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  • Indian J Psychol Med
  • v.43(2); 2021 Mar

A Student’s Guide to the Classification and Operationalization of Variables in the Conceptualization and Design of a Clinical Study: Part 1

Chittaranjan andrade.

1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.

Students without prior research experience may not know how to conceptualize and design a study. This article explains how an understanding of the classification and operationalization of variables is the key to the process. Variables describe aspects of the sample that is under study; they are so called because they vary in value from subject to subject in the sample. Variables may be independent or dependent. Independent variables influence the value of other variables; dependent variables are influenced in value by other variables. A hypothesis states an expected relationship between variables. A significant relationship between an independent and dependent variable does not prove cause and effect; the relationship may partly or wholly be explained by one or more confounding variables. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. These and other concepts are explained with the help of clinically relevant examples.

Key Message:

This article explains the following concepts: Independent variables, dependent variables, confounding variables, operationalization of variables, and construction of hypotheses.

In any body of research, the subject of study requires to be described and understood. For example, if we wish to study predictors of response to antidepressant drugs (ADs) in patients with major depressive disorder (MDD), we might select patient age, sex, age at onset of MDD, number of previous episodes of depression, duration of current depressive episode, presence of psychotic symptoms, past history of response to ADs, and other patient and illness characteristics as potential predictors. These characteristics or descriptors are called variables. Whether or not the patient responds to AD treatment is also a variable. A solid understanding of variables is the cornerstone in the conceptualization and preparation of a research protocol, and in the framing of study hypotheses. This subject is presented in two parts. This article, Part 1, explains what independent and dependent variables are, how an understanding of these is important in framing hypotheses, and what operationalization of a variable entails.

Variables are defined as characteristics of the sample that are examined, measured, described, and interpreted. Variables are so called because they vary in value from subject to subject in the study. As an example, if we wish to examine the relationship between age and height in a sample of children, age and height are the variables of interest; their values vary from child to child. In the earlier example, patients vary in age, sex, duration of current depressive episode, and response to ADs. Variables are classified as dependent and independent variables and are usually analyzed as categorical or continuous variables.

Independent and Dependent Variables

Independent variables are defined as those the values of which influence other variables. For example, age, sex, current smoking, LDL cholesterol level, and blood pressure are independent variables because their values (e.g., greater age, positive for current smoking, and higher LDL cholesterol level) influence the risk of myocardial infarction. Dependent variables are defined as those the values of which are influenced by other variables. For example, the risk of myocardial infarction is a dependent variable the value of which is influenced by variables such as age, sex, current smoking, LDL cholesterol level, and blood pressure. The risk is higher in older persons, in men, in current smokers, and so on.

There may be a cause–effect relationship between independent and dependent variables. For example, consider a clinical trial with treatment (iron supplement vs placebo) as the independent variable and hemoglobin level as the dependent variable. In children with anemia, an iron supplement will raise the hemoglobin level to a greater extent than will placebo; this is a cause–effect relationship because iron is necessary for the synthesis of hemoglobin. However, consider the variables teeth and weight . An alien from outer space who has no knowledge of human physiology may study human children below the age of 5 years and find that, as the number of teeth increases, weight increases. Should the alien conclude that there is a cause–effect relationship here, and that growing teeth causes weight gain? No, because a third variable, age, is a confounding variable 1 – 3 that is responsible for both increase in the number of teeth and increase in weight. In general, therefore, it is more proper to state that independent variables are associated with variations in the values of the dependent variables rather than state that independent variables cause variations in the values of the dependent variables. For causality to be asserted, other criteria must be fulfilled; this is out of the scope of the present article, and interested readers may refer to Schunemann et al. 4

As a side note, here, whether a particular variable is independent or dependent will depend on the question that is being asked. For example, in a study of factors influencing patient satisfaction with outpatient department (OPD) services, patient satisfaction is the dependent variable. But, in a study of factors influencing OPD attendance at a hospital, OPD attendance is the dependent variable, and patient satisfaction is merely one of many possible independent variables that can influence OPD attendance.

Importance of Variables in Stating the Research Objectives

Students must have a clear idea about what they want to study in order to conceptualize and frame a research protocol. The first matters that they need to address are “What are my research questions?” and “What are my hypotheses?” Both questions can be answered only after choosing the dependent variables and then the independent variables for study.

In the case of a student who is interested in studying predictors of AD outcomes in patients with MDD, treatment response is the dependent variable and patient and clinical characteristics are possible independent variables. So, the selection of dependent and independent variables helps defines the objectives of the study:

  • To determine whether sociodemographic variables, such as age and sex, predict the outcome of an episode of depression in MDD patients who are treated with an AD.
  • To determine whether clinical variables, such as age at onset of depression, number of previous depressive episodes, duration of current depressive episode, and the presence of soft neurological signs, predict the outcome of an episode of depression in MDD patients who are treated with an AD.

Note that in a formal research protocol, the student will need to state all the independent variables and not merely list examples. The student may also choose to include additional independent variables, such as baseline biochemical, psychophysiological, and neuroradiological measures.

Importance of Variables in Framing Hypotheses

A hypothesis is a clear statement of what the researcher expects to find in the study. As an example, a researcher may hypothesize that longer duration of current depression is associated with poorer response to ADs. In this hypothesis, the duration of the current episode of depression is the independent variable and treatment response is the dependent variable. It should be obvious, now, that a hypothesis can also be defined as the statement of an expected relationship between an independent and a dependent variable . Or, expressed visually, (independent variable) (arrow) (dependent variable) = hypothesis.

It would be a waste of time and energy to do a study to examine only one question: whether duration of current depression predicts treatment response. So, it is usual for research protocols to include many independent variables and many dependent variables in the generation of many hypotheses, as shown in Table 1 . Pairing each variable in the “independent variable” column with each variable in the “dependent variable” column would result in the generation of these hypotheses. Table 2 shows how this is done for age. Sets of hypotheses can likewise be constructed for the remaining independent and dependent variables in Table 1 . Importantly, the student must select one of these hypotheses as the primary hypothesis; the remaining hypotheses, no matter how many they are, would be secondary hypotheses. It is necessary to have only one hypothesis as the primary hypothesis in order to calculate the sample size necessary for an adequately powered study and to reduce the risk of false positive findings in the analysis. 5 In rare situations, two hypotheses may be considered equally important and may be stated as coprimary hypotheses.

Independent Variables and Dependent Variables in a Study on Sociodemographic and Clinical Prediction of Response of Major Depressive Disorder to Antidepressant Drug Treatment

Combinations of Age with Dependent Variables in the Generation of Hypotheses

Operationalization of Variables

In Table 1 , suicidality is listed as an independent variable and severity of depression, as a dependent variable. These variables need to be operationalized; that is, stated in a way that explains how they will be measured. Table 3 presents three ways in which suicidality can be measured and four ways in which (reduction in) the severity of depression can be measured. Now, each way of measurement in the “independent variable” column can be paired with a way of measurement in the “dependent variable” column, making a total of 12 possible hypotheses. In like manner, the many variables listed in Table 1 can each be operationalized in several different ways, resulting in the generation of a very large number of hypotheses. As already stated, the student must select only one hypothesis as the primary hypothesis.

Possible Ways of Operationalization of Suicidality and Depression

HAM-D: Hamilton Depression Rating Scale, MADRS: Montgomery–Asberg Depression Rating Scale.

Much thought should be given to the operationalization of variables because variables that are carelessly operationalized will be poorly measured; the data collected will then be of poor quality, and the study will yield unreliable results. For example, socioeconomic status may be operationalized as lower, middle, or upper class, depending on the patient’s monthly income, on the total monthly income of the family, or using a validated socioeconomic status assessment scale that takes into consideration income, education, occupation, and place of residence. The student must choose the method that would best suit the needs of the study, and the method that has the greatest scientific acceptability. However, it is also permissible to operationalize the same variable in many different ways and to include all these different operationalizations in the study, as shown in Table 3 . This is because conceptualizing variables in different ways can help understand the subject of the study in different ways.

Operationalization of variables requires a consideration of the reliability and validity of the method of operationalization; discussions on reliability and validity are out of the scope of this article. Operationalization of variables also requires specification of the scale of measurement: nominal, ordinal, interval, or ratio; this is also out of the scope of the present article. Finally, operationalization of variables can also specify details of the measurement procedure. As an example, in a study on the use of metformin to reduce olanzapine-associated weight gain, we may state that we will obtain the weight of the patient but fail to explain how we will do it. Better would be to state that the same weighing scale will be used. Still better would be to state that we will use a weighing instrument that works on the principle of moving weights on a levered arm, and that the same instrument will be used for all patients. And best would be to add that we will weigh patients, dressed in standard hospital gowns, after they have voided their bladder but before they have eaten breakfast. When the way in which a variable will be measured is defined, measurement of that variable becomes more objective and uniform

Concluding Notes

The next article, Part 2, will address what categorical and continuous variables are, why continuous variables should not be converted into categorical variables and when this rule can be broken, and what confounding variables are.

Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author received no financial support for the research, authorship, and/or publication of this article.

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4.2 The Variety of Theories in Psychology

Learning objectives.

  • Describe three dimensions along which theories in psychology vary.
  • Give examples of several different types of theories in psychology.

Researchers in psychology have found that many different types of theories can help them to organize phenomena, predict what will happen in new situations, and generate new research. It is important for beginning researchers to be aware of the different types so that they recognize theories when they see them in the research literature. (They are not always clearly labeled as “theories.”) It is also important for them to see that some types of theories are well within their ability to understand, use, and even construct. In this section, we look at the variety of psychological theories in terms of three important dimensions: formality, scope, and theoretical approach.

Psychological theories vary widely in their formality —the extent to which the components of the theory and the relationships among them are specified clearly and in detail. At the informal end of this dimension are theories that consist of simple verbal descriptions of a few important components and relationships. The habituation theory of expressive-writing effects on health is relatively informal in this sense. So is the drive theory of social facilitation and inhibition. At the more precise, formal end of this dimension are theories that are expressed in terms of mathematical equations or computer programs.

Formal Theories in Psychology

People who are not familiar with scientific psychology are sometimes surprised to learn that psychological theories can take the form of mathematical equations and computer programs. The following formal theories are among the best known and most successful in the field.

  • ACT-R. A comprehensive theory of human cognition that is akin to a programming language, within which more specific models can be created. See http://act-r.psy.cmu.edu .
  • Prospect theory. A formal theory of decision making under uncertainty. Psychologist Daniel Kahneman won the Nobel Prize in economics based in part on prospect theory. Read about Kahneman’s Nobel Prize work at http://www.nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman-autobio.html .
  • Rescorla-Wagner model. A theory of classical conditioning that features an equation describing how the strength of the association between unconditioned and conditioned stimuli changes when the two are paired. For more on this formal theory—including an interactive version—see http://psych.hanover.edu/javatest/rescrolawagner .

Both informal and formal theories have their place in psychological research. Informal theories tend to be easier to create and to understand but less precise in their predictions, which can make them more difficult to test. They are especially appropriate, however, in the early stages of research when the phenomena of interest have not yet been described in detail. Formal theories tend to be more difficult to create and to understand—sometimes requiring a certain amount of mathematical or computer programming background—but they also tend to be more precise in their predictions and therefore easier to test. They are especially appropriate in the later stages of research when the phenomena of interest have been described in detail

Theories in psychology also vary widely in their scope —the number and diversity of the phenomena they explain or interpret. Many early psychological theories were extremely broad in that they attempted to interpret essentially all human behavior. Freud and his followers, for example, applied his theory not only to understanding psychological disorders but also to slips of the tongue and other everyday errors, dreaming, sexuality, art, politics, and even civilization itself (Fine, 1979). Such theories have fallen out of favor in scientific psychology, however, because they tend to be imprecise and difficult to test. In addition, they have not been particularly successful at organizing or predicting the range and complexity of human behavior at the level of detail that scientific researchers usually seek.

