes research studies the end results (outcomes) of the structure and processes of the health care system on the health and well-being of patients and populations. These studies look at clinical practices to see if there are better ways for doctors to help patients manage their medical care. Outcomes research often considers patients’ experiences, preferences, and values – all of which may affect whether or not a medical treatment is best for them. 

Community-based - Community-based research is done through a true partnership of community leaders and organizations with a UNC researcher or research team. The ideas are driven by community members and the research incorporates voices of all involved.  These studies aim to understand problems impacting communities and contribute to solutions through policy or social change. 

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Introduction to Research Methods in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

three types of research studies

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

three types of research studies

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

stevecoleimages/Getty Images

Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

  • Case studies
  • Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

  • A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
  • A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

  • Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
  • Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

  • A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
  • In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In:  Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Unit 3 – The Science of Nutrition

3.3 Types of Research Studies

The field of nutrition is dynamic, and our understanding and practices are always evolving. Nutrition scientists are continuously conducting new research and publishing their findings in peer-reviewed journals. This adds to scientific knowledge, but it’s also of great interest to the public, so nutrition research often shows up in the news and other media sources. You might be interested in nutrition research to inform your own eating habits, or if you work in a health profession, so that you can give evidence-based advice to others. Making sense of science requires that you understand the types of research studies used and their limitations.

Types of Research Studies

There are various types of scientific studies on humans that can be used to provide supporting evidence for a particular hypothesis. These include epidemiological studies, interventional clinical trials, and randomized clinical trials. Valuable nutrition knowledge also is obtained from animal studies and cellular and molecular biology research.

Table 3.1 Types of Scientific Studies

The Hierarchy of Nutrition Evidence

Researchers use many different types of study designs depending on the question they are trying to answer, as well as factors such as time, funding, and ethical considerations. The study design affects how we interpret the results and the strength of the evidence as it relates to real-life nutrition decisions. It can be helpful to think about the types of studies within a pyramid (Figure 3.3)  representing a hierarchy of evidence, where the studies at the bottom of the pyramid usually give us the weakest evidence with the least relevance to real-life nutrition decisions, and the studies at the top offer the strongest evidence, with the most relevance to real-life nutrition decisions .

The image shows a triangle, divided horizontally into 4 sections, from bottom to top, labeled as follows: non-human studies in red color; observational studies in blue color; intervention studies in green color, and meta-analyses and systematic reviews in yellow color. At left is an arrow pointing diagonally from bottom to top, labeled "LOW--Strength of evidence/Relevance to real-life nutrition decisions--HIGH."

The pyramid also represents a few other general ideas. There tend to be more studies published using the methods at the bottom of the pyramid, because they require less time, money, and other resources. When researchers want to test a new hypothesis , they often start with the study designs at the bottom of the pyramid , such as in vitro, animal, or observational studies. Intervention studies are more expensive and resource-intensive, so there are fewer of these types of studies conducted. But they also give us higher quality evidence, so they’re an important next step if observational and non-human studies have shown promising results. Meta-analyses and systematic reviews combine the results of many studies already conducted, so they help researchers summarize scientific knowledge on a topic.

Non-Human Studies: In Vitro & Animal Studies

The simplest form of nutrition research is an in vitro study . In vitro means “within glass,” (although plastic is used more commonly today) and these experiments are conducted within flasks, dishes, plates, and test tubes. These studies are performed on isolated cells or tissue samples, so they’re less expensive and time-intensive than animal or human studies. In vitro studies are vital for zooming in on biological mechanisms, to see how things work at the cellular or molecular level. However, these studies shouldn’t be used to draw conclusions about how things work in humans (or even animals), because we can’t assume that the results will apply to a whole, living organism.

Two photos representing lab research. At left, a person appearing to be a woman with long dark hair and dark skin handles tiny tubes in a black bucket of ice. More tubes surround the bucket on the table. At right, a white mouse with red eyes peers out of an opening of a cage.

Animal studies are one form of  in vivo research, which translates to “within the living.” Rats and mice are the most common animals used in nutrition research. Animals are often used in research that would be unethical to conduct in humans. Another advantage of animal dietary studies is that researchers can control exactly what the animals eat. In human studies, researchers can tell subjects what to eat and even provide them with the food, but they may not stick to the planned diet. People are also not very good at estimating, recording, or reporting what they eat and in what quantities. In addition, animal studies typically do not cost as much as human studies.

There are some important limitations of animal research. First, an animal’s metabolism and physiology are different from humans. Plus, animal models of disease (cancer, cardiovascular disease, etc.), although similar, are different from human diseases. Animal research is considered preliminary, and while it can be very important to the process of building scientific understanding and informing the types of studies that should be conducted in humans, animal studies shouldn’t be considered relevant to real-life decisions about how people eat.

Observational Studies

Observational studies in human nutrition collect information on people’s dietary patterns or nutrient intake and look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health. These types of study designs can only identify correlations (relationships) between nutrition and health; they can’t show that one factor causes another. (For that, we need intervention studies, which we’ll discuss in a moment.) Observational studies that describe factors correlated with human health are also called epidemiological studies . 1

One example of a nutrition hypothesis that has been investigated using observational studies is that eating a Mediterranean diet reduces the risk of developing cardiovascular disease. (A Mediterranean diet focuses on whole grains, fruits and vegetables, beans and other legumes, nuts, olive oil, herbs, and spices. It includes small amounts of animal protein (mostly fish), dairy, and red wine. 2 ) There are three main types of observational studies, all of which could be used to test hypotheses about the Mediterranean diet:

  • Cohort studies follow a group of people (a cohort) over time, measuring factors such as diet and health outcomes. A cohort study of the Mediterranean diet would ask a group of people to describe their diet, and then researchers would track them over time to see if those eating a Mediterranean diet had a lower incidence of cardiovascular disease.
  • Case-control studies compare a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes. For example, researchers might compare a group of people with cardiovascular disease with a group of healthy controls to see whether there were more controls or cases that followed a Mediterranean diet.
  • Cross-sectional studies collect information about a population of people at one point in time. For example, a cross-sectional study might compare the dietary patterns of people from different countries to see if diet correlates with the prevalence of cardiovascular disease in the different countries.

Prospective cohort studies, which enroll a cohort and follow them into the future, are usually considered the strongest type of observational study design. Retrospective studies look at what happened in the past, and they’re considered weaker because they rely on people’s memory of what they ate or how they felt in the past. There are several well-known examples of prospective cohort studies that have described important correlations between diet and disease:

  • Framingham Heart Study : Beginning in 1948, this study has followed the residents of Framingham, Massachusetts, to identify risk factors for heart disease.
  • Health Professionals Follow-Up Study : This study started in 1986 and enrolled 51,529 male health professionals (dentists, pharmacists, optometrists, osteopathic physicians, podiatrists, and veterinarians), who complete diet questionnaires every 2 years.
  • Nurses Health Studies : Beginning in 1976, these studies have enrolled three large cohorts of nurses with a total of 280,000 participants. Participants have completed detailed questionnaires about diet, other lifestyle factors (smoking and exercise, for example), and health outcomes.

Observational studies have the advantage of allowing researchers to study large groups of people in the real world, looking at the frequency and pattern of health outcomes and identifying factors that correlate with them. But even very large observational studies may not apply to the population as a whole. For example, the Health Professionals Follow-Up Study and the Nurses Health Studies include people with above-average knowledge of health. In many ways, this makes them ideal study subjects, because they may be more motivated to be part of the study and to fill out detailed questionnaires for years. However, the findings of these studies may not apply to people with less baseline knowledge of health.

