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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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Research Variables 101

Independent variables, dependent variables, control variables and more

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

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.

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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!

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.

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!

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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• 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?

Table of contents

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 .

Categorical variables

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

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

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

Example data sheet

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

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

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

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

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

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

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

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.

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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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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Independent and Dependent Variables

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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).

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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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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The Independent Variable vs. Dependent Variable in Research

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In any scientific research, there are typically two variables of interest: independent variables and dependent variables. In forming the backbone of scientific experiments , they help scientists understand relationships, predict outcomes and, in general, make sense of the factors that they're investigating.

Understanding the independent variable vs. dependent variable is so fundamental to scientific research that you need to have a good handle on both if you want to design your own research study or interpret others' findings.

To grasp the distinction between the two, let's delve into their definitions and roles.

What Is an Independent Variable?

What is a dependent variable, research study example, predictor variables vs. outcome variables, other variables, the relationship between independent and dependent variables.

The independent variable, often denoted as X, is the variable that is manipulated or controlled by the researcher intentionally. It's the factor that researchers believe may have a causal effect on the dependent variable.

In simpler terms, the independent variable is the variable you change or vary in an experiment so you can observe its impact on the dependent variable.

The dependent variable, often represented as Y, is the variable that is observed and measured to determine the outcome of the experiment.

In other words, the dependent variable is the variable that is affected by the changes in the independent variable. The values of the dependent variable always depend on the independent variable.

Let's consider an example to illustrate these concepts. Imagine you're conducting a research study aiming to investigate the effect of studying techniques on test scores among students.

In this scenario, the independent variable manipulated would be the studying technique, which you could vary by employing different methods, such as spaced repetition, summarization or practice testing.

The dependent variable, in this case, would be the test scores of the students. As the researcher following the scientific method , you would manipulate the independent variable (the studying technique) and then measure its impact on the dependent variable (the test scores).

You can also categorize variables as predictor variables or outcome variables. Sometimes a researcher will refer to the independent variable as the predictor variable since they use it to predict or explain changes in the dependent variable, which is also known as the outcome variable.

When conducting an experiment or study, it's crucial to acknowledge the presence of other variables, or extraneous variables, which may influence the outcome of the experiment but are not the focus of study.

These variables can potentially confound the results if they aren't controlled. In the example from above, other variables might include the students' prior knowledge, level of motivation, time spent studying and preferred learning style.

As a researcher, it would be your goal to control these extraneous variables to ensure you can attribute any observed differences in the dependent variable to changes in the independent variable. In practice, however, it's not always possible to control every variable.

The distinction between independent and dependent variables is essential for designing and conducting research studies and experiments effectively.

By manipulating the independent variable and measuring its impact on the dependent variable while controlling for other factors, researchers can gain insights into the factors that influence outcomes in their respective fields.

Whether investigating the effects of a new drug on blood pressure or studying the relationship between socioeconomic factors and academic performance, understanding the role of independent and dependent variables is essential for advancing knowledge and making informed decisions.

Correlation vs. Causation

Understanding the relationship between independent and dependent variables is essential for making sense of research findings. Depending on the nature of this relationship, researchers may identify correlations or infer causation between the variables.

Correlation implies that changes in one variable are associated with changes in another variable, while causation suggests that changes in the independent variable directly cause changes in the dependent variable.

Control and Intervention

In experimental research, the researcher has control over the independent variable, allowing them to manipulate it to observe its effects on the dependent variable. This controlled manipulation distinguishes experiments from other types of research designs.

For example, in observational studies, researchers merely observe variables without intervention, meaning they don't control or manipulate any variables.

Context and Analysis

Whether it's intentional or unintentional, independent, dependent and other variables can vary in different contexts, and their effects may differ based on various factors, such as age, characteristics of the participants, environmental influences and so on.

Researchers employ statistical analysis techniques to measure and analyze the relationships between these variables, helping them to draw meaningful conclusions from their data.

We created this article in conjunction with AI technology, then made sure it was fact-checked and edited by a HowStuffWorks editor.

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Types of Variables in Psychology Research

Examples of Independent and Dependent Variables

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

 James Lacy, MLS, is a fact-checker and researcher.

Dependent and Independent Variables

• Intervening Variables
• Extraneous Variables
• Controlled Variables
• Confounding Variables
• Operationalizing Variables

Frequently Asked Questions

Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Variables in psychology play a critical role in the research process. By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships.

The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about psychological phenomena.

This article discusses different types of variables that are used in psychology research. It also covers how to operationalize these variables when conducting experiments.

Students often report problems with identifying the independent and dependent variables in an experiment. While this task can become more difficult as the complexity of an experiment increases, in a psychology experiment:

• The independent variable is the variable that is manipulated by the experimenter. An example of an independent variable in psychology: In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.
• The dependent variable is the variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

So how do you differentiate between the independent and dependent variables? Start by asking yourself what the experimenter is manipulating. The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.

Intervening Variables in Psychology

Intervening variables, also sometimes called intermediate or mediator variables, are factors that play a role in the relationship between two other variables. In the previous example, sleep problems in university students are often influenced by factors such as stress. As a result, stress might be an intervening variable that plays a role in how much sleep people get, which may then influence how well they perform on exams.

Extraneous Variables in Psychology

Independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

For example, in our previous example of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so any impact can be controlled for.

There are two basic types of extraneous variables:

• Participant variables : These extraneous variables are related to the individual characteristics of each study participant that may impact how they respond. These factors can include background differences, mood, anxiety, intelligence, awareness, and other characteristics that are unique to each person.
• Situational variables : These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

Other extraneous variables include the following:

• Demand characteristics : Clues in the environment that suggest how a participant should behave
• Experimenter effects : When a researcher unintentionally suggests clues for how a participant should behave

Controlled Variables in Psychology

In many cases, extraneous variables are controlled for by the experimenter. A controlled variable is one that is held constant throughout an experiment.

In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors don't interfere with the results. Holding these variables constant is important for an experiment because it allows researchers to be sure that all other variables remain the same across all conditions.  

Using controlled variables means that when changes occur, the researchers can be sure that these changes are due to the manipulation of the independent variable and not caused by changes in other variables.

It is important to also note that a controlled variable is not the same thing as a control group . The control group in a study is the group of participants who do not receive the treatment or change in the independent variable.

All other variables between the control group and experimental group are held constant (i.e., they are controlled). The dependent variable being measured is then compared between the control group and experimental group to see what changes occurred because of the treatment.

Confounding Variables in Psychology

If a variable cannot be controlled for, it becomes what is known as a confounding variabl e. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Operationalizing Variables in Psychology

An operational definition describes how the variables are measured and defined in the study. Before conducting a psychology experiment , it is essential to create firm operational definitions for both the independent variable and dependent variables.

For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is "Students who are sleep deprived will score significantly lower on a test," then we would have a few different concepts to define:

• Students : First, what do we mean by "students?" In our example, let’s define students as participants enrolled in an introductory university-level psychology course.
• Sleep deprivation : Next, we need to operationally define the "sleep deprivation" variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test.
• Test variable : Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.

Once all the variables are operationalized, we're ready to conduct the experiment.

Variables play an important part in psychology research. Manipulating an independent variable and measuring the dependent variable allows researchers to determine if there is a cause-and-effect relationship between them.

A Word From Verywell

Understanding the different types of variables used in psychology research is important if you want to conduct your own psychology experiments. It is also helpful for people who want to better understand what the results of psychology research really mean and become more informed consumers of psychology information .

Independent and dependent variables are used in experimental research. Unlike some other types of research (such as correlational studies ), experiments allow researchers to evaluate cause-and-effect relationships between two variables.

Researchers can use statistical analyses to determine the strength of a relationship between two variables in an experiment. Two of the most common ways to do this are to calculate a p-value or a correlation. The p-value indicates if the results are statistically significant while the correlation can indicate the strength of the relationship.