Still, contemporary theories in psychology can vary in their scope. At the broad end of this dimension are theories that apply to many diverse phenomena. Cognitive dissonance theory, for example, assumes that when people hold inconsistent beliefs, this creates mental discomfort that they are motivated to reduce by changing one or both of the beliefs. This theory has been applied to a wide variety of phenomena, including the persistence of irrational beliefs and behaviors (e.g., smoking), the effectiveness of certain persuasion and sales techniques (e.g., asking for a small favor before asking for a big one), and even placebo effects. At the narrow end of this dimension are theories that apply to a small number of closely related phenomena. Consider, for example, a very specific quantitative ability called subitizing. This refers to people’s ability to quickly and accurately perceive the number of objects in a scene without counting them—as long as the number is four or fewer. Several theories have been proposed to explain subitizing. Among them is the idea that small numbers of objects are associated with easily recognizable patterns. For example, people know immediately that there are three objects in a scene because the three objects tend to form a “triangle” and it is this pattern that is quickly perceived (Logan & Sbrodoff, 2003).

As with informal and formal theories, both broad and narrow theories have their place in psychological research. Broad theories organize more phenomena but tend to be less formal and less precise in their predictions. Narrow theories organize fewer phenomena but tend to be more formal and more precise in their predictions.

Theoretical Approach

In addition to varying in formality and scope, theories in psychology vary widely in the kinds of theoretical ideas they are constructed from. We will refer to this as the theoretical approach .

Functional theories explain psychological phenomena in terms of their function or purpose. For example, one prominent theory of repeated self-injury (e.g., cutting) is that people do it because it produces a short-term reduction in the intensity of negative emotions that they are feeling (Tantam & Huband, 2009). Note that this theory does not focus on how this happens, but on the function of self-injury for the people who engage in it. Theories from the perspective of evolutionary psychology also tend to be functional—assuming that human behavior has evolved to solve specific adaptive problems faced by our distant ancestors. Consider the phenomenon of sex differences in human mating strategies (Buss & Schmitt, 1993). Men are somewhat more likely than women to seek short-term partners and to value physical attractiveness over material resources in a mate. Women are somewhat more likely than men to seek long-term partners and to value material resources over physical attractiveness in a mate. But why? The standard evolutionary theory holds that because the male investment in becoming a parent is relatively small, men reproduce more successfully by seeking several short-term partners who are young and healthy (which is signaled by physical attractiveness). But because the female investment in becoming a parent is quite large, women reproduce more successfully by seeking a long-term partner who has resources to contribute to raising the child.

Mechanistic theories , on the other hand, focus on specific variables, structures, and processes, and how they interact to produce the phenomena. The drive theory of social facilitation and inhibition and the multistore model of human memory are mechanistic theories in this sense. Figure 4.4 “Simplified Representation of One Contemporary Theory of Hypochondriasis” represents another example—a contemporary cognitive theory of hypochondriasis—an extreme form of health anxiety in which people misinterpret ordinary bodily symptoms (e.g., headaches) as signs of a serious illness (e.g., a brain tumor; Williams, 2004). This theory specifies several key variables and the relationships among them. Specifically, people who are high in the personality trait of neuroticism (also called negative emotionality) start to pay excessive attention to negative health information—especially if they have had a significant illness experience as a child (e.g., a seriously ill parent). This attention to negative health information then leads to health anxiety and hypochondriasis, especially among people who are low in effortful control, which is the ability to shift attention away from negative thoughts and feelings.

Figure 4.4 Simplified Representation of One Contemporary Theory of Hypochondriasis

Simplified Representation of One Contemporary Theory of Hypochondriasis

This theory focuses on key variables and the relationships among them.

Mechanistic theories can also be expressed in terms of biological structures and processes. With advances in genetics and neuroscience, such theories are becoming increasingly common in psychology. For example, researchers are currently constructing and testing theories that specify the brain structures associated with the storage and rehearsal of information in the short-term store, the transfer of information to the long-term store, and so on. Theories of psychological disorders are also increasingly likely to focus on biological mechanisms. Schizophrenia, for example, has been explained in terms of several biological theories, including theories that focus on genetics, neurotransmitters, brain structures, and even prenatal exposure to infections.

Finally, there are also theoretical approaches that provide organization without necessarily providing a functional or mechanistic explanation. These include stage theories , which specify a series of stages that people pass through as they develop or adapt to their environment. Famous stage theories include Abraham Maslow’s hierarchy of needs and Jean Piaget’s theory of cognitive development. Typologies provide organization by categorizing people or behavior into distinct types. These include theories that identify several basic emotions (e.g., happiness, sadness, fear, surprise, anger, and disgust), several distinct types of intelligence (e.g., spatial, linguistic, mathematical, kinesthetic, musical, interpersonal, and intrapersonal), and distinct types of personalities (e.g., Type A vs. Type B).

Researchers in psychology have found that there is a place for all these theoretical approaches. In fact, multiple approaches are probably necessary to provide a complete understanding of any set of phenomena. A complete understanding of emotions, for example, is likely to require identifying the basic emotions that people experience, explaining why we have those emotions, and describing how those emotions work in terms of underlying psychological and biological variables, structures, and processes.

Key Takeaway

  • Theories in psychology vary widely in terms of their formality, scope, and theoretical approach. The different types of theories all play important roles in psychological research.
  • Practice: Find an empirical research report in a professional journal, identify a theory that the researchers present, and then describe the theory in terms of its formality (informal vs. formal), scope (broad vs. narrow), and theoretical approach (functional, mechanistic, etc.).
  • Discussion: Do you think there will ever be a single theory that explains all psychological disorders? Why or why not?

Buss, D. M., & Schmitt, D. P. (1993). Sexual strategies theory: A contextual evolutionary analysis of human mating. Psychological Review, 100 , 204–232.

Fine, R. (1979). A history of psychoanalysis . New York, NY: Columbia University Press.

Logan, G. D., & Sbrodoff, N. J. (2003). Subitizing and similarity: Toward a pattern-matching theory of enumeration. Psychonomic Bulletin & Review, 10 , 676–682.

Tantam, D., & Huband, N. (2009). Understanding repeated self-injury: A multidisciplinary approach . New York, NY: Palgrave Macmillan.

Williams, P. G. (2004). The psychopathology of self-assessed health: A cognitive approach to health anxiety and hypochondriasis. Cognitive Therapy and Research, 28 , 629–644.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Variables In Psychological Research

March 7, 2021 - paper 2 psychology in context | research methods.

  • Back to Paper 2 - Research Methods

Variables: Independent And Dependent Variable

There are  two main  variables when it comes to psychological research, these are;

(1)  The Independent Variable (IV)    the variable that is manipulated/changed

(2)  The Dependent Variable (DV)  the variable that is measured (e.g. it measures whether or not the IV has influence human behaviour).

When carrying out a piece of research, a psychologists main concern is looking at the effects of  just  the IV on the DV, in order to do this, all other extraneous variables (EVs) need to be controlled.

Between the control condition and the experimental condition the only thing that should change is the IV   for example,  when looking at the effects of music on memory, in the control condition the participants should complete a memory test with  no music playing,  in the experimental condition, the participants should complete a memory test   with music playing.  The only thing that should change across these conditions is whether the participants complete the memory test with or without music. All other variables the memory test difficulty, age of participant, gender of participant, background noise, temperature of the room etc should remain consistent.

If a researcher controls for extraneous variables and the only variable to change across the control and experimental condition is the IV it can be seen that the research has been carried out successfully. This means that the researcher has observed the effects of  just  the  IV on the DV,  which also means that the researcher can establish a  cause and effect relationship ( they can be confident that the IV has been the only variable to effect the DV)  and therefore can say that their experiment has  high internal validity .  High internal validity is when the researcher is confident that they have measured what they intended to measure (i.e. the effects of just the IV on the DV) and that all extraneous variables (EVs) have been controlled and that there are no confounding variables (CVs) in their study.

Extraneous Variables

Extraneous Variables (EVs):  These are variables that researchers do not want in their research. It is important that before a researcher conducts a study they carry out a  pilot study  to ensure that there are no EVs that could ruin their results. There are four main extraneous variables that you need to know in your exam. It is important that you are able to describe what is meant by these four EVs and that you are able to give examples of each of the four EVs.

The four extraneous variables are:

(1) Participant Variables:   This refers to anything specific to the participant that could affect the results of the research,   for example,  a participant’s age, gender, intelligence, personality etc

(2) Demand Characteristics:  This refers to environmental clues and cues in an investigation that cause participants to behave unnaturally. Participants respond in one of the following ways:

a. Attempt to please the experimenter                                                                       b. Attempt to ruin the results (‘screw you’ effect)                                                    c. Become more self-conscious

(3) Situational Variables:   Refers to the experimental setting and surrounding environment must be controlled between conditions to avoid them impacting on the results,  for example,  the temperature of the room in which the experiment is taking place, the time of day, the weather etc

(4) Experimenter Effects:  This refers to anything specific to the experimenter that could affect the results of the research,  for example,  the gender of the experimenter (e.g. if an experiment was taking place investigating the social life of university students a 50+ researcher may not be the best person to obtain this information from the participants as the participants may feel this person would judge their behaviours this could lead to the participants not being honest). The mood and personality of the researcher could also be experimenter effects that could impact on the results of the study. 

It is important that the researcher plans their research carefully in order to remove any potential extraneous variables (EV). If an EV isn’t controlled and interferes with a study this would prevent the researcher from establishing a  cause and effect relationship  and would lead the study to having  low internal validity   the researcher will not be able to conclude that the IV is the only variable to effect the DV as an EV has been present in the study.

When a study is carried out with an extraneous variable (EV) present, this EV becomes a  confounding variable (CV)  due to the fact that it’s presence confounds the results of the study.

Operationalising Independent Variables (IVs) and Dependent Variables (DVs):

In experiments, the researcher manipulates the IV to find the effect it has on the DV. To preserve the internal validity of an experiment, the IV and DV must be operationalised.

Operationalising The IV And DV

Operationalisation  means defining the variables (both the independent variable (IV) and the dependent variable (DV)) in such a way that they can be precisely tested and measured. More simply, operationalising variables means  stating  how the IV and the DV  have been measured.  The process of operationalising variables allows other researchers to  replicate  previous research studies precisely.

For example,  if a researcher was looking at the effects of hunger on memory, they would have to consider how they are going to measure the IV ‘hunger’ and how they are going to measure the DV ‘memory.’

There are a number of ways in which  hunger can be operationaised/measured:

(1) a questionnaire assessing hunger, the higher the score on the questionnaire could indicate a high level of hunger                                                                                                                    

(2) the amount of ghrelin present in the participant’s stomach a high amount of ghrelin indicates that the participant is hungry                                                                                                    

(3) the amount of time passed since the person last ate (assuming that the more time that has passed since a persons last meal the more hungry they are).

There are a number of ways in which  memory  can be operationaised/measured  the most popular method would be giving the participants a memory tests and observing the score that the participants obtain on this test.

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Let's take memory abilities and shoe size; these seem like completely random concepts. But what if they're not? For researchers to establish if there is a link between the two, they need to conduct empirical, valid and reliable scientific research. When researchers conduct psychological research, they are usually trying to find out the relationship between two variables . There are different types of variables in psychology research; let's explore these. 