We’ve already mentioned another important limitation of observational studies—that they can only determine correlation, not causation. A prospective cohort study that finds that people eating a Mediterranean diet have a lower incidence of heart disease can only show that the Mediterranean diet is correlated with lowered risk of heart disease. It can’t show that the Mediterranean diet directly prevents heart disease. Why? There are a huge number of factors that determine health outcomes such as heart disease, and other factors might explain a correlation found in an observational study. For example, people who eat a Mediterranean diet might also be the same kind of people who exercise more, sleep more, have higher income (fish and nuts can be expensive!), or be less stressed. These are called confounding factors ; they’re factors that can affect the outcome in question (i.e., heart disease) and also vary with the factor being studied (i.e., Mediterranean diet).

Intervention Studies

Intervention studies , also sometimes called experimental studies or clinical trials, include some type of treatment or change imposed by the researcher. Examples of interventions in nutrition research include asking participants to change their diet, take a supplement, or change the time of day that they eat. Unlike observational studies, intervention studies can provide evidence of cause and effect , so they are higher in the hierarchy of evidence pyramid.

The gold standard for intervention studies is the randomized controlled trial (RCT) . In an RCT, study subjects are recruited to participate in the study. They are then randomly assigned into one of at least two groups, one of which is a control group (this is what makes the study controlled ). In an RCT to study the effects of the Mediterranean diet on cardiovascular disease development, researchers might ask the control group to follow a low-fat diet (typically recommended for heart disease prevention) and the intervention group to eat a Mediterranean diet. The study would continue for a defined period of time (usually years to study an outcome like heart disease), at which point the researchers would analyze their data to see if more people in the control or Mediterranean diet had heart attacks or strokes. Because the treatment and control groups were randomly assigned, they should be alike in every other way except for diet, so differences in heart disease could be attributed to the diet. This eliminates the problem of confounding factors found in observational research, and it’s why RCTs can provide evidence of causation, not just correlation.

Imagine for a moment what would happen if the two groups weren’t randomly assigned. What if the researchers let study participants choose which diet they’d like to adopt for the study? They might, for whatever reason, end up with more overweight people who smoke and have high blood pressure in the low-fat diet group, and more people who exercised regularly and had already been eating lots of olive oil and nuts for years in the Mediterranean diet group. If they found that the Mediterranean diet group had fewer heart attacks by the end of the study, they would have no way of knowing if this was because of the diet or because of the underlying differences in the groups. In other words, without randomization, their results would be compromised by confounding factors, with many of the same limitations as observational studies.

In an RCT of a supplement, the control group would receive a placebo—a “fake” treatment that contains no active ingredients, such as a sugar pill. The use of a placebo is necessary in medical research because of a phenomenon known as the placebo effect. The placebo effect results in a beneficial effect because of a subject’s belief in the treatment, even though there is no treatment actually being administered.

A cartoon depicts the study described in the text. At left is shown the "super duper sports drink" (sports drink plus food coloring) in orange. At right is the regular sports drink in green. A cartoon guy with yellow hair is pictured sprinting. The time with the super duper sports drink is 10.50 seconds, and the time with the regular sports drink is 11.00 seconds. The image reads "the improvement is the placebo effect."

For example, imagine an athlete who consumes a sports drink and then runs 100 meters in 11.0 seconds  as illustrated in Figure 3.4. On a different day, under the exact same conditions, the athlete is given a Super Duper Sports Drink and again runs 100 meters, this time in 10.5 seconds. But what the athlete didn’t know was that the Super Duper Sports Drink was the same as the regular sports drink—it just had a bit of food coloring added. There was nothing different between the drinks, but the athlete believed that the Super Duper Sports Drink was going to help him run faster, so he did. This improvement is due to the placebo effect. Ironically, a study similar to this example was published in 2015, demonstrating the power of the placebo effect on athletic performance. 3

Blinding is a technique to prevent bias in intervention studies. In a study without blinding, the subject and the researchers both know what treatment the subject is receiving. This can lead to bias if the subject or researcher have expectations about the treatment working, so these types of trials are used less frequently. It’s best if a study is double-blind , meaning that neither the researcher nor the subject know what treatment the subject is receiving. It’s relatively simple to double-blind a study where subjects are receiving a placebo or treatment pill, because they could be formulated to look and taste the same. In a single-blind study , either the researcher or the subject knows what treatment they’re receiving, but not both. Studies of diets—such as the Mediterranean diet example—often can’t be double-blinded because the study subjects know whether or not they’re eating a lot of olive oil and nuts. However, the researchers who are checking participants’ blood pressure or evaluating their medical records could be blinded to their treatment group, reducing the chance of bias.

Like all studies, RCTs and other intervention studies do have some limitations. They can be difficult to carry on for long periods of time and require that participants remain compliant with the intervention. They’re also costly and often have smaller sample sizes. Furthermore, it is unethical to study certain interventions. (An example of an unethical intervention would be to advise one group of pregnant mothers to drink alcohol to determine its effects on pregnancy outcomes, because we know that alcohol consumption during pregnancy damages the developing fetus.)

Watch this short TED Ed video that describes that not all scientific studies are equal.

VIDEO: “ Not all scientific studies are created equal ” by David H. Schwartz, YouTube (April 28, 2014), 4:26.

Meta-Analyses and Systematic Reviews

However, even systematic reviews  systematic reviews and meta-analyses aren’t the final word on scientific questions. For one thing, they’re only as good as the studies that they include. The Cochrane Collaboration is an international consortium of researchers who conduct systematic reviews in order to inform evidence-based healthcare, including nutrition, and their reviews are among the most well-regarded and rigorous in science. For example,  a recent Cochrane review of the Mediterranean diet and cardiovascular disease, two authors independently reviewed studies published on this question. Based on their inclusion criteria, 30 RCTs with a total of 12,461 participants were included in the final analysis. However, after evaluating and combining the data, the authors concluded that “despite the large number of included trials, there is still uncertainty regarding the effects of a Mediterranean‐style diet on cardiovascular disease occurrence and risk factors in people both with and without cardiovascular disease already.” Part of the reason for this uncertainty is that different trials found different results, and the quality of the studies was low to moderate. Some had problems with their randomization procedures, for example, and others were judged to have unreliable data. That doesn’t make them useless, but it adds to the uncertainty about this question, and uncertainty pushes the field forward towards more and better studies. The Cochrane review authors noted that they found seven ongoing trials of the Mediterranean diet, so we can hope that they’ll add more clarity to this question in the future. 5

To find the most recent studies about nutrition and health, go to the PubMed.gov website.  The site, sponsored by the National Institute of Health’s Library of Medicine, includes a search engine that allows users to explore by type of study, date of publication,  human vs animal subjects and a host of other parameters.

Science is an ongoing process. It’s often a slow process, and it contains a lot of uncertainty, but it’s our best method of building knowledge of how the world and human life works. Many different types of studies can contribute to scientific knowledge. None are perfect—all have limitations—and a single study is never the final word on a scientific question. Part of what advances science is that researchers are constantly checking each other’s work, asking how it can be improved and what new questions it raises.

