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Variables in Research – Definition, Types and Examples

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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:

Independent Variable

This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.

Dependent Variable

This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.

Confounding Variable

This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.

Mediating Variable

This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.

Moderator Variable

This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.

Control Variable

This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.

Continuous Variable

This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.

Categorical Variable

This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

Discrete Variable

This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.

Dummy Variable

This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.

Extraneous Variable

This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.

Latent Variable

This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.

Moderator-mediator Variable

This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.

Variables Analysis Methods

There are different methods to analyze variables in research, including:

Descriptive statistics: This involves analyzing and summarizing data using measures such as mean, median, mode, range, standard deviation, and frequency distribution. Descriptive statistics are useful for understanding the basic characteristics of a data set.
Inferential statistics : This involves making inferences about a population based on sample data. Inferential statistics use techniques such as hypothesis testing, confidence intervals, and regression analysis to draw conclusions from data.
Correlation analysis: This involves examining the relationship between two or more variables. Correlation analysis can determine the strength and direction of the relationship between variables, and can be used to make predictions about future outcomes.
Regression analysis: This involves examining the relationship between an independent variable and a dependent variable. Regression analysis can be used to predict the value of the dependent variable based on the value of the independent variable, and can also determine the significance of the relationship between the two variables.
Factor analysis: This involves identifying patterns and relationships among a large number of variables. Factor analysis can be used to reduce the complexity of a data set and identify underlying factors or dimensions.
Cluster analysis: This involves grouping data into clusters based on similarities between variables. Cluster analysis can be used to identify patterns or segments within a data set, and can be useful for market segmentation or customer profiling.
Multivariate analysis : This involves analyzing multiple variables simultaneously. Multivariate analysis can be used to understand complex relationships between variables, and can be useful in fields such as social science, finance, and marketing.

Examples of Variables

Age : This is a continuous variable that represents the age of an individual in years.
Gender : This is a categorical variable that represents the biological sex of an individual and can take on values such as male and female.
Education level: This is a categorical variable that represents the level of education completed by an individual and can take on values such as high school, college, and graduate school.
Income : This is a continuous variable that represents the amount of money earned by an individual in a year.
Weight : This is a continuous variable that represents the weight of an individual in kilograms or pounds.
Ethnicity : This is a categorical variable that represents the ethnic background of an individual and can take on values such as Hispanic, African American, and Asian.
Time spent on social media : This is a continuous variable that represents the amount of time an individual spends on social media in minutes or hours per day.
Marital status: This is a categorical variable that represents the marital status of an individual and can take on values such as married, divorced, and single.
Blood pressure : This is a continuous variable that represents the force of blood against the walls of arteries in millimeters of mercury.
Job satisfaction : This is a continuous variable that represents an individual’s level of satisfaction with their job and can be measured using a Likert scale.

Applications of Variables

Variables are used in many different applications across various fields. Here are some examples:

Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
Business and marketing: Variables are used in business and marketing to understand customer behavior and to make decisions about product development and marketing strategies. For example, businesses may study variables such as consumer preferences, spending habits, and market trends to identify opportunities for growth.
Healthcare : Variables are used in healthcare to monitor patient health and to make treatment decisions. For example, doctors may use variables such as blood pressure, heart rate, and cholesterol levels to diagnose and treat cardiovascular disease.
Education : Variables are used in education to measure student performance and to evaluate the effectiveness of teaching strategies. For example, teachers may use variables such as test scores, attendance, and class participation to assess student learning.
Social sciences : Variables are used in social sciences to study human behavior and to understand the factors that influence social interactions. For example, sociologists may study variables such as income, education level, and family structure to examine patterns of social inequality.

Purpose of Variables

Variables serve several purposes in research, including:

To provide a way of measuring and quantifying concepts: Variables help researchers measure and quantify abstract concepts such as attitudes, behaviors, and perceptions. By assigning numerical values to these concepts, researchers can analyze and compare data to draw meaningful conclusions.
To help explain relationships between different factors: Variables help researchers identify and explain relationships between different factors. By analyzing how changes in one variable affect another variable, researchers can gain insight into the complex interplay between different factors.
To make predictions about future outcomes : Variables help researchers make predictions about future outcomes based on past observations. By analyzing patterns and relationships between different variables, researchers can make informed predictions about how different factors may affect future outcomes.
To test hypotheses: Variables help researchers test hypotheses and theories. By collecting and analyzing data on different variables, researchers can test whether their predictions are accurate and whether their hypotheses are supported by the evidence.

Characteristics of Variables

Characteristics of Variables are as follows:

Measurement : Variables can be measured using different scales, such as nominal, ordinal, interval, or ratio scales. The scale used to measure a variable can affect the type of statistical analysis that can be applied.
Range : Variables have a range of values that they can take on. The range can be finite, such as the number of students in a class, or infinite, such as the range of possible values for a continuous variable like temperature.
Variability : Variables can have different levels of variability, which refers to the degree to which the values of the variable differ from each other. Highly variable variables have a wide range of values, while low variability variables have values that are more similar to each other.
Validity and reliability : Variables should be both valid and reliable to ensure accurate and consistent measurement. Validity refers to the extent to which a variable measures what it is intended to measure, while reliability refers to the consistency of the measurement over time.
Directionality: Some variables have directionality, meaning that the relationship between the variables is not symmetrical. For example, in a study of the relationship between smoking and lung cancer, smoking is the independent variable and lung cancer is the dependent variable.

Advantages of Variables

Here are some of the advantages of using variables in research:

Control : Variables allow researchers to control the effects of external factors that could influence the outcome of the study. By manipulating and controlling variables, researchers can isolate the effects of specific factors and measure their impact on the outcome.
Replicability : Variables make it possible for other researchers to replicate the study and test its findings. By defining and measuring variables consistently, other researchers can conduct similar studies to validate the original findings.
Accuracy : Variables make it possible to measure phenomena accurately and objectively. By defining and measuring variables precisely, researchers can reduce bias and increase the accuracy of their findings.
Generalizability : Variables allow researchers to generalize their findings to larger populations. By selecting variables that are representative of the population, researchers can draw conclusions that are applicable to a broader range of individuals.
Clarity : Variables help researchers to communicate their findings more clearly and effectively. By defining and categorizing variables, researchers can organize and present their findings in a way that is easily understandable to others.

Disadvantages of Variables

Here are some of the main disadvantages of using variables in research:

Simplification : Variables may oversimplify the complexity of real-world phenomena. By breaking down a phenomenon into variables, researchers may lose important information and context, which can affect the accuracy and generalizability of their findings.
Measurement error : Variables rely on accurate and precise measurement, and measurement error can affect the reliability and validity of research findings. The use of subjective or poorly defined variables can also introduce measurement error into the study.
Confounding variables : Confounding variables are factors that are not measured but that affect the relationship between the variables of interest. If confounding variables are not accounted for, they can distort or obscure the relationship between the variables of interest.
Limited scope: Variables are defined by the researcher, and the scope of the study is therefore limited by the researcher’s choice of variables. This can lead to a narrow focus that overlooks important aspects of the phenomenon being studied.
Ethical concerns: The selection and measurement of variables may raise ethical concerns, especially in studies involving human subjects. For example, using variables that are related to sensitive topics, such as race or sexuality, may raise concerns about privacy and discrimination.

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Types of Variables in Research | Definitions & Examples

Types of Variables in Research | Definitions & Examples

Published on 19 September 2022 by Rebecca Bevans . Revised on 28 November 2022.

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 .

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?

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, 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 variable 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 .

Discrete vs continuous variables
Type of variable	What does the data represent?	Examples
Discrete variables (aka integer variables)	Counts of individual items or values.
Continuous variables (aka ratio variables)	Measurements of continuous or non-finite values.

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.

Binary vs nominal vs ordinal variables
Type of variable	What does the data represent?	Examples
Binary variables (aka dichotomous variables)	Yes/no outcomes.
Nominal variables	Groups with no rank or order between them.
Ordinal variables	Groups that are ranked in a specific order.

*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 colour-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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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.

Independent vs dependent vs control variables
Type of variable	Definition	Example (salt tolerance experiment)
Independent variables (aka treatment variables)	Variables you manipulate in order to affect the outcome of an experiment.	The amount of salt added to each plant’s water.
Dependent variables (aka response variables)	Variables that represent the outcome of the experiment.	Any measurement of plant health and growth: in this case, plant height and wilting.
Control variables	Variables that are held constant throughout the experiment.	The temperature and light in the room the plants are kept in, and the volume of water given to each plant.

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.

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 variable are listed below.

Type of variable	Definition	Example (salt tolerance experiment)
	A variable that hides the true effect of another variable in your experiment. This can happen when another variable is closely related to a variable you are interested in, but you haven’t controlled it in your experiment.	Pot size and soil type might affect plant survival as much as or more than salt additions. In an experiment, you would control these potential confounders by holding them constant.
Latent variables	A variable that can’t be directly measured, but that you represent via a proxy.	Salt tolerance in plants cannot be measured directly, but can be inferred from measurements of plant health in our salt-addition experiment.
Composite variables	A variable that is made by combining multiple variables in an experiment. These variables are created when you analyse data, not when you measure it.	The three plant-health variables could be combined into a single plant-health score to make it easier to present your findings.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

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).

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 .

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In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

Independent and Dependent Variables Examples

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

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By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

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What (exactly) is a variable?

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

How someone’s age impacts their sleep quality
How different teaching methods impact learning outcomes
How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

Independent variables (IV)
Dependant variables (DV)
Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

Need a helping hand?

What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
Students’ scores (DV) could be impacted by teaching methods (IV)
Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

Temperature
Time of day
Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

A control variable is intentionally held constant (it doesn't vary) to ensure it doesn’t have an influence on any other variables.

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

Moderating variables
Mediating variables
Confounding variables
Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

Mediating variables can explain the relationship between the independent and dependent variable, including whether it's causal or not.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

It must be correlated with the independent variable (this can be causal or not)
It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

Emotional intelligence
Quality of life
Business confidence
Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study.

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

Independent variables (the “cause”)
Dependent variables (the “effect”)
Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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Variables in Research | Types, Definiton & Examples

Introduction

What is a variable, what are the 5 types of variables in research, other variables in research.

Variables are fundamental components of research that allow for the measurement and analysis of data. They can be defined as characteristics or properties that can take on different values. In research design , understanding the types of variables and their roles is crucial for developing hypotheses , designing methods , and interpreting results .

This article outlines the the types of variables in research, including their definitions and examples, to provide a clear understanding of their use and significance in research studies. By categorizing variables into distinct groups based on their roles in research, their types of data, and their relationships with other variables, researchers can more effectively structure their studies and achieve more accurate conclusions.

A variable represents any characteristic, number, or quantity that can be measured or quantified. The term encompasses anything that can vary or change, ranging from simple concepts like age and height to more complex ones like satisfaction levels or economic status. Variables are essential in research as they are the foundational elements that researchers manipulate, measure, or control to gain insights into relationships, causes, and effects within their studies. They enable the framing of research questions, the formulation of hypotheses, and the interpretation of results.

Variables can be categorized based on their role in the study (such as independent and dependent variables ), the type of data they represent (quantitative or categorical), and their relationship to other variables (like confounding or control variables). Understanding what constitutes a variable and the various variable types available is a critical step in designing robust and meaningful research.

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Variables are crucial components in research, serving as the foundation for data collection , analysis , and interpretation . They are attributes or characteristics that can vary among subjects or over time, and understanding their types is essential for any study. Variables can be broadly classified into five main types, each with its distinct characteristics and roles within research.

This classification helps researchers in designing their studies, choosing appropriate measurement techniques, and analyzing their results accurately. The five types of variables include independent variables, dependent variables, categorical variables, continuous variables, and confounding variables. These categories not only facilitate a clearer understanding of the data but also guide the formulation of hypotheses and research methodologies.

Independent variables

Independent variables are foundational to the structure of research, serving as the factors or conditions that researchers manipulate or vary to observe their effects on dependent variables. These variables are considered "independent" because their variation does not depend on other variables within the study. Instead, they are the cause or stimulus that directly influences the outcomes being measured. For example, in an experiment to assess the effectiveness of a new teaching method on student performance, the teaching method applied (traditional vs. innovative) would be the independent variable.

The selection of an independent variable is a critical step in research design, as it directly correlates with the study's objective to determine causality or association. Researchers must clearly define and control these variables to ensure that observed changes in the dependent variable can be attributed to variations in the independent variable, thereby affirming the reliability of the results. In experimental research, the independent variable is what differentiates the control group from the experimental group, thereby setting the stage for meaningful comparison and analysis.

Dependent variables

Dependent variables are the outcomes or effects that researchers aim to explore and understand in their studies. These variables are called "dependent" because their values depend on the changes or variations of the independent variables.

Essentially, they are the responses or results that are measured to assess the impact of the independent variable's manipulation. For instance, in a study investigating the effect of exercise on weight loss, the amount of weight lost would be considered the dependent variable, as it depends on the exercise regimen (the independent variable).