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Let's take memory abilities and shoe size; these seem like completely random concepts. But what if they're not? For researchers to establish if there is a link between the two, they need to conduct empirical, valid and reliable scientific research. When researchers conduct psychological research, they are usually trying to find out the relationship between two variables . There are different types of variables in psychology research; let's explore these.

  • We will start by looking at the types of variables in psychology research. We will explore some psychological variables examples to ensure you can identify the differences between the types of variables .
  • We will look at the types of extraneous variables in psychology, including the topics of situational variables: psychology and participant variables: psychology.
  • To finish off, we will discuss confounding variables: psychology.

Types of Variables in Psychology Research

In experimental scientific research, the researcher's goal is to provide evidence supporting a phenomenon regarding whether it exists or doesn't exist.

Psychology research has three variables: independent (IV) , dependent (DV) , and extraneous variables . Let us take a look at the definition of each variable.

The independent variable is the variable that is manipulated to test its effect on the dependent variable.

The IV is what the researcher proposes as the cause of a phenomenon. Thus, by manipulating changes in the IV, the researcher can establish if the changes affect the DV.

The researcher doesn't always have to manipulate the IV. Sometimes it can be naturally occurring. A researcher can't manipulate the sun, but it can measure how exposed the subject is to sunshine.

Types of variable, bored child whilst doing homework, StudySmarter

Now let's move on to understand what the DV is.

The dependent variable is the outcome that is measured.

The DV is what the researcher expects to be the outcome/ changes that occur due to manipulating the IV; therefore, it is considered the effect.

In experimental research, cause-and-effect can be determined as the researcher measures the IV and DV whilst controlling external influences.

Psychological Variables Examples

To help us understand the concept of IVs and DVs, let us take a look at some examples.

Remember, the independent variable causes an effect on the dependent variable.

Each example describes a hypothetical hypothesis and then identifies the IV and DV of the study.

The amount of sleep students get will affect their exam scores.

Independent variable: the amount of sleep

Dependent variable: exam scores

In this research design, the researchers may compare sleep-deprived versus non-sleep-deprived students' test scores.

Playing piano music will help babies fall asleep faster.

Independent variable: piano music

Dependent variable: how fast babies fell asleep

You can probably see the pattern, but in this study, the researchers may explore how long it took for babies to fall asleep who were listening to piano music versus those who were not.

Extraneous Variables in Research

Extraneous variables are variables that could unknowingly affect the dependent variable. In an experiment, the independent variable should be the only thing that affects the dependent variable. However, sometimes there may be other 'nuisance' variables that actually affect the dependent variable.

A theoretical experiment investigated the effect of the IV; whether children received Kumon lessons or not on the DV; end-of-year-grades.' If the study found that children who received Kumon lessons received better grades than those who did not, the researcher can infer that Kumon may boost academic performance.

However, there may be other variables we didn't consider that affected the dependent variable.

Perhaps whether children attended a private or public school influenced their test scores (DV), their parent's educational background, the quality of teaching received in school, whether the children had test anxiety or just their general health on the day.

These other variables that could affect the dependent variable are called extraneous variables .

Types of Extraneous Variables in Psychology

There are two basic types of extraneous variables: participant and situational variables .

Let's look at both in more detail.

Situational Variables: Psychology

Situational variables are factors in the environment that could affect participants' performance. Some examples of these are the temperature of the room and the noise in the room.

In our example experiment, music would be a situational variable and academic performance a participant variable.

An example of how researchers can control situational variables is:

  • Standardisation is when the experiment procedure is the same for each participant, So all participants experience the same things when they undergo the study.

When conducting a study, the researcher should give the same instructions to each participant; this usually involves the researcher reading from a script.

And the participants should be tested in the same conditions, e.g., same time of day, temperature, same noise levels and in the same venue.

The presence of situational variables reduces the validity of the experiment.

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Participant Variables: Psychology

Participant variables are individual traits of a participant that could affect their performance in the experiment. Some examples of these are gender, age, intelligence and socioeconomic status.

There are a few different ways to reduce extraneous variables, these are:

Random allocation : this is exactly what the name implies. Participants are randomly allocated to a group instead of the experimenter assigning participants to groups.

Single or double-blind technique : a single-blind technique is when participants don't know what the experimental conditions are; this way, they won't behave in a way they think the experimenters would want and bias the results.

A single-blind technique is when the participant doesn't know if they are in the control or experimental group.

A double-blind technique is when participants and experimenters interacting with the participants both don't know who is in the experimental versus the control group (group not exposed to IV). So, researchers won't act in a manner that biases the results.

Suppose a researcher would like to find out the effects of meditation on anxiety levels. One group is given a particular meditation recording to play and follow along each day. In contrast, the other group are taught some breathing exercises to do whenever they're feeling anxious.

If the group that's given the meditation recording knows at the end of the experiment their group is supposed to feel more at peace with low anxiousness compared to the other group, this can be an issue. When reporting their feelings, they may deliberately say they're feeling great because they think that's what the researchers want.

When participants guess the hypothesis and act accordingly, this issue is known as the Hawthorne effect, which reduces the research's validity.

When looking at studies, there will usually always be some extraneous variable you can find. Recognising these extraneous variables will give you an edge in critically analysing studies, leading to extra marks in evaluation.

Confounding Variables in Psychology Research

A confounding variable is an extraneous variable related to the independent and dependent variables. I.e., a confounding variable is correlated with the independent variable and has a direct effect on the dependent variable.

A high standard of research is ensured by taking into account confounding variables. In this way, researchers can be reasonably confident that the independent variable is the only thing affecting the dependent variable.

Some ways to reduce confounding variables are:

  • Restriction: only recruiting participants with similar characteristics or qualities, such as only using females age 30 for a study.
  • Matching participants: in this method, one participant is matched with another, and these pairs are split into two groups. And only one of the groups experiences the IV, and the other doesn't.
  • Randomisation: in this method, the researcher randomly allocates participants to each group. In this way, any confounding factors should be evenly distributed between the groups, thus not affecting the independent and dependent variables.

Randomisation is the ideal sampling method used in psychology as it involves the least research involvement.

Usually, this technique uses number generators to assign participants to groups so that the researcher's subjective opinion doesn't influence the research.

Types of Variable - Key takeaways

When researchers conduct psychological research, they are usually trying to find out the relationship between two variables.

The independent variable is the variable manipulated to test its effect on the dependent variable. And t he dependent variable is the outcome that is measured.

Extraneous variables are variables that could unknowingly affect the dependent variable. And these reduce the validity of the findings.

There are two types of extraneous variables: participant variables and situational variables.

Some ways to reduce extraneous variables are standardisation, random allocation, and single or blind technique.

Frequently Asked Questions about Types of Variable

--> what are the three types of variables.

In psychological research, the three types of variables are independent, dependent and extraneous variables.

--> What are variables in psychological research?

Variables are what are studied in research to try and establish a cause-and-effect relationship. Researchers try to find out if an independent variable affects a dependent variable. 

--> What are situational variables in psychology?

Situational variables are things in the environment that could affect participants’ performance. Some examples of these are the temperature of the room and the noise in the room. 

--> What is the dependent variable in psychology? 

The dependent variable is the outcome that is measured in the study.

--> What is the independent variable in psychology?

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Is this a participant or situation variable: academic performance?

Which of the following is thought of as the cause of a phenomenon? 

Which of the following is thought of as the effect of a phenomenon? 

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When researchers conduct psychological research, what are they trying to find out?

The cause-and-effect relationship between two variables. 

What is an independent variable?

The variable manipulated to test its effect on the dependent variable.

What is a dependent variable?

The outcome measured.

In an experiment investigating  the amount of sleep students get and exam scores,  what is the independent variable?

Amount of sleep

What is the dependent variable in an experiment investigating  if piano music can help babies fall asleep faster?

How fast babies fell asleep.

What are extraneous variables?

Variables that could unknowingly affect the dependent variable.

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14 Key Terms for Psychological Research

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Methodology

  • Types of Variables in Research & Statistics | Examples

Types of Variables in Research & Statistics | Examples

Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.

In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

  • What type of data does the variable contain?
  • What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

  • Quantitative data represents amounts
  • Categorical data represents groupings

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables .

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .

Example data sheet showing types of variables in a plant salt tolerance experiment

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key variables in psychology research

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

Example of a data sheet showing dependent and independent variables for a plant salt tolerance experiment.

What about correlational research?

When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.

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

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic

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You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

  • The  independent variable  is the amount of nutrients added to the crop field.
  • The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

  • Discrete variables represent counts (e.g. the number of objects in a collection).
  • Continuous variables represent measurable amounts (e.g. water volume or weight).

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Research Hypothesis In Psychology: Types, & Examples

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.

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

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

  • A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
  • It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
  • A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
  • Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
  • For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
  • Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.
Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

  • Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

hypothesis

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.
  • Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
  • However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

  • Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
  • Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
  • Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
  • Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
  • Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

  • The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
  • The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

  • Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
  • Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
  • Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
  • Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
  • Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
  • Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
  • Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
  • Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Multiple Independent Variables

Learning objectives.

  • Explain why researchers often include multiple independent variables in their studies.
  • Define factorial design, and use a factorial design table to represent and interpret simple factorial designs.
  • Distinguish between main effects and interactions, and recognize and give examples of each.
  • Sketch and interpret bar graphs and line graphs showing the results of studies with simple factorial designs.

Just as it is common for studies in psychology to include multiple dependent variables, it is also common for them to include multiple independent variables. Schnall and her colleagues studied the effect of both disgust and private body consciousness in the same study. Researchers’ inclusion of multiple independent variables in one experiment is further illustrated by the following actual titles from various professional journals:

  • The Effects of Temporal Delay and Orientation on Haptic Object Recognition
  • Opening Closed Minds: The Combined Effects of Intergroup Contact and Need for Closure on Prejudice
  • Effects of Expectancies and Coping on Pain-Induced Intentions to Smoke
  • The Effect of Age and Divided Attention on Spontaneous Recognition
  • The Effects of Reduced Food Size and Package Size on the Consumption Behaviour of Restrained and Unrestrained Eaters

Just as including multiple dependent variables in the same experiment allows one to answer more research questions, so too does including multiple independent variables in the same experiment. For example, instead of conducting one study on the effect of disgust on moral judgment and another on the effect of private body consciousness on moral judgment, Schnall and colleagues were able to conduct one study that addressed both questions. But including multiple independent variables also allows the researcher to answer questions about whether the effect of one independent variable depends on the level of another. This is referred to as an interaction between the independent variables. Schnall and her colleagues, for example, observed an interaction between disgust and private body consciousness because the effect of disgust depended on whether participants were high or low in private body consciousness. As we will see, interactions are often among the most interesting results in psychological research.

Factorial Designs

By far the most common approach to including multiple independent variables in an experiment is the factorial design. In a  factorial design , each level of one independent variable (which can also be called a  factor ) is combined with each level of the others to produce all possible combinations. Each combination, then, becomes a condition in the experiment. Imagine, for example, an experiment on the effect of cell phone use (yes vs. no) and time of day (day vs. night) on driving ability. This is shown in the  factorial design table  in Figure 8.1. The columns of the table represent cell phone use, and the rows represent time of day. The four cells of the table represent the four possible combinations or conditions: using a cell phone during the day, not using a cell phone during the day, using a cell phone at night, and not using a cell phone at night. This particular design is referred to as a 2 × 2 (read “two-by-two”) factorial design because it combines two variables, each of which has two levels. If one of the independent variables had a third level (e.g., using a handheld cell phone, using a hands-free cell phone, and not using a cell phone), then it would be a 3 × 2 factorial design, and there would be six distinct conditions. Notice that the number of possible conditions is the product of the numbers of levels. A 2 × 2 factorial design has four conditions, a 3 × 2 factorial design has six conditions, a 4 × 5 factorial design would have 20 conditions, and so on.