In an experiment on how sugar affects short-term memory, sugar intake would be the independent variable and scores on a short-term memory task would be the independent variable.

In an experiment looking at how caffeine intake affects test anxiety, the amount of caffeine consumed before a test would be the independent variable and scores on a test anxiety assessment would be the dependent variable.

Just as with other types of research, the independent variable in a cognitive psychology study would be the variable that the researchers manipulate. The specific independent variable would vary depending on the specific study, but it might be focused on some aspect of thinking, memory, attention, language, or decision-making.

American Psychological Association. Operational definition . APA Dictionary of Psychology.

American Psychological Association. Mediator . APA Dictionary of Psychology.

Altun I, Cınar N, Dede C. The contributing factors to poor sleep experiences in according to the university students: A cross-sectional study .  J Res Med Sci . 2012;17(6):557-561. PMID:23626634

Skelly AC, Dettori JR, Brodt ED. Assessing bias: The importance of considering confounding .  Evid Based Spine Care J . 2012;3(1):9-12. doi:10.1055/s-0031-1298595

• Evans, AN & Rooney, BJ. Methods in Psychological Research. Thousand Oaks, CA: SAGE Publications; 2014.
• Kantowitz, BH, Roediger, HL, & Elmes, DG. Experimental Psychology. Stamfort, CT: Cengage Learning; 2015.

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

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2.2: Concepts, Constructs, and Variables

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• Anol Bhattacherjee
• University of South Florida via Global Text Project

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We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to “gravitate” to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualize. A construct is an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary , syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ ( intelligence quotient ) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical (abstract) plane, while variables are operationalized and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled for in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as grade point average is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts is more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variable are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualize a nomological network linking these abstract constructs.

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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

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.

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:

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 .

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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.

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Exploring the Relationship Between Early Life Exposures and the Comorbidity of Obesity and Hypertension: Findings from the 1970 The British Cohort Study (BCS70)

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Background Epidemiological research commonly investigates single exposure-outcome relationships, while children’s experiences across a variety of early lifecourse domains are intersecting. To design realistic interventions, epidemiological research should incorporate information from multiple risk exposure domains to assess effect on health outcomes. In this paper we identify exposures across five pre-hypothesised childhood domains and explored their association to the odds of combined obesity and hypertension in adulthood.

Methods We used data from 17,196 participants in the 1970 British Cohort Study. The outcome was obesity (BMI of ≥30) and hypertension (blood pressure>140/90mm Hg or self-reported doctor’s diagnosis) comorbidity at age 46. Early life domains included: ‘prenatal, antenatal, neonatal and birth’, ‘developmental attributes and behaviour’, ‘child education and academic ability’, ‘socioeconomic factors’ and ‘parental and family environment’. Stepwise backward elimination selected variables for inclusion for each domain. Predicted risk scores of combined obesity and hypertension for each cohort member within each domain were calculated. Logistic regression investigated the association between domain-specific risk scores and odds of obesity-hypertension, controlling for demographic factors and other domains.

Results Adjusting for demographic confounders, all domains were associated with odds of obesity-hypertension. Including all domains in the same model, higher predicted risk values across the five domains remained associated with increased odds of obesity-hypertension comorbidity, with the strongest associations to the parental and family environment domain (OR1.11 95%CI 1.05-1.18) and the socioeconomic factors domain (OR1.11 95%CI 1.05-1.17).

Conclusions Targeted prevention interventions aimed at population groups with shared early-life characteristics could have an impact on obesity-hypertension prevalence which are known risk factors for further morbidity including cardiovascular disease.

Competing Interest Statement

R.O. is a member of the National Institute for Health and Care Excellence (NICE) Technology Appraisal Committee, member of the NICE Decision Support Unit (DSU), and associate member of the NICE Technical Support Unit (TSU). She has served as a paid consultant to the pharmaceutical industry and international reimbursement agencies, providing unrelated methodological advice. She reports teaching fees from the Association of British Pharmaceutical Industry (ABPI). R.H. is a member of the Scientific Board of the Smith Institute for Industrial Mathematics and System Engineering.

Funding Statement

This work is part of the multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) project which is supported by the National Institute for Health Research (NIHR203988). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics approval for this work has been obtained from the University of Southampton Faculty of Medicine Ethics committee (ERGO II Reference 66810).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability Statement

The BCS70 datasets generated and analysed in the current study are available from the UK Data Archive repository (available here: http://www.cls.ioe.ac.uk/page.aspx?&sitesectionid=795 ).

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Nonlinear association of triglyceride-glucose index with hyperuricemia in US adults: a cross-sectional study

• Linjie Qiu 1   na1 ,
• Yan Ren 1   na1 ,
• Jixin Li 1   na1 ,
• Meijie Li 1 ,
• Wenjie Li 2 ,
• Lingli Qin 1 ,
• Chunhui Ning 1 ,
• Jin Zhang 1 &
• Feng Gao 1  

Lipids in Health and Disease volume  23 , Article number:  145 ( 2024 ) Cite this article

Metrics details

Despite abundant evidence on the epidemiological risk factors of metabolic diseases related to hyperuricemia, there is still insufficient evidence regarding the nonlinear relationship between triglyceride-glucose (TyG) index and hyperuricemia. Thus, the purpose of this research is to clarify the nonlinear connection between TyG and hyperuricemia.

From 2011 to 2018, a cross-sectional study was carried out using data from the National Health and Nutrition Examination Survey (NHANES). This study had 8572 participants in all. TyG was computed as Ln [triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The outcome variable was hyperuricemia. The association between TyG and hyperuricemia was examined using weighted multiple logistic regression, subgroup analysis, generalized additive models, smooth fitting curves, and two-piecewise linear regression models.

In the regression model adjusting for all confounding variables, the OR (95% CI) for the association between TyG and hyperuricemia was 2.34 (1.70, 3.21). There is a nonlinear and reverse U-shaped association between TyG and hyperuricemia, with a inflection point of 9.69. The OR (95% CI) before the inflection point was 2.64 (2.12, 3.28), and after the inflection point was 0.32 (0.11, 0.98). The interaction in gender, BMI, hypertension, and diabetes analysis was statistically significant.

Additional prospective studies are required to corroborate the current findings, which indicate a strong positive connection between TyG and hyperuricemia among adults in the United States.

Introduction

Uric acid is produced when purine nucleotides are metabolized. The condition known as hyperuricemia occurs when uric acid levels rise over a certain point due to either excessive uric acid synthesis or inadequate uric acid elimination. It affects patients of all ages and genders, and its prevalence is on the rise globally [ 1 , 2 ]. Up to 2016, the global prevalence of hyperuricemia has reached 21% [ 3 ], and the prevalence of hyperuricemia varies by geographic region. For example, in South Korea, it is 11.4% [ 4 ], and a survey conducted among adults aged 18–59 in China showed a prevalence of 15% for hyperuricemia [ 5 ]. Data from the 2007–2016 National Health and Nutrition Examination Survey (NHANES) show that 14.6–20% of Americans suffer with hyperuricemia [ 6 ]. Furthermore, hyperuricemia poses a serious threat to public health as numerous epidemiological studies have confirmed that it is a significant risk factor for a number of chronic diseases, including gout, cardiovascular diseases, chronic kidney disease, hypertension, metabolic syndrome, and many others [ 7 , 8 , 9 , 10 ], posing a serious threat to public health.