The identification and measurement of the dependent variable are crucial for testing the hypothesis and drawing conclusions from the research. It allows researchers to quantify the effect of the independent variable , providing evidence for causal relationships or associations. In experimental settings, the dependent variable is what is being tested and measured across different groups or conditions, enabling researchers to assess the efficacy or impact of the independent variable's variation.

To ensure accuracy and reliability, the dependent variable must be defined clearly and measured consistently across all participants or observations. This consistency helps in reducing measurement errors and increases the validity of the research findings. By carefully analyzing the dependent variables, researchers can derive meaningful insights from their studies, contributing to the broader knowledge in their field.

Categorical variables

Categorical variables, also known as qualitative variables, represent types or categories that are used to group observations. These variables divide data into distinct groups or categories that lack a numerical value but hold significant meaning in research. Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis.

Categorical variables can be further classified into two subtypes: nominal and ordinal. Nominal variables are categories without any inherent order or ranking among them, such as blood type or ethnicity. Ordinal variables, on the other hand, imply a sort of ranking or order among the categories, like levels of satisfaction (high, medium, low) or education level (high school, bachelor's, master's, doctorate).

Understanding and identifying categorical variables is crucial in research as it influences the choice of statistical analysis methods. Since these variables represent categories without numerical significance, researchers employ specific statistical tests designed for a nominal or ordinal variable to draw meaningful conclusions. Properly classifying and analyzing categorical variables allow for the exploration of relationships between different groups within the study, shedding light on patterns and trends that might not be evident with numerical data alone.

Continuous variables

Continuous variables are quantitative variables that can take an infinite number of values within a given range. These variables are measured along a continuum and can represent very precise measurements. Examples of continuous variables include height, weight, temperature, and time. Because they can assume any value within a range, continuous variables allow for detailed analysis and a high degree of accuracy in research findings.

The ability to measure continuous variables at very fine scales makes them invaluable for many types of research, particularly in the natural and social sciences. For instance, in a study examining the effect of temperature on plant growth, temperature would be considered a continuous variable since it can vary across a wide spectrum and be measured to several decimal places.

When dealing with continuous variables, researchers often use methods incorporating a particular statistical test to accommodate a wide range of data points and the potential for infinite divisibility. This includes various forms of regression analysis, correlation, and other techniques suited for modeling and analyzing nuanced relationships between variables. The precision of continuous variables enhances the researcher's ability to detect patterns, trends, and causal relationships within the data, contributing to more robust and detailed conclusions.

Confounding variables

Confounding variables are those that can cause a false association between the independent and dependent variables, potentially leading to incorrect conclusions about the relationship being studied. These are extraneous variables that were not considered in the study design but can influence both the supposed cause and effect, creating a misleading correlation.

Identifying and controlling for a confounding variable is crucial in research to ensure the validity of the findings. This can be achieved through various methods, including randomization, stratification, and statistical control. Randomization helps to evenly distribute confounding variables across study groups, reducing their potential impact. Stratification involves analyzing the data within strata or layers that share common characteristics of the confounder. Statistical control allows researchers to adjust for the effects of confounders in the analysis phase.

Properly addressing confounding variables strengthens the credibility of research outcomes by clarifying the direct relationship between the dependent and independent variables, thus providing more accurate and reliable results.

Beyond the primary categories of variables commonly discussed in research methodology , there exists a diverse range of other variables that play significant roles in the design and analysis of studies. Below is an overview of some of these variables, highlighting their definitions and roles within research studies:

Discrete variables : A discrete variable is a quantitative variable that represents quantitative data , such as the number of children in a family or the number of cars in a parking lot. Discrete variables can only take on specific values.
Categorical variables : A categorical variable categorizes subjects or items into groups that do not have a natural numerical order. Categorical data includes nominal variables, like country of origin, and ordinal variables, such as education level.
Predictor variables : Often used in statistical models, a predictor variable is used to forecast or predict the outcomes of other variables, not necessarily with a causal implication.
Outcome variables : These variables represent the results or outcomes that researchers aim to explain or predict through their studies. An outcome variable is central to understanding the effects of predictor variables.
Latent variables : Not directly observable, latent variables are inferred from other, directly measured variables. Examples include psychological constructs like intelligence or socioeconomic status.
Composite variables : Created by combining multiple variables, composite variables can measure a concept more reliably or simplify the analysis. An example would be a composite happiness index derived from several survey questions .
Preceding variables : These variables come before other variables in time or sequence, potentially influencing subsequent outcomes. A preceding variable is crucial in longitudinal studies to determine causality or sequences of events.

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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

You need to understand and be able to evaluate their application in other people's research.
You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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Types of Variables – A Comprehensive Guide

Published by Carmen Troy at August 14th, 2021 , Revised On October 26, 2023

A variable is any qualitative or quantitative characteristic that can change and have more than one value, such as age, height, weight, gender, etc.

Before conducting research, it’s essential to know what needs to be measured or analysed and choose a suitable statistical test to present your study’s findings.

In most cases, you can do it by identifying the key issues/variables related to your research’s main topic.

Example: If you want to test whether the hybridisation of plants harms the health of people. You can use the key variables like agricultural techniques, type of soil, environmental factors, types of pesticides used, the process of hybridisation, type of yield obtained after hybridisation, type of yield without hybridisation, etc.

Variables are broadly categorised into:

Independent variables
Dependent variable
Control variable

Independent Vs. Dependent Vs. Control Variable

Type of variable

Definition

Example

Independent Variable (Stimulus)

It is the variable that influences other variables.

Dependent variable (Response)

The dependent variable is the outcome of the influence of the independent variable.

You want to identify “How refined carbohydrates affect the health of human beings?”

: refined carbohydrates

: the health of human beings

You can manipulate the consumption of refined carbs in your human participants and measure how those levels of consuming processed carbohydrates influence human health.

Control Variables

Control variables are variables that are not changed and kept constant throughout the experiment.

The research includes finding ways:

To change the independent variables.
To prevent the controlled variables from changing.
To measure the dependent variables.

Note: The term dependent and independent is not applicable in correlational research as this is not a controlled experiment. A researcher doesn’t have control over the variables. The association and between two or more variables are measured. If one variable affects another one, then it’s called the predictor variable and outcome variable.

Example: Correlation between investment (predictor variable) and profit (outcome variable)

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Types of Variables Based on the Types of Data

A data is referred to as the information and statistics gathered for analysis of a research topic. Data is broadly divided into two categories, such as:

Quantitative/Numerical data is associated with the aspects of measurement, quantity, and extent.

Categorial data is associated with groupings.

A qualitative variable consists of qualitative data, and a quantitative variable consists of a quantitative variable.

Quantitative Variable

The quantitative variable is associated with measurement, quantity, and extent, like how many . It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population.

Example: Find out the weight of students of the fifth standard studying in government schools.

The quantitative variable can be further categorised into continuous and discrete.

Type of variable

Definition

Example

Continuous Variable

A continuous variable is a quantitative variable that can take a value between two specific values.

Discrete Variable

A discrete variable is a quantitative variable whose attributes are separated from each other.

Literacy rate, gender, and nationality.

Scale: Nominal and ordinal.

Categorial Variable

The categorical variable includes measurements that vary in categories such as names but not in terms of rank or degree. It means one level of a categorical variable cannot be considered better or greater than another level.

Example: Gender, brands, colors, zip codes

The categorical variable is further categorised into three types:

Type of variable	Definition	Example
Dichotomous (Binary) Variable	This is the categorical variable with two possible results (Yes/No)	Alcoholic (Yes/No)
Nominal Variable	Nominal Variable can take the value that is not organised in terms of groups, degree, or rank.
Ordinal Variable	Ordinal Variable can take the value that can be logically ordered or ranked.

Note: Sometimes, an ordinal variable also acts as a quantitative variable. Ordinal data has an order, but the intervals between scale points may be uneven.

Example: Numbers on a rating scale represent the reviews’ rank or range from below average to above average. However, it also represents a quantitative variable showing how many stars and how much rating is given.

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Other Types of Variables

It’s important to understand the difference between dependent and independent variables and know whether they are quantitative or categorical to choose the appropriate statistical test.

There are many other types of variables to help you differentiate and understand them.

Also, read a comprehensive guide written about inductive and deductive reasoning .

Type of variable

Definition

Example

Confounding variables

The confounding variable is a hidden variable that produces an association between two unrelated variables because the hidden variable affects both of them.

There is an association between water consumption and cold drink sales.

The confounding variable could be the and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat.

Latent Variable

These are the variables that cannot be observed or measured directly.

Self-confidence and motivation cannot be measured directly. Still, they can be interpreted through other variables such as habits, achievements, perception, and lifestyle.

Composite variables

A composite variable is a combination of multiple variables. It is used to measure multidimensional aspects that are difficult to observe.

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Database management, storage, and retrieval

Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

Independent
Confounding
Categorical
Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

What is a Variable?

By Jim Frost Leave a Comment

The definition of a variable changes depending on the context. Typically, a letter represents them, and it stands in for a numerical value. In algebra, a variable represents an unknown value that you need to find. For mathematical functions and equations, you input their values to calculate the output. In an equation, a coefficient is a fixed value by which you multiply the variable.

In statistics , a variable is a characteristic of interest that you measure, record, and analyze. Statisticians understand them by defining the type of information they record and their role in an experiment or study.

In this post, learn about the different kinds of variables in statistics and their functions in experiments.

Variables Record Different Types of Information

Statisticians have devised various methods for categorizing variables to help you understand their differences. Below are several key ways to group them by the information they record.

Quantitative vs. Qualitative

Quantitative variables record amounts and quantities. For example, you used 15.7 gallons on your latest road trip. You walked 11,353 steps yesterday. The plant grew 5.6 cm in a week. Each of these examples quantifies a characteristic.

Qualitative or categorical variables define groups in your data. Frequently, you use descriptive language for these groups. For example, marital status, college major, type of fiction (drama, comedy, science fiction, etc.), and architectural style are all categorical and form groups in your data.

In an experiment, the treatment condition is a categorical variable that forms the experimental groups. In a plant fertilizer experiment, treatment condition divides the specimens into the control group and other groups based on fertilizer type.

Learn more about Quantitative vs. Qualitative Data .

Discrete vs. Continuous

When you have a quantitative variable, it can be discrete or continuous.

In broad terms, the difference between the two is the following:

You count discrete data.
You measure continuous data .

Discrete variables can only take on specific values that you cannot subdivide. Frequently, discrete data are values that you count and, consequently, are nonnegative integers. For example, you can count the number of people in your household and the number of steps per day.

Continuous variables can assume any value and you can meaningfully divide them into smaller parts, such as fractional and decimal values. Theoretically, continuous data have infinite values between any two values. Typically, you measure them using a scale.

For example, you have continuous data when measuring weight, height, length, time, and temperature.

Related post : Discrete vs. Continuous Data

Statisticians have devised various methods for categorizing data by the types of information they contain. To learn about another approach for organizing data types, read my post about Nominal, Ordinal, Interval, and Ratio Scales .

Random Variables

In statistics, most of the data you analyze are random variables, which are functions describing all values that occur during a series of random events or experiments. They can represent categorical, discrete, and continuous data. Examples include the following:

Flipping coins or rolling dice and recording the results.
Drawing a random sample and measuring heights.
Performing a fertilizer experiment and recording plant growth.

In the preceding examples, an event provides a single value. However, a random variable comprises the entire set of possible values in your sample space.

For random variables, statisticians frequently assess the distribution of possible values, including the central tendency, spread, and skewness . Additionally, probability distribution functions describe the likelihood of obtaining particular values. All these properties provide vital information about the attribute you’re studying.

Related posts : Measures of Central Tendency , Measures of Variability , and Understanding Probability Distributions

Variables Play Different Roles in an Experiment

Finally, thinking about a variable’s role in an experiment or statistical study can help you better understand it.

Dependent Variables

In an experiment, you measure an outcome variable of interest. If you’re studying plant growth, infection rates, or bone density, that will be the outcome you measure. We call these dependent variables because their values depend on other variables in the study that I discuss below.

Independent Variables

In true experiments, researchers control the experimental conditions by assigning each subject to a treatment or control group. In other words, they can set the value of the variable they think will cause changes in the outcome. For example, in a plant growth study, the researchers control whether each plant receives fertilizer or not. When determining if a new vaccine reduces infection rates, they assign participants to either the vaccine or placebo group. Statisticians refer to this type of variable as an independent variable.

Learn more about Independent and Dependent Variables .

Control Variables

Control variables are not the primary focus of the research, but they are properties that researchers need to monitor because they can influence the outcome. Failure to incorporate them into a study can bias the findings. To prevent this bias, scientists can either hold these characteristics constant during the study or let them vary and include them in their models to control them statistically.

Suppose you’re performing a plant growth experiment, and you’re using several types of fertilizer and a control group with no fertilizer. The researchers might measure additional attributes that also affect plant growth. For example, they can record the temperature, moisture, and light conditions.