Review Questions

Attributions:

  • Lane Community College’s Nutrition: Science and Everyday Application   “ Types of Research and How to Interprete Them” CC BY-NC 4.0

References:

  • 1 Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica , 24 (2), 199–210. https://doi.org/10.11613/BM.2014.022
  • 2 Harvard T.H. Chan School of Public Health. (2018, January 16). Diet Review: Mediterranean Diet . The Nutrition Source. https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/mediterranean-diet/
  • 3 Ross, R., Gray, C. M., & Gill, J. M. R. (2015). Effects of an Injected Placebo on Endurance Running Performance. Medicine and Science in Sports and Exercise , 47 (8), 1672–1681. https://doi.org/10.1249/MSS.0000000000000584
  • 4 Hooper, A. (n.d.). LibGuides: Systematic Review Resources: Systematic Reviews vs Other Types of Reviews . Retrieved February 7, 2020, from //libguides.sph.uth.tmc.edu/c.php?g=543382&p=5370369
  • 5 Rees, K., Takeda, A., Martin, N., Ellis, L., Wijesekara, D., Vepa, A., Das, A., Hartley, L., & Stranges, S. (2019). Mediterranean‐style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews , 3 . https://doi.org/10.1002/14651858.CD009825.pub3
  • Figure 2.3. The hierarchy of evidence by Alice Callahan, is licensed under CC BY 4.0
  • Research lab photo by National Cancer Institute on Unsplas h ; mouse photo by vaun0815 on Unsplash
  • Figure 2.4. “Placebo effect example” by Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR

A systematic review is a summary of all of the literature on a particular topic, that meets pre-defined eligibility criteria.

A subset of systematic reviews in which the results are combined and statistically analyzed.

It allows for single conclusion and many similar studies that has greater statistical power.

Introduction to Nutrition and Wellness Copyright © 2022 by Janet Colson; Sandra Poirier; and Yvonne Dadson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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3.3: Types of Research Studies and How To Interpret Them

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  • Alice Callahan, Heather Leonard, & Tamberly Powell
  • Lane Community College via OpenOregon

The field of nutrition is dynamic, and our understanding and practices are always evolving. Nutrition scientists are continuously conducting new research and publishing their findings in peer-reviewed journals. This adds to scientific knowledge, but it’s also of great interest to the public, so nutrition research often shows up in the news and other media sources. You might be interested in nutrition research to inform your own eating habits, or if you work in a health profession, so that you can give evidence-based advice to others. Making sense of science requires that you understand the types of research studies used and their limitations.

The Hierarchy of Nutrition Evidence

Researchers use many different types of study designs depending on the question they are trying to answer, as well as factors such as time, funding, and ethical considerations. The study design affects how we interpret the results and the strength of the evidence as it relates to real-life nutrition decisions. It can be helpful to think about the types of studies within a pyramid representing a hierarchy of evidence, where  the  studies at the bottom of the pyramid usually give us the weakest evidence with the least relevance to real-life nutrition decisions, and the studies at the top offer the strongest evidence, with the most relevance to real-life nutrition  decisions .

three types of research studies

The pyramid also represents a few other general ideas. There tend to be more studies published using the methods at the bottom of the pyramid, because they require less time, money, and other resources. When researchers want to test a new hypothesis , they often start with the study designs at the bottom of the pyramid , such as in vitro, animal, or observational studies. Intervention studies are more expensive and resource-intensive, so there are fewer of these types of studies conducted. But they also give us higher quality evidence, so they’re an important next step if observational and non-human studies have shown promising results. Meta-analyses and systematic reviews combine the results of many studies already conducted, so they help researchers summarize scientific knowledge on a topic.

Non-Human Studies: In Vitro & Animal Studies

The simplest form of nutrition research is an in vitro study . In vitro means “within glass,” (although plastic is used more commonly today) and these experiments are conducted within flasks, dishes, plates, and test tubes. These studies are performed on isolated cells or tissue samples, so they’re less expensive and time-intensive than animal or human studies. In vitro studies are vital for zooming in on biological mechanisms, to see how things work at the cellular or molecular level. However, these studies shouldn’t be used to draw conclusions about how things work in humans (or even animals), because we can’t assume that the results will apply to a whole, living organism.

Two photos representing lab research. At left, a person appearing to be a woman with long dark hair and dark skin handles tiny tubes in a black bucket of ice. More tubes surround the bucket on the table. At right, a white mouse with red eyes peers out of an opening of a cage.

Animal studies are one form of in vivo research, which translates to “within the living.” Rats and mice are the most common animals used in nutrition research. Animals are often used in research that would be unethical to conduct in humans. Another advantage of animal dietary studies is that researchers can control exactly what the animals eat. In human studies, researchers can tell subjects what to eat and even provide them with the food, but they may not stick to the planned diet. People are also not very good at estimating, recording, or reporting what they eat and in what quantities. In addition, animal studies typically do not cost as much as human studies.

There are some important limitations of animal research. First, an animal’s metabolism and physiology are different from humans. Plus, animal models of disease (cancer, cardiovascular disease, etc.), although similar, are different from human diseases. Animal research is considered preliminary, and while it can be very important to the process of building scientific understanding and informing the types of studies that should be conducted in humans, animal studies shouldn’t be considered relevant to real-life decisions about how people eat.

Observational Studies

Observational studies  in human nutrition collect information on people’s dietary patterns or nutrient intake and look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health. These types of study designs can only identify  correlations  (relationships) between nutrition and health; they can’t show that one factor  causes  another. (For that, we need intervention studies, which we’ll discuss in a moment.) Observational studies that describe factors correlated with human health are also called  epidemiological studies . 1

One example of a nutrition hypothesis that has been investigated using observational studies is that eating a Mediterranean diet reduces the risk of developing cardiovascular disease. (A Mediterranean diet focuses on whole grains, fruits and vegetables, beans and other legumes, nuts, olive oil, herbs, and spices. It includes small amounts of animal protein (mostly fish), dairy, and red wine. 2 ) There are three main types of observational studies, all of which could be used to test hypotheses about the Mediterranean diet:

  • Cohort studies follow a group of people (a cohort) over time, measuring factors such as diet and health outcomes. A cohort study of the Mediterranean diet would ask a group of people to describe their diet, and then researchers would track them over time to see if those eating a Mediterranean diet had a lower incidence of cardiovascular disease.
  • Case-control studies compare a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes. For example, researchers might compare a group of people with cardiovascular disease with a group of healthy controls to see whether there were more controls or cases that followed a Mediterranean diet.
  • Cross-sectional studies collect information about a population of people at one point in time. For example, a cross-sectional study might compare the dietary patterns of people from different countries to see if diet correlates with the prevalence of cardiovascular disease in the different countries.

Prospective cohort studies, which enroll a cohort and follow them into the future, are usually considered the strongest type of observational study design. Retrospective studies look at what happened in the past, and they’re considered weaker because they rely on people’s memory of what they ate or how they felt in the past. There are several well-known examples of prospective cohort studies that have described important correlations between diet and disease:

  • Framingham Heart Study : Beginning in 1948, this study has followed the residents of Framingham, Massachusetts to identify risk factors for heart disease.
  • Health Professionals Follow-Up Study : This study started in 1986 and enrolled 51,529 male health professionals (dentists, pharmacists, optometrists, osteopathic physicians, podiatrists, and veterinarians), who complete diet questionnaires every 2 years.
  • Nurses Health Studies : Beginning in 1976, these studies have enrolled three large cohorts of nurses with a total of 280,000 participants. Participants have completed detailed questionnaires about diet, other lifestyle factors (smoking and exercise, for example), and health outcomes.

Observational studies have the advantage of allowing researchers to study large groups of people in the real world, looking at the frequency and pattern of health outcomes and identifying factors that correlate with them. But even very large observational studies may not apply to the population as a whole. For example, the Health Professionals Follow-Up Study and the Nurses Health Studies include people with above-average knowledge of health. In many ways, this makes them ideal study subjects, because they may be more motivated to be part of the study and to fill out detailed questionnaires for years. However, the findings of these studies may not apply to people with less baseline knowledge of health.