Insulin resistance (IR) is a physiological and pathological process closely associated with hyperuricemia [ 8 ]. Epidemiological studies have confirmed the close association between IR and serum urate concentration [ 11 , 12 ]. High insulin levels induced by IR lead to reduced uric acid excretion and increased production, resulting in uric acid accumulation [ 13 ]. Reducing IR has been shown in studies to lower uric acid levels and lower the chance of developing gout [ 14 ]. An animal experimental study from Japan also found that insulin can promote uric acid reabsorption through urate transporter 1 and ATP-binding cassette sub-family G member 2 [ 15 ]. Additionally, a nationwide cohort study confirmed a significant association between insulin resistance and an increased risk of hyperuricemia [ 16 ]. When assessing IR, the glucose clamp method is regarded as the gold standard. However, the use of this detection technology in clinical practice is restricted because of its complexity and comparatively expensive cost. The body’s level of IR can be determined simply using the triglyceride-glucose (TyG) index [ 17 ]. The two main factors used to compute TyG are fasting triglycerides (TG) and fasting glucose (FPG). Multiple studies have confirmed its reliability in predicting various diseases related to IR [ 18 , 19 , 20 ]. TyG and hyperuricemia are significantly correlated in individuals with non-alcoholic fatty liver disease, diabetic nephropathy, and primary hypertension, according to earlier Chinese research [ 21 , 22 , 23 ]. Li et al. discovered that TyG might predict the coexistence of hypertension and hyperuricemia in the elderly population [ 24 ]. An additional cross-sectional study conducted in Northeast China examined the validity of TyG in determining the risk of hyperuricemia in people 40 years of age and older [ 25 ]. While prior research has indicated a connection between hyperuricemia and TyG index, these investigations mostly examined the Chinese population and had rather small sample sizes. The relationship between the TyG index and hyperuricemia is understudied in the US population. Wang et al. found a positive correlation between hyperuricemia and the TyG index in non-diabetic populations in the United States [ 26 ]. Furthermore, there are no reports on the relationship between the TyG index and hyperuricemia in the general adult population in the United States.

Therefore, for this cross-sectional analysis, NHANES data from 2011 to 2018 were used. This study aims to explore the relationship between TyG and adult Americans’ hyperuricemia.

Study design and population

This study made use of cross-sectional data from the National Center for Health Statistics (NCHS) 2011–2018 NHANES, a nationwide survey that used a sophisticated sampling design. The survey, conducted biennially since 1999, covers demographic, dietary, examination, laboratory, and questionnaire data [ 27 , 28 ]. All participants completed informed permission forms, and the NHANES survey procedures and detailed data are available on the official website after being approved by NCHS.

After excluding 16,539 participants under the age of 20, 247 pregnant participants, 2,440 participants with missing FPG and TG data, and 11,358 participants with missing data on uric acid BMI, blood glucose, hypertension and related covariates, the final analysis includes 8,572 participants in total (Fig.  1 ).

From chart of sample selection from the NHANES 2011–2018

Definitions of the exposure and outcome variables

Employing an automatic analyzer, blood samples from individuals fasting for at least 8 h but less than 24 h were measured for TG and FPG using enzymatic methods. The TyG can be computed using the formula Ln [TG (mg/dL) × FPG (mg/dL)/2] [ 29 ]. By using uricase and H 2 O 2 to undergo enzymatic oxidation, the concentration of uric acid in serum was determined and reported in milligrams per deciliter (mg/dL). This can be multiplied by 59.48 to get micromoles per liter (µmol/L). Serum uric acid levels ≥ 416 µmol/L (7 mg/dL) in men and ≥ 357 µmol/L (6 mg/dL) in women are classified as hyperuricemia, given the diagnostic criteria for the condition [ 30 ].

Definition of covariates

To examine the distinct link between hyperuricemia and TyG, we adjusted for potential confounding factors, including demographics, lifestyle, anthropometric measurements, laboratory examinations, and health conditions. Age, gender, race, marital status, degree of education, and the ratio of household income to poverty were the main demographic factors; lifestyle mainly encompassed smoking status, alcohol consumption, and physical activity; anthropometric measurements primarily incorporated BMI; laboratory examination data mainly included HbA1c, LDL, HDL, eGFR, and serum creatinine; health conditions comprised hypertension, diabetes, arthritis, stroke, and coronary heart disease.

According to survey findings, “Yes” indicates that a person has smoked at least 100 cigarettes in their lifetime, whereas “no” indicates that they have smoked fewer than 100 [ 31 ]. Similarly, alcohol consumption is classified as “yes” (consuming at least 12 drinks per year) or “no” (consuming fewer than 12 drinks per year) [ 32 ]. Physical activity is grouped into three categories—active, moderately active, and inactive—following the guidelines for physical activity [ 33 ]. Three categories are used to classify education levels: below high school, high school, and above high school. Parameters such as HDL, LDL, HbA1c, and serum creatinine are measured from each participant’s fasting venous blood using an automated analyzer. Conditions like high blood pressure, heart disease, stroke, and arthritis are categorized based on self-reported medical diagnosis. The three factors used to identify diabetes are a self-reported medical diagnosis, a glycosylated hemoglobin (HbA1c) of 6.5% or above, or a fasting blood glucose level of 7.0 mmol/L or higher. The widely accepted algorithm developed by the Chronic Kidney Disease Epidemiology Collaboration is used to calculate the estimated glomerular filtration rate (eGFR) [ 34 ].

Statistical analyses

Sample weights were appropriately applied in statistical analyses to account for complex sampling designs, following guidelines from the NHANES official website. All of the study population’s descriptive statistics were calculated, and the TyG index was divided into quartiles. The categorical data were reported as percentages, and the continuous variables were shown as mean ± standard deviation (SD). To examine differences between continuous and categorical data, weighted chi-square tests and weighted linear regression models were employed, respectively. In accordance with the STROBE statement [ 35 ], three distinct multivariate logistic regressions were run to investigate the relationship between TyG and hyperuricemia. While Model 2 and Model 3 adjusted for age, gender, and race, educational level, marital status, RIP, smoking, alcohol consumption, physical activity, BMI, HDL, LDL, HbA1c, serum creatinine, eGFR, hypertension, diabetes, arthritis, stroke, and coronary heart disease, Model 1 left covariates unadjusted. Relationship consistency was verified by a linear trend test, and nonlinear relationships were investigated using a Generalized Additive Model (GAM) with smooth curve fitting. In the presence of nonlinearity, a recursive algorithm identified significant turning points in the TyG and hyperuricemia relationship. Threshold effect analysis assessed differences between logistic regression models and two-part logistic regression models. Additionally, subgroup analyses and interaction tests were performed for age, gender, BMI, hypertension, diabetes, stroke, arthritis, and coronary heart disease, with adjustments for corresponding confounding factors. The results were considered credible if the interaction P -value was not significant; if it was, then likely subgroup variations were considered. EmpowerStats ( http://www.empowerstats.com ) and R (version 4.2.2) were used for all statistical analyses, with a P -value < 0.05 denoting statistical significance.

Baseline characteristics of participants

Table  1 displays the baseline attributes of the individuals in the TyG index. Compared to the lowest TyG quartile, individuals in the TyG Q4 group exhibited a tendency towards older age, male gender, Mexican American ethnicity, lower educational attainment, marital status, non-smoking behavior, lower RIP levels, lower HDL, lower eGFR, and higher prevalence of hypertension, diabetes, coronary heart disease, arthritis, stroke. Additionally, they displayed higher levels of BMI, HbA1c, FPG, TG, LDL, serum creatinine, and uric acid (all P  < 0.05). Notably, there was a significantly increased frequency of hyperuricemia ( P  < 0.05) in participants with high TyG levels.