Learn more about Control Variables .

For more information about graphing and analyzing data for different types of variables, read the following posts:

Data Types and How to Graph Them
Hypothesis Testing by Data Types
Choosing the Correct Type of Regression Analysis

Stevens, S.S., On the Theory of Scales of Measurement, Science, 1946

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Types of Variables in Research – Definition & Examples

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A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design . This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes.

Inhaltsverzeichnis

1 Types of Variables in Research – In a Nutshell
2 Definition: Types of variables in research
3 Types of variables in research – Quantitative vs. Categorical
4 Types of variables in research – Independent vs. Dependent
5 Other useful types of variables in research

Types of Variables in Research – In a Nutshell

A variable is an attribute of an item of analysis in research.
The types of variables in research can be categorized into: independent vs. dependent , or categorical vs. quantitative .
The types of variables in research (correlational) can be classified into predictor or outcome variables.
Other types of variables in research are confounding variables , latent variables , and composite variables.

Definition: Types of variables in research

A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas , or other research objects . There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study.

Note that the correct variable will help with your research design , test selection, and result interpretation.

In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors.

Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents.

Types of variables in research – Quantitative vs. Categorical

Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes.

Quantitative or numerical data represents amounts, while categorical data represents collections or groupings.

The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better.

Quantitative variables

The scores you record when collecting quantitative data usually represent real values you can add, divide , subtract , or multiply . There are two types of quantitative variables: discrete variables and continuous variables .

The table below explains the elements that set apart discrete and continuous types of variables in research:


Discrete or integer variables	Individual item counts or values	• Number of employees in a company • Number of students in a school district
Continuous or ratio variables	Measurements of non-finite or continuous scores	• Age • Weight • Volume • Distance

Categorical variables

Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers . However, the numbers represent categories instead of real amounts.

There are three categorical types of variables in research: nominal variables, ordinal variables , and binary variables . Here is a tabular summary.


Binary/dichotomous variables	YES/NO outcomes	• Win/lose in a game • Pass/fail in an exam
Nominal variables	No-rank groups or orders between groups	• Colors • Participant name • Brand names
Ordinal variables	Groups ranked in a particular order	• Performance rankings in an exam • Rating scales of survey responses

It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete.

Data sheet of quantitative and categorical variables

A data sheet is where you record the data on the variables in your experiment.

In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet.

The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process.

Below is an example of a data sheet containing binary, nominal, continuous , and ordinal types of variables in research.


A	12	0	-	-	-
A	18	50	-	-	-
B	11	0	-	-	-
B	15	50	-	-	-
C	25	0	-	-	-
C	31	50	-	-	-

Types of variables in research – Independent vs. Dependent

types-of-variables-in-research-Dependent-independet-and-constant-variable

The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival.

Therefore, the researcher manipulates the independent variables and measures the dependent variables . Additionally, you may have control variables that you hold constant.

The table below summarizes independent variables, dependent variables , and control variables .


Independent/ treatment variables	The variables you manipulate to affect the experiment outcome	The amount of salt added to the water
Dependent/ response variables	The variable that represents the experiment outcomes	The plant’s growth or survival
Control variables	Variables held constant throughout the study	Temperature or light in the experiment room

Data sheet of independent and dependent variables

In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent.

Below is a data sheet based on our experiment:

Types of variables in correlational research

The types of variables in research may differ depending on the study.

In correlational research , dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables.

However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable.

Other useful types of variables in research

The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study.

Below are other types of variables in research worth understanding.


Confounding variables	Hides the actual impact of an alternative variable in your study	Pot size and soil type
Latent variables	Cannot be measured directly	Salt tolerance
Composite variables	Formed by combining multiple variables	The health variables combined into a single health score

What is the definition for independent and dependent variables?

An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study.

What are quantitative and categorical variables?

Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design.

Discrete and continuous variables: What is their difference?

Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight.

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Research Variables

The research variables, of any scientific experiment or research process, are factors that can be manipulated and measured.

This article is a part of the guide:

Experimental Research
Pretest-Posttest
Third Variable
Research Bias
Independent Variable

Browse Full Outline

1 Experimental Research
2.1 Independent Variable
2.2 Dependent Variable
2.3 Controlled Variables
2.4 Third Variable
3.1 Control Group
3.2 Research Bias
3.3.1 Placebo Effect
3.3.2 Double Blind Method
4.1 Randomized Controlled Trials
4.2 Pretest-Posttest
4.3 Solomon Four Group
4.4 Between Subjects
4.5 Within Subject
4.6 Repeated Measures
4.7 Counterbalanced Measures
4.8 Matched Subjects

Any factor that can take on different values is a scientific variable and influences the outcome of experimental research .

Most scientific experiments measure quantifiable factors, such as time or weight, but this is not essential for a component to be classed as a variable.

As an example, most of us have filled in surveys where a researcher asks questions and asks you to rate answers. These responses generally have a numerical range, from ‘1 - Strongly Agree’ through to ‘5 - Strongly Disagree’. This type of measurement allows opinions to be statistically analyzed and evaluated.

Dependent and Independent Variables

The key to designing any experiment is to look at what research variables could affect the outcome.

There are many types of variable but the most important, for the vast majority of research methods, are the independent and dependent variables.

The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design . For many physical experiments , isolating the independent variable and measuring the dependent is generally easy.

If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated independent variable is time and the dependent measured variable is temperature.

In other fields of science, the variables are often more difficult to determine and an experiment needs a robust design. Operationalization is a useful tool to measure fuzzy concepts which do not have one obvious variable.

The Difficulty of Isolating Variables

In biology , social science and geography, for example, isolating a single independent variable is more difficult and any experimental design must consider this.

For example, in a social research setting, you might wish to compare the effect of different foods upon hyperactivity in children. The initial research and inductive reasoning leads you to postulate that certain foods and additives are a contributor to increased hyperactivity. You decide to create a hypothesis and design an experiment , to establish if there is solid evidence behind the claim.

The type of food is an independent variable, as is the amount eaten, the period of time and the gender and age of the child. All of these factors must be accounted for during the experimental design stage. Randomization and controls are generally used to ensure that only one independent variable is manipulated.

To eradicate some of these research variables and isolate the process, it is essential to use various scientific measurements to nullify or negate them.

For example, if you wanted to isolate the different types of food as the manipulated variable, you should use children of the same age and gender.

The test groups should eat the same amount of the food at the same times and the children should be randomly assigned to groups. This will minimize the physiological differences between children. A control group , acting as a buffer against unknown research variables, might involve some children eating a food type with no known links to hyperactivity.

In this experiment, the dependent variable is the level of hyperactivity, with the resulting statistical tests easily highlighting any correlation . Depending upon the results , you could try to measure a different variable, such as gender, in a follow up experiment.

Converting Research Variables Into Constants

Ensuring that certain research variables are controlled increases the reliability and validity of the experiment, by ensuring that other causal effects are eliminated. This safeguard makes it easier for other researchers to repeat the experiment and comprehensively test the results.

What you are trying to do, in your scientific design, is to change most of the variables into constants, isolating the independent variable. Any scientific research does contain an element of compromise and inbuilt error , but eliminating other variables will ensure that the results are robust and valid .

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Martyn Shuttleworth (Aug 9, 2008). Research Variables. Retrieved Jun 30, 2024 from Explorable.com: https://explorable.com/research-variables

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Types of variables and commonly used statistical designs.

Jacob Shreffler ; Martin R. Huecker .

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Last Update: March 6, 2023 .

Definition/Introduction

Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study. [1] Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis. [1] Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of the types of variables and commonly used designs to facilitate this understanding. [2]

Issues of Concern

Individuals who attempt to conduct research and choose an inappropriate design could select a faulty test and make flawed conclusions. This decision could lead to work being rejected for publication or (worse) lead to erroneous clinical decision-making, resulting in unsafe practice. [1] By understanding the types of variables and choosing tests that are appropriate to the data, individuals can draw appropriate conclusions and promote their work for an application. [3]

To determine which statistical design is appropriate for the data and research plan, one must first examine the scales of each measurement. [4] Multiple types of variables determine the appropriate design.

Ordinal data (also sometimes referred to as discrete) provide ranks and thus levels of degree between the measurement. [5] Likert items can serve as ordinal variables, but the Likert scale, the result of adding all the times, can be treated as a continuous variable. [6] For example, on a 20-item scale with each item ranging from 1 to 5, the item itself can be an ordinal variable, whereas if you add up all items, it could result in a range from 20 to 100. A general guideline for determining if a variable is ordinal vs. continuous: if the variable has more than ten options, it can be treated as a continuous variable. [7] The following examples are ordinal variables:

Likert items
Cancer stages
Residency Year

Nominal, Categorical, Dichotomous, Binary

Other types of variables have interchangeable terms. Nominal and categorical variables describe samples in groups based on counts that fall within each category, have no quantitative relationships, and cannot be ranked. [8] Examples of these variables include:

Service (i.e., emergency, internal medicine, psychiatry, etc.)
Mode of Arrival (ambulance, helicopter, car)

A dichotomous or a binary variable is in the same family as nominal/categorical, but this type has only two options. Binary logistic regression, which will be discussed below, has two options for the outcome of interest/analysis. Often used as (yes/no), examples of dichotomous or binary variables would be:

Alive (yes vs. no)
Insurance (yes vs. no)
Readmitted (yes vs. no)

With this overview of the types of variables provided, we will present commonly used statistical designs for different scales of measurement. Importantly, before deciding on a statistical test, individuals should perform exploratory data analysis to ensure there are no issues with the data and consider type I, type II errors, and power analysis. Furthermore, investigators should ensure appropriate statistical assumptions. [9] [10] For example, parametric tests, including some discussed below (t-tests, analysis of variance (ANOVA), correlation, and regression), require the data to have a normal distribution and that the variances within each group are similar. [6] [11] After eliminating any issues based on exploratory data analysis and reducing the likelihood of committing type I and type II errors, a statistical test can be chosen. Below is a brief introduction to each of the commonly used statistical designs with examples of each type. An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs.

Commonly Used Statistical Designs

Independent Samples T-test

An independent samples t-test allows a comparison of two groups of subjects on one (continuous) variable. Examples in biomedical research include comparing results of treatment vs. control group and comparing differences based on gender (male vs. female).

Example: Does adherence to the ketogenic diet (yes/no; two groups) have a differential effect on total sleep time (minutes; continuous)?

Paired T-test

A paired t-test analyzes one sample population, measuring the same variable on two different occasions; this is often useful for intervention and educational research.

Example : Does participating in a research curriculum (one group with intervention) improve resident performance on a test to measure research competence (continuous)?

One-Way Analysis of Variance (ANOVA)

Analysis of variance (ANOVA), as an extension of the t-test, determines differences amongst more than two groups, or independent variables based on a dependent variable. [11] ANOVA is preferable to conducting multiple t-tests as it reduces the likelihood of committing a type I error.

Example: Are there differences in length of stay in the hospital (continuous) based on the mode of arrival (car, ambulance, helicopter, three groups)?

Repeated Measures ANOVA

Another procedure commonly used if the data for individuals are recurrent (repeatedly measured) is a repeated-measures ANOVA. [1] In these studies, multiple measurements of the dependent variable are collected from the study participants. [11] A within-subjects repeated measures ANOVA determines effects based on the treatment variable alone, whereas mixed ANOVAs allow both between-group effects and within-subjects to be considered.

Within-Subjects Example: How does ketamine effect mean arterial pressure (continuous variable) over time (repeated measurement)?

Mixed Example: Does mean arterial pressure (continuous) differ between males and females (two groups; mixed) on ketamine throughout a surgical procedure (over time; repeated measurement)?

Nonparametric Tests

Nonparametric tests, such as the Mann-Whitney U test (two groups; nonparametric t-test), Kruskal Wallis test (multiple groups; nonparametric ANOVA), Spearman’s rho (nonparametric correlation coefficient) can be used when data are ordinal or lack normality. [3] [5] Not requiring normality means that these tests allow skewed data to be analyzed; they require the meeting of fewer assumptions. [11]

Example: Is there a relationship between insurance status (two groups) and cancer stage (ordinal)?

A Chi-square test determines the effect of relationships between categorical variables, which determines frequencies and proportions into which these variables fall. [11] Similar to other tests discussed, variants and extensions of the chi-square test (e.g., Fisher’s exact test, McNemar’s test) may be suitable depending on the variables. [8]

Example: Is there a relationship between individuals with methamphetamine in their system (yes vs. no; dichotomous) and gender (male or female; dichotomous)?

Correlation

Correlations (used interchangeably with ‘associations’) signal patterns in data between variables. [1] A positive association occurs if values in one variable increase as values in another also increase. A negative association occurs if variables in one decrease while others increase. A correlation coefficient, expressed as r, describes the strength of the relationship: a value of 0 means no relationship, and the relationship strengthens as r approaches 1 (positive relationship) or -1 (negative association). [5]

Example: Is there a relationship between age (continuous) and satisfaction with life survey scores (continuous)?