We’ve already mentioned another important limitation of observational studies—that they can only determine correlation, not causation. A prospective cohort study that finds that people eating a Mediterranean diet have a lower incidence of heart disease can only show that the Mediterranean diet is correlated with lowered risk of heart disease. It can’t show that the Mediterranean diet directly prevents heart disease. Why? There are a huge number of factors that determine health outcomes such as heart disease, and other factors might explain a correlation found in an observational study. For example, people who eat a Mediterranean diet might also be the same kind of people who exercise more, sleep more, have higher income (fish and nuts can be expensive!), or be less stressed. These are called confounding factors ; they’re factors that can affect the outcome in question (i.e., heart disease) and also vary with the factor being studied (i.e., Mediterranean diet).

Intervention Studies

Intervention studies , also sometimes called experimental studies or clinical trials, include some type of treatment or change imposed by the researcher. Examples of interventions in nutrition research include asking participants to change their diet, take a supplement, or change the time of day that they eat. Unlike observational studies, intervention studies can provide evidence of cause and effect , so they are higher in the hierarchy of evidence pyramid.

The gold standard for intervention studies is the randomized controlled trial (RCT) . In an RCT, study subjects are recruited to participate in the study. They are then randomly assigned into one of at least two groups, one of which is a control group (this is what makes the study controlled ). In an RCT to study the effects of the Mediterranean diet on cardiovascular disease development, researchers might ask the control group to follow a low-fat diet (typically recommended for heart disease prevention) and the intervention group to eat a Mediterrean diet. The study would continue for a defined period of time (usually years to study an outcome like heart disease), at which point the researchers would analyze their data to see if more people in the control or Mediterranean diet had heart attacks or strokes. Because the treatment and control groups were randomly assigned, they should be alike in every other way except for diet, so differences in heart disease could be attributed to the diet. This eliminates the problem of confounding factors found in observational research, and it’s why RCTs can provide evidence of causation, not just correlation.

Imagine for a moment what would happen if the two groups weren’t randomly assigned. What if the researchers let study participants choose which diet they’d like to adopt for the study? They might, for whatever reason, end up with more overweight people who smoke and have high blood pressure in the low-fat diet group, and more people who exercised regularly and had already been eating lots of olive oil and nuts for years in the Mediterranean diet group. If they found that the Mediterranean diet group had fewer heart attacks by the end of the study, they would have no way of knowing if this was because of the diet or because of the underlying differences in the groups. In other words, without randomization, their results would be compromised by confounding factors, with many of the same limitations as observational studies.

In an RCT of a supplement, the control group would receive a placebo—a  “fake” treatment that contains no active ingredients, such as a sugar pill. The use of a placebo is necessary in medical research because of a phenomenon known as the placebo effect. The placebo effect results in a beneficial effect because of a subject’s belief in the treatment, even though there is no treatment actually being administered.

A cartoon depicts the study described in the text. At left is shown the "super duper sports drink" (sports drink plus food coloring) in orange. At right is the regular sports drink in green. A cartoon guy with yellow hair is pictured sprinting. The time with the super duper sports drink is 10.50 seconds, and the time with the regular sports drink is 11.00 seconds. The image reads "the improvement is the placebo effect."

Blinding is a technique to prevent bias in intervention studies. In a study without blinding, the subject and the researchers both know what treatment the subject is receiving. This can lead to bias if the subject or researcher have expectations about the treatment working, so these types of trials are used less frequently. It’s best if a study is double-blind , meaning that neither the researcher nor the subject know what treatment the subject is receiving. It’s relatively simple to double-blind a study where subjects are receiving a placebo or treatment pill, because they could be formulated to look and taste the same. In a single-blind study , either the researcher or the subject knows what treatment they’re receiving, but not both. Studies of diets—such as the Mediterranean diet example—often can’t be double-blinded because the study subjects know whether or not they’re eating a lot of olive oil and nuts. However, the researchers who are checking participants’ blood pressure or evaluating their medical records could be blinded to their treatment group, reducing the chance of bias.

Like all studies, RCTs and other intervention studies do have some limitations. They can be difficult to carry on for long periods of time and require that participants remain compliant with the intervention. They’re also costly and often have smaller sample sizes. Furthermore, it is unethical to study certain interventions. (An example of an unethical intervention would be to advise one group of pregnant mothers to drink alcohol to determine its effects on pregnancy outcomes, because we know that alcohol consumption during pregnancy damages the developing fetus.)

VIDEO: “ Not all scientific studies are created equal ” by David H. Schwartz, YouTube (April 28, 2014), 4:26.

Meta-Analyses and Systematic Reviews

At the top of the hierarchy of evidence pyramid are systematic reviews and meta-analyses .  You can think of these as “studies of studies.” They attempt to combine all of the relevant studies that have been conducted on a research question and summarize their overall conclusions. Researchers conducting a  systematic review  formulate a research question and then systematically and independently identify, select, evaluate, and synthesize all high-quality evidence that relates to the research question. Since systematic reviews combine the results of many studies, they help researchers produce more reliable findings. A  meta-analysis  is a type of systematic review that goes one step further, combining the data from multiple studies and using statistics to summarize it, as if creating a mega-study from many smaller studies . 4

However, even systematic reviews and meta-analyses aren’t the final word on scientific questions. For one thing, they’re only as good as the studies that they include. The  Cochrane Collaboration  is an international consortium of researchers who conduct systematic reviews in order to inform evidence-based healthcare, including nutrition, and their reviews are among the most well-regarded and rigorous in science. For the most recent Cochrane review of the Mediterranean diet and cardiovascular disease, two authors independently reviewed studies published on this question. Based on their inclusion criteria, 30 RCTs with a total of 12,461 participants were included in the final analysis. However, after evaluating and combining the data, the authors concluded that “despite the large number of included trials, there is still uncertainty regarding the effects of a Mediterranean‐style diet on cardiovascular disease occurrence and risk factors in people both with and without cardiovascular disease already.” Part of the reason for this uncertainty is that different trials found different results, and the quality of the studies was low to moderate. Some had problems with their randomization procedures, for example, and others were judged to have unreliable data. That doesn’t make them useless, but it adds to the uncertainty about this question, and uncertainty pushes the field forward towards more and better studies. The Cochrane review authors noted that they found seven ongoing trials of the Mediterranean diet, so we can hope that they’ll add more clarity to this question in the future. 5

Science is an ongoing process. It’s often a slow process, and it contains a lot of uncertainty, but it’s our best method of building knowledge of how the world and human life works. Many different types of studies can contribute to scientific knowledge. None are perfect—all have limitations—and a single study is never the final word on a scientific question. Part of what advances science is that researchers are constantly checking each other’s work, asking how it can be improved and what new questions it raises.

Attributions:

  • “Chapter 1: The Basics” from Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR , CC BY-NC-SA 4.0
  • “ The Broad Role of Nutritional Science ,” section 1.3 from the book An Introduction to Nutrition (v. 1.0), CC BY-NC-SA 3.0

References:

  • 1 Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica , 24 (2), 199–210. https://doi.org/10.11613/BM.2014.022
  • 2 Harvard T.H. Chan School of Public Health. (2018, January 16). Diet Review: Mediterranean Diet . The Nutrition Source. https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/mediterranean-diet/
  • 3 Ross, R., Gray, C. M., & Gill, J. M. R. (2015). Effects of an Injected Placebo on Endurance Running Performance. Medicine and Science in Sports and Exercise , 47 (8), 1672–1681. https://doi.org/10.1249/MSS.0000000000000584
  • 4 Hooper, A. (n.d.). LibGuides: Systematic Review Resources: Systematic Reviews vs Other Types of Reviews . Retrieved February 7, 2020, from //libguides.sph.uth.tmc.edu/c.php?g=543382&p=5370369
  • 5 Rees, K., Takeda, A., Martin, N., Ellis, L., Wijesekara, D., Vepa, A., Das, A., Hartley, L., & Stranges, S. (2019). Mediterranean‐style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews , 3 . doi.org/10.1002/14651858.CD009825.pub3
  • Figure 2.3. The hierarchy of evidence by Alice Callahan, is licensed under CC BY 4.0
  • Research lab photo by National Cancer Institute on Unsplas h ; mouse photo by vaun0815 on Unsplash
  • Figure 2.4. “Placebo effect example” by Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

research methods3

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :
  • Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
  • Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
  • One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
  • Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