Association between TyG and its components and hyperuricemia

Table  2 displays the relationship between TyG and its components and hyperuricemia. After adjusting for potential confounding variables (Model 3), the study found a significant positive correlation between TG and hyperuricemia (OR = 1.68, 95% CI: 1.38, 2.04). Further dividing TG into quartiles, in Model 3, participants in the highest quartile of TG had a 1.95-fold higher risk of hyperuricemia compared to those in the lowest quartile (OR: 2.95, 95% CI: 1.83, 4.75). Additionally, a significant dose-response relationship was found ( P  < 0.05). However, after adjusting for potential confounding variables (Model 3), the study did not find a significant association between FPG and hyperuricemia (OR = 1.00, 95% CI: 0.99, 1.01). Further dividing FPG into quartiles, in Model 3, participants in quartile 4 of FPG had a significantly positive correlation with hyperuricemia compared to Q1 (OR = 1.84, 95% CI: 1.14, 2.99). Our study also found a significant dose-response relationship ( P  < 0.05). Moreover, the investigation’s findings demonstrated a positive correlation between TyG and the likelihood of hyperuricemia. Variable adjustments bolstered this association, and all multivariate logistic regression models (model 1: OR = 1.70, 95% CI: 1.51,1.91; model 2: OR = 1.69, 95% CI: 1.50,1.92; model 3: OR = 2.34, 95% CI: 1.70,3.21) showed positive correlations regardless of whether confounding variables were adjusted. It’s interesting to note that a unit increase in the TyG index was linked to a 1.34-fold increase in the risk of hyperuricemia after controlling for possible confounding variables (model 3; Table  2 ). When TyG was further split into quartiles using Q1 as the reference group and different variables were taken into account in model 3, the risk of hyperuricemia was 3.85 times higher for those in the highest quartile of the TyG index than for those in the lowest quartile (OR: 4.85, 95% CI: 3.03, 7.78) (Table  2 ). Furthermore, a noteworthy dose-response correlation ( P  < 0.05) was noted.

However, the odds ratios (ORs) for Q2, Q3, and Q4 show that there might be a non-linear correlation; the 95% confidence intervals (CIs) for these three questions are 1.54 (0.98, 2.14), 2.17 (1.44, 3.25), and 4.85 (3.03, 7.78), respectively. Using GAM and smooth curve fitting, a non-linear association between TyG and hyperuricemia was found (Fig.  2 ), adding to the validity of the results. Further exploration through threshold effect analysis revealed a turning point at 9.69 (Table  3 ). Before the turning point, TyG and hyperuricemia exhibited a significant positive correlation, with an OR (95% CI) of 2.64 (2.12, 3.28). Subsequently, after the turning point, TyG and hyperuricemia showed a significant negative correlation, with an OR (95% CI) of 0.32 (0.11, 0.98) (Table  3 ). Additionally, after stratification by age and gender, our results also indicate a non-linear relationship between TyG and hyperuricemia (Figs.  3 and 4 ).

Smooth curve fitting for TyG and hyperuricemia. Non-linear relationship between TyG and hyperuricemia was detected by the generalized additive model. The solid red line represents the smooth curve fit between variables. Blue dotted lines represent the 95% CI from the fit. Adjustment factors included age, sex, race, educational level, marital status, smoking status, alcohol consumption, physical activity, BMI, RIP, LDL, HDL, HbA1c, Serum creatinine, eGFR, hypertension, diabetes, arthritis, coronary heart disease and Stroke

The association between TyG and hyperuricemia stratified by gender. Age, race, educational level, marital status, smoking status, alcohol consumption, physical activity, BMI, RIP, LDL, HDL, HbA1c, Serum creatinine, eGFR, hypertension, diabetes, arthritis, coronary heart disease and Stroke were adjusted

The association between TyG and hyperuricemia stratified by age. Gender, race, educational level, marital status, smoking status, alcohol consumption, physical activity, BMI, RIP, LDL, HDL, HbA1c, Serum creatinine, eGFR, hypertension, diabetes, arthritis, coronary heart disease and Stroke were adjusted

To further evaluate the association between TyG and hyperuricemia in various categories, we also conducted interaction tests and stratified analysis accounting for gender, age, BMI, hypertension, diabetes, coronary heart disease, arthritis, and stroke. The positive link between TyG and hyperuricemia does not appear to be influenced by age, arthritis, coronary heart disease, or stroke, according to the results of our study. However, interactions were observed in gender, BMI, diabetes, and hypertension, with particular significance in female, non-obese, non-hypertensive, and non-diabetic populations (OR: 2.98, 95% CI: 2.27, 3.92), (OR: 3.33, 95% CI: 2.56, 4.33), (OR: 2.62, 95% CI: 2.05, 3.35), (OR: 2.92, 95% CI: 2.32, 3.69) (Fig.  5 ). Therefore, we further explored the non-linear relationship between TyG and hyperuricemia through stratification. After stratification by gender, we found that their non-linear relationship still exists (Fig.  3 ). Furthermore, after stratification by BMI, hypertension, and diabetes, we still observed a non-linear association (Supplementary Figs.  1 , 2 and 3 ).

Subgroup and interaction analyses of the TyG index and hyperuricemia. Adjustment factors included age, sex, race, educational level, marital status, smoking status, alcohol consumption, physical activity, BMI, RIP, LDL, HDL, HbA1c, Serum creatinine, eGFR, hypertension, diabetes, arthritis, coronary heart disease and Stroke

Based on NHANES data from 2011 to 2018, our large-sample cross-sectional analysis demonstrates a strong correlation between elevated TyG and a higher risk of hyperuricemia. Even when categorizing the TyG into quartiles (Q1-Q4), this positive correlation persists. In the adult population in the United States, we found a non-linear association between hyperuricemia and the TyG index after applying a smooth curve. There is a segmented inhibitory effect between the TyG index and hyperuricemia, with 9.69 as a significant inflection point. Before this point, a significant increase in hyperuricemia risk was reported with the increasing TyG, and after this point, a significant decrease in hyperuricemia risk was observed with increasing TyG index. Additionally, our study presents the most detailed stratified analysis.

The TyG index and hyperuricemia had a linear positive connection, according to a prior cross-sectional study from northeastern China, with a 54.1% rise in the probability of hyperuricemia for every unit increase in the TyG [ 25 ]. A cross-sectional study conducted in Thailand also found that among Royal Thai Army members, the TyG index and hyperuricemia had a substantial positive connection that persisted regardless of the soldiers’ obesity condition [ 36 ]. Qing et al. evaluated the relationship between TyG and hyperuricemia in a cohort study involving 42,387 Chinese patients having physical exams. The findings demonstrated a favorable relationship between hyperuricemia and the TyG index [ 37 ]. These studies support our findings. In addition, our research revealed a strong positive association between TyG and hyperuricemia, with each unit rise in TyG associated with a 1.34-fold increase in the risk of hyperuricemia. It was also discovered that interactions occurred regardless of obesity, however in non-obese people this link was stronger.

In addition, after conducting subgroup analyses and interaction tests, our study found that gender, hypertension, and diabetes interacted with the relationship between TyG and hyperuricemia, especially in females, and this association was more pronounced in non-hypertensive and non-diabetic populations. Gender variations have been observed in the TyG index’s ability to detect hyperuricemia in the past, particularly in females [ 38 ], which is consistent with our study results. This may be because estrogen is a uric acid generator and is associated with complex metabolic endocrine factors, thereby affecting lipid metabolism and causing gender differences in lipid metabolism [ 39 ]. In hypertensive people with an average age of 63.81 years, a study in China demonstrated a positive connection between TyG and hyperuricemia (OR = 2.04; 95%CI: 1.87 to 2.24) [ 40 ]. An additional cross-sectional study conducted in Chinese hospitals investigated the relationship between hyperuricemia and TyG in patients with hypertension. TyG and hyperuricemia were shown to positively correlate in hypertensive individuals; this correlation was more pronounced in patients with grade 1–2 hypertension than in those with grade 3 hypertension [ 22 ]. This is consistent with the trend observed in our study. Regardless of the existence of hypertension, we discovered a favorable connection between TyG and hyperuricemia, but this correlation was more pronounced in non-hypertensive individuals. Differences in demographic characteristics and research methods may explain this discrepancy. Further research is needed to uncover these underlying factors. Through a retrospective analysis, Han et al. [ 41 ] discovered a substantial positive connection between TyG and hyperuricemia in patients with diabetes, whereas our study discovered an interaction between TyG and hyperuricemia in patients without diabetes. The observed occurrence could potentially be attributed to variations in the study population, ethnicity, and sample size. More study is required to validate these findings because there is a dearth of information regarding the connection between TyG and hyperuricemia in both diabetic and non-diabetic groups.