Linear Regression

Regression allows researchers to determine the degrees of relationships between a dependent variable and independent variables and results in an equation for prediction. [11] A large number of variables are usable in regression methods.

Example: Which admission to the hospital metrics (multiple continuous) best predict the total length of stay (minutes; continuous)?

Binary Logistic Regression

This type of regression, which aims to predict an outcome, is appropriate when the dependent variable or outcome of interest is binary or dichotomous (yes/no; cured/not cured). [12]

Example: Which panel results (multiple of continuous, ordinal, categorical, dichotomous) best predict whether or not an individual will have a positive blood culture (dichotomous/binary)?

An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs.

(See Types of Variables and Statistical Designs Table 1)

Clinical Significance

Though numerous other statistical designs and extensions of methods covered in this article exist, the above information provides a starting point for healthcare providers to become acquainted with variables and commonly used designs. Researchers should study types of variables before determining statistical tests to obtain relevant measures and valid study results. [6] There is a recommendation to consult a statistician to ensure appropriate usage of the statistical design based on the variables and that the assumptions are upheld. [1] With the variety of statistical software available, investigators must a priori understand the type of statistical tests when designing a study. [13] All providers must interpret and scrutinize journal publications to make evidence-based clinical decisions, and this becomes enhanced by a limited but sound understanding of variables and commonly used study designs. [14]

Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to be familiar with study design and the variables used in studies to accurately evaluate new data and studies as they are published and apply the latest data to patient care and drive optimal outcomes.

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Types of Variables and Statistical Designs Table 1 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

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

Cite this Page Shreffler J, Huecker MR. Types of Variables and Commonly Used Statistical Designs. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Elements of Research

The purpose of all research is to describe and explain in the world. Variance is simply the difference; that is, variation that occurs naturally in the world or change that we create as a result of a manipulation. Variables are names that are given to the variance we wish to explain.

A variable is either a result of some force or is itself the force that causes a change in another variable. In experiments, these are called and variables respectively. When a researcher gives an active drug to one group of people and a placebo , or inactive drug, to another group of people, the independent variable is the drug treatment. Each person's response to the active drug or is called the dependent variable. This could be many things depending upon what the drug is for, such as high blood pressure or muscle pain. Therefore in experiments, a researcher manipulates an independent variable to determine if it causes a change in the dependent variable.

As we learned earlier in a descriptive study, variables are not manipulated. They are observed as they naturally occur and then associations between variables are studied. In a way, all the variables in descriptive studies are dependent variables because they are studied in relation to all the other variables that exist in the setting where the research is taking place. However, in descriptive studies, variables are not discussed using the terms "independent" or "dependent." Instead, the names of the variables are used when discussing the study. For example, there is more diabetes in people of Native American heritage than people who come from Eastern Europe. In a descriptive study, the researcher would examine how diabetes (a variable) is related to a person's genetic heritage (another variable).

Variables are important to understand because they are the basic units of the information studied and interpreted in research studies. Researchers carefully analyze and interpret the value(s) of each variable to make sense of how things relate to each other in a descriptive study or what has happened in an experiment.

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The Different Types Of Variables Used In Research And Statistics

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Scientists and statisticians conduct experiments on a regular basis. Scientists use these experiments to identify cause and effect, while Statisticians use variables to represent the unknown or varied data in their experiments.

Determining which variables to use is vital to the experiment. Also, choosing the right variables will lead to clearer analyses and more accurate results.

What Is a Variable?

A variable is something you can control, manipulate, or measure when conducting research or experiments. They are characteristics, numbers, or quantities and can represent specific items, people, places, or an idea. The variables may be referred to as data items.

The variables in an experiment will vary depending on the desired outcome. All scientific experiments and statistical studies will analyze a variable.

They are referred to as variables because the values can vary. A variable’s value can change within a single experiment. Whether there is a change between the groups being studied or the value changes over time, it may not necessarily be a constant.

Designing Experiments

As noted, all scientific experiments and statistical studies will control, manipulate, or measure a variable. In fact, the experiments are usually designed to determine the effect one variable has on another variable, cause, and effect.

When designing experiments, it is extremely important to choose the right variables. Choosing incorrectly can skew the results and derail the experiment or study completely. Choosing right can help an experiment or study run much more smoothly and produce more accurate results.

It is not just the specific variable within the experiment that needs to be determined, but the variable type as well. Knowing the variable type will allow you to interpret the results of the experiment or study.

It should be noted, though, that categorizing variables is a little subjective. Scientists and statisticians have some wiggle room when they categorize their experiment variables.

Generally, you will need to know what data the variable represents and what part of the experiment the variable represents in order to determine the variable type.

Independent, Dependent, and Control Variables

Typically, there will be an independent variable, dependent variable, and control variable in every experiment or study conducted.

Independent variables. Independent variables are the variables in your experiment that are being manipulated. They are referred to as independent variables due to the fact that their value is independent of other variables, which means that the other variables cannot change the independent variable.

Dependent variables. Dependent variables are the variables in your experiment that rely on other variables and can be changed or manipulated by the other variables being measured.

Control variables. Control variables are the variables in your experiment that are constant. They do not change over the course of the experiment or study and will have no direct effect on the other variables being measured.

Qualitative Versus Quantitative Variables

Every single variable you include in your experiment will need to be categorized as either a qualitative variable or a quantitative variable.

Qualitative variables. Also referred to as categorical variables, qualitative variables are any variables that hold no numerical value. They are nominal labels. For example, eye color would be a qualitative variable. The data being recorded is not a number but a color.

These variables don’t necessarily measure, but they describe a characteristic of the data set. They can be broken down further as either ordinal variables or nominal variables (see below for definitions).

Quantitative variables. Quantitative variables, or numeric variables, are the variables in your experiment that hold a numerical value. Unsurprisingly, they will represent a measurable quantity and will be recorded as a number.

These variables will measure “how many” or “how much” of the data being collected. These can be broken down further as either continuous variables or discrete variables (see below for definitions).

30 Other Variable Types Used in Experiments

This is by no means a comprehensive list, as the list of all variable types would be difficult to document in one place.

Below are many of the common and some less common variable types used in scientific experiments and statistical studies. Included is a brief overview of what that variable type measures.

Active variable. An active variable is a variable that can be manipulated by those running the experiment.

Antecedent variable. Antecedent variables come before the independent and dependent variables. With “antecedent” meaning “preceding in time or order,” this is not surprising.

Attribute variable. An attribute variable also called a passive variable, is not manipulated during the experiment. It may be a fixed variable or simply a variable that is not manipulated for one experiment but could be for another.

Binary variable. Binary variables only have two values. Typically, this will be represented as a zero or one but can be yes/no or another two-value combination.

Categorical variable. Categorical variables are variables that can be divided into larger buckets or categories. Shoe brands, for instance, could include Nike, Reebok, or Adidas.

Composite variable. This variable type is a bit different from others. A composite variable is made up of two or more other variables. The individual variables that make up the composite variable will be closely related either conceptually or statistically.

Confounding variable. Confounding variables are not good. They can affect both independent and dependent variables and invalidate results. Sometimes referred to as a lurking variable, these variables are considered “extra” and were not accounted for during the designing phase.

Continuous variable. Continuous variables have an infinite number of values between the highest point and lowest point. Distance is a continuous variable.

Covariate variable. A covariate variable can affect the dependent variable in addition to the independent variable. It will not be of interest in the results of the experiment, though.

Criterion variable. This is a statistical variable only. It is another name for the dependent variable.

Dichotomous variable. This is another name for a binary variable. Dichotomous variables will have two values only.

Discrete variable. Discrete variables are the opposite of continuous variables. Where continuous variables have an infinite number of possible values, discrete variables have a finite number.

Endogenous variable. Endogenous variables are dependent on other variables and are used only in statistical studies, in econometrics specifically. The value of these variables is determined by the model.

Exogenous variable. An exogenous variable is the opposite of an endogenous variable. The value of this type of variable is determined outside of the model and will have an impact on other variables within the model.

Explanatory variable. This is a commonly used name for the independent variable or the variable that is being manipulated by those running the experiment.

Grouping variable. A grouping variable is used to sort, or split up, the data set into groups or categories.

Interval variable. Interval variables show the meaningful difference between the two values.

Intervening variable. Intervening variables, or mediator variables, explains the cause, connection, or relationship between two other variables being measured.

Manifest variable. A manifest variable is a variable that can be directly observed or measured within the experiment.

Moderating variable. A moderating variable can affect the relationship between the independent variable and dependent variable. It can either strengthen, diminish, or negate the relationship.

Nominal variable. This is another way of saying categorical value. Nominal values will have two or more categories.

Observed variable. Observed variables are variables that are being measured during the experiment.

Ordinal variable. Ordinal variables are similar to categorical or nominal variables but have a clear ordering of categories. Examples such as High to low and like to dislike would both be ordinal variables.

Polychotomous variable. Polychotomous variables have more than two possible categories or values. These can be either nominal or ordinal.

Ranked variable. Ranked variables are ordinal variables. The researcher may not know the exact value, but they will know the order in which the data points should fall.

Ratio variable. Ratio variables are similar to interval variables but have a clear definition of zero.

Responding variable. Responding variables are the effect or outcome of the experiment. Similar to dependent variables, responding variables will “respond” to changes being made in the experiment.

Scale variable. A scale variable is a variable that has a numeric value that can be ordered with a meaningful metric. It will be the amount or number of something.

Study variable. Often referred to as a research variable, a study variable is any variable used that has some kind of cause and effect relationship.

Test variable. A test variable also referred to as the dependent variable, is a variable that represents the outcome of the experiment.

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Chris Kolmar is a co-founder of Zippia and the editor-in-chief of the Zippia career advice blog. He has hired over 50 people in his career, been hired five times, and wants to help you land your next job. His research has been featured on the New York Times, Thrillist, VOX, The Atlantic, and a host of local news. More recently, he's been quoted on USA Today, BusinessInsider, and CNBC.

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Methodology

Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

The type of knowledge you aim to produce
The type of data you will collect and analyze
The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

Type of research	What’s the difference?	What to consider
Basic vs. applied	Basic research aims to , while applied research aims to .	Do you want to expand scientific understanding or solve a practical problem?
vs.	Exploratory research aims to , while explanatory research aims to .	How much is already known about your research problem? Are you conducting initial research on a newly-identified issue, or seeking precise conclusions about an established issue?
	aims to , while aims to .	Is there already some theory on your research problem that you can use to develop , or do you want to propose new theories based on your findings?

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Type of research	What’s the difference?	What to consider
Primary research vs secondary research	Primary data is (e.g., through or ), while secondary data (e.g., in government or scientific publications).	How much data is already available on your topic? Do you want to collect original data or analyze existing data (e.g., through a )?
	, while .	Is your research more concerned with measuring something or interpreting something? You can also create a research design that has elements of both.
vs	Descriptive research gathers data , while experimental research .	Do you want to identify characteristics, patterns and or test causal relationships between ?

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Type of research	What’s the difference?	What to consider
	allows you to , while allows you to draw conclusions .	Do you want to produce knowledge that applies to many contexts or detailed knowledge about a specific context (e.g. in a )?
vs	Cross-sectional studies , while longitudinal studies .	Is your research question focused on understanding the current situation or tracking changes over time?
Field research vs laboratory research	Field research takes place in , while laboratory research takes place in .	Do you want to find out how something occurs in the real world or draw firm conclusions about cause and effect? Laboratory experiments have higher but lower .
Fixed design vs flexible design	In a fixed research design the subjects, timescale and location are begins, while in a flexible design these aspects may .	Do you want to test hypotheses and establish generalizable facts, or explore concepts and develop understanding? For measuring, testing and making generalizations, a fixed research design has higher .

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

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

Research bias

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

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What is a Zestimate?

The Zestimate® home valuation model is Zillow’s estimate of a home’s market value. A Zestimate incorporates public, MLS and user-submitted data into Zillow’s proprietary formula, also taking into account home facts, location and market trends. It is not an appraisal and can’t be used in place of an appraisal.

How accurate is the Zestimate?

The nationwide median error rate for the Zestimate for on-market homes is 2.4%, while the Zestimate for off-market homes has a median error rate of 7.49%. The Zestimate’s accuracy depends on the availability of data in a home’s area. Some areas have more detailed home information available — such as square footage and number of bedrooms or bathrooms — and others do not. The more data available, the more accurate the Zestimate value will be.

These tables break down the accuracy of Zestimates for both active listings and off-market listings.

Active listings accuracy

Last updated: April 27, 2023

Note: The Zestimate’s accuracy is computed by comparing the final sale price to the Zestimate that was published on or just prior to the sale date.

Download an Excel spreadsheet of this data .

How is the Zestimate calculated?

Zillow publishes Zestimate home valuations for 104 million homes across the country, and uses state of the art statistical and machine learning models that can examine hundreds of data points for each individual home.