Figure 8.1 Factorial Design Table Representing a 2 × 2 Factorial Design

In principle, factorial designs can include any number of independent variables with any number of levels. For example, an experiment could include the type of psychotherapy (cognitive vs. behavioural), the length of the psychotherapy (2 weeks vs. 2 months), and the sex of the psychotherapist (female vs. male). This would be a 2 × 2 × 2 factorial design and would have eight conditions. Figure 8.2 shows one way to represent this design. In practice, it is unusual for there to be more than three independent variables with more than two or three levels each. This is for at least two reasons: For one, the number of conditions can quickly become unmanageable. For example, adding a fourth independent variable with three levels (e.g., therapist experience: low vs. medium vs. high) to the current example would make it a 2 × 2 × 2 × 3 factorial design with 24 distinct conditions. Second, the number of participants required to populate all of these conditions (while maintaining a reasonable ability to detect a real underlying effect) can render the design unfeasible (for more information, see the discussion about the importance of adequate statistical power in Chapter 13). As a result, in the remainder of this section we will focus on designs with two independent variables. The general principles discussed here extend in a straightforward way to more complex factorial designs.

Figure 8.2 Factorial Design Table Representing a 2 × 2 × 2 Factorial Design

Assigning Participants to Conditions

Recall that in a simple between-subjects design, each participant is tested in only one condition. In a simple within-subjects design, each participant is tested in all conditions. In a factorial experiment, the decision to take the between-subjects or within-subjects approach must be made separately for each independent variable. In a  between-subjects factorial design , all of the independent variables are manipulated between subjects. For example, all participants could be tested either while using a cell phone  or  while not using a cell phone and either during the day  or  during the night. This would mean that each participant was tested in one and only one condition. In a within-subjects factorial design, all of the independent variables are manipulated within subjects. All participants could be tested both while using a cell phone and  while not using a cell phone and both during the day  and  during the night. This would mean that each participant was tested in all conditions. The advantages and disadvantages of these two approaches are the same as those discussed in Chapter 6. The between-subjects design is conceptually simpler, avoids carryover effects, and minimizes the time and effort of each participant. The within-subjects design is more efficient for the researcher and controls extraneous participant variables.

It is also possible to manipulate one independent variable between subjects and another within subjects. This is called a  mixed factorial design . For example, a researcher might choose to treat cell phone use as a within-subjects factor by testing the same participants both while using a cell phone and while not using a cell phone (while counterbalancing the order of these two conditions). But he or she might choose to treat time of day as a between-subjects factor by testing each participant either during the day or during the night (perhaps because this only requires them to come in for testing once). Thus each participant in this mixed design would be tested in two of the four conditions.

Regardless of whether the design is between subjects, within subjects, or mixed, the actual assignment of participants to conditions or orders of conditions is typically done randomly.

Nonmanipulated Independent Variables

In many factorial designs, one of the independent variables is a nonmanipulated independent variable . The researcher measures it but does not manipulate it. The study by Schnall and colleagues is a good example. One independent variable was disgust, which the researchers manipulated by testing participants in a clean room or a messy room. The other was private body consciousness, a participant variable which the researchers simply measured. Another example is a study by Halle Brown and colleagues in which participants were exposed to several words that they were later asked to recall (Brown, Kosslyn, Delamater, Fama, & Barsky, 1999) [1] . The manipulated independent variable was the type of word. Some were negative health-related words (e.g.,  tumor, coronary ), and others were not health related (e.g.,  election, geometry ). The nonmanipulated independent variable was whether participants were high or low in hypochondriasis (excessive concern with ordinary bodily symptoms). The result of this study was that the participants high in hypochondriasis were better than those low in hypochondriasis at recalling the health-related words, but they were no better at recalling the non-health-related words.

Such studies are extremely common, and there are several points worth making about them. First, nonmanipulated independent variables are usually participant variables (private body consciousness, hypochondriasis, self-esteem, and so on), and as such they are by definition between-subjects factors. For example, people are either low in hypochondriasis or high in hypochondriasis; they cannot be tested in both of these conditions. Second, such studies are generally considered to be experiments as long as at least one independent variable is manipulated, regardless of how many nonmanipulated independent variables are included. Third, it is important to remember that causal conclusions can only be drawn about the manipulated independent variable. For example, Schnall and her colleagues were justified in concluding that disgust affected the harshness of their participants’ moral judgments because they manipulated that variable and randomly assigned participants to the clean or messy room. But they would not have been justified in concluding that participants’ private body consciousness affected the harshness of their participants’ moral judgments because they did not manipulate that variable. It could be, for example, that having a strict moral code and a heightened awareness of one’s body are both caused by some third variable (e.g., neuroticism). Thus it is important to be aware of which variables in a study are manipulated and which are not.

Graphing the Results of Factorial Experiments

The results of factorial experiments with two independent variables can be graphed by representing one independent variable on the  x -axis and representing the other by using different kinds of bars or lines. (The  y -axis is always reserved for the dependent variable.) Figure 8.3 shows results for two hypothetical factorial experiments. The top panel shows the results of a 2 × 2 design. Time of day (day vs. night) is represented by different locations on the  x -axis, and cell phone use (no vs. yes) is represented by different-coloured bars. (It would also be possible to represent cell phone use on the  x -axis and time of day as different-coloured bars. The choice comes down to which way seems to communicate the results most clearly.) The bottom panel of Figure 8.3 shows the results of a 4 × 2 design in which one of the variables is quantitative. This variable, psychotherapy length, is represented along the  x -axis, and the other variable (psychotherapy type) is represented by differently formatted lines. This is a line graph rather than a bar graph because the variable on the x-axis is quantitative with a small number of distinct levels. Line graphs are also appropriate when representing measurements made over a time interval (also referred to as time series information) on the x -axis.

Figure 8.3 Two Ways to Plot the Results of a Factorial Experiment With Two Independent Variables

Main Effects and Interactions

In factorial designs, there are two kinds of results that are of interest: main effects and interaction effects (which are also just called “interactions”). A main effect  is the statistical relationship between one independent variable and a dependent variable—averaging across the levels of the other independent variable. Thus there is one main effect to consider for each independent variable in the study. The top panel of Figure 8.3 shows a main effect of cell phone use because driving performance was better, on average, when participants were not using cell phones than when they were. The blue bars are, on average, higher than the red bars. It also shows a main effect of time of day because driving performance was better during the day than during the night—both when participants were using cell phones and when they were not. Main effects are independent of each other in the sense that whether or not there is a main effect of one independent variable says nothing about whether or not there is a main effect of the other. The bottom panel of Figure 8.3 , for example, shows a clear main effect of psychotherapy length. The longer the psychotherapy, the better it worked.

There is an  interaction  effect (or just “interaction”) when the effect of one independent variable depends on the level of another. Although this might seem complicated, you already have an intuitive understanding of interactions. It probably would not surprise you, for example, to hear that the effect of receiving psychotherapy is stronger among people who are highly motivated to change than among people who are not motivated to change. This is an interaction because the effect of one independent variable (whether or not one receives psychotherapy) depends on the level of another (motivation to change). Schnall and her colleagues also demonstrated an interaction because the effect of whether the room was clean or messy on participants’ moral judgments depended on whether the participants were low or high in private body consciousness. If they were high in private body consciousness, then those in the messy room made harsher judgments. If they were low in private body consciousness, then whether the room was clean or messy did not matter.

The effect of one independent variable can depend on the level of the other in several different ways. This is shown in Figure 8.4 . In the top panel, independent variable “B” has an effect at level 1 of independent variable “A” but no effect at level 2 of independent variable “A.” (This is much like the study of Schnall and her colleagues where there was an effect of disgust for those high in private body consciousness but not for those low in private body consciousness.) In the middle panel, independent variable “B” has a stronger effect at level 1 of independent variable “A” than at level 2. This is like the hypothetical driving example where there was a stronger effect of using a cell phone at night than during the day. In the bottom panel, independent variable “B” again has an effect at both levels of independent variable “A,” but the effects are in opposite directions. Figure 8.4 shows the strongest form of this kind of interaction, called a crossover interaction. One example of a crossover interaction comes from a study by Kathy Gilliland on the effect of caffeine on the verbal test scores of introverts and extraverts (Gilliland, 1980) [2] . Introverts perform better than extraverts when they have not ingested any caffeine. But extraverts perform better than introverts when they have ingested 4 mg of caffeine per kilogram of body weight.

Figure 8.4 Bar Graphs Showing Three Types of Interactions. In the top panel, one independent variable has an effect at one level of the second independent variable but not at the other. In the middle panel, one independent variable has a stronger effect at one level of the second independent variable than at the other. In the bottom panel, one independent variable has the opposite effect at one level of the second independent variable than at the other.

Figure 8.5 shows examples of these same kinds of interactions when one of the independent variables is quantitative and the results are plotted in a line graph. Note that in a crossover interaction, the two lines literally “cross over” each other.

In the top panel, one independent variable has an effect at one level of the second independent variable but not at the other. In the middle panel, one independent variable has a stronger effect at one level of the second independent variable than at the other. In the bottom panel, one independent variable has the opposite effect at one level of the second independent variable than at the other.

In many studies, the primary research question is about an interaction. The study by Brown and her colleagues was inspired by the idea that people with hypochondriasis are especially attentive to any negative health-related information. This led to the hypothesis that people high in hypochondriasis would recall negative health-related words more accurately than people low in hypochondriasis but recall non-health-related words about the same as people low in hypochondriasis. And of course this is exactly what happened in this study.

Key Takeaways

  • Researchers often include multiple independent variables in their experiments. The most common approach is the factorial design, in which each level of one independent variable is combined with each level of the others to create all possible conditions.
  • In a factorial design, the main effect of an independent variable is its overall effect averaged across all other independent variables. There is one main effect for each independent variable.
  • There is an interaction between two independent variables when the effect of one depends on the level of the other. Some of the most interesting research questions and results in psychology are specifically about interactions.
  • Practice: Return to the five article titles presented at the beginning of this section. For each one, identify the independent variables and the dependent variable.
  • Practice: Create a factorial design table for an experiment on the effects of room temperature and noise level on performance on the MCAT. Be sure to indicate whether each independent variable will be manipulated between-subjects or within-subjects and explain why.
  • No main effect of A; no main effect of B; no interaction
  • Main effect of A; no main effect of B; no interaction
  • No main effect of A; main effect of B; no interaction
  • Main effect of A; main effect of B; no interaction
  • Main effect of A; main effect of B; interaction
  • Main effect of A; no main effect of B; interaction
  • No main effect of A; main effect of B; interaction
  • No main effect of A; no main effect of B; interaction
  • Brown, H. D., Kosslyn, S. M., Delamater, B., Fama, A., & Barsky, A. J. (1999). Perceptual and memory biases for health-related information in hypochondriacal individuals. Journal of Psychosomatic Research, 47 , 67–78. ↵
  • Gilliland, K. (1980). The interactive effect of introversion-extroversion with caffeine induced arousal on verbal performance. Journal of Research in Personality, 14 , 482–492. ↵

Research Methods in Psychology Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Descriptive Statistics

56 Key Takeaways and Exercises

Key takeaways.