The mechanism of TyG in hyperuricemia is not yet clear, but the following biological mechanisms can be explained. Since TyG is computed by summing up TG and FPG, there is a strong correlation between the pathophysiology of hyperuricemia and TG and FPG levels in the human body. Abnormalities in lipid metabolism result from the breakdown of elevated quantities of TG into free fatty acids, which are then transferred to different parts of the body and speed up the breakdown of adenosine triphosphate. Lipid metabolism abnormalities cause kidney damage, reduce uric acid excretion, and consequently increase serum uric acid levels [ 42 ]. Furthermore, high TG levels inhibit insulin receptor activity and quantity on adipocytes, competing with glucose to block insulin’s ability to bind to receptors and cause IR [ 43 ]. Excessive accumulation of glucose leads to hyperglycemia, alters the expression and activity of glucose transporter proteins in tissues, and reduces insulin sensitivity [ 44 , 45 ]. Notably, with an inflection point of 9.69, our study discovered a strong segmental inhibitory effect between TyG and hyperuricemia. TyG and hyperuricemia had a substantial positive correlation up to 9.69, whereas a significant negative correlation followed after 9.69. This differs from the results reported in previous studies, and one possible reason is speculated to be racial differences. Previous correlation studies have mainly focused on Asian countries such as China and Thailand, and racial differences have been shown to affect insulin sensitivity [ 46 ]. Also differences in demographic characteristics and research methods may be potential factors. To sum up, additional pertinent research is required to validate our findings, particularly in the US population.

There are various restrictions on this study. First off, because the study is cross-sectional, we are unable to determine if TyG and hyperuricemia are causally related. The conclusions reached must be supported by further research. Second, although we included many relevant covariates and adjusted accordingly, there may still be interference from other confounding factors, such as hyperthyroidism, alcoholism, renal insufficiency, drugs, tumors, and other factors that affect uric acid levels. To substantiate the connection between hyperuricemia and the TyG index, more intervention studies ought to be carried out. Additionally, serum uric acid levels are influenced by diets rich in purines, and the data on dietary questionnaires in NHANES are very limited, so we cannot determine whether participants have a high-purine diet.

In general, hyperuricemia and the TyG index have a reverse U-shaped connection. In patients with TyG < 9.69, a higher risk of hyperuricemia is significantly correlated with a greater TyG. On the other hand, a higher TyG is substantially linked to a decreased risk of hyperuricemia in patients with TyG > 9.69. These results imply that the prevention and treatment of hyperuricemia may benefit from reducing or raising TyG levels within a specific range. Confirming the causal relationship and underlying mechanisms between them will require more investigation.

Data availability

Data is provided within the supplementary information files.

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Acknowledgements

The authors thank all team members and participants in the NHANES study.

This study was funded by the Science and Technology Innovation Project of the Chinese Academy of Traditional Chinese Medicine (CI2021A03005).

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Linjie Qiu, Yan Ren, Jixin Li, Meijie Li, Lingli Qin, Chunhui Ning, Jin Zhang & Feng Gao

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LQ, YR, JZ, and FG drafted the manuscript and made substantial contributions to its conception and design. JL, ML and WL extracted the data used for the study from the NHANES official website, while LQ and CN were responsible for the production of photographs and tables for this study. LQ, YR, JZ, and FG were responsible for data analysis and interpretation of the results for this research. All authors have thoroughly reviewed and approved the final manuscript.

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Qiu, L., Ren, Y., Li, J. et al. Nonlinear association of triglyceride-glucose index with hyperuricemia in US adults: a cross-sectional study. Lipids Health Dis 23 , 145 (2024). https://doi.org/10.1186/s12944-024-02146-5

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The effect of organizational culture, supplier trust and information sharing on supply chain viability

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• Mehmet Fatih Acar 1 ,
• Alev Özer Torgalöz 2 ,
• Enes Eryarsoy 3 ,
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This study investigates the impact of intangible resources such as adhocracy culture (ADC), information sharing with suppliers (IS), and supplier trust (ST) on supply chain viability (SCV) under high inflation environment. To do this, a conceptual model is developed to analyze the associations between these suggested variables. Using on a cross-sectional survey, data are collected from 216 supply chain (SC) and production practitioners based in Türkiye who are medium- to senior-level managers. To analyze our theoretical model, we processed our data and model using lavaan package in R. The results show a significant relationship between ADC and SCV. Additionally, both of IS and ST capabilities are found to have a strong mediating effect on the ADC and SCV relationship. The results of this study will provide insight for managers and researchers to prevent the negative effects of SC disruptions due to the high inflation or other type of stress tests. Extant research has investigated the SCV with different crises like COVID-19 pandemic however, the study is the first research that examines SCV under high inflation stress test. Moreover, ADC, IS and ST have not widely appeared in SCV literature. In this regard, this research also contributes to the ongoing efforts of investigating the antecedents of SCV.

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Appendix 1 Measurement of constructs*

• *Seven-point Likert, ranging from 1 (strongly disagree) to 7 (strongly agree) for SCV, ADC and SCD, five-point Likert, ranging from 1 (strongly disagree) to 5 (strongly agree) for ST and IS

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Acar, M.F., Torgalöz, A.Ö., Eryarsoy, E. et al. The effect of organizational culture, supplier trust and information sharing on supply chain viability. Oper Manag Res (2024). https://doi.org/10.1007/s12063-024-00491-3

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CD163 + macrophages in the triple-negative breast tumor microenvironment are associated with improved survival in the Women’s Circle of Health Study and the Women’s Circle of Health Follow-Up Study

• Angela R. Omilian 1 ,
• Rikki Cannioto 1 ,
• Lucas Mendicino 1 ,
• Leighton Stein 2 ,
• Wiam Bshara 2 ,
• Bo Qin 3 , 4 ,
• Elisa V. Bandera 3 , 4 ,
• Nur Zeinomar 3 , 4 ,
• Scott I. Abrams 5 ,
• Chi-Chen Hong 1 ,
• Song Yao 1 ,
• Thaer Khoury 2 &
• Christine B. Ambrosone 1  

Breast Cancer Research volume  26 , Article number:  75 ( 2024 ) Cite this article

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Metrics details

Tumor-associated macrophages (TAMs) are a prominent immune subpopulation in the tumor microenvironment that could potentially serve as therapeutic targets for breast cancer. Thus, it is important to characterize this cell population across different tumor subtypes including patterns of association with demographic and prognostic factors, and breast cancer outcomes.

We investigated CD163 + macrophages in relation to clinicopathologic variables and breast cancer outcomes in the Women’s Circle of Health Study and Women’s Circle of Health Follow-up Study populations of predominantly Black women with breast cancer. We evaluated 611 invasive breast tumor samples (507 from Black women, 104 from White women) with immunohistochemical staining of tissue microarray slides followed by digital image analysis. Multivariable Cox proportional hazards models were used to estimate hazard ratios for overall survival (OS) and breast cancer-specific survival (BCSS) for 546 cases with available survival data (median follow-up time 9.68 years (IQR: 7.43–12.33).

Women with triple-negative breast cancer showed significantly improved OS in relation to increased levels of tumor-infiltrating CD163 + macrophages in age-adjusted (Q3 vs. Q1: HR = 0.36; 95% CI 0.16–0.83) and fully adjusted models (Q3 vs. Q1: HR = 0.30; 95% CI 0.12–0.73). A similar, but non-statistically significant, association was observed for BCSS. Macrophage infiltration in luminal and HER2+ tumors was not associated with OS or BCSS. In a multivariate regression model that adjusted for age, subtype, grade, and tumor size, there was no significant difference in CD163 + macrophage density between Black and White women (RR = 0.88; 95% CI 0.71–1.10).

Conclusions

In contrast to previous studies, we observed that higher densities of CD163 + macrophages are independently associated with improved OS and BCSS in women with invasive triple-negative breast cancer.

Trial registration

Not applicable.