To calculate a Zestimate, Zillow uses a sophisticated neural network-based model that incorporates data from county and tax assessor records and direct feeds from hundreds of multiple listing services and brokerages. The Zestimate also incorporates:

Home characteristics including square footage, location or the number of bathrooms.
On-market data such as listing price, description, comparable homes in the area and days on the market
Off-market data — tax assessments, prior sales and other publicly available records
Market trends, including seasonal changes in demand

Currently, we have data for over 110 million U.S. homes and we publish Zestimates for 104 million of them.

What changes are in the latest Zestimate?

The latest Zestimate model is our most accurate Zestimate yet. It’s based on a neural network model and uses even more historical data to produce off-market home valuations. This means the Zestimate is more responsive to market trends & seasonality that may affect a home’s market value. We also reduced overall errors and processing time in the Zestimate.

My Zestimate seems too low or too high. What gives?

The amount of data we have for your home and homes in your area directly affects the Zestimate’s accuracy, including the amount of demand in your area for homes. If the data is incorrect or incomplete, update your home facts — this may affect your Zestimate. To ensure the most accurate Zestimate, consider reporting any home updates to your local tax assessor. Unreported additions, updates and remodels aren’t reflected in the Zestimate.

Check that your tax history and price history (the sale price and date you bought your home) are accurate on Zillow. If data is missing or incorrect, let us know .

Be aware that the model that creates the Zestimate factors in changing market trends, including seasonal fluctuations in demand. So in some cases that may be the reason for a change in your Zestimate.

I just listed my home for sale. Why did my Zestimate change?

When a home goes on the market, new data can be incorporated into the Zestimate algorithm. In the simplest terms, the Zestimate for on-market homes includes listing data that provides valuable signals about the home’s eventual sale price. This data isn’t available for off-market homes.

My home is on the market. Why is the Zestimate so far off?

Properties that have been listed for a full year transition to off-market valuations because they have been listed longer than normal for that local market. This can result in a large difference between the list price and the Zestimate.

I just changed my home facts. When will my Zestimate update?

Updates to your home facts are factored into the Zestimate. However, if the updates are not significant enough to affect the home’s value (eg: paint colors), your Zestimate may not change. Zestimates for all homes update multiple times per week, but on rare occasions this schedule is interrupted by algorithmic changes or new analytical features.

How are changes to my home facts (like an additional bedroom or bathroom) valued?

The Zestimate is based on complex and proprietary algorithms that can incorporate millions of data points. The algorithms determine the approximate added value that an additional bedroom or bathroom contributes, though the amount of the change depends on many factors, including local market trends, location and other home facts.

Is the Zestimate an appraisal?

No. The Zestimate is not an appraisal and can’t be used in place of an appraisal. It is a computer-generated estimate of the value of a home today, given the available data.

We encourage buyers, sellers and homeowners to supplement the Zestimate with other research, such as visiting the home, getting a professional appraisal of the home, or requesting a comparative market analysis (CMA) from a real estate agent.

Why do I see home values for the past?

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Do you ever change historical Zestimates?

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However, we never allow future information to influence a historical Zestimate (for example, a sale in 2019 could not influence a 2018 Zestimate). Historical Zestimates only use information known prior to the date of that Zestimate.

Does the Zestimate algorithm ever change?

Yes — Zillow’s team of researchers and engineers work every day to make the Zestimate more accurate. Since Zillow’s founding in 2006, we have deployed multiple major Zestimate algorithm updates and other incremental improvements are consistently released between major upgrades.

How often are Zestimates for homes updated?

We refresh Zestimates for all homes multiple times per week, but on rare occasions this schedule is interrupted by algorithmic changes or new analytical features.

Are foreclosure sales included in the Zestimate algorithm?

No. The Zestimate is intended to provide an estimate of the price that a home would fetch if sold for its full value, where the sale isn’t for partial ownership of the property or between family members. Our extensive analysis of foreclosure resale transactions supports the conclusion that these sales are generally made at substantial discounts compared to non-foreclosure sales. For this reason, the Zestimate does not incorporate data about these sales.

Who calculates the Zestimate? Can someone tamper with my home’s Zestimate?

The Zestimate is an automated valuation model calculated by a software process. It’s not possible to manually alter the Zestimate for a specific property.

Can the Zestimate be updated?

Yes. The Zestimate’s accuracy depends on the amount of data we have for the home. Public records can be outdated or lag behind what homeowners and real estate agents know about a property, so it’s best to update your home facts and fix any incorrect or incomplete information — this will help make your Zestimate as accurate as possible.

You can also add info about the architectural style, roof type, heat source, building amenities and more. Remember: updating home information doesn’t guarantee an increase in the value of Zestimate, but will increase the Zestimate’s accuracy.

Does Zillow delete Zestimates? Can I have my Zestimate reviewed if I believe there are errors?

We do not delete Zestimates. However, for some homes we may not have enough data to provide a home valuation that meets our standards for accuracy. In these instances, we do not publish the Zestimate until more data can be obtained. The Zestimate is designed to be a neutral estimate of the fair market value of a home, based on publicly available and user-submitted data. For this purpose, it is important that the Zestimate is based on information about all homes (e.g., beds, baths, square footage, lot size, tax assessment, prior sale price) and that the algorithm itself is consistently applied to all homes in a similar manner.

I don’t know of any homes that have sold recently in my area. How are you calculating my Zestimate?

Zestimates rely on much more than comparable sales in a given area. The home’s physical attributes, historical information and on-market data all factor into the final calculation. The more we know about homes in an area (including your home), the better the Zestimate. Our models can find neighborhoods similar to yours and use sales in those areas to extrapolate trends in your housing market. Our estimating method differs from that of a comparative market analysis completed by a real estate agent. We use data from a geographical area that is much larger than your neighborhood — up to the size of a county — to help calculate the Zestimate. Though there may not be any recent sales in your neighborhood, even a few sales in the area allow us to extrapolate trends in the local housing market.

I’m trying to sell my home and I think my Zestimate should be higher.

The Zestimate was created to give customers more information about homes and the housing market. It is intended to provide user-friendly data to promote transparent real estate markets and allow people to make more informed decisions — it should not be used to drive up the price of a home. Zestimates are designed to track the market, not drive it.

Can I use the Zestimate to get a loan?

No. The Zestimate is an automated value model and not an appraisal. Most lending professionals and institutions will only use professional appraisals when making loan-related decisions.

I have two Zestimates for my home. How do I fix this?

If you see two Zestimates for the same property, please let us know by visiting the Zillow Help Center and s e lecting Submit a request. You may see more than one Zestimate for your address if you are a homeowner with multiple parcels of land. Zillow matches the parcels on record with the county. If you officially combine parcels, the county will send us updated information.

What’s the Estimated Sale Range?

While the Zestimate is the estimated market value for an individual home, the Estimated Sale Range describes the range in which a sale price is predicted to fall, including low and high estimated values. For example, a Zestimate may be $260,503, while the Estimated Sale Range is $226,638 to $307,394. This range can vary for different homes and regions. A wider range generally indicates a more uncertain Zestimate, which might be the result of unique home factors or less data available for the region or that particular home. It’s important to consider the size of the Estimated Sale Range because it offers important context about the Zestimate’s anticipated accuracy.

How can real estate professionals work with the Zestimate?

Millions of consumers visit Zillow every month. When combined with the guidance of real estate professionals, the Zestimate can help consumers make more informed financial decisions about their homes. Real estate professionals can also help their clients claim their home on Zillow, update the home facts and account for any work they have done on the property. A home’s Zillow listing is often the first impression for prospective buyers, and accurate information helps attract interest.

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On Commercial Construction Activity's Long and Variable Lags

David Glancy
Robert Kurtzman
Lara Loewenstein

We use microdata on the phases of commercial construction projects to document three facts regarding time-to-plan lags: (1) plan times are long—about 1.5 years—and highly variable, (2) roughly 40 percent of projects are abandoned in planning, and (3) property price appreciation reduces the likelihood of abandonment. We construct a model with endogenous planning starts and abandonment that matches these facts. The model has the testable implication that supply is more elastic when there are more "shovel ready" projects available to advance to construction. We use local projections to validate that this prediction holds in the cross-section for US cities.

Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.

Suggested Citation

Glancy, David, Robert Kurtzman, and Lara Loewenstein. 2024. “On Commercial Construction Activity's Long and Variable Lags.” Federal Reserve Bank of Cleveland, Working Paper No. 24-14. https://doi.org/10.26509/frbc-wp-202414

Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released.

Volume 30, Number 8—August 2024

Research Letter

Persistence of influenza h5n1 and h1n1 viruses in unpasteurized milk on milking unit surfaces.

Acknowledgments

We thank the Lakdawala lab members, Centers of Excellence for Influenza Research and Response (CEIRR) risk assessment pipeline meeting attendees, Rachel Duron, and Linsey Marr for useful feedback.

This project was funded in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract no. 75N93021C00015 and a National Institutes of Health award (no. UC7AI180311) from the National Institute of Allergy and Infectious Diseases supporting the operations of the University of Pittsburgh Regional Biocontainment Laboratory in the Center for Vaccine Research. H5N1 studies were performed in accordance with select agent permit no. 20230320-074008 at the University of Pittsburgh.

This article was preprinted at https://www.medrxiv.org/content/10.1101/2024.05.22.24307745v1 .

Centers for Disease Control and Prevention . H5N1 bird flu: current situation summary [ cited 2024 Jun 13 ]. https://www.cdc.gov/flu/avianflu/avian-flu-summary.htm
Burrough ER , Magstadt DR , Petersen B , Timmermans SJ , Gauger PC , Zhang J , et al. Highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b virus infection in domestic dairy cattle and cats, United States, 2024. Emerg Infect Dis . 2024 ; 30 : 1335 – 43 . DOI PubMed Google Scholar
Odorčić M , Rasmussen MD , Paulrud CO , Bruckmaier RM . Review: Milking machine settings, teat condition and milking efficiency in dairy cows. Animal . 2019 ; 13 ( S1 ): s94 – 9 . DOI PubMed Google Scholar
Centers for Disease Control and Prevention . Avian influenza (bird flu): reducing risk for people working with or exposed to animals [ cited 2024 Jun 20 ]. https://www.cdc.gov/bird-flu/prevention/worker-protection-ppe.html
Qian Z , Morris DH , Avery A , Kormuth KA , Le Sage V , Myerburg MM , et al. Variability in donor lung culture and relative humidity impact the stability of 2009 pandemic H1N1 influenza virus on nonporous surfaces. Appl Environ Microbiol . 2023 ; 89 : e0063323 . DOI PubMed Google Scholar
Kormuth KA , Lin K , Qian Z , Myerburg MM , Marr LC , Lakdawala SS . Environmental persistence of influenza viruses is dependent upon virus type and host origin. MSphere . 2019 ; 4 : e00552 – 19 . DOI PubMed Google Scholar
Kormuth KA , Lin K , Prussin AJ II , Vejerano EP , Tiwari AJ , Cox SS , et al. Influenza virus infectivity is retained in aerosols and droplets independent of relative humidity. J Infect Dis . 2018 ; 218 : 739 – 47 . DOI PubMed Google Scholar
Figure 1 . Illustration of milking unit surfaces tested in a study of persistence of influenza H5N1 and H1N1 viruses in unpasteurized milk. Before attaching the milking unit (claw), a dairy worker...
Figure 2 . Viral titers in a study of persistence of influenza H5N1 and H1N1 viruses in unpasteurized milk on milking unit surfaces. A) Viral titers of bovine A(H5N1) virus diluted 1:10...

Suggested citation for this article : Le Sage V, Campbell AJ, Reed DS, Duprex WP, Lakdawala SS. Persistence of influenza H5N1 and H1N1 viruses in unpasteurized milk on milking unit surfaces. Emerg Infect Dis. 2024 Aug [ date cited ]. https://doi.org/10.3201/eid3008.240775

DOI: 10.3201/eid3008.240775

Original Publication Date: June 24, 2024

1 These first authors contributed equally to this article.

Table of Contents – Volume 30, Number 8—August 2024

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Open access
Published: 25 June 2024

Rejection in romantic relationships: Does rejection sensitivity modulate emotional responses to perceptions of negative interactions?

Marianne Richter 1 ,
Georgia Kouri 2 ,
Nathalie Meuwly 1 &
Dominik Schoebi 1

BMC Psychology volume 12 , Article number: 365 ( 2024 ) Cite this article

117 Accesses

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Rejection is a highly stressful experience and individuals tend to avoid it whenever possible. In intimate relationships, experiences of rejection can shape the interaction dynamics between partners. Highly rejection sensitive people fear that their romantic partner will reject them and they overreact to any ambiguous cues that might indicate rejection. Furthermore, because they focus on the threat of rejection, they may have difficulty disengaging from rejection-related emotions, persevere in a rejection-focused state and have a reduced capacity to regulate their emotions. The prolonged experience of strong negative emotions, together with maladaptive attempts to respond to rejection, may undermine key relationship maintenance processes that contribute to relationship functioning and lead to negative reciprocity in interactions. The goal of the present study was to shed light on how individuals experience rejection-related emotions and determine whether, following perceptions of negative interactions, rejection sensitivity was associated with stronger negative responses and less efficient downregulation of negative emotions. In addition, we examined whether dyadic patterns of rejection sensitivity were associated with negative emotion dynamics following perceptions of negative interactions.