  • Every variable has a distribution—a way that the scores are distributed across the levels. The distribution can be described using a frequency table and histogram. It can also be described in words in terms of its shape, including whether it is unimodal or bimodal, and whether it is symmetrical or skewed.
  • The central tendency, or middle, of a distribution can be described precisely using three statistics—the mean, median, and mode. The mean is the sum of the scores divided by the number of scores, the median is the middle score, and the mode is the most common score.
  • The variability, or spread, of a distribution can be described precisely using the range and standard deviation. The range is the difference between the highest and lowest scores, and the standard deviation is the average amount by which the scores differ from the mean.
  • The location of a score within its distribution can be described using percentile ranks or  z  scores. The percentile rank of a score is the percentage of scores below that score, and the  z  score is the difference between the score and the mean divided by the standard deviation.
  • Differences between groups or conditions are typically described in terms of the means and standard deviations of the groups or conditions or in terms of Cohen’s  d  and are presented in bar graphs.
  • Cohen’s  d  is a measure of relationship strength (or effect size) for differences between two group or condition means. It is the difference of the means divided by the standard deviation. In general, values of ±0.20, ±0.50, and ±0.80 can be considered small, medium, and large, respectively.
  • Correlations between quantitative variables are typically described in terms of Pearson’s  r  and presented in line graphs or scatterplots.
  • Pearson’s  r  is a measure of relationship strength (or effect size) for relationships between quantitative variables. It is the mean cross-product of the two sets of  z  scores. In general, values of ±.10, ±.30, and ±.50 can be considered small, medium, and large, respectively.
  • In an APA-style article, simple results are most efficiently presented in the text, while more complex results are most efficiently presented in graphs or tables.
  • APA style includes several rules for presenting numerical results in the text. These include using words only for numbers less than 10 that do not represent precise statistical results, and rounding results to two decimal places, using words (e.g., “mean”) in the text and symbols (e.g., “ M ”) in parentheses.
  • APA style includes several rules for presenting results in graphs and tables. Graphs and tables should add information rather than repeating information, be as simple as possible, and be interpretable on their own with a descriptive caption (for graphs) or a descriptive title (for tables).
  • Raw data must be prepared for analysis by examining them for possible errors, organizing them, and entering them into a spreadsheet program.
  • Preliminary analyses on any data set include checking the reliability of measures, evaluating the effectiveness of any manipulations, examining the distributions of individual variables, and identifying outliers.
  • Outliers that appear to be the result of an error, a misunderstanding, or a lack of effort can be excluded from the analyses. The criteria for excluded responses or participants should be applied in the same way to all the data and described when you present your results. Excluded data should be set aside rather than destroyed or deleted in case they are needed later.
  • Descriptive statistics tell the story of what happened in a study. Although inferential statistics are also important, it is essential to understand the descriptive statistics first.
  • 11, 8, 9, 12, 9, 10, 12, 13, 11, 13, 12, 6, 10, 17, 13, 11, 12, 12, 14, 14
  • Practice: For the data in Exercise 1, compute the mean, median, mode, standard deviation, and range.
  • the percentile ranks for scores of 9 and 14
  • the  z  scores for scores of 8 and 12.
  • Practice: The following data represent scores on the Rosenberg Self-Esteem Scale for a sample of 10 Japanese university students and 10 American university students. (Although hypothetical, these data are consistent with empirical findings [Schmitt & Allik, 2005] [1] .) Compute the means and standard deviations of the two groups, make a bar graph, compute Cohen’s  d , and describe the strength of the relationship in words.
  • Practice: The hypothetical data that follow are extraversion scores and the number of Facebook friends for 15 university students. Make a scatterplot for these data, compute Pearson’s  r , and describe the relationship in words.
  • in a figure
  • Discussion: What are at least two reasonable ways to deal with each of the following outliers based on the discussion in this chapter? (a) A participant estimating ordinary people’s heights estimates one woman’s height to be “84 inches” tall. (b) In a study of memory for ordinary objects, one participant scores 0 out of 15. (c) In response to a question about how many “close friends” she has, one participant writes “32.”
  • Schmitt, D. P., & Allik, J. (2005). Simultaneous administration of the Rosenberg Self-Esteem Scale in 53 nations: Exploring the universal and culture-specific features of global self-esteem. Journal of Personality and Social Psychology, 89 , 623–642. ↵
  • Buss, D. M., & Schmitt, D. P. (1993). Sexual strategies theory: A contextual evolutionary analysis of human mating. Psychological Review, 100 , 204–232. ↵

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 8: Complex Research Designs

8.1 multiple dependent variables, learning objectives.

  • Explain why researchers often include multiple dependent variables in their studies.
  • Explain what a manipulation check is and when it would be included in an experiment.

Imagine that you have made the effort to find a research topic, review the research literature, formulate a question, design an experiment, obtain institutional review board (IRB) approval, recruit research participants, and manipulate an independent variable. It would seem almost wasteful to measure a single dependent variable. Even if you are primarily interested in the relationship between an independent variable and one primary dependent variable, there are usually several more questions that you can answer easily by including multiple dependent variables .

Measures of Different Constructs

Often a researcher wants to know how an independent variable affects several distinct dependent variables. For example, Schnall and her colleagues were interested in how feeling disgusted affects the harshness of people’s moral judgments, but they were also curious about how disgust affects other variables, such as people’s willingness to eat in a restaurant. As another example, researcher Susan Knasko was interested in how different odors affect people’s behavior (Knasko, 1992). She conducted an experiment in which the independent variable was whether participants were tested in a room with no odor or in one scented with lemon, lavender, or dimethyl sulfide (which has a cabbagelike smell). Although she was primarily interested in how the odors affected people’s creativity, she was also curious about how they affected people’s moods and perceived health—and it was a simple enough matter to measure these dependent variables too. Although she found that creativity was unaffected by the ambient odor, she found that people’s moods were lower in the dimethyl sulfide condition, and that their perceived health was greater in the lemon condition.

When an experiment includes multiple dependent variables, there is again a possibility of carryover effects. For example, it is possible that measuring participants’ moods before measuring their perceived health could affect their perceived health or that measuring their perceived health before their moods could affect their moods. So the order in which multiple dependent variables are measured becomes an issue. One approach is to measure them in the same order for all participants—usually with the most important one first so that it cannot be affected by measuring the others. Another approach is to counterbalance, or systematically vary, the order in which the dependent variables are measured.

Manipulation Checks

When the independent variable is a construct that can only be manipulated indirectly—such as emotions and other internal states—an additional measure of that independent variable is often included as a manipulation check . This is done to confirm that the independent variable was, in fact, successfully manipulated. For example, Schnall and her colleagues had their participants rate their level of disgust to be sure that those in the messy room actually felt more disgusted than those in the clean room. Manipulation checks are usually done at the end of the procedure to be sure that the effect of the manipulation lasted throughout the entire procedure and to avoid calling unnecessary attention to the manipulation.

Manipulation checks become especially important when the manipulation of the independent variable turns out to have no effect on the dependent variable. Imagine, for example, that you exposed participants to happy or sad movie music—intending to put them in happy or sad moods—but you found that this had no effect on the number of happy or sad childhood events they recalled. This could be because being in a happy or sad mood has no effect on memories for childhood events. But it could also be that the music was ineffective at putting participants in happy or sad moods. A manipulation check—in this case, a measure of participants’ moods—would help resolve this uncertainty. If it showed that you had successfully manipulated participants’ moods, then it would appear that there is indeed no effect of mood on memory for childhood events. But if it showed that you did not successfully manipulate participants’ moods, then it would appear that you need a more effective manipulation to answer your research question.

Measures of the Same Construct

Another common approach to including multiple dependent variables is to operationally define and measure the same construct, or closely related ones, in different ways. Imagine, for example, that a researcher conducts an experiment on the effect of daily exercise on stress. The dependent variable, stress, is a construct that can be operationally defined in different ways. For this reason, the researcher might have participants complete the paper-and-pencil Perceived Stress Scale and measure their levels of the stress hormone cortisol. This is an example of the use of converging operations. If the researcher finds that the different measures are affected by exercise in the same way, then he or she can be confident in the conclusion that exercise affects the more general construct of stress.

When multiple dependent variables are different measures of the same construct—especially if they are measured on the same scale—researchers have the option of combining them into a single measure of that construct. Recall that Schnall and her colleagues were interested in the harshness of people’s moral judgments. To measure this construct, they presented their participants with seven different scenarios describing morally questionable behaviors and asked them to rate the moral acceptability of each one. Although they could have treated each of the seven ratings as a separate dependent variable, these researchers combined them into a single dependent variable by computing their mean.

When researchers combine dependent variables in this way, they are treating them collectively as a multiple-response measure of a single construct. The advantage of this is that multiple-response measures are generally more reliable than single-response measures. However, it is important to make sure the individual dependent variables are correlated with each other by computing an internal consistency measure such as Cronbach’s α. If they are not correlated with each other, then it does not make sense to combine them into a measure of a single construct. If they have poor internal consistency, then they should be treated as separate dependent variables.

Key Takeaways

  • Researchers in psychology often include multiple dependent variables in their studies. The primary reason is that this easily allows them to answer more research questions with minimal additional effort.
  • When an independent variable is a construct that is manipulated indirectly, it is a good idea to include a manipulation check. This is a measure of the independent variable typically given at the end of the procedure to confirm that it was successfully manipulated.
  • Multiple measures of the same construct can be analyzed separately or combined to produce a single multiple-item measure of that construct. The latter approach requires that the measures taken together have good internal consistency.
  • Practice: List three independent variables for which it would be good to include a manipulation check. List three others for which a manipulation check would be unnecessary.
  • Practice: Imagine a study in which the independent variable is whether the room where participants are tested is warm (80°) or cool (65°). List three dependent variables that you might treat as measures of separate variables. List three more that you might combine and treat as measures of the same underlying construct.

Knasko, S. C. (1992). Ambient odor’s effect on creativity, mood, and perceived health. Chemical Senses , 17 , 27–35.

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Confounding Variables in Psychology Research

Cathy Cassata is a freelance writer who specializes in stories around health, mental health, medical news, and inspirational people.

key variables in psychology research

Getty Images / Andrew Brookes

  • Real World Examples

Confounding variables are external factors (typically a third variable) in research that can interfere with the relationship between dependent and independent variables .

At a Glance

A confounding variable alters the risk of the condition being studied and confuses the “true” relationship between the variables. The role of confounding variables in research is critical to understanding the causes of all kinds of physical, mental, and behavioral conditions and phenomena.

Real World Examples of Confounding Variables

Typical examples of confounding variables often relate to demographics and social and economic outcomes.

For instance, people who are relatively low in socioeconomic status during childhood tend to do, on average, worse financially than others do when they reach adulthood, explains Glenn Geher , PhD, professor of psychology at State University of New York at New Paltz and author of “Own Your Psychology Major!” While he said we could simply think this because poverty begets poverty, he also says there are other variables that are conflated with poverty.

People with lower economic means tend to have less access to high quality education, which is also related to fiscal success in adulthood, Geher explained. Furthermore, poverty is often associated with limited access to healthcare and, thus, with increased risk of adverse health outcomes. These factors can also play roles in fiscal success in adulthood.

“The bottom line here is that when looking to find factors that predict adult economic success, there are many variables that predict this outcome, and so many of these factors are confounded with one another,” Geher said. 

The Impact of Confounding Variables on Research

Psychology researchers must be diligent in controlling for confounding variables, because if they are not, they may draw inaccurate conclusions.

For example, during a research project, Geher’s team found the number of stitches one received in childhood predicted one’s sexual activity in adulthood.

However, Geher said "to conclude that getting stitches causes promiscuous behavior would be unwarranted and odd. In fact, it is much more likely that childhood health outcomes, such as getting stitches, predicts environmental instability during childhood, which has been found to indirectly bear on adult sexual and relationship outcomes,” said Geher.