The tumor-immune microenvironment (TIME) has a key role in pathologic complete response and patient survival in breast cancer [ 1 , 2 , 3 , 4 ]. While tumor-infiltrating lymphocytes (TILs) in aggregate and various T cell subpopulations have been routinely examined, tumor-associated macrophages (TAMs) and other cells of the myeloid lineage have received less attention, despite being a prevalent immune subpopulation in breast carcinoma. Typically, high macrophage counts in breast tumors are regarded as being associated with tumor progression and poorer survival [ 5 , 6 , 7 , 8 ]. However, much prior work on macrophage markers in relation to breast cancer outcomes had small study samples that precluded analyses stratified by subtype, or adequately powered analyses adjusted for prognostic factors that are known to influence breast cancer survival. Moreover, most of these earlier studies were overwhelmingly conducted in populations of White or Asian women, and representation of Black women on this topic is poor, with only a handful of studies to date [ 9 , 10 , 11 ].

Novel therapeutic approaches that target macrophages are an increasingly important area of clinical study, and thus it is important to understand how specific macrophage markers vary in accordance with demographic and clinical factors [ 12 , 13 ]. As part of our ongoing work that investigates the breast TIME in relation to aggressive disease and poorer outcomes in Black women, we investigated the macrophage marker CD163 among women participating in the Women’s Circle of Health Study and Women’s Circle of Health Follow-up Study. Our objective was to compare macrophage infiltration between Black and White women and to investigate the association of CD163 + cells with overall and breast cancer-specific survival in a study sample that was large enough to allow stratification by subtype and adjustment for known prognostic factors in breast cancer.

Study population

We used data and tissue samples from women participating in the Women’s Circle of Health Study (WCHS), a multi-site, case–control study designed to evaluate the risk factors for aggressive breast cancer in Black and White women, and the Women’s Circle of Health Follow-up Study (WCHFS), a population-based cohort study of Black breast cancer survivors, both of which have been described extensively in our previous work and in the Additional file 1 : Methods [ 14 , 15 , 16 , 17 ]. The WCHS and WCHFS used the same methods for recruitment, interviews, and eligibility. Briefly, participants were 20–75 years old; self-identified as Black or White (for WCHS); had primary, histologically confirmed invasive breast cancer or ductal carcinoma in situ (DCIS); and had no previous history of cancer other than non-melanoma skin cancer. Women in WCHS were diagnosed between 2001 and 2013 and included Black and White cases from New York City and New Jersey; while cases in WCHFS included only Black women diagnosed from 2013 to 2019 in New Jersey. Clinical and tumor pathology variables were extracted from the pathology reports. All women provided informed consent and the study protocol was approved by the Institutional Review Boards at Rutgers Cancer Institute of New Jersey and Roswell Park Comprehensive Cancer Center.

Tissue samples

Formalin-fixed and paraffin-embedded (FFPE) invasive breast tumor tissues were built into tissue microarrays (TMAs) under the guidance of an experienced breast pathologist (TK). TMA cores ranged in size from 0.6 to 1.2 mm in diameter, and the majority of patient tumors (67.2%) were represented by at least 3 TMA cores (range 1–6 cores). We aimed to include both tumor nests and stromal regions when selecting regions for coring and avoid the tumor margins. TMA construction was completed in 2017 from patients recruited up until this point with incident, primary, and treatment-naïve invasive breast cancer. As the WCHS and WCHFS focused on recruiting Black women, the number of cases from Black women in our dataset exceeds the number of White cases (Black: N = 507, White: N = 104).

Immunohistochemical staining and image analysis

CD163 has long been established as a clinical antibody for detecting histiocytes that has greater specificity than CD68 [ 18 ], and is commonly used to represent immunosuppressive macrophages in the TIME in research studies [ 19 ]. Immunohistochemistry (IHC) was performed by the Pathology Network Shared Resource at Roswell Park following standard procedures. To reduce staining variability that can occur with IHC, we used an automated staining platform, clinical-grade reagents, and stained all TMAs in a single batch. Briefly, TMA sections were cut at 4 μm, placed on charged slides, dried, and deparaffinized. Bond Epitope Retrieval 2 (Leica AR9640) was used for antigen retrieval. Slides were stained on the Leica Bond Rx autostainer with the CD163 antibody (Leica Biosystems, clone 10D6) and the Bond Polymer Refine Detection kit (Leica DS9800). Diaminobenzidine (DAB) was used for marker visualization. TMA cores were excluded if the tumor could not be reliably scored for CD163 marker expression (e.g., the tissue was folded or damaged) or there was insufficient tumor cellularity (cutoff set at 100 tumor cells).

Slides were digitally scanned using Aperio AT2 (Leica Biosystems, Inc., Buffalo Grove, IL) with 20X bright-field microscopy. Aperio ImageScope version 12.4.3.8007 (Leica Biosystems, Inc., Buffalo Grove, IL) was used for image analysis. Slide image data fields were populated, and images were visually examined for quality and amended as necessary (e.g., core excluded if there was excessive folding or damage). An annotation layer was created for each core and our study pathologist who was blinded to sample characteristics made an image analysis algorithm macro that was used to quantify the number of cells that were positive for CD163 stain. Details pertaining to the algorithm and scoring are described in the Additional file 1 : Methods. The number of CD163 + cells in each patient sample were reported per square millimeter of tumor tissue and the average CD163 + cell density across multiple cores from each patient was used for analyses.

Epidemiological and tumor variables

Women self-identified their race in the baseline questionnaire. Tumor and clinicopathological factors were abstracted from the patient pathology report and included AJCC stage, grade, tumor size, node status, and treatment (surgery, chemotherapy, radiation therapy, and/or hormone therapy). Breast cancer subtypes were inferred from estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status information from the pathology reports as follows: luminal (HR+/HER2−), HER2-positive (HR+/HER2+ or HR-/HER2+), and triple-negative (HR-/HER2−), where hormone receptor (HR+) refers to ER+ and/or PR+. Other factors, including age and body mass index (BMI), were obtained by interviewer- and self-administered questionnaires at baseline and have been previously described [ 20 ].

Breast cancer outcomes

Data on vital status, including dates and causes of death, were ascertained through linkage with the New Jersey State Cancer Registry files, and were available for 546 cases. Primary outcomes of interest in the study were overall survival (OS) and breast cancer-specific survival (BCSS). The ICD-10 code (C50) was used to identify breast cancer mortality. Time to follow-up was calculated from the date of diagnosis until the date of last follow-up (August 2023) or death from any cause or death from breast cancer.

Statistical analyses

Demographic and clinical factors were summarized using the mean and standard deviation for normally distributed continuous variables and the median and interquartile range (IQR) otherwise, and number and percentage for categorical variables. A negative binomial regression model was used to resolve overdispersion of CD163 + cell density and non-normally distributed residuals seen with a linear model. A zero-inflation parameter was included due to underfitting of zero values and an offset term for the log of total cell density to account for tumor cellularity differences across patients. Model assumptions were verified graphically. Beta coefficients were exponentiated to obtain Rate Ratios (RR) and 95% Confidence Intervals (CI) representing the change in CD163 + cell density in terms of percentage increase or decrease. Separate models were used to model CD163 + cell density as a function of race and clinical/tumor factors. F tests about the appropriate contrasts of model estimates were used to evaluate, within race, the association between CD163 + cell density and each factor. A multivariable model was formulated to assess the association between race and CD163 + macrophage density, adjusted for age, subtype, grade, and tumor size.