Sample Target Population

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

  • Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
  • Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
  • Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
  • Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
  • Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
  • Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
  • Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

  • The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
  • The dependent variable is the thing being measured, or the results of the experiment.

variables

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.
  • Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
  • Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
  • Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
  • The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
  • They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
  • Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

  • Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
  • Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

types of correlation. Scatter plot. Positive negative and no correlation

  • If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
  • If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
  • A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

causation correlation

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

  • Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
  • Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :
  • Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
  • Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
  • Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
  • Natural : Here, spontaneous behavior is recorded in a natural setting.
  • Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
  • Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

  • Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
  • Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

  • Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
  • Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
  • Primary data is first-hand data collected for the purpose of the investigation.
  • Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

  • Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
  • Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
  • Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
  • Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

  • Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
  • Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
  • Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
  • Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
  • Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
  • Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

  • Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
  • To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
  • Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
  • All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
  • It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
  • Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
  • Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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  • Cohort study a nonexperimental design that can be prospective or retrospective. In a prospective cohort study, participants are enrolled before the potential causal event has occurred. In a retrospective cohort study, the study begins after the dependent event occurs. See also "longitudinal study."
  • Cross-sectional design study an experimental design in which multiple measures are collected over a period of time from two or more groups of different ages (birth cohorts), ethnicities, or other factos. These designs combine aspects of longitudinal design and cohort-sequential design.
  • Literature review a narrative summary and evaluation of the findings or theories within a literature base. Also known as "narrative literature review."
  • Longitudinal study a study that involves the observation of a variable or group of variables in the same cases or individuals using the same set of measurements (or attributes) over a period of time (i.e., at multiple times or occasions). A longitudinal study that evaluates a group of randomly chosen individuals is referred to as a panel study, whereas a longitudinal study that evaluates a group of individuals possessing some common characteristic (usually age) is referred to as a cohort study. This multiple observational structure may be combined with almost any other research design—ones with and without experimental manipulations, randomized clinical trials, or any other study type. Also known as "longitudinal research," "longitudinal design."
  • Prospective sampling (cohort) a sampling method in which cases are selected for inclusion in experiments or other research based on their exposure to a risk factor. Participants are then followed to see if a condition of interest develops.
  • Qualitatiive research study approaches to research used to generate knowledge about human experience and/or action, including social processes. These research methods typically produce descriptive (non-numerical) data, such as observations of behavior or personal accounts of experiences. The goal of gathering qualitative data is to examine how individuals perceive the world from different vantage points. Also known as "qualitative design," "qualitative inquiry," "qualitative method," "qualitative study." more... less... Qualitative methods share four central characteristics: Involve the analysis of natural language and other forms of human expression rather than the translation of meaning into numbersCentralize an iterative process in which data are analyzed and meanings are generated in a circular and self-correcting process of checking and refining findingsSeek to present findings in a manner that emphasizes the study's context and situation in timeRecursively combine inquiry with methods that require researchers' reflexivity (i.e., self-examination) about their influence upon the research process.
  • Qualitative meta analysis study a form of inquiry in which qualitative research findings about a process or experience are aggregated or integrated across research studies. Aims can involve synthesizing qualitative findings across primary studies, generating new theoretical or conceptual models, identifying gaps in research, or generating new questions.
  • Quantitative research study approaches to research in which observed outcomes are numerically represented. These research methods rely on measuring variables using a numerical system, analyzing measurements using statistical models, and reporting relationships and associations among the studied variables. The goal of gathering quantitative data is to understand, describe, and predict the nature of a phenomenon, particularly through the development of models and theories. Also known as "quantitative design," "quantitative inquiry," "quantitative method," "quantitative study."
  • Quantitative Meta analysis a technique for synthesizing the results of multiple studies of a phenomenon by combining the effect size estimates from each study into a single estimate of the combined effect size or into a distribution of effect sizes. Effect size estimates from individual studies are the inputs to the analyses. Although meta-analyses are ideally suited for summarizing a body of literature in terms of impact, limitations, and implications, they are limited by having no required minimum number of studies or participants. Information of potential interest may also be missing from the original research reports upon which the procedure must rely.
  • Randomized controlled (clinical) trial an experimental design in which patients are randomly assigned to a group that will receive an experimental treatment, such as a new drug, or to one that will receive a comparison treatment, a standard-of-care treatment, or a placebo. The random assignment occurs after recruitment and assessment of eligibility but before the intervention. There may be multiple experimental and comparison groups, but each patient is assigned to one group only.
  • Retrospective cohort study (sampling) the study begins after the dependent event occurs; a technique in which participants or cases from the general population are selected for inclusion in experiments or other research based on their previous exposure to a risk factor or the completion of some particular process. Participants are then examined in the present to see if a particular condition or state exists, often in comparison to others who were not exposed to the risk or who did not complete the particular process.
  • Please consult the following sources for more information on these types of studies and terminology related to the studies.

    • APA Style JARS Supplemental Glossary This webpage provides supplemental information on the terms used in APA Style JARS. This glossary is meant to supplement Chapter 3 of the Publication Manual of the American Psychological Association, Seventh Edition. It is not an exhaustive list of all terms employed in quantitative, qualitative, or mixed methods research, nor does it include all possible definitions for each term; definitions in addition to or different from those reported in this glossary may be found in other sources.
    • APA Dictionary of Psychology More than 25,000 authoritative entries across 90 subfields of psychology.
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    • Last Updated: Feb 27, 2024 9:33 AM
    • URL: https://libguides.ggc.edu/EXSC_3250_Wludyga

    Long-Term Outcomes of Resynchronization-Defibrillation for Heart Failure

    Collaborators.

    • RAFT Long-Term Study Team : John L Sapp ,  Ahmed Mokhtar ,  Ratika Parkash ,  Karen Giddens ,  Aileen Davis ,  Jeff S Healey ,  Stuart McKinlay ,  Angela Frechette ,  Calum J Redpath ,  Bernard Thibault ,  Blandine Mondesert ,  Mario Talajic ,  Tammy Knight ,  Sarah Beaudoin ,  Caroline Girard ,  Francois Lemarbre ,  Anthony S L Tang ,  Habib Khan ,  Jaimie Manlucu ,  Derek V Exner ,  Glen Sumner ,  Sabrina Wall ,  Zina Zein Abden ,  Samuel Triemstra ,  Jennifer McKeage ,  Karen Cowan ,  Laurence D Sterns ,  Caitlin Patterson ,  Jake Gray ,  Soori Sivakumaran ,  Shane Kimber ,  Harald Becher ,  Ross Tsuyuki ,  Nhat Hung N Lam ,  Jean Rouleau ,  C Elizabeth McCarron ,  George Wells ,  Patricia Theoret-Patrick ,  Janel Dhooma

    Affiliation

    • 1 From QEII Health Sciences Centre, Dalhousie University, Halifax, NS (J.L.S., R.P.), the Mazankowski Alberta Heart Institute, University of Alberta, Edmonton (S.S., S.K.), the University of Ottawa Heart Institute, Ottawa (C.J.R., N.H.N.L., G.W.), Schulich School of Medicine and Dentistry, Western University, London, ON (H.K., J.M., C.E.M., A.S.L.T.), Libin Cardiovascular Institute, Calgary, AB (D.V.E., G.S.), McMaster University, Hamilton, ON (J.S.H.), Montreal Heart Institute, Montreal (B.T., B.M., M.T., J.R.), Royal Jubilee Hospital, Victoria, BC (L.D.S.), and the University of Toronto, Toronto (S.M.) - all in Canada; and King Abdulaziz University, Jeddah, Saudi Arabia (A.M.).
    • PMID: 38231622
    • DOI: 10.1056/NEJMoa2304542

    Background: The Resynchronization-Defibrillation for Ambulatory Heart Failure Trial (RAFT) showed a greater benefit with respect to mortality at 5 years among patients who received cardiac-resynchronization therapy (CRT) than among those who received implantable cardioverter-defibrillators (ICDs). However, the effect of CRT on long-term survival is not known.