In other words, the number of stitches is confounded with environmental instability in childhood. It's not that the number of stitches is directly correlated with sexual activity.

Another example that shows confounding variables is the idea that there is a positive correlation between ice cream sales and homicide rates. However, in fact, both these variables are confounded with time of year, said Geher. “They are both higher in summer when days are longer, days are hotter, and people are more likely to encounter others in social contexts because in the winter when it is cold people are more likely to stay home—so they are less likely to buy ice cream cones and to kill others,” he said. 

Both of these are examples of how it is in the best interest of researchers to ensure that they control for confounding variables to increase the likelihood that their conclusions are truly warranted.

Universal confounding variables across research on a particular topic can also be influential. In an evaluation of confounding variables that assessed the effect of alcohol consumption on the risk of ischemic heart disease, researchers found a large variation in the confounders considered across observational studies.

While 85 of 87 studies that the researchers analyzed made a connection to alcohol and ischemic heart disease, confounding variables that could influence ischemic heart disease included, smoking, age, and BMI, height, and/or weight. This means that these factors could have also affected heart disease, not just alcohol.

While most studies mentioned or alluded to “confounding” in their Abstract or Discussion sections, only one stated that their main findings were likely to be affected by confounding variables. The authors concluded that almost all studies ignored or eventually dismissed confounding variables in their conclusions.

Because study results and interpretations may be affected by the mix of potential confounders included within models, the researchers suggest that “efforts are necessary to standardize approaches for selecting and accounting for confounders in observational studies.”

Techniques to Identify Confounding Variables

The best way to control for confounding variables is to conduct “true experimental research,” which means researchers experimentally manipulate a variable that they think causes a certain outcome. They typically do this by randomly assigning study participants to different levels of the first variable, which is referred to as the “independent variable.”

For example, if researchers want to determine if, separate from other factors, receiving a full high-quality education, including a four-year college degree from a respected school, causes positive fiscal outcomes in adulthood, they would need to find a pool of participants, such as a group of young adults from the same broad socioeconomic group as one another. Once the group is selected, half of them would need to be randomly assigned to receive a free, high-quality education and the other half would need to be randomly assigned to not receive such an education.

“This methodology would allow you to see if there are fiscal outcomes on average for the two groups later in life and, if so, you could reasonably conclude that the cause of the differential fiscal outcomes is found in the educational differences across the two groups,” said Geher. “You can draw this conclusion because you randomly assigned the participants to these different groups—and process that naturally controls for confounding variables.” 

However, with this process, different problems emerge. For instance, it would not be ethical or practical to randomly assign some participants to a “high-quality education” group and others to a “no-education” group.

“[Controlling] confounding variables via experimental manipulation is not always feasible,” Geher said. 

Because of this, there are also statistical ways to try to control for confounding variables, such as “partial correlation,” which looks at a correlation between two variables (e.g., childhood SES and adulthood SES) while factoring out the effects of a potential confounding variable (e.g., educational attainment).

However, statistical control that addresses confounding by measurement can point to confounding through inappropriate control.

“This statistically oriented process is definitely not considered the gold standard compared with true experimental procedures, but often, it is the best you can do given ethical and/or practical constraints,” said Geher.

The Importance of Addressing Confounding Variables in Research

Controlling for confounding variables is critical in research primarily because it allows researchers to make sure that they are drawing valid and accurate conclusions. 

“If you don’t correct for confounding variables, you put yourself at risk for drawing conclusions regarding relationships between variables that are simply wrong (at the worst) or incomplete (at the best),” said Geher.

Controlling for confounding variables includes a basic set of skills when it comes to the social and behavioral sciences, he added. 

The Role of Confounding Variables in Valid Research

Human behavior is highly complex and any single action often has a broad array of variables that underlie it. 

“Understanding the concept of confounding variables, as well as how to control for these variables, makes for better behavioral science with conclusions that are, simply, more valid that research that does not effectively take confounding variables into account,” Geher said.

Wallach JD, Serghiou S, Chu L, et al. Evaluation of confounding in epidemiologic studies assessing alcohol consumption on the risk of ischemic heart disease. BMC Med Res Methodol. 2020;20(1):64. https://doi.org/10.1186/s12874-020-0914-6

Pourhoseingholi MA, Baghestani AR, Vahedi M. How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench. 2012;5(2):79-83. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/

By Cathy Cassata Cathy Cassata is a freelance writer who specializes in stories around health, mental health, medical news, and inspirational people.

Organizational health is (still) the key to long-term performance

For decades we’ve seen companies’ fortunes rise and fall based on their ability to react to, and recover quickly from, geopolitical shocks, technological advances, economic uncertainty, competitors’ bold moves, and other disruptions. Amid this volatility, which these days is accelerating rather than abating, many have a hard time staying the course. But some continue to survive and thrive despite the challenges. Why do these companies manage to succeed, year after year—operationally, financially, and otherwise—while others don’t?

Twenty-plus years of proprietary McKinsey research tells us that one of the main reasons is organizational health.

Organizational health refers to how effectively leaders “run the place”—that is, how they make decisions, allocate resources, operate day to day, and lead their teams with the goal of delivering high performance, both near term and over time. Organizational health comprises three elements: how well the entire organization rallies around a common vision and strategy, how well the organization executes its strategy, and how well the organization innovates and renews itself over time.

Our latest research on the topic reiterates the degree to which organizational health is not just nice to have; it’s required for sustained performance and organizational success. McKinsey’s Organizational Health Index (OHI) continues to show, for instance, that, over the long term, healthy organizations deliver three times the total shareholder returns (TSR) of unhealthy organizations, regardless of industry. 1 Aaron De Smet, Bill Schaninger, and Matthew Smith, “ The hidden value of organizational health—and how to capture it ,” McKinsey Quarterly , April 1, 2014. Other findings point to greater resilience and higher financial performance in healthy organizations, even as the world around them has become that much more complicated (see sidebar, “What is the Organizational Health Index?”).

What is the Organizational Health Index?

The Organizational Health Index (OHI) is a diagnostic that measures critical elements of a high-performing culture in an organization. The index draws from a proprietary database of more than eight million respondents across more than 2,500 organizations in a range of geographies and industries. The index aggregates employees’ and managers’ views on the management practices (and employee experiences) that inform an organization’s performance across nine dimensions, or outcomes. An overall score is assigned so companies can see how they compare with others in the database. The result is a detailed view of the health and internal-network dynamics of an organization (exhibit).

Launched in 2003, the OHI model is updated regularly to reflect advances in organizational science and changes in the state of organizations more broadly. The 2023 update includes factors—such as agility, resilience, inclusivity, and employee experience and well-being—that have become more pronounced in the wake of the global pandemic, macroeconomic shifts, geopolitical unease, and other global trends.

In this article, we look at the latest OHI results and highlight a few of the more compelling insights that the index reveals about leadership, data and technology, and talent management. We also identify several principles for building or maintaining organizational health over time—something that leaders often tell us they have limited time and resources to do.

It’s important to make the time, however—not just to spin up new activities but rather to think about how to run the business differently and factor both health and performance into daily actions. The causes of, and conditions for, organizational health are always changing. Just as medical associations continually update their recommendations on diet and fitness, so must the business community regularly monitor its practices and performance. The companies that do can differentiate themselves from others in the marketplace. They can more readily identify the kind of talent they need and the specific behaviors it will take to achieve their organizational objectives.

Organizational health can put companies on a fast track to performance —and a commitment to sustained health can keep them there.

The staying power of organizational health

There is no one right path to sustained success, but the fact is, healthier organizations do tend to perform better than unhealthy ones, especially in times of uncertainty. And that performance advantage increases over time. 2 “ Where companies with a long-term view outperform their peers ,” McKinsey Global Institute, February 8, 2017. According to our research, organizational health is the strongest predictor of value creation and a critical factor in sustained competitive advantage. In one evaluation of 1,500 companies in 100 countries, for instance, we saw that companies that had improved their organizational health realized 18 percent increases in their EBITDA  after one year.

Consider the following data points.

Health and M&A . In merger situations, healthy organizations—those that applied various health interventions during the integration phase and emphasized organizational health throughout the integration—gained a 5 percent median change in TSR  compared with industry peers after two years. The change for unhealthy companies was –17 percent over the same period.

Health and transformations . In large transformations, companies that embedded organizational-health investments and initiatives in their change programs across an 18-month period saw 35 percent higher TSR  than companies that did not invest in health.

Health and resiliency . Healthy organizations are not just higher performers, they are also more resilient and better able to manage downside risk. For instance, from 2020 to 2021, during the COVID-19 pandemic, healthy organizations were 59 percent less likely than unhealthy organizations to show signs of financial distress .

Health and safety . Companies with superior organizational health are better able than their peers to provide safe work environments, thereby limiting their exposure to financial, operational, and reputational risks. Indeed, companies in the top quartile in organizational health have six times fewer safety incidents  than those in the bottom quartile.

The relationship between health and performance can be quantified in other ways, too, including in the areas of talent and culture . In our experience, employees and leaders in unhealthy cultures often focus on what made them successful in the past rather than on what may be required going forward—and their entrenched behaviors and ways of working can take on a life of their own. Consider the situation at one global company: employees had reported in company satisfaction and pulse surveys that they felt motivated to do their jobs—and yet, the company’s performance remained stagnant. The CEO and executive team could not determine how to break through.

An organizational-health diagnostic revealed the problem: misaligned behaviors had dulled the company’s performance edge. Employees were producing day to day—but not in the areas that mattered most for meeting the organization’s long-term strategic goals. They were engaged but comfortable—“like being in a warm bath.” To change the energy, the CEO and executive team embarked on a multiyear transformation in which they reengineered business processes, instituted different working norms for leadership teams, changed their protocols for meetings and communications, activated change agents across the organization, and pushed more decisions down to those on the front lines. Over time, employees’ enthusiasm increased, and descriptions of “what it felt like to work there” became livelier and more focused on achieving great things together. Performance was on the upswing.

A pulse check: How should leaders think about organizational health?

Clearly, organizational health matters as much now as it ever has. The latest OHI results reiterate what we know from McKinsey’s 2023 State of Organizations research  about how companies are faring in an era of unprecedented change. But in these latest OHI findings, three trends in particular stand out: how leaders are leading; the links between technology, data, and innovation; and the value of talent mobility.

1. Leadership is undergoing a generational transformation

It’s fair to say that few—if any—executives anticipated the deeply disruptive business (and societal) changes that would emerge because of the 2020 global pandemic and the speed at which organizations needed to transform themselves. As they have reckoned with changes in where and how work gets done, leaders are learning that they need to be both decisive and empowering .

To that point, the OHI research indicates that decisive leadership is now one of the best predictors of organizational health. Unlike authoritative leadership, in which leaders use influence and authority to get things done, decisive leadership reflects leaders’ quick decisions and their commitment to act on them. During the COVID-19 pandemic, senior leaders at Amazon made quick commitments—within days and weeks, not months—to prioritize essential supplies, protect customers from price gouging, raise the minimum wage for hourly workers, and increase overtime pay. They allowed unlimited paid time off, as well as two weeks of sick pay to those affected by COVID-19. The company also rapidly expanded the capacity in its data center to meet the surge in demand for cloud computing services, which resulted in increased operational efficiency and growth for Amazon Web Services. 3 Karen Weise, “Amazon’s profit soars 220 percent as pandemic drives shopping online,”  New York Times , April 29, 2021.

Would you like to learn more about the Organizational Health Index ?