Multivariable Cox regression models were used to compute hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of CD163 + cell density with OS and BCSS for each breast cancer subtype. As there are currently no established cutoffs in the literature, CD163 + cell density was divided into tertiles. Other cutoffs were examined, including dividing CD163 + cell density at the median, and by quantiles and quintiles. Variables that were significantly associated with CD163 + cell density or survival in the univariate setting were added to a multivariable model and sequentially removed while assessing model fit using a likelihood ratio test (Additional file 1 : Tables S1 and S2). Covariates were retained in the final model if their inclusion improved model fit. Model covariates differed by breast cancer subtype. Model 1 was adjusted for age at diagnosis. For OS, Model 2 was adjusted for age, BMI, stage, and tumor size in the luminal subtype; age plus tumor size for the HER2+ subtype; and age, stage, grade, and node status for the triple-negative subtype. For BCSS, Model 2 was adjusted for age and BMI in the luminal subtype; no additional covariates were retained for the HER2+ subtype; and age and stage for the triple-negative subtype. The proportional hazards assumption was verified graphically by analyzing the correlation between time and scaled Schoenfeld residuals. All statistical analyses were conducted in R (version 4.2.0) and two-sided p values ≤ 0.05 were considered statistically significant. Analyses are reported according to REMARK guidelines [ 21 ].

Characteristics of the cohort

Cohort characteristics are shown in Table  1 and the study sampling schema is shown in Additional file 1 : Figure S1. In total, there were 611 women with invasive breast cancer (507 Black and 104 White); of these 546 women had available survival data. Compared with White women, Black women were significantly more likely to have higher BMI (30.5 vs 26.6 kg/m 2 ), have tumors that were ER-negative (33.5 vs 21.2, p  = 0.01), triple-negative (25.5 vs 14.4, p  = 0.04), and tumors with higher grade (54.1 grade 3 vs 35.6 p  = 0.003). Black women were also more likely than White women to have received radiation therapy (68.5 vs 46.5, p  < 0.001). There were no statistically significant differences between Black and White women in age, the distribution of breast cancer stage, mean tumor size, node status, and the receipt of surgery, chemotherapy, or hormone therapy.

Macrophage densities, race, and clinical prognostic factors

Staining is shown for cores representative of low, intermediate, and high levels of CD163 + macrophage infiltration in Fig.  1 . Almost all women in the WCHS had macrophages in their tumors; CD163 + macrophages were not detected in only 6 out of 611 women. In univariate analyses, Black women had a significantly higher density of CD163 + cells ( p  = 0.0099, Fig.  2 a). CD163 + macrophage densities were also higher in triple-negative tumors ( p  < 0.0001, Fig.  2 b), and higher-grade tumors ( p  < 0.0001, Fig.  2 c). Black women with the triple-negative subtype (median 574.3 cells/μm 2 , p  < 0.001), Black women with the HER2 + subtype (314.6 cells/μm 2 , p  < 0.001), and White women with the HER2 + subtype (281.5 cells/μm 2 , p  = 0.035) had significantly higher densities of CD163 + macrophages compared to White women with the luminal subtype (Fig.  2 d). In the overall study population, CD163 + macrophage density was significantly associated with age ( p  = 0.025), breast cancer subtype ( p  < 0.001), stage ( p  < 0.001), grade ( p  < 0.001), and tumor size ( p  = 0.002); similar associations were observed when the Black population was examined separately (Table  2 ). In a multivariate negative binomial regression model that adjusted for age, subtype, grade, and tumor size, there were no significant differences in CD163 + macrophage densities between Black and White women (RR = 0.88; 95% CI 0.71–1.10). To investigate a possible cohort effect given that recruitment for White women ended earlier than that for Black women, we compared Black and White cases up until the last timepoint that White women were enrolled and observed similar results (RR = 0.88; 95% CI 0.67–1.16).

Representative CD163 immunohistochemical staining in breast tissue microarray cores. Two representative cores are shown from each of three categories of infiltration: a low, b intermediate, c high

Boxplots comparing CD163 + cell density by a race, b breast cancer subtype, c tumor grade, and d combination of race and breast cancer subtype. Comparisons tested using negative binomial regression. ns non-significant, * p  < 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001

Survival outcomes and CD163 + macrophages

Data for survival analyses were available for 546 women, with 127 deaths, 66 of which were due to breast cancer. The median follow-up time was 9.68 years (IQR: 7.43–12.33) years. For the overall cohort, increasing tertiles of CD163 + macrophage density were not associated with a significant improvement in OS or BCSS in the age-adjusted models (Table  3 ). For the fully adjusted models, there was a significant association for OS (Q3 vs. Q1: HR = 0.59; 95% CI 0.37–0.94), but not BCSS (Q3 vs. Q1: HR = 0.59; 95% CI 0.30–1.14; Table  3 ). In both age-adjusted and fully adjusted models stratified by subtype, increasing tertiles of CD163 + macrophage density were associated with a significant improvement in OS (Q3 vs. Q1: HR = 0.30; 95% CI 0.12–0.73; Table  4 ) in the triple-negative subtype. A statistically significant association between CD163 + macrophage densities and OS was not observed for the luminal and HER2+ subtypes. A similar pattern was observed for BCSS, in which increasing CD163 + macrophage densities were associated with better survival in the triple-negative subtype only (Q3 vs. Q1: HR = 0.38; 95% CI 0.10–1.44), although the associations were not significant (Table  4 ).

To ensure that race and grade were not confounding the associations that we observed in the triple-negative subtype, additional multivariable analyses that added race and grade as variables in the fully adjusted models were investigated. Again, we observed that increasing CD163 + macrophage density was associated with a significant improvement in OS for the triple-negative subtype (Q3 vs. Q1: HR = 0.28; 95% CI 0.11–0.69), but not for the luminal or HER2+ subtypes (Additional file 1 : Table S3). Several additional sensitivity analyses were performed to ensure our results were robust. Additional cut points of CD163 marker density were examined, such as dividing at the cohort median to differentiate high vs low CD163 density, as well as quantiles and quintiles (Additional file 1 : Tables S4 and S5). We stratified by ER status rather than breast cancer subtype (Additional file 1 : Table S6). Lastly, we performed the analysis in Black patients only (Additional file 1 : Table S7). For all these additional analyses, we observed that increasing levels of CD163 + macrophage infiltration were associated with improved OS in the triple-negative subtype (or ER-negative group for analyses stratified by ER status), and this effect was not observed for the luminal or HER2 + subtypes.

In this study, we found that increasing densities of CD163 + macrophages in the breast TIME were associated with a pronounced and significant improvement in OS for women with the triple-negative subtype. Prior studies investigating the association between TAMs and breast cancer prognosis have contributed to a general consensus that high levels of TAMs in the breast TIME, especially M2-like macrophages, are associated with adverse survival outcomes [ 5 , 6 , 7 , 9 , 22 ]. So, what might explain the differing results in our study? First, we have a relatively large population of Black women allowing us to stratify by subtype and adjust for confounding factors. As subtypes of breast cancer differ in their patterns of short and long-term survival, stratification by subtype can reveal different associations in relation to prognostic or risk factors [ 23 , 24 , 25 ]. This holds true for patterns of immune infiltration in the breast TIME that are known to vary by subtype and show differing associations with survival [ 1 , 26 , 27 ]. The majority of prior studies that examined TAM infiltration in breast carcinoma were underpowered for subtype-specific associations, especially for the triple-negative subtype, in which sample sizes were extremely small [ 5 , 6 , 9 , 11 ].

Second, macrophages are a complex immune cell population with a variety of phenotypes and functional states that can be tissue specific and dependent on microenvironmental cues and/or spatial proximity to other immune subsets [ 28 , 29 , 30 ]. Moreover, there are no standardized methods for macrophage detection and different studies have used different markers (e.g., CD68, CD163, or CD206) and staining platforms to make conclusions about the prognostic value of macrophages in invasive breast cancer. Methods for quantifying macrophages in the breast TIME are also heterogeneous (e.g., density, percentage) as well as the tissue compartment in which macrophages are assessed (e.g., tumor compartment vs. stroma or both). The cutoff values for what constitute high versus low macrophage infiltration also varies by study, as well as what factors are included in multivariable models.