    Methods: We randomly assigned patients with New York Heart Association (NYHA) class II or III heart failure, a left ventricular ejection fraction of 30% or less, and an intrinsic QRS duration of 120 msec or more (or a paced QRS duration of 200 msec or more) to receive either an ICD alone or a CRT defibrillator (CRT-D). We assessed long-term outcomes among patients at the eight highest-enrolling participating sites. The primary outcome was death from any cause; the secondary outcome was a composite of death from any cause, heart transplantation, or implantation of a ventricular assist device.

    Results: The trial enrolled 1798 patients, of whom 1050 were included in the long-term survival trial; the median duration of follow-up for the 1050 patients was 7.7 years (interquartile range, 3.9 to 12.8), and the median duration of follow-up for those who survived was 13.9 years (interquartile range, 12.8 to 15.7). Death occurred in 405 of 530 patients (76.4%) assigned to the ICD group and in 370 of 520 patients (71.2%) assigned to the CRT-D group. The time until death appeared to be longer for those assigned to receive a CRT-D than for those assigned to receive an ICD (acceleration factor, 0.80; 95% confidence interval, 0.69 to 0.92; P = 0.002). A secondary-outcome event occurred in 412 patients (77.7%) in the ICD group and in 392 (75.4%) in the CRT-D group.

    Conclusions: Among patients with a reduced ejection fraction, a widened QRS complex, and NYHA class II or III heart failure, the survival benefit associated with receipt of a CRT-D as compared with ICD appeared to be sustained during a median of nearly 14 years of follow-up. (RAFT ClinicalTrials.gov number, NCT00251251 .).

    Copyright © 2024 Massachusetts Medical Society.

    Publication types

    • Comparative Study
    • Multicenter Study
    • Randomized Controlled Trial
    • Cardiac Resynchronization Therapy*
    • Defibrillators, Implantable*
    • Electrocardiography
    • Follow-Up Studies
    • Heart Failure* / mortality
    • Heart Failure* / physiopathology
    • Heart Failure* / therapy
    • Kaplan-Meier Estimate
    • Stroke Volume
    • Time Factors
    • Treatment Outcome
    • Ventricular Function, Left

    Associated data

    • ClinicalTrials.gov/NCT00251251

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    • Knowledge Base

    Methodology

    • What Is a Research Design | Types, Guide & Examples

    What Is a Research Design | Types, Guide & Examples

    Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

    A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

    • Your overall research objectives and approach
    • Whether you’ll rely on primary research or secondary research
    • Your sampling methods or criteria for selecting subjects
    • Your data collection methods
    • The procedures you’ll follow to collect data
    • Your data analysis methods

    A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

    Table of contents

    Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

    • Introduction

    Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

    There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

    The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

    Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

    Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

    It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

    Practical and ethical considerations when designing research

    As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

    • How much time do you have to collect data and write up the research?
    • Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
    • Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
    • Will you need ethical approval ?

    At each stage of the research design process, make sure that your choices are practically feasible.

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    Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

    Types of quantitative research designs

    Quantitative designs can be split into four main types.

    • Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
    • Descriptive and correlational designs allow you to measure variables and describe relationships between them.

    With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

    Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

    Types of qualitative research designs

    Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

    The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

    Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

    In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

    Defining the population

    A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

    For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

    The more precisely you define your population, the easier it will be to gather a representative sample.

    • Sampling methods

    Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

    To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

    Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

    For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

    Case selection in qualitative research

    In some types of qualitative designs, sampling may not be relevant.

    For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

    In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

    For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

    Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

    You can choose just one data collection method, or use several methods in the same study.

    Survey methods

    Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

    Observation methods

    Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

    Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

    Other methods of data collection

    There are many other ways you might collect data depending on your field and topic.

    If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

    Secondary data

    If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

    With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

    Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

    However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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    As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

    Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

    Operationalization

    Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

    If you’re using observations , which events or actions will you count?

    If you’re using surveys , which questions will you ask and what range of responses will be offered?

    You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

    Reliability and validity

    Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

    For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

    If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

    Sampling procedures

    As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

    That means making decisions about things like:

    • How many participants do you need for an adequate sample size?
    • What inclusion and exclusion criteria will you use to identify eligible participants?
    • How will you contact your sample—by mail, online, by phone, or in person?

    If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

    If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

    Data management

    It’s also important to create a data management plan for organizing and storing your data.

    Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

    Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

    On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

    Quantitative data analysis

    In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

    Using descriptive statistics , you can summarize your sample data in terms of:

    • The distribution of the data (e.g., the frequency of each score on a test)
    • The central tendency of the data (e.g., the mean to describe the average score)
    • The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

    The specific calculations you can do depend on the level of measurement of your variables.

    Using inferential statistics , you can:

    • Make estimates about the population based on your sample data.
    • Test hypotheses about a relationship between variables.

    Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

    Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

    Qualitative data analysis

    In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

    Two of the most common approaches to doing this are thematic analysis and discourse analysis .

    There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

    • Simple random sampling
    • Stratified sampling
    • Cluster sampling
    • Likert scales
    • Reproducibility

     Statistics

    • Null hypothesis
    • Statistical power
    • Probability distribution
    • Effect size
    • Poisson distribution

    Research bias

    • Optimism bias
    • Cognitive bias
    • Implicit bias
    • Hawthorne effect
    • Anchoring bias
    • Explicit bias

    A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

    A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

    Quantitative research designs can be divided into two main categories:

    • Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
    • Experimental and quasi-experimental designs are used to test causal relationships .

    Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

    The priorities of a research design can vary depending on the field, but you usually have to specify:

    • Your research questions and/or hypotheses
    • Your overall approach (e.g., qualitative or quantitative )
    • The type of design you’re using (e.g., a survey , experiment , or case study )
    • Your data collection methods (e.g., questionnaires , observations)
    • Your data collection procedures (e.g., operationalization , timing and data management)
    • Your data analysis methods (e.g., statistical tests  or thematic analysis )

    A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

    In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

    Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

    A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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    Qualitative study.

    Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

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    Last Update: September 18, 2022 .

    • Introduction

    Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

    Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

    However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

    Examples of Qualitative Research Approaches

    Ethnography

    Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

    Grounded Theory

    Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

    Phenomenology

    Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

    Narrative Research

    One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

    Research Paradigm

    Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

    Positivist vs Postpositivist

    To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

    Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

    Constructivist

    Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

    So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

    Data Sampling 

    The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

    • Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
    • Criterion sampling-selection based on pre-identified factors.
    • Convenience sampling- selection based on availability.
    • Snowball sampling- the selection is by referral from other participants or people who know potential participants.
    • Extreme case sampling- targeted selection of rare cases.
    • Typical case sampling-selection based on regular or average participants. 

    Data Collection and Analysis

    Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

    While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

    After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

    Dissemination

    To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

    Examples of Application

    Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

    A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

    A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

    In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

    The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

    The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

    The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

    The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

    If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

    The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

    Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.  Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

    • Issues of Concern

    As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

    • Internal validity--- Credibility
    • External validity---Transferability
    • Reliability---Dependability
    • Objectivity---Confirmability

    In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.  

    Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

    • Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
    • Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

    ‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

    • Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
    • Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

    One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

    • Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
    • Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
    • Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
    • Clinical Significance

    Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

    • Enhancing Healthcare Team Outcomes

    Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research.  Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc.  Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc. 

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    Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

    Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

    Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

    This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

    • Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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    UK study: Chemicals in plastics could be a risk factor for Alzheimer’s

    Anika Hartz, Ph.D., has a joint appointment in the colleges of Medicine and Pharmacy. She is also affiliated with the UK Sanders-Brown Center on Aging. Pete Comparoni | UK Photo

    LEXINGTON, Ky. (Feb. 28, 2024) — Researchers at the University of Kentucky are studying how elements of our natural surroundings can be potential risk factors for Alzheimer’s disease — including chemicals widely used in plastics.

    “Identifying environmental risk factors for Alzheimer’s is critical to mitigate cognitive decline in humans,” said Anika Hartz, Ph.D., a professor with a joint appointment in the Department of Pharmacology and Nutritional Sciences in the College of Medicine and the Department of Pharmaceutical Sciences in the College of Pharmacy . She is the study’s principal investigator.

    “Bisphenols can accelerate Alzheimer’s disease and lead to cognitive deficits. Simply: Be smart and stay smart by avoiding plastics.”

    Hartz is also affiliated with the UK Sanders-Brown Center on Aging (SBCoA) , one of the nation’s leading centers on aging, Alzheimer’s disease and related neurodegenerative disorders and one of 33 National Institute on Aging funded Alzheimer’s Disease Research Centers in the United States.

    Alzheimer’s is a progressive and irreversible neurological disorder. It’s estimated that 6.2 million Americans aged 65 and older are living with the disease that affects cognitive function, memory and behavior. 

    The study titled “Bisphenol-Induced Blood-Brain Barrier Dysfunction in Alzheimer’s Disease” is funded by a grant from the National Institute of Neurological Disorders and Stroke , part of the National Institutes of Health (NIH).

    Hartz and her colleagues are examining three types of bisphenols (BPA, BPF and BPS), which are chemical compounds used in the production of polycarbonate plastics and epoxy resins. These compounds are commonly found in food containers, water bottles and the lining of cans.

    “Human exposure to bisphenols is inevitable due to their widespread presence in the environment,” said Hartz. “Our data show that bisphenols trigger blood-brain barrier dysfunction and memory problems, both hallmarks of Alzheimer’s, indicating that environmental bisphenols are a critical yet underrecognized risk factor for the disease.”

    Hartz’s research team provided some of the first evidence that the chemicals are a clinically relevant environmental risk factor for Alzheimer’s. Bisphenols are a concern because previous research has shown they can affect the endocrine system, which regulates hormones. The brain plays a complex role in that system.

    UK researchers want to better understand how the chemicals’ disruption of the endocrine system impacts the blood-brain barrier function, potentially driving cognitive decline and accelerating Alzheimer’s disease.

    “The goal of this project is to develop fundamental knowledge of environmental impacts on human health that will help promote healthier lives and reduce the burden of diseases and conditions related to aging,” said Hartz. “New insights are expected from our study that will open the door for future evidence-based health management aimed at preserving cognition in health and disease.”

    The work builds on preliminary data funded by a pilot grant from the UK Center for Appalachian Research in Environmental Sciences (UK-CARES) .

    “Without the pilot funding, the strong support of the UK-CARES leadership Drs. Ellen Hahn and Erin Haynes, the entire UK-CARES team, my colleagues Drs. Bjoern Bauer, Kevin Pearson, Richard Kryscio, Bernhard Hennig, Peter Nelson, Scott Stanley and the continuous support from Dr. Linda Van Eldik and the team at the Sanders-Brown Center on Aging, this would have not been possible,” said Hartz.

    This study brings together a multidisciplinary group of researchers spanning the colleges of Medicine, Pharmacy, Public Health and Martin-Gatton College of Agriculture, Food and Environment . Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number R01NS133250 and the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number P30ES026529. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

    UK HealthCare is the hospitals and clinics of the University of Kentucky. But it is so much more. It is more than 10,000 dedicated health care professionals committed to providing advanced subspecialty care for the most critically injured and ill patients from the Commonwealth and beyond. It also is the home of the state’s only National Cancer Institute (NCI)-designated Comprehensive Cancer Center, a Level IV Neonatal Intensive Care Unit that cares for the tiniest and sickest newborns, the region’s only Level 1 trauma center and Kentucky’s top hospital ranked by U.S. News & World Report.

    As an academic research institution, we are continuously pursuing the next generation of cures, treatments, protocols and policies. Our discoveries have the potential to change what’s medically possible within our lifetimes. Our educators and thought leaders are transforming the health care landscape as our six health professions colleges teach the next generation of doctors, nurses, pharmacists and other health care professionals, spreading the highest standards of care. UK HealthCare is the power of advanced medicine committed to creating a healthier Kentucky, now and for generations to come. 

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    Study: Isometrics can help lower blood pressure than other forms of exercise

    Feb 29, 2024 by kristin emery 5 min read.

    three types of research studies

    The health benefits of exercise are well-known, especially when it comes to better cardiovascular health and lowering blood pressure. Now, new research contends doing something as simple as a few wall squats or planks per week can help lower blood pressure even better than other types of exercise.

    The study published recently in the British Journal of Sports Medicine suggests that isometric exercises are better at lowering blood pressure than other types of exercise. Isometrics are moves in which you tense muscles and hold them still for a period of time. Researchers in this study combined results of 270 trials from around the world between 1990 and 2023 and examined results from nearly 16,000 participants. The goal was to compare how much a person’s resting blood pressure dropped after following different exercise programs.

    Five different categories of exercise were studied, including aerobic exercise like walking and jogging, resistance training such as lifting weights, a combined program of weights and aerobics, high-intensity interval training (HIIT) including sprint intervals and isometric exercises, like wall squats and planks. The results showed all five regimens helped to lower blood pressure, but isometric exercise proved to be most beneficial. Wall squats (propping your back against a wall with your thighs parallel to the ground) were the most effective exercise out of all those studied.

    What is isometric exercise?

    Frank Velasquez Jr., Director of Sports Performance for the Allegheny Health Network (AHN), explains there are three types of muscle contractions: isometric, isotonic and isokinetic. “Isometric is where the muscle is contracting, but it’s not changing length,” says Velasquez. “If you’re sitting in a chair and straighten your right leg and just tighten up your quad muscle and hold it, that’s an isometric contraction. You’re contracting that muscle and it’s not increasing or decreasing in length.”

    Isometrics place a different stress on the body as compared to aerobic exercise. Increasing tension in a muscle and holding it causes a sudden rush of blood to the area when you relax, and this increased blood flow helps circulation and cardiovascular health.

    So, should you include isometrics in your exercise routine?

    “I’ll say what’s old is new again. Isometrics have been around a long time and used a ton for rehabilitation,” Velasquez said. “We will start you off with some isometric exercises and then build up from there. It’s kind of like one of the foundation blocks for strength training. I don’t think they’re wrong, but I don’t think it’s going to be a growing trend of how we train to improve our cardiovascular fitness.”

    He cautioned to start very slowly and with small time increments.

    “I will tell you this, if you’ve never done a wall squat … to do one for two minutes? You could probably struggle through it, but you may not be able to walk the next day. They’re saying to do it two minutes with a two-minute break and do that four times, three times a week. You really have to be elite to do something like that.”