The participants ( N = 298) were couples experiencing the transition to parenthood. A multilevel modelling approach was used to assess the associations between rejection sensitivity, perceptions of negative interactions and emotional states. The analyses included repeated daily reports for both rejection and emotions.

The results suggest that rejection sensitive individuals do not report higher negative emotions when they perceive negative interactions. Moreover, rejection sensitive men and women did not remain longer in a negative emotional state after they perceived negative interactions with their partner. Finally, when both men and women partners reported higher levels of rejection sensitivity, neither reported having higher negative emotions after experiencing negative interaction perceptions.

Conclusions

Our findings provide further insights into emotional dynamics and rejection sensitivity in romantic relationships. Our results do not provide evidence for a link between rejection sensitivity and higher negative emotions or slower recovery after reports of negative interactions. If individuals suppress their emotions, they may not benefit from regulation with their partner and instead may protect themselves over their relationships. However, in this context, rejection sensitivity might also not constitute a strong predictor of daily emotion fluctuations, but other variables– such as relationship satisfaction – might. Future research may investigate emotional responses in a sample with higher levels of rejection sensitivity and use more diverse measures of perceptions of negative interactions.

Peer Review reports

For better or for worse, intimate relationships affect us emotionally [ 1 ]. Social rejection is also a painful interpersonal experience [ 2 , 3 ] and feeling rejected by an intimate partner may be particularly hurtful because people reveal their most vulnerable side in their intimate relationships [ 4 ]. It may not be surprising, therefore, that critical interpersonal situations with a perceived risk of rejection trigger anxiety and negative emotions. However, people likely differ in the extent to which they suffer emotionally and recover from the pain of rejection [ 2 , 5 ]. Rejection sensitive individuals are thought to be more sensitive and vigilant to rejection than other individuals [ 6 ]. On the one hand, this may contribute to their anxiety in interpersonal situations with their partner, they may perceive rejection more readily [ 6 ] and show stronger and more lasting emotional responses to negative or ambiguous interactions. On the other hand, as a self-protective strategy, these individuals may become less emotionally involved with their partners, which limits the risk of acute rejection experiences [ 7 ]. Identification of such tendencies is important, as they may undermine lasting closeness and intimacy [ 8 , 9 ]. The current research examined the associations between rejection sensitivity and emotional responses after potentially hurtful interpersonal situations.

Perceptions of rejection in romantic relationships

Rejection experiences can be highly subjective and involve the interpretation of social cues. They may therefore arise in diverse interpersonal situations, such as perceiving a lack of support when support is expected, or experiencing unresponsive behaviours or disinterest from the partner [ 10 ]. For instance, self-report perceptions that the partner acted in a hurtful way, was critical or unpleasant, have been used as proxies for rejection-related events [ 10 , 11 ]. Therefore, conflicts and tense interpersonal situations, as well as other situations where loyalty, support, approval, or validation are expected from a partner, bear the potential to make an individual feel threatened and rejected [ 2 , 11 ]. Because experiencing unconditional acceptance from a close partner is a basis for feeling validated, supported and understood [ 12 ], rejection cues are hurtful and undermine intimacy [ 4 ]. People are therefore motivated to try and avoid or prevent such experiences [ 13 , 14 ] and are likely to show strong affective reactions if they occur [ 15 , 16 ].

Rejection sensitivity and emotional responses to perceived rejection

Because perceived rejection jeopardises an individual’s sense of acceptance, it triggers negative emotions and motivates regulatory behaviours to help them cope with the threat and restore acceptance [ 15 , 17 , 18 ]. Indeed, experimental manipulations of rejection cause individuals to experience more intense sadness and hurt feelings [ 18 ]. These emotional responses accompany negative behavioural responses, which may prolong negative interactions and interfere with the downregulation of negativity. For example, situations that elicit jealousy are perceived as rejecting and not only foster anger and fear but can also prompt aggressive behaviours [ 19 , 20 , 21 ].

In intimate relationships, rejection sensitive individuals tend to anxiously expect rejection from their partner and therefore readily perceive ambiguous cues as rejection [ 6 ]. High levels of rejection sensitivity have been linked to higher levels of emotional or behavioural dysregulation, and insecure attachment [ 6 , 22 , 23 ]. Rejection sensitive individuals often react strongly to ambiguous situations by providing diminished emotional support or they respond with maladaptive behaviours, such as controlling and self-silencing behaviours [ 6 , 24 , 25 ]. They may have learnt that seeking support or acceptance from a significant other may lead to rejection [ 6 ]; hence, anxious expectations of rejection foster hypervigilance to rejection-related cues. As a result, rather than being responsive and supportive, rejection sensitive individuals are prone to show defensive and self-protective behaviours in critical relational situations [ 6 ]. In turn, their partners may reciprocate their negative behaviours, which ultimately results in prolonged interpersonal distress [ 7 ].

The disposition to be sensitive to negative cues and respond to them with negative emotions and/or defensive or hostile behaviour is likely to increase difficulties with emotion regulation and adjustment in intimate relationships [ 1 , 26 ]. Consequently, negative emotional states may persist over time. For example, rejection sensitive students in committed relationships show more emotional distance from their partner, and in turn, are more dissatisfied in their close relationships [ 6 , 27 ]. Rejection sensitivity is also associated with increased reciprocation of the partner’s behaviours and attitudes. While rejection sensitive individuals are warm in response to their partner’s positive affect, they are distant and cold toward a partner’s negative affect. Importantly, when a significant other is distressed, rejection sensitive individuals fail to respond with warmth [ 28 ]. In a similar vein, anxious and avoidant attachment orientations are associated with reactions to rejection, although in different ways; whereas anxious individuals show more personal distress and guilt in response to rejection, avoidant individuals tend to respond with greater hostility or emotional suppression [ 11 , 29 ]. It is important to consider these contrasting differences when individuals are faced with rejection or are more sensitive to threatening interactions with their partners.

It is also possible that rejection sensitive individuals try to prevent negative emotional states that result from expected rejection. For example, they may avoid intimacy and closeness situations because they involve the risk of feeling rejected [ 7 ] and the negative affective experiences associated with rejection. Hence, keeping emotional distance from the partner and disconnecting from rejection-related emotions may appear as a viable strategy to reduce the experience of rejection [ 25 , 27 ]. Rejection sensitive individuals, like those with an avoidant attachment style or individuals with low self-esteem, may prefer to maintain a sense of safety by reducing their closeness to significant others [ 4 , 7 , 20 ]. The risk regulation model [ 4 , 30 , 31 ] offers an interesting framework to better understand partner interactions. The model explains how in interdependent relationships, individuals must take risks and show themselves as vulnerable to fulfil their need for connectedness and closeness to their partners. Accordingly, if an individual evaluates a situation as safe and their partner as accepting and responsive to their needs, they tend to choose self-disclosure over distancing. However, if they doubt that their partner will be responsive, they prefer self-protective behaviours over self-disclosure and connection [ 32 ] If individuals are prone to perceive rejection, they are likely to behave in self-protecting ways even in situations that harbour no objective threat [ 25 ]. As a result, they are likely to miss opportunities for building and maintaining intimate bonds with their partner and instead worry about rejection. This may further contribute to their emotional instability and impaired interpersonal adjustment [ 1 , 33 ], potentially prolonging their negative emotional response to rejection.

Taken together, negative emotional and behavioural responses to perceived rejection are likely to trigger further cues of rejection from the partner [ 28 , 34 ]. These responses may interfere with the downregulation of negative emotions and interpersonal adjustment [ 5 , 6 ]. Therefore, rejection sensitive individuals may focus more persistently on and perceive threat cues from their partner, thus remaining in a negative emotional state for longer.

Dyadic patterns of rejection sensitivity and negative emotions in interpersonal interactions

Rejection sensitive people may find it difficult to disengage from rejection-related emotions and thoughts in the face of tense or ambiguous interactions with their partner. They may respond in maladaptive ways to the rejection cues they perceive from their partner, which fosters continued distressing interactions [ 5 ]. Arguably, if we assume that rejection sensitive individuals show stronger emotional reactivity to negative or ambiguous cues from their partner, then having a partner who tends to show accommodating behaviour will dampen further negative responses. However, having a rejection sensitive partner who is susceptible to showing negative reactions to negative or ambiguous cues will amplify the negativity. Thus, if both partners are prone to perceiving rejection and reacting negatively to such perceptions [ 5 , 34 ], a negative feedback loop may occur, with both partners reciprocating each other’s negative affective responses. Similar effects of dyadic patterns between partners have been examined regarding the associations between attachment orientation and communication [ 35 ] and between attachment and coregulation of affect [ 36 ]. Specifically, a study of avoidance-oriented individuals showed that they were more likely to communicate in a negative way if their partner also displayed negative communication behaviours [ 37 ]. To our knowledge, no studies have investigated the effects of dyadic patterns of rejection sensitivity, even though it seems plausible that a partner’s response tendencies are relevant to the outcomes of interactions. Investigating dyadic effects may therefore contribute to our understanding of the role of rejection sensitivity in intimate interaction dynamics.

Therefore, along with a reduced capacity to regulate emotions [ 13 ], we would expect that elevated rejection sensitivity in both partners would give rise to increased negative reciprocal dynamics in distressing interactions and contribute to more prolonged negativity. In contrast, in couples where only one partner shows elevated rejection sensitivity, these dynamics and emotional responses might be still present but less pronounced, because the other partner may buffer the negativity during dyadic interactions.

The current study

In the current study, we examined emotional responses to perceptions of negative interactions, including rejection or disregard from a romantic partner. We tested the individual and dyadic effects of rejection sensitivity on emotional change associated with or subsequent to negative interactions. Specifically, we expected that, when perceiving negative interactions, rejection sensitive individuals would report stronger increases in negative emotions than individuals low in rejection sensitivity (Hypothesis 1; H1). Furthermore, we expected that rejection sensitive individuals would show a less rapid downregulation of negative emotions (Hypothesis 2; H2). Footnote 1 Finally, we expected that dyadic patterns (combinations) of rejection sensitivity would predict increased negative emotions following perceptions of negative interactions, above and beyond the rejection sensitivity of the two partners (Hypothesis 3; H3). We expected that mutually high levels of rejection sensitivity in both partners would be associated with stronger negative emotions (Hypothesis 3a; H3a), and a reduced downregulation of negative emotions over time (Hypothesis 3b; H3b), than in dyadic patterns of one partner or none of the partners being high in rejection sensitivity. Footnote 2 Because rejection sensitivity has been associated with marital dissatisfaction in both rejection sensitive individuals and their partners [ 6 ] we included relationship satisfaction as a control variable. To test our hypotheses, we used data from the first measurement in a project on couples’ transition to parenthood, collected before the birth of their child.

The present study was preregistered (during data collection) on the open science platform osf.io ( https://osf.io/wyz4r ). To assess emotion dynamics and rejection, we used an ecological momentary assessment approach in that the participants reported their daily experiences and emotional states four times per day over seven consecutive days. These data allowed us to model within-person variability of emotional states and trends of within-person emotional change over time, as associated with negative interpersonal interactions and rejection sensitivity, using a multilevel modelling approach.

Materials and methods

Participants.

Participating couples were recruited during the second or third trimester of their first pregnancy, and data collection took place in the second or third trimester of pregnancy (further measurements, which were not included in this current study, were taken at six, 12 and 18 months after birth). Recruitment began in March 2019 and ended in February 2022. The inclusion criteria included being in a mixed-gender relationship, being fluent in one of the study languages (German or French), being over 18 years old, living in a shared household with their partner, and expecting their first child. Each couple was compensated with the equivalent of approximately 900 USD for participation in all parts of the study.

The participants were recruited via flyers through midwives, gynaecologists, birth centres and prenatal courses, as well as via social media and word-of-mouth. Additionally, posters were distributed in universities, pharmacies, supermarkets, and hospitals. Potential participants could contact the study team via email or phone to obtain more information about the study. They were provided with a detailed information sheet and had the opportunity to ask questions and further discuss their potential participation in the study with a researcher on the phone. Both partners were required to provide consent to participate in the study.