Decisive leadership is not just for times of crisis, however; it’s a requirement for any business that just wants to keep up. 4 Aaron De Smet, Gerald Lackey, and Leigh M. Weiss, “ Untangling your organization’s decision making ,” McKinsey Quarterly , June 21, 2017. To that end, a number of organizations have taken steps to empower frontline workers. Senior leaders at TJ Maxx, for instance, have empowered more than 1,200 buyers across all stores, each of whom controls millions of dollars, to cut deals on the spot with manufacturers. By committing to a system of delegated decision making, leaders have ensured that items get into stores quicker—within a week, in most cases—than they would have under a more traditional, hierarchical review process. 5 “The Economics of T.J. Maxx’s recession-proof pricing strategy, explained,” Wall Street Journal , June 1, 2023. Leaders at Southwest Airlines have made a concerted effort to put critical customer information in frontline employees’ hands: “Not only are [employees] able to work more quickly, but they are also providing a more tailored experience to customers,” James Ashworth, vice president for customer support and services, told Forbes magazine. The end result has been “a lift in our customer satisfaction scores, as well as a decrease in our call handle times,” he says. 6 Tiffani Bova, “Southwest on the importance of employee experience,” Forbes , November 17, 2020.

According to the OHI research, companies with leaders who take decisive actions—and who commit to those decisions once they are made—are 4.2 times more likely to be healthy, as compared with their peers.

But it’s not enough just to be fast with those decisions; our OHI research shows that decisive leaders who empower their employees (giving those closest to the work the autonomy to make their own decisions) are 85 percent more likely to improve the quality of organizational decisions, as compared with their peers. This supports previous McKinsey research pointing to a paradigm shift in leadership and, among other new requirements, the need for executives to shift from being controllers to becoming coaches  who engage employees and help foster in them a bold mindset of testing, learning, and fast adaptation. 7 Aaron De Smet, Arne Gast, Johanne Lavoie, and Michael Lurie, “ New leadership for a new era of thriving organizations ,”  McKinsey Quarterly , May 4, 2023.

Bank Mandiri, for instance, is using digital tools to ensure that individuals across all parts of the company have access to data analytics. Previously, information requests and report generation at the bank could take weeks, and critical business information had to be pulled from a tangle of systems. Through a new self-service system, employees can now access the data that are most relevant to them in a timelier manner—in a matter of days rather than weeks—allowing employees to make better, faster decisions.

2. Data is the fuel for everyday innovation

Leaders have traditionally thought of innovation as a process for bringing “the next big idea” to life . But our latest OHI data reveal that companies are more likely to succeed with innovation initiatives if “big bang ideas” are supported by data-driven insights and supplemented with smaller, more frequent ideas that target improvements in everyday processes or ways of working.

In many organizations, the ideas for “little i” innovation often come from the people closest to customers— frontline employees . 8 People & Organization Blog , “ Empower the front line for a thriving organization ,” blog entry by Kelli Moles and Michael Park, McKinsey, August 28, 2023. And, as it turns out, it pays to listen to them: the OHI data show that organizations that actively listen and act on recommendations from frontline employees are 80 percent more likely than others to consistently implement new and better ways of doing things.

The research also reiterates that all forms of innovation are more likely to succeed when decisions are grounded in data and facts. According to the research, organizations that emphasize data-driven decision making are 63 percent more likely than others to adapt to a changing business environment.

One of the best recent examples of data-informed innovation comes from Major League Baseball. The rise of data analytics prompted significant changes in many teams’ operations; managers built their rosters and managed their lineups according to batting percentages, probabilities, and other data captured across the league. The downside of that data-driven innovation, however, was longer games (more pitching changes) and a product that was less appealing to younger viewers. Again, the league turned to data—this time conducting surveys, focus groups, and spending time with younger fans—to learn what was important to them. Based on that feedback, the league engaged in some experiments. It implemented rule changes in 2023 (pitch clocks, larger bases, pitching-change limits, and so on) that fundamentally altered the pace and action of the game. The league continues to embrace innovation and technology, not only to improve the game but the overall fan experience. 9 Erik Roth, “ The Committed Innovator: How Major League Baseball built an innovation machine ,” McKinsey, October 27, 2023.

3. The dynamic deployment of talent is becoming even more of a competitive advantage

Workforce dynamics have been completely upended over the past few years, which has left organizations with an increasingly difficult HR-related task: ensuring that they have the right talent on board to tackle the highest-value-creating activities and successfully execute on their strategies. 10 Patrick Guggenberger, Dana Maor, Michael Park, and Patrick Simon, “ The State of Organizations 2023: Ten shifts transforming organizations ,” McKinsey, April 26, 2023. Our OHI research shows that the dynamic deployment of talent can be a powerful lever for both employee attraction and retention. It can also help organizations pivot quickly as markets change or new technologies and global trends emerge. 

Companies that encourage and even facilitate internal role changes can sharpen employees’ skills, maximize their versatility, and provide avenues for growth. According to our OHI findings, employees that experience more mobility at work are 27 percent less likely to report feeling burned out, 47 percent less likely to report intentions to leave their organization, and 2.3 times more likely to recommend their companies to others.

Employee rotations and upskilling became core components of one Latin American bank’s digital transformation. When HR leaders realized that 62 percent of the company’s technology workforce needed to be upskilled to meet the bank’s transformation goals, they launched a large training initiative, which involved more than 1,500 courses focused on about 820 technology skills, 60 boot camps, and countless individual, on-the-job coaching sessions. The HR organization embedded this focus on technology coaching and capability building into all performance management discussions.

As a result of this effort, about 60 percent of the total technology workforce is engaged in upskilling, attrition is low, and what started as a “special transformation program” is now considered business as usual and a cornerstone of the bank’s learning and development efforts. 11 Vincent Bérubé, Dana Maor, Maria Ocampo, and Alex Sukharevsky, “ HR rewired: An end-to-end approach to attracting and retaining top tech talent ,” McKinsey, June 27, 2023.

It's worth noting that more and more organizations are following the bank’s lead and exploring the move to skills-based hiring —in part to address shortages in certain skill areas like technology but also to create pathways for “nontraditional” job candidates, or those who might not have a college degree or a formal certificate of expertise. 12 Bryan Hancock and Brooke Weddle, “ Right skills, right person, right role ,” McKinsey, October 25, 2023.

Getting and staying healthy

Sustained organizational success really comes down to leaders gathering the data that will help them understand which behaviors can help them to meet their performance goals as well as the type and scale of health improvements their organization should target.

It’s critical for leaders to establish a baseline of the organization’s current strengths as well as the strengths it is targeting. With that baseline in mind, leaders can set clear behavioral priorities and begin to act—but it’s also critical to remember that context matters. Organizations will need to launch health interventions that are specific to the business, their performance goals, and their customer value proposition. Two hotel chains—one luxury, one economy—may offer similar services in the market, but each requires different kinds of behaviors to deliver on their value propositions and meet their performance targets. Regardless of their starting points, each will need to track progress against goals and adapt as needed along the way.

McKinsey research points to four foundational behaviors, what we call power practices , that can have disproportionate effects on organizational performance—and whose absence can create a significant drag on organizations: strategic clarity, role clarity, personal ownership, and competitive insights. 13 It is worth noting that the list of power practices has changed over time, and likely will again, but three practices routinely show up: strategic clarity, role clarity, and personal ownership.

  • Strategic clarity . Healthy organizations effectively translate vision and strategy into actionable and measurable objectives that are clearly articulated and shared with employees at all levels.
  • Role clarity . Healthy organizations tend to have structures, processes, and working norms that speed up decision making, remove layers of bureaucracy, and make it easy for employees to get things done—even when situations are new or ambiguous.
  • Personal ownership . Healthy organizations hire and develop managers who have a deep sense of personal ownership for their work and who foster that same sense of ownership in their teams and employees.
  • Competitive insights . Healthy organizations tend to have a clear view of where and how they fit in the competitive landscape and of their value propositions; they use these insights to set strategic priorities, make decisions, and allocate resources.

If any of these power practices are missing or at risk, organizations should take steps to address them; it’s a no-regrets move for achieving good organizational health.

In addition to this list, companies also need to identify which kinds of talent and behaviors are required for them to truly differentiate themselves from competitors—the organizations’ so-called “secret sauce.” Industry insights and benchmarks can provide some direction, but the final list of behaviors that convey competitive advantage to one company and not others can only be identified by an organization’s senior leaders.

The “born remote” technology company GitLab provides a good example. Long before these days of remote and hybrid workplaces, the company established foundational norms  to get the most out of its distributed workforce. Ways of working were designed to be independent of time and place. Employees are encouraged to “write things down,” for instance, and playbooks are readily available online. GitLab’s operating model emphasizes a shared reality, equal contributions, decision velocity, and measurement clarity. The central behaviors at the company’s core have given it an advantage as other companies continue to try to define remote and hybrid working models . And GitLab has demonstrated top-decile performance against OHI benchmarks. As this and many other examples show, leaders in outperforming companies always have a plan to be “good enough” at everything and “truly excellent” at the handful of things that matter for them and the organization. And when it comes to how they run the place, they emphasize cultural consistency across the organization. 14 Carolyn Dewar and Scott Keller, “Three steps to a high-performance culture,” Harvard Business Review , January 26, 2012.

The leadership imperative

Our research makes a clear and compelling case that organizational health is the foundation for companies’ ability to successfully create value, attain profitability, build resilience, and thrive in so many other areas.

So why don’t more senior executives make it a priority?

In our experience, there are several common obstacles. The first is inconsistency in how leaders think and talk about organizational health: conversations about organizational health often anchor on employee engagement as the default, and executives often consider organizational health as being separate from performance. In fact, they are actually one and the same. Leaders should be asking themselves, “How do I run the place each and every day—in each and every meeting—in ways that are both healthy and conducive to creating high performance?”

Related, senior leaders may not see the trees for the forest; many will discuss organizational health as a top-level theme but are much less often involved in the interventions and implementation required to achieve and sustain organizational health. Third, realizing improvements in organizational health takes time—and executives often need to move fast. The default here is to focus on putting out fires rather than fixing the system.

And finally, there’s a sense that bad health implies bad leadership. C-suite leaders must make organizational health a central component of their leadership styles and manage it as rigorously as they do their P&L. Otherwise, they may not actually recognize unhealthy actions when they see them. For this reason, leaders may need to spend extra time, attention, and resources on health interventions. They may need to reframe quarterly discussions and incentives and other elements of performance management around the idea of maintaining organizational health.

Even for those companies that are seemingly in great shape, it’s important to continue to monitor the organization for symptoms of upset or disruption. Just as top athletes can lose time or distance or skill if they skip workouts for an extended period, so can companies fall behind competitors if they take a break and rest on their laurels. Commitment is crucial.

Alex Camp is a partner in McKinsey’s New York office, Arne Gast is a senior partner in the Amsterdam office, Drew Goldstein is a partner in the Charlotte, North Carolina, office, and Brooke Weddle is a partner in the Washington, DC, office.

The authors wish to thank Aaron De Smet,  Ben Fletcher, David Mendelsohn, John Parsons, and Laura Pineault for their contributions to this article.

This article was edited by Roberta Fusaro, an editorial director in the Boston office.

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New leadership for a new era of thriving organizations

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Why Are Bullies Popular? Brain Science Can Explain

Research documents the way brains can harness empathy for cruelty..

Posted February 16, 2024 | Reviewed by Monica Vilhauer

  • How to Handle Bullying
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  • Brain science has learned the key role empathy plays in abusive behaviors.
  • We think our empathy should stop us from bullying, but it can facilitate maltreatment.
  • Bullies and abusers use empathy to bond with community, while at the same time targeting victims with cruelty.
  • Research shows empathy manifests in two opposing ways that impact our conduct, making it caring or cruel.