    He agreed that including isometrics in an exercise program makes for a well-rounded regimen. To get started, Velasquez suggests two isometrics that work the front and then the back of your thighs. Start with 20- to- 30-second wall squats to build up leg strength and work the quadriceps on the front of your thighs. Then, do it seated on a chair with your left leg slightly bent, toes in the air and heel on the ground. Place your right hand behind your left hamstring on the back of your thigh and press your left heel into the ground. That creates an isometric hamstring exercise for the back of your leg.

    Velasquez also recommends high-intensity interval training, especially a method called Tabata, named after the Japanese exercise physiologist who created it. Tabata is clinically proven to improve anaerobic capacity and aerobic fitness by doing only a four-minute program.

    “You do 20 seconds of work, then 10 seconds of rest and you repeat that eight cycles,” Velasquez said. “Let’s say you were jumping rope. You jump rope for 20 seconds; you rest for 10 seconds. You do that eight times. It sounds easy, but it’s difficult.”

    The benefit is concentrating on cardiovascular fitness by focusing on intense but brief bursts of activity. An added bonus is that Tabata workouts don’t take much time and you can get the most bang for your buck in just a four-minute workout a few times per week.

    If you’re thinking of trying Tabata training or isometrics, just know that you should start slowly and not overdo it at the start.

    “If you’re going to go down the path of isometrics, just make sure you ease in,” Velasquez said. “Don’t read this and try to do eight minutes of wall squats or planks. You’re going to wake up regretting it. Ease in and make it part of a well-rounded program.”

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    IMAGES

    1. Three Types Of Research Methodology

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      three types of research studies

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      three types of research studies

    VIDEO

    1. What is research

    2. What is Research??

    3. Research methodology... Meaning of Research, Types of Research

    4. biology studies....different types... #gk

    5. Lecture 01: Basics of Research

    6. Research part 1/overview of research

    COMMENTS

    1. What types of studies are there?

      A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards. The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

    2. 6 Basic Types of Research Studies (Plus Pros and Cons)

      Here are six common types of research studies, along with examples that help explain the advantages and disadvantages of each: 1. Meta-analysis. A meta-analysis study helps researchers compile the quantitative data available from previous studies. It's an observational study in which the researchers don't manipulate variables.

    3. Types of Research

      Classification of Types of Research. There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It's important to note that a research project will not be limited to one type of research, but will likely use several.

    4. Research Methods--Quantitative, Qualitative, and More: Overview

      About Research Methods. This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. As Patten and Newhart note in the book Understanding Research Methods, "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge.

    5. Research Methods

      Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:

    6. 1.3: Types of Research Studies and How To Interpret Them

      Epidemiology is defined as the study of human populations. These studies often investigate the relationship between dietary consumption and disease development. There are three main types of epidemiological studies: cross-sectional, case-control, and prospective cohort studies. Figure 2.2: Types of epidemiology.

    7. 3.2 Psychologists Use Descriptive, Correlational, and Experimental

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

    8. Types of studies and research design

      Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...

    9. Research Study Types

      These are studies done in laboratories on cells, tissue, or animals. Strengths: Laboratories provide strictly controlled conditions and are often the genesis of scientific ideas that go on to have a broad impact on human health. They can help understand the mechanisms of disease. Weaknesses: Laboratory and animal studies are only a starting point.

    10. Types of Research Designs Compared

      Types of Research Designs Compared | Guide & Examples. Published on June 20, 2019 by Shona McCombes.Revised on June 22, 2023. When you start planning a research project, developing research questions and creating a research design, you will have to make various decisions about the type of research you want to do.. There are many ways to categorize different types of research.

    11. Types of Research within Qualitative and Quantitative

      What are the main types of qualitative approaches to research? While there are many different investigations that can be done, a study with a qualitative approach generally can be described with the characteristics of one of the following three types: Historical research describes past events, problems, issues and facts. Data are gathered from ...

    12. Primary Research

      There are three types of observational studies: cross-sectional studies, cohort studies, and case-control studies. If you decide to conduct observational research, you can choose the one that's best for you. All three are quite straightforward and easy to design—just beware of confounding variables and observer bias creeping into your analysis.

    13. Study designs: Part 1

      Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the ...

    14. How Do the Different Types of Research Studies Work?

      Making sense of this new information requires us to understand the different kinds of research. There are three main kinds of health research studies. These are preclinical, experimental, and epidemiologic research. Epidemiologic and preclinical studies often come first, followed by clinical trials involving human participants.

    15. 1.9: Types of Research Studies and How To Interpret Them

      Figure 2.3. The hierarchy of evidence shows types of research studies relative to their strength of evidence and relevance to real-life nutrition decisions, with the strongest studies at the top and the weakest at the bottom. The pyramid also represents a few other general ideas.

    16. Types of Research Studies and How To Interpret Them

      Figure 2.3. The hierarchy of evidence shows types of research studies relative to their strength of evidence and relevance to real-life nutrition decisions, with the strongest studies at the top and the weakest at the bottom. The pyramid also represents a few other general ideas.

    17. What are some different types of research studies?

      Generally, there are two major types of studies available on Research for Me @UNC: research studies and clinical trials. When a research study is about disease or human health, it is called a clinical research study. When a research study involves drugs or other therapies that aim to slow or stop a disease, then it is called a clinical trial.

    18. Overview of the Types of Research in Psychology

      An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants. 2. Descriptive Research . Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are: Case studies

    19. 3.3 Types of Research Studies

      There are various types of scientific studies on humans that can be used to provide supporting evidence for a particular hypothesis. These include epidemiological studies, interventional clinical trials, and randomized clinical trials. Valuable nutrition knowledge also is obtained from animal studies and cellular and molecular biology research.

    20. Types of Study in Medical Research

      This is done on the basis of a selective literature search concerning study types in medical research, in addition to the authors' own experience. Results. Three main areas of medical research can be distinguished by study type: basic (experimental), clinical, and epidemiological research. Furthermore, clinical and epidemiological studies can ...

    21. 3.3: Types of Research Studies and How To Interpret Them

      Figure 2.3. The hierarchy of evidence shows types of research studies relative to their strength of evidence and relevance to real-life nutrition decisions, with the strongest studies at the top and the weakest at the bottom. The pyramid also represents a few other general ideas.

    22. Research Methods In Psychology

      Olivia Guy-Evans, MSc. Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

    23. Types of Research Studies

      APA Style JARS Supplemental Glossary (2019) This glossary provides supplemental information on terms used in APA Style JARS and is meant to supplement Chapter 3 of the Publication Manual of the American Psychological Association, Seventh Edition. It is not an exhaustive list of all terms employed in quantitative, qualitative, or mixed methods research, nor does it include all possible ...

    24. Long-Term Outcomes of Resynchronization-Defibrillation for Heart

      We assessed long-term outcomes among patients at the eight highest-enrolling participating sites. The primary outcome was death from any cause; the secondary outcome was a composite of death from any cause, heart transplantation, or implantation of a ventricular assist device. Results: The trial enrolled 1798 patients, of whom 1050 were ...

    25. What Is a Research Design

      Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies. Other interesting articles.

    26. Qualitative Study

      Qualitative research is a type of research that explores and provides deeper insights into real-world problems.[1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants ...

    27. UK study: Chemicals in plastics could be a risk factor for Alzheimer's

      LEXINGTON, Ky. (Feb. 28, 2024) — Researchers at the University of Kentucky are studying how elements of our natural surroundings can be potential risk factors for Alzheimer's disease — including chemicals widely used in plastics. "Identifying environmental risk factors for Alzheimer's is critical to mitigate cognitive decline in ...

    28. Study: Isometrics can help lower blood pressure than other forms of

      The health benefits of exercise are well-known, especially when it comes to better cardiovascular health and lowering blood pressure. Now, new research contends doing something as simple as a few wall squats or planks per week can help lower blood pressure even better than other types of exercise. The study published recently in the British […]