By December 2021, 149 couples ( N = 298 participants) had completed the first part of the couples’ transition to parenthood study, and their data were used for the current study. The average age was 31.55 years ( SD = 3.67) for women and 33.20 years ( SD = 4.06) for men. At the time of recruitment, couples had been in a relationship for an average of 6.73 years ( SD = 3.01). The participants reported a relatively high level of education, with 65.6% of the sample holding a university degree, 11.9% had another type of advanced education/training, 10.3% had completed an apprenticeship, 5.6% were undergraduate students, 5% had completed high school, and 1.7% had a secondary school degree. At the time of data collection, 70.9% of the sample were employed, 9.6% were executive employees or in a managerial position, 10.9% were self-employed, and 8.6% were not working. The median individual net income was the equivalent of approximately 5600 USD.

All the couples completed the first set of assessments in the second or third trimester of pregnancy. All the participants were informed about the nature of the procedure and both partners provided informed consent before data collection began. The data used in the present study were collected as part of an online questionnaire completed after enrolment in the study, and a seven-day smartphone-based momentary assessment that began after the participants completed the online questionnaire. The online questionnaire included questions about the participants’ relationship and their mental health and well-being.

Ecological momentary assessments were prompted four times per day (8:00 a.m., 12:00 p.m., 6:00 p.m. and 9:30 p.m.) over seven consecutive days. Both partners were prompted to complete the assessment at the same time. Adherence was good ( M = 26.7 reports per week out of a maximum of 28 reports; 4.52% missing data). The momentary assessment included questions about the participant’s daily interactions with their partner, their own and their partner’s affect, interpersonal behaviours, stress, perceptions of negative interactions, intimacy, and relationship satisfaction. Other assessments in the main study included a mental health diagnostic interview, home visits with interaction tasks, and physiological measures during the home visits and on three consecutive days of their daily lives (cortisol and heart rate frequency); these data were not part of the current study. The project obtained approval from the ethics review board of the regional government.

Adult rejection sensitivity

Rejection sensitivity was measured using the Adult Rejection Sensitivity Questionnaire (A-RSQ; [ 25 , 38 , 39 ], a revised version of the Rejection Sensitivity Questionnaire [ 6 ]. Participants were presented with vignettes of nine different situations in which rejection might be possible (e.g. “After a bitter argument, you call or approach your significant other because you want to make up.”). Two items with a 6-point response scale were used to assess participants’ perceptions of each situation: (1) the likelihood of rejection (1 = very unlikely ; 6 = very likely ); (2) the degree of concern regarding the possible outcome of each situation (1 = unconcerned ; 6 = very concerned ). Following the recommendations of the original measure, we multiplied the concern ratings by the expectancy ratings for each situation and averaged the scores. The internal consistency was high (α = 0.81) for the concern scale and moderate for the expectancy scale (α = 0.71).

Affect. At each of the 28 reports, participants were asked how they felt “in the moment” and had to answer the question by reporting on four different descriptors of negative affective states: “irritated”, “lonely”, “depressed”, and “worried”. For each descriptor, participants reported how they felt on a 10-point scale (1 = not at all ; 10 = very ). The four negative item reports were averaged to obtain a negative affect score. Using McDonald’s omega, the internal consistency for negative affect was satisfactory for both the women (within: ω = 0.67; between: ω = 0.86) and the men (within: ω = 0.68; between: ω = 0.89).

Perceptions of negative interactions

To build a variable that reflected perceptions of negative interpersonal interactions and disregard from the partner, we used momentary reports on a variety of interpersonal experiences. Specifically, we chose assessments of situations that have been used in previous literature as indicators of potential rejection from a significant other [ 6 , 31 , 40 ]. Two questions directly assessed whether participants felt [ 1 ] rejected and [ 2 ] mistreated by their partner in the last hour, using a 10-point scale (1 = not at all ; 10 = very ). We also included another variable that reflected the perception of the partner as being distant in the relationship (“During our last contact, my partner was aloof.”). We then computed a mean of the three items; this represented perceptions of negative interactions with the partner. Higher scores indicated more negative perceptions of negative interactions. Internal consistency was satisfactory for women (within: ω = 0.68; between: ω = 0.89) and men (within: ω = 0.73; between: ω = 0.96). Overall, out of all the participant’s daily reports, 23.6% were reports of perceptions of negative interpersonal interactions; this represented 1892 reports out of a potential total of 6128 reports. We centred this variable at the person mean to reflect within-person variability in perceptions of negative interactions.

Relationship satisfaction

Participants rated their satisfaction with their relationship each day with a single item (“At the moment, I feel satisfied in my relationship.”) on a 10-point scale (1 = not at all ; 10 = extremely ). Higher scores indicated higher satisfaction with the relationship. We computed a single score per person, averaged across all ratings, and centred this variable at the grand mean. We tested this variable as a control variable alongside rejection sensitivity.

The current study included dyadic data that featured repeated measures. We used a multilevel modelling approach to model the non-independence of emotional states at the within- and between-person levels [ 41 ], testing equations that included separate coefficients for the two partners in the couple. Because our sample included mixed-gender couples, dyad members were distinguishable by their reported gender [ 42 ]. Daily reports of both partners (Level 1) were modelled as nested within couples (Level 2), while the women and men partners were represented by separate coefficients in the equation. The effects of perceptions of negative interactions (Level 1 predictor) were estimated at the within-couple level. At this level, we only examined actor effects (H1 and H2), with participants’ own reported perceptions of negative interactions predicting their ratings of negative emotions at the same time points (for H1) and at two subsequent time points (for H2). The effects of rejection sensitivity (Level 2 predictor) were modelled at the between-person level, along with the control variables (i.e. average negative emotions and relationship satisfaction). For H3a and H3b, we also examined partner effects, testing whether the participants’ rejection sensitivity predicted their partners’ ratings of negative emotions. All the variables entered at Level 2 were centred at the grand mean.

H1 was tested based on Eq. 1 (for clarity, Eq. 1 does not display the parameters for relationship satisfaction, which we incorporated alongside rejection sensitivity):

Negative emotions tj represent the current report of negative emotions from one participant i (man or woman) at time t . The estimate b 0j reflects the mean level of a participant’s report of negative emotions when all other predictors are held constant. The estimate for b 1j reflects the within-subject actor effect of perceptions of a participant i at time t ; that is, the association between the perceived negative interactions of a person with their own negative emotions. The estimate b 2j reflects the between-subject actor effect of rejection sensitivity of participant i. The estimate for b 3j reflects the interaction effect of a participant’s rejection sensitivity and their own perception of negative interactions at time t . This estimate represents a cross-level interaction and can be interpreted as the moderator effect of rejection sensitivity on the effect of participant’s perceived rejection on their negative emotions. The estimate b 4j captures the extent to which the current emotion report is predicted by the prior emotion report (the autocorrelation). Controlling for the prior emotion report renders the outcome interpretable as a change score. The error term r tj reflects the residual variance. For the H1, H3a and H3b models, we estimated random intercepts and random slopes. For H2, because there was no convergence, we did not estimate random slopes.

To test whether negative emotional states decreased more slowly in rejection sensitive individuals than in their less rejection sensitive counterparts (H2), we examined whether rejection sensitivity was associated with negative emotion trends in the hours after the perception of a negative interaction. To this end, we analysed data from the time points where negative interactions were reported as well as the two subsequent reports. This allowed us to estimate a linear slope that reflected a linear negative emotion trend over three time points at Level 1, starting at the time of perceived negative interaction. These trends were estimated using a trend variable that was coded to indicate the temporal sequence of reports after the negative interaction perception (0 = negative interaction; 1 and 2 for the two subsequent reports, respectively). In this model, the intercept captured the negative affect at the report of a perception of a negative interaction, and the estimate for the trend variable captured the linear trend of negative emotional states after the perception of a negative interaction. Finally, we added the interaction term between the participant’s rejection sensitivity score and the trend variable. The estimate for the interaction term reflected the degree to which participants’ degree of linear change in negative emotional states after perceiving rejection differed as a function of their level of rejection sensitivity.

Finally, to test whether dyadic patterns of rejection sensitivity might be predictive of stronger negative emotional states following rejection-related perceptions (H3a, H3b), we extended the models at Level 2 by including the partner’s rejection sensitivity and the interaction term between both partners’ rejection sensitivity scores as well as the participant’s own rejection sensitivity variable. To examine the effect of individual and dyadic rejection sensitivity on emotional responses to and emotional dynamics after perceived rejection, we estimated the effects of the cross-level interaction terms between rejection sensitivity predictors and the perceived rejection parameter (H3a), or the linear time trends (H3b). For all the hypothesis tests, we also controlled for relationship satisfaction. However, the H3b model did not include the interaction terms due to lack of convergence.

To ensure that our results could be discussed in the light of possible gender differences, we tested and compared a model that distinguished between partners against a model that did not distinguish between partners [ 43 ]. Because a significantly better fit resulted for the models that distinguished the partners by gender, these are the results we present in this paper (model for H1: X 2 (25) = 595.89, p < .001; model for H2: X 2 (8) = 215.23, p < .001; model for H3a: X 2 (16) = 649.51, p < .001; model for H3b: X 2 (15) = 255.10, p < .001). The models were run in R using the nlme [ 44 , 45 ]. The datasets generated and analysed in the current study are available in the Open Science Framework (OSF) repository, https://osf.io/az9vg/ Footnote 3 .

Descriptive statistics

The correlation matrix for the within and between subjects is presented in Table 1 . The average rejection sensitivity scores were comparable for men ( M = 5.80; SD = 2.78) and women ( M = 5.77; SD = 3.23) and the difference was not significant (paired-samples t (141) = 0.07, p = .942). The mean level of perceptions of negative interactions was M = 0.67 ( SD = 0.92) for men and M = 0.41 ( SD = 0.86) for women, and the gender difference was significant (paired-samples t (143) = 2.86, p = .005). Men and women reported slightly different levels of negative affect (men: M = 1.03; SD = 0.92; women: M = 0.85; SD = 0.90; paired-samples t (143) = 1.89, p = .061). On average, both men and women reported high levels of satisfaction with their relationship (men: M = 9.07; SD = 1.08; women: M = 9.15; SD = 0.96) and there were no significant differences between the two partners (paired-samples t (143) = –0.95; p = .345).

Association of higher rejection sensitivity with stronger negative affect

For H1, we examined whether individuals higher in rejection sensitivity experienced higher negative emotions when perceiving negative interactions, compared to less rejection sensitive individuals. As shown in Table 2 , when perceiving negative interactions, we found a significant increase in both men’s and women’s negative affect (men: b = 0.193, p < .001; women: b = 0.358, p < .001). However, when reporting perceptions of negative interactions, rejection sensitivity in both men and women was not associated with changes in negative emotional states since the prior report of affect (men: b = –0.001, p = .984; women: b = –0.026, p = .125). Controlling for relationship satisfaction, higher relationship satisfaction predicted lower negative emotions for both women ( b = –0.213; p < .001) and men ( b = –0.451; p < .001). Moreover, when reporting perceptions of negative interactions, men with higher levels of relationship satisfaction reported lower levels of negative emotions ( b = –0.062; p = .010), but this was not the case for women ( b = –0.061; p = .195).

In H2, we expected that following perceptions of negative interactions from the partner, high rejection sensitive individuals would show a slower decrease in negative emotions than their less rejection sensitive counterparts. The results are presented in Table 3 . Rejection sensitivity was not associated with the degree to which individuals recovered from their negative emotions after perceiving negative interactions (men: b = –0.026, p = .159; women: b = 0.006, p = .685). Moreover, for men, the recovery of negative emotion after perceiving negative interactions was dependent on relationship satisfaction ( b = –0.112, p = .009).

Dyadic patterns of rejection sensitivity and modulation of negative affect

To examine H3a, we tested whether dyadic patterns of rejection sensitivity were associated with stronger negative emotions after perceptions of negative interactions (see Table 4 ). Specifically, we expected that mutually high rejection sensitivity would be associated with more negative affect in partners compared to when only one or none of the partners scored relatively high in rejection sensitivity. We propose that the three-way interaction between both partners’ rejection sensitivity and perceptions of negative interactions captures the extent to which the dyadic rejection sensitivity scores explain the additional variance in perceptions of negative interactions that predict negative emotions, over and above the partners’ own rejection sensitivity scores. Testing this interaction indicated that the combination of partners’ rejection sensitivity did not explain the between-person differences in negative emotional states when reporting perceptions of negative interactions (men: b = –0.001, p = .965; women: b = 0.001, p = .947).

Next, we examined whether negative emotion trends after perceptions of negative interactions were associated with dyadic rejection sensitivity patterns (H3b). The results are presented in Table 5 . We propose that the interaction between both partners’ rejection sensitivity scores and recovery captures the effects of the dyadic combination of rejection sensitivity scores and emotional recovery after reports of negative interactions, beyond individual rejection sensitivity scores. Testing this did not confirm our expectations; dyadic patterns of rejection sensitivity did not explain the variance in trends of negative emotional states after perceptions of negative interactions for women ( b = –0.006, p = .327) or men ( b = –0.003, p = .622). That is, both rejection sensitive individuals and their rejection sensitive partners did not show a slower recovery of negative emotions after perceiving rejection.