Brains harnessing empathy for cruelty is counter-intuitive. In fact, we usually think of someone who is empathic as utterly unable to bully others. Empathy is our innate capacity to recognize what others are thinking, feeling, and intending. Dr. Helen Reiss explains we are born wired for empathy: studies have shown that infants will imitate facial expressions very early as they are mirroring those caring for them.

Robbie Ross / Pixabay

Researchers have seen that animals who identify the distress or pain of another in their species will halt aggressive behaviour in response. Primatologist Jules Masserman and colleagues conducted research in 1964 that showed rhesus monkeys would not pull a chain to access food when they learned it meant other monkeys would get an electric shock as a result. They chose not to have the food if it caused others to suffer. That is empathy at work.

Empathy is our capacity to walk in someone else’s shoes, see the world from their point of view, feel their pain. Our empathy is critical to our social interactions and our chance of safety and survival by living in community. Bullying is the opposite: it causes pain; it divides people; it shames and conveys the message to the targets that they don’t belong. Bullying does not acknowledge our essential human bond; instead, it dehumanizes.

Bullies are often well-liked in their communities

This is why it’s perplexing that individuals who bully and abuse are also well-liked in the community. They’re often popular, charismatic , and sometimes even have cult-followings. Even children who bully seem able to turn off and turn on their cruel conduct so that only victims are targeted, while other children are treated with kindness. Even children who bully can cover up their harmful behaviour when adults are present.

With adults who bully and abuse, it is more sophisticated. As is extensively documented, they are adept at grooming higher-ups in the workplace, masquerading as the pillar of the community in social circles, as well as virtue-signalling to ensure they are not identified as abusive. This dual personality — one that exudes respectable kindness and the other that defaults to maltreatment of victims — frequently acts as an effective cover-up, even from the law.

Individuals in recent media scandals such as Harvey Weinstein, Larry Nassar, and Bill Cosby are classic examples of abusive individuals who are well-established and honoured in their communities. Their abuse goes on for decades by being systemically ignored as if it is not possible that such respected, powerful, prestigious people could also be extremely harmful to targets. One minute the person is kind and caring, the next minute he’s humiliating someone. How can this person be a “bully-empath”? How can someone be both empathic and abusive?

Empathy is not one brain system, but two

A chilling answer to the bully-empath split is provided in the research and work of neuroscientist Dr. Simon Baron-Cohen. He and his colleagues refer to those who do harm to others as “Zero-Negative” on the empathy spectrum. Those who are Zero-Negative can include individuals who are diagnosed as borderline, narcissistic , and psychopathic . What these individuals have in common from a neuroscience perspective is a severely underactive empathy circuit. Their brains behave in atypical ways when examining the ten interactive regions of the empathy circuit. When researchers look separately at the two empathy systems within the circuit, those who harm others have only one empathy system that is intact and the other that is eroded.

Baron-Cohen’s research offers an answer to the confusing fact that those who bully and abuse also appear to have empathy. A psychopath has “intact cognitive empathy but reduced affective empathy.” In other words, a psychopath who lies, maltreats, abuses, harms others in a variety of ways, and doesn’t care at all about it, has a brain with eroded affective empathy. Our affective empathy is how we feel someone else’s pain. We can see their pain, hear it, and actually experience it. If you see someone cut their hand, you are likely to physically react, recoil, wince.

The psychopath does not feel someone else’s pain. They lack affective empathy. However, they still have access to cognitive empathy. This gives them the advantage of being able to read others. In a cold, calculating way, they can think very adeptly about what someone thinks, what emotions they have, and what intentions they plan. The psychopath – without affective reactions like remorse, guilt , anguish – uses their cognitive insights to create a following and to destroy targets.

key variables in psychology research

When the bullying or abusive individual is reported on or confronted with the harm they are causing, they deny it, and call upon their followers (those they treat with kindness and offer advantages to) in order to vouch for them. The bully or abuser is aware that they are causing harm and they are motivated to cover it up.

Textbook case of Zero-Negative empathy

A textbook example of this is nurse Lucy Letby in the U.K. who doctors reported as suspiciously involved in far too many infant deaths. She accused them of bullying her. The doctors had to issue an apology and this allowed her to continue as a serial killer of babies. Ultimately she was charged and convicted. While Letby was killing the babies, she was also comforting the devastated parents who were thankful for her care and kindness.

Nurse Letby has cognitive empathy. While she did not hesitate to kill seven babies and tried to kill six more, she knew how to manipulate doctors and administrators, and most tragically, she knew how to read grieving parents. If Letby’s brain was studied by Baron-Cohen and his team of neuroscientists, it would show atypical, eroded affective empathy.

Baron-Cohen asserts at the end of his book The Science of Evil that empathy “is the most valuable resource in our world” and he expresses profound concern that it is not the cornerstone of education . He’d like to see empathy prioritized in parenting and policing and especially politics . The erosion of empathy is complex, but environment plays an outsized role. Abuse begets abuse. The neglected, harmed, verbally put down child is far more likely to have atypical affective empathy which can lead to bullying and abusive behaviours. Knowing how critical our two empathy systems (affective and cognitive) are for all individuals, communities, and the world makes us realize how much we need to invest in it.

Baron-Cohen, S. (2011). The Science of Evil. New York: Hachette.

Jennifer Fraser Ph.D.

Jennifer Fraser, Ph.D., is an award-winning educator and bestselling author. Her latest book, The Bullied Brain: Heal Your Scars and Restore Your Health , hit shelves and airwaves in April 2022.

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Popcorn

Popcorn brain: could the snack be the key to understanding why it’s so hard to concentrate?

Struggling to focus? Overwhelmed by your phone alerts? Experts say the popping kernels are a useful metaphor to explain overstimulation in the digital age

Name: Popcorn brain.

Appearance : Like overexcited popcorn kernels popping around in a pan. Or, if you prefer, in your microwave.

Can popcorn kernels be overexcited? Seems unlikely. Not the point. “Popcorn brain” is a metaphor to explain the multitasking and overstimulation caused by the digital world. It was first coined in 2011 by David M Levy , whose books include Mindful Tech and No Time to Think. Now, he says, the design of many of our most-used apps “seem uniquely suited to scatter focus ”.

Sorry, I wasn’t paying attention. I was watching TikTok panda fails. Pandas are so useless. Why aren’t they extinct? Focus! Levy’s suggestion is that the brain has become so accustomed to incessant digital yip-yap – notification dings, new tabs, adverts, fatuous content, cute pandas – that it mimics that frenetic pace.

I see the former Pussycat Doll Nicole Scherzinger won an award for being in that play. The Pussycat Dolls were woeful, weren’t they? Please, will you concentrate? Apparently, the popcorn brain phenomenon has got worse in the past two decades, in line with the rise of social media. According to one survey, the time a person can focus on one thing has declined from about two and a half minutes to about 47 seconds over the past 20 years.

Aston Villa’s home form is a concern. When is Tyrone Mings coming back to shore up Villains’ leaky defence? Stop this mindless scrolling. According to the psychologist Dannielle Haig, social media platforms use algorithms to feed us information, notifications and entertainment. Each piece of new information triggers a dopamine release, rewarding our brain and encouraging us to continue this cycle of seeking and receiving new stimuli.

I wonder if anyone got to taste that lifesize Taylor Swift cake someone made for the Super Bowl? Haig says: “Over time, this constant demand for attention and the rapid switching between tasks can lead to a feeling of mental restlessness or the brain ‘bouncing around’ as it struggles to maintain focus on any one task for an extended period.”

Oh, God, she’s right! How can I stop wasting my life on studying Villa’s home form and Scherzinger’s CV? There are lots of self-help books advising us on how to reclaim focus, such as Aditi Nerurkar’s The 5 Resets: Rewire Your Brain and Body for Less Stress and More Resilience.

Yawn! Even the subtitle sounds boring. What can I do that doesn’t involve reading self-help books? Set tech-free times, put your phone in another room (with notifications off) and periodically delete apps, suggests clinical psychologist Dr Daniel Glazer . Or, if you’re lucky, accidentally leave your phone on the bus, like I did.

Don’t say: “Can there ever be too many cute kitten videos? That question is rhetorical.”

Do say: “I’m a member of homo sapiens, not – pauses for dramatic effect – phono sapiens.”

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    There are two main variables when it comes to psychological research, these are; (1) The Independent Variable (IV) the variable that is manipulated/changed. (2) The Dependent Variable (DV) the variable that is measured (e.g. it measures whether or not the IV has influence human behaviour). When carrying out a piece of research, a psychologists ...

  14. Types of Variable: Examples, Types & Research

    Psychology research has three variables: independent (IV), dependent (DV), and extraneous variables. Let us take a look at the definition of each variable.

  15. Key Terms for Psychological Research

    Key Terms for Psychological Research - Introduction to Psychology & Neuroscience 14 Key Terms for Psychological Research archival research method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships attrition

  16. Types of Variables in Research & Statistics

    There are three types of categorical variables: binary, nominal, and ordinal variables. *Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn't need to be kept as discrete integers.

  17. Key Takeaways and Exercises

    Key Takeaways. Researchers often include multiple independent variables in their experiments. The most common approach is the factorial design, in which each level of one independent variable is combined with each level of the others to create all possible conditions. ... Some of the most interesting research questions and results in psychology ...

  18. Research Hypothesis In Psychology: Types, & Examples

    Examples. A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

  19. Multiple Independent Variables

    Overview. By far the most common approach to including multiple independent variables in an experiment is the factorial design. In a factorial design, each level of one independent variable (which can also be called a factor) is combined with each level of the others to produce all possible combinations.

  20. Key Takeaways and Exercises

    Key Takeaways. Every variable has a distribution—a way that the scores are distributed across the levels. The distribution can be described using a frequency table and histogram. It can also be described in words in terms of its shape, including whether it is unimodal or bimodal, and whether it is symmetrical or skewed.

  21. Variables in Research

    Definition: In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest. Types of Variables in Research Types of Variables in Research are as follows:

  22. 8.1 Multiple Dependent Variables

    Key Takeaways. Researchers in psychology often include multiple dependent variables in their studies. The primary reason is that this easily allows them to answer more research questions with minimal additional effort. When an independent variable is a construct that is manipulated indirectly, it is a good idea to include a manipulation check.

  23. Confounding Variables in Psychology Research

    Types of Variables in Psychology Research The Impact of Confounding Variables on Research Psychology researchers must be diligent in controlling for confounding variables, because if they are not, they may draw inaccurate conclusions.

  24. Natural Experiments: Missed Opportunities for Causal Inference in

    An instrumental variable allows one to target variation in the treatment that is plausibly unconfounded. In that manner, an instrumental variable (Z) allows researchers to unbiasedly estimate the causal effect of some other treatment variable (X) on an outcome (Y), even if there is unobserved confounding between X and Y (U; see Fig. 3).

  25. Organizational health is (still) the key to long-term performance

    McKinsey research points to four foundational behaviors, what we call power practices, that can have disproportionate effects on organizational performance—and whose absence can create a significant drag on organizations: strategic clarity, role clarity, personal ownership, and competitive insights. 13 It is worth noting that the list of ...

  26. Why Are Bullies Popular? Brain Science Can Explain

    Key points. Brain science has learned the key role empathy plays in abusive behaviors. We think our empathy should stop us from bullying, but it can facilitate maltreatment.

  27. Popcorn brain: could the snack be the key to understanding why it's so

    Appearance: Like overexcited popcorn kernels popping around in a pan.Or, if you prefer, in your microwave. Can popcorn kernels be overexcited? Seems unlikely. Not the point. "Popcorn brain" is ...