We conducted several quality controls and performed several sensitivity analyses to ensure that our findings were robust. First, we used a clinical-grade CD163 antibody that is approved for in vitro diagnostic purposes. Second, quality control for staining specificity was performed by an experienced breast pathologist. Third, automated image analysis was performed ensuring that the quantification of CD163 positive cells was standardized and objective across each TMA core. Fourth, all TMAs were stained in a single batch to eliminate inter-batch variability that is known to occur with IHC. From an analysis standpoint, we examined different cutoffs for what constitutes high or low CD163 + macrophage infiltration, dividing the cohort at the median, tertiles, quantiles, and quintiles. We examined associations when stratifying by ER status instead of subtype. Lastly, we examined Black women separately. The same general patterns of improved OS and BCSS in the triple-negative subtype (or ER- group) were observed across all these additional analyses.

As shown in our results and in the literature, high macrophage infiltration in breast cancer is correlated with several factors that indicate poor survival, like the triple-negative subtype, and higher grade and stage [ 5 , 6 , 7 , 8 ]. In prior studies that could not account for these factors, the associations of high macrophage densities with poor survival may have been largely driven by these correlated factors. A recent study that investigated multiple macrophage markers in relation to breast cancer outcomes showed that when examining the ER-positive versus ER-negative groups separately, high expression of CD163 was associated with improved OS in ER− cases, but not in ER+ cancers [ 31 ]. When examining CD163 expression by tumor locations, Fortis et al. found that disease-free survival (DFS) and OS were prolonged in patients with CD163 expression that was low in the tumor center but high at the invasive margins compared to the inverse (i.e., high in tumor center and low in the invasive margin) [ 32 ]. Collectively, these findings together with those reported in our study add to the existing body of evidence suggesting that tumor-associated macrophages have distinct programs that vary by tissue context or breast cancer subtype. While CD163 + macrophages are usually regarded as immune-suppressing and tumor-promoting, human macrophages are likely to concurrently exhibit phenotypic characteristics of both M1-like and M2-like subtypes. Therefore, to gain a broader appreciation of the macrophage response in breast cancer outcomes, phenotypic studies combined with comprehensive functional and transcriptomic analyses may strengthen translational relevance to prognosis.

Univariate analyses showed that CD163 + cell densities differed between Black and White women, but these differences were attenuated in the multivariable analyses that adjusted for age, grade, tumor size, and breast cancer subtype. Earlier work has shown that immune profiles vary in breast tumors from Black and White women [ 14 , 15 , 33 , 34 ]. While other studies have compared macrophage markers in Black and White women, to our knowledge, only a couple studies have compared CD163 marker expression specifically [ 9 , 10 ]. Koru-Sengul et al. reported that Black women had higher levels of CD163 + macrophages, however multivariable analyses were not performed [ 11 ]. In a more recent study, Bauer et al. found that the frequency of CD163 + macrophages varied by region within African populations and a population from Germany; West African women had the highest numbers of CD163 + macrophages [ 35 ].

The strengths of this work are accompanied by some limitations. While our study sample exceeds that of several prior studies of CD163 in relation to breast cancer prognosis, it is nonetheless not as large as some of the more well-characterized T cell populations like CD8 + T cells [ 4 ], and our findings need to be replicated in additional cohorts. As the WCHS and WCHFS prioritized recruitment of Black women, our findings may not be generalizable to more demographically or clinically diverse populations. As the vast majority (89.5%) of our cases were obtained through the New Jersey Cancer registry, our sample is largely population-based. Nonetheless, potential sources of bias include women who agreed to participate verses those who did not. However, the distributions of tumor stage and grade are similar among participants in the WCHFS and all eligible breast cancer cases in the New Jersey State Cancer Registry in the same counties, suggesting that tumor characteristics in our study are representative of Black women diagnosed with breast cancer in New Jersey [ 16 ]. Recall bias is minimized as the data pertaining to the tumor characteristics were obtained by independent review of pathology reports. Despite adjusting for important clinical and demographic prognostic factors, we cannot rule out the possibility of residual confounding due to unmeasured variables. Lastly, although whole sections are ideal for studies of the TIME, a study of this size is not practicable with whole sections, and therefore TMAs are commonly used in large studies of marker expression in breast cancer [ 4 , 36 , 37 ]. Importantly, we cored the interior of the tumor block for TMA construction and thus our results are specific to this region and do not apply to the tumor interface or other non-tumor regions. Macrophages are a complex population and our future work will build on this fundamental finding, making use of multiplexed panels to more fully define macrophage phenotypes in women with invasive breast cancer, as well as their spatial distribution, which could further influence their prognostic relevance [ 32 ].

We observed that higher densities CD163 + macrophages are independently associated with improved OS and BCSS in the triple-negative subtype. Future investigations will expand upon this work in a larger cohort, incorporating more comprehensive multiplexed staining technologies to further define the complexity of macrophage functional states and compare their localization within the TIME to prognosis in women with invasive breast cancer.

Availability of data and materials

Epidemiological data and image data are available from the corresponding author upon reasonable request.

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Acknowledgements

Biospecimens or research pathology services for this study were provided by the Pathology Network Shared Resource and the DataBank and Biorepository Shared Resource, which are funded by the National Cancer Institute (NCI P30CA16056) as Cancer Center Support Grant shared resources.

This work was supported by the National Cancer Institute (R01 CA10059, R01 CA185623, R01 CA247281, R01 CA133264, P01 CA151135, R03 CA238792, P30 CA16056). The New Jersey State Cancer Registry, Cancer Epidemiology Services, New Jersey Department of Health are funded by the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute under contract No. HHSN261201300021I and No. N01-PC-2013-00021, the National Program of Cancer Registries (NPCR), Centers for Disease Control and Prevention under Grant No. NU5U58DP006279-02-00, and the State of New Jersey and the Rutgers Cancer Institute of New Jersey. Dr. Ambrosone is supported by the Breast Cancer Research Foundation.

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Angela R. Omilian, Rikki Cannioto, Lucas Mendicino, Chi-Chen Hong, Song Yao & Christine B. Ambrosone

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Conception and design of the work: A.R.O., R.C. Acquisition and/or analysis of the data: A.R.O, R.C., L.M., L.S., W.B., B.Q., E.V.B., C.H., T.K., S.Y., C.B.A. Interpretation of data: A.R.O., R.C., L.M., S.I.A., S.Y., T.K., C.B.A., N.Z, E.V.B, B.Q. Drafted the manuscript: A.R.O. Approved the submitted version: All authors. All authors have agreed both to be personally accountable for their contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

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Supplementary Information

Additional file 1: table s1..

Univariate Cox regression models assessing associations of additional CD163 + cell density cutoffs and cohort characteristics with overall survival (OS) within subtype. Table S2. Univariate Cox regression models assessing associations of additional CD163 + cell density cutoffs and cohort characteristics with breast cancer-specific survival (BCSS) within subtype. Table S3.  Multivariable Cox regression models assessing associations between CD163 + cell density tertiles with OS and BCSS within subtype, additionally adjusting for self-identified race and grade in Model 2. Table S4.  Multivariable Cox regression models assessing associations of additional CD163 + cell density cutoffs with OS within subtype. Table S5. . Multivariable Cox regression models assessing associations of additional CD163 + cell density cutoffs with BCSS within subtype. Table S6.  Multivariable Cox regression models assessing associations between CD163 + cell density tertiles with OS and BCSS by estrogen receptor (ER) status. Table S7.  Multivariable Cox regression models assessing associations between CD163 + cell density tertiles with OS and BCSS within Black cases. Figure S1. Diagram of participant availability for CD163 profiling in the Women’s Circle of Health Study.

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Omilian, A.R., Cannioto, R., Mendicino, L. et al. CD163 + macrophages in the triple-negative breast tumor microenvironment are associated with improved survival in the Women’s Circle of Health Study and the Women’s Circle of Health Follow-Up Study. Breast Cancer Res 26 , 75 (2024). https://doi.org/10.1186/s13058-024-01831-8

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DOI : https://doi.org/10.1186/s13058-024-01831-8

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Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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