The current study aimed to investigate whether individuals’ rejection sensitivity and dyadic patterns of rejection sensitivity were associated with higher and persistent emotional responses in the context of daily perceptions of negative interactions. H1 predicted that when perceiving negative interactions, individuals with higher levels of rejection sensitivity would report more negative affect than their less rejection sensitive counterparts. The results did not support this hypothesis for either men or women. First, the data showed a strong positive association between perceptions of negative interactions and elevated negative emotional states, suggesting that individuals reported stronger negative emotional states when they reported perceptions of negative interactions compared to when they reported no perceptions of negative interactions, which is in line with the results of previous studies [ 18 , 21 ]. However, the results suggest that individual differences in the strength of this effect were not attributable to differential levels of rejection sensitivity. In other words, individuals with higher levels of rejection sensitivity did not report stronger negative emotions when they perceived rejection from their partner. Moreover, H2 predicted that following perceptions of negative interactions, rejection sensitive individuals would recover less rapidly from these perceptions, as reflected in a slower decrease in negative emotions. The data did not support this prediction; rejection sensitive individuals did not differ significantly in their emotional recovery compared to less rejection sensitive individuals.

There are several possible reasons for the absence of rejection sensitivity effects on emotional responses and recovery. First, the items used to reflect perceptions of negative interactions might not point to situations that are challenging enough to reveal rejection sensitivity effects. The literature suggests that individuals may be sensitive to rejection in specific situations. For instance, men tend to perceive rejection in conflictual situations or in situations that threaten their status [ 3 ] whereas for women, perceptions of negative interactions occur when they perceive their partner to be inattentive [ 46 ]. Moreover, rejection sensitivity in men has been associated with heightened jealousy and controlling behaviours after rejection [ 6 ] and aggression [ 46 , 47 ]. Rejection sensitive women are less supportive, distance themselves and tend to conform to maintain their relationship [ 6 , 27 , 40 ]. These contrasting differences in men’s and women’s perceptions of rejection and their responses to them may be reflected in our results. Indeed, the questionnaire items we used reflected subjective perceptions of negative interactions and were quite broad to specifically capture an interaction where rejection might have occurred. In our sample, the emotions of both high rejection sensitive men and women were not affected by their perceptions of negative interactions. It may be that the questionnaire items did not represent threatening interactions that were sufficient to trigger stronger negative emotions. Alternatively, the participants might not have felt prompted to respond in self-protective ways or to seek closeness with their partner [ 4 ], and this may have been reflected in the absence of stronger emotional responses and slower downregulation.

It is also possible that rejection sensitive individuals respond to perceptions of threatening interactions by attempting to reduce emotional arousal, thus suppressing their emotions to avoid being hurt and to protect themselves and their relationship [ 25 , 40 ]. However, by engaging in such behaviours and choosing distance over closeness [ 4 ], such individuals may not benefit from their partner’s help in regulating their negative emotions [ 1 ]. In particular, if individuals feel chronically undervalued [ 7 ], these patterns may regularly repeat and accumulate. In addition, similarly to individuals with avoidance attachment orientation [ 20 , 48 ], through interactions with their partner, a rejection sensitive person may have learned to divert their attention away from such threats. As a result, they may not show or report distress when faced with potential rejection [ 25 ].

H3a and H3b predicted dyadic effects of rejection sensitivity, expecting that when both partners are highly rejection sensitive, they experience stronger negative emotions compared to when only one or none of the partners have higher levels of rejection sensitivity. We also expected that when both partners were rejection sensitive, they would remain longer in a negative emotional state related to rejection. This assumption was based on the reasoning that mutual sensitivity to rejection enhances negative reciprocal dynamics between partners, and thus increases response intensity and prolongs negative emotional states. However, our results showed that neither women nor men with higher levels of rejection sensitivity reported stronger negative emotional responses to perceived negative interactions when their partner was also rejection sensitive. Furthermore, neither men nor women experienced prolonged emotional states compared to when one or none of the partners were highly rejection sensitive.

Our data did not show negative patterns of reciprocity between rejection sensitive partners. Research on insecure attachment, a correlate of rejection sensitivity [ 6 ], offers a possible explanation for this finding [ 29 ], suggesting that anxious-avoidant and anxious-ambivalent individuals are more likely to be rejection sensitive [ 49 ]. When faced with rejection from a significant other, individuals with an avoidant attachment style inhibit strong emotions to avoid threatening thoughts that might activate their attachment needs. Such individuals are also less likely to react with anger when they are confronted with their partner’s negative behaviour and are less distressed after a hurtful event [ 20 ]. Instead, they typically distance themselves from their partner and show more hostility [ 29 ]. Furthermore, evidence suggests that when both partners are insecure, they engage in mutual avoidance and withdraw from communication [ 35 ]. To the extent that these findings can be applied to rejection sensitivity, mutual use of avoidance strategies in couples with higher levels of rejection sensitivity in both partners could feed avoidance cycles. However, these negative dyadic dynamics are more likely to manifest in heightened avoidance [ 25 ] than in intense mutual expressions of negative emotions. If this effect was active in our study, high rejection sensitive individuals may not have reported stronger negative emotions when they perceived negative interactions. Nonetheless, they may have felt more distant, and these dynamics will still contribute to dysfunctional interactions in the long run [ 50 ]. Another possibility is that these individuals may have attempted to de-escalate negative interactions, rather than engage in emotional avoidance or disengagement. It is important to stress that these possibilities are all very speculative and that further research is needed to confirm whether they are viable explanations.

Finally, when controlling for relationship satisfaction, our results showed that higher levels of relationship satisfaction in men predicted lower negative emotions, and a slower recovery from the perceived negative interactions. We did not expect these results and offer some possible explanations for them. Interestingly, while high levels of satisfaction in men predicted lower negative emotions, men recovered less quickly from these negative emotional states. A reason for these results may be that individuals highly satisfied in their relationship hold high expectations of their romantic relationship and their partner [ 51 ]. As a result, these individuals may still report negative emotion, though significantly less, but it may take them longer to recover from this emotional state. They may react with a heightened sensitivity to such interactions because they consider and care for their romantic relationship more [ 51 ]. Besides, the context may also contribute to such responses. The transition to parenthood is often depicted as a joyful period, where future parents rejoice in this common challenge [ 52 ]. Thus, partners may report higher levels of satisfaction, which would render them more sensitive to threatening interactions. In addition, disclosing emotions contribute to relationship maintenance [ 53 ], which suggest that reporting negative emotions does not point to negative relational processes. Instead, partners may need such moments to increase intimacy and to foster their relationship [ 51 ]. Additional research specifically focusing on these aspects is needed to understand the mechanisms that underlie them. How individuals respond to their partner, and their ability to regulate and recover from their emotions, plays a crucial role in relationship satisfaction and how couples deal with conflict, for example [ 54 ].

The current study has several limitations. First, high levels of rejection sensitivity were relatively rare in our sample. Therefore, most participants may be unlikely to display the strong emotional responses to negative interactions needed to reveal higher levels of rejection sensitivity. Second, the measure of rejection sensitivity was based on self-reported experiences in response to general interpersonal situations with different people. Such reports do not necessarily reflect rejection responses to everyday interactions, or in this context, to a romantic partner. Self-reports on specific daily rejection-relevant interactions with the partner may capture different types of responses that represent more immediate reactions to rejection. Moreover, the questions included in the momentary assessment did not all refer to the same time points and this may have been a source of systematic error variance. Importantly, when measuring emotions after a certain event, a three- to six-hour difference between each report might have been too wide to capture relevant emotions. Hence, further studies including a smaller time difference between each self-report questionnaire are needed to fully grasp the potential effects of rejection on emotional reactions; this would allow a more fine-grained measurement (e.g. in minutes rather than hours). Similarly, although we were primarily interested in emotion dynamics as an outcome, the distinction between soft affect (e.g. hurt, sadness) and hard affect (e.g. anger) in relation to rejection may be of interest for future research, as these affective responses may serve different social functions relevant to interpersonal interactions [ 55 ]. Whereas soft affects may reflect vulnerability and promote or facilitate affiliation, hard affects are associated with assertiveness and threat and tend to promote interpersonal distance [ 56 , 57 ].

Part of the present study examined reports of emotions and perceptions of negative interactions at the same point in time. While some useful data was obtained, this design precluded obtaining causal effects. Therefore, future studies may aim at establishing predictions of prospective change or use experimental approaches to allow for stronger causal interpretations. Finally, the couples in our sample were all expecting their first child. This situation is unique and the relationship experience of expectant couples may differ from that of other types of couples. Hence, the generalizability of our results is limited. It is unclear how this unique relationship situation may have affected our results. On the one hand, emotional responses may be different at this time. Partners may be more focused on their future child, and be more willing to override negative relational sentiments through experiences of togetherness and cooperation [ 58 , 59 , 60 ]. On the other hand, although expectant couples continue to experience common difficulties in their daily interactions [ 61 , 62 , 63 , 64 ], the period of expecting a first child is associated with adjustment difficulties and increased stress [ 65 , 66 ]. This may exacerbate relationship insecurities [ 67 ] and thus, negatively affect perceptions of the relationship [ 68 ]. Moreover, rejection sensitive individuals in committed long-term relationships may show different emotional patterns as a response to rejection compared to individuals in the early stages of a relationship, as they may have developed regulation strategies to manage their relational experiences [ 69 ].

While our results did not show an effect of rejection sensitivity on the emotion regulation capacities of individuals, therapists may nonetheless benefit from these findings. Rejection sensitive individuals are concerned about possible rejection; therefore, it might be of interest to specifically target relationship-based anxiety. For instance, a brief psychoeducational intervention has shown how behaviours such as self-silencing or partner accommodation can change significantly following intervention [ 70 ]. Moreover, emotionally focused couple therapy is associated with decreased anxious and avoidance attachment [ 71 ]. In both options, partners learn how to communicate with each other [ 70 ] and de-escalate negative interactions [ 71 ]. Such interventions might prevent the perpetuation of negative feedback cycles through both partners’ dyadic communication and understanding of each other’s experiences, especially when they are both highly sensitive to rejection.

In conclusion, the current study did not fully support the notion that rejection sensitivity plays a role in modulating emotional responses and regulation following perceptions of negative interactions in intimate relationships, or that dyadic patterns of rejection modify emotional responses on a daily basis. Rejection sensitive individuals may suppress their emotions and protect themselves over their relationship, which in the long run, may be detrimental to the relationship. Additionally, the absence of emotional responses underscores the need to identify what kind of interactions may prompt negative emotions in rejection sensitive individuals. Finally, the identified association between relationship satisfaction and negative emotional responses is a new finding and a possible avenue for future research on emotion regulation and relationship outcomes.

Our findings are important because they contribute to the gap in the research on daily emotional dynamics and rejection sensitivity. Rejection sensitivity emotional responses may not necessarily become more apparent in the specific rejection interactions perceptions we used. Future studies should shed more light on the relevance of rejection sensitivity for negative emotion dynamics in such relationships by focusing on samples with higher levels of rejection sensitivity and adapting diary studies to more accurately capture the possible aftermath of perceptions of negative interactions.

Data availability

The datasets analyzed in this study are available on the OSF repository, https://osf.io/az9vg/ .

Contrary to the preregistration protocol, because of a lack of coherence, we did not include Hypothesis 2b, which predicted a slower recovery of positive emotions in rejection sensitive individuals, in the current paper. The results can be found online: https://osf.io/gtj5u/ .

Contrary to the preregistration protocol for the current study, we did not test our initial Hypothesis 3b, in which we predicted more frequent negative emotions when both partners are rejection sensitive. Because the available data on emotions were not categorical, we could not straightforwardly assess the frequency of negative emotions. Therefore, we did not include this hypothesis in the paper.

Several changes have occurred since the preregistration, partly due to the operationalization of our hypotheses as well as to addressing reviewers’ comments. Mainly, measures of rejection sensitivity, perceptions of negative interactions, and the time trend variable were modified to better respond to our hypotheses.

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The research has been supported by the grant 100014_175620 from the Swiss National Science Foundation, awarded to the last author.

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Richter, M., Kouri, G., Meuwly, N. et al. Rejection in romantic relationships: Does rejection sensitivity modulate emotional responses to perceptions of negative interactions?. BMC Psychol 12 , 365 (2024). https://doi.org/10.1186/s40359-024-01864-w

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Variables in Research – Definition, Types and Examples

Variables in Research

Types of Variables in Research

Independent Variable

Dependent Variable

Confounding Variable

Mediating Variable

Moderator Variable

Control Variable

Continuous Variable

Categorical Variable

Discrete Variable

Dummy Variable

Extraneous Variable

Latent Variable

Moderator-mediator Variable

Variables Analysis Methods

Examples of Variables

Applications of Variables

Purpose of Variables

Characteristics of Variables

Advantages of Variables

Disadvantages of Variables

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Independent and Dependent Variables

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Variables in Research | Types, Definiton & Examples

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