Variables: Definition, Examples, Types of Variables in Research

Variables: Definition, Examples, Types Of Variables In Research

What is a Variable?

Within the context of a research investigation, concepts are generally referred to as variables. A variable is, as the name applies, something that varies.

Examples of Variable

These are all examples of variables because each of these properties varies or differs from one individual to another.

  • income and expenses,
  • family size,
  • country of birth,
  • capital expenditure,
  • class grades,
  • blood pressure readings,
  • preoperative anxiety levels,
  • eye color, and
  • vehicle type.

What is Variable in Research?

A variable is any property, characteristic, number, or quantity that increases or decreases over time or can take on different values (as opposed to constants, such as n , that do not vary) in different situations.

When conducting research, experiments often manipulate variables. For example, an experimenter might compare the effectiveness of four types of fertilizers.

In this case, the variable is the ‘type of fertilizers.’ A social scientist may examine the possible effect of early marriage on divorce. Her early marriage is variable.

A business researcher may find it useful to include the dividend in determining the share prices . Here, the dividend is the variable.

Effectiveness, divorce, and share prices are variables because they also vary due to manipulating fertilizers, early marriage, and dividends .

11 Types of Variables in Research

Qualitative variables.

An important distinction between variables is the qualitative and quantitative variables.

Qualitative variables are those that express a qualitative attribute, such as hair color, religion, race, gender, social status, method of payment, and so on. The values of a qualitative variable do not imply a meaningful numerical ordering.

The value of the variable ‘religion’ (Muslim, Hindu.., etc..) differs qualitatively; no ordering of religion is implied. Qualitative variables are sometimes referred to as categorical variables.

For example, the variable sex has two distinct categories: ‘male’ and ‘female.’ Since the values of this variable are expressed in categories, we refer to this as a categorical variable.

Similarly, the place of residence may be categorized as urban and rural and thus is a categorical variable.

Categorical variables may again be described as nominal and ordinal.

Ordinal variables can be logically ordered or ranked higher or lower than another but do not necessarily establish a numeric difference between each category, such as examination grades (A+, A, B+, etc., and clothing size (Extra large, large, medium, small).

Nominal variables are those that can neither be ranked nor logically ordered, such as religion, sex, etc.

A qualitative variable is a characteristic that is not capable of being measured but can be categorized as possessing or not possessing some characteristics.

Quantitative Variables

Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person’s age.

Age can take on different values because a person can be 20 years old, 35 years old, and so on. Likewise, family size is a quantitative variable because a family might be comprised of one, two, or three members, and so on.

Each of these properties or characteristics referred to above varies or differs from one individual to another. Note that these variables are expressed in numbers, for which we call quantitative or sometimes numeric variables.

A quantitative variable is one for which the resulting observations are numeric and thus possess a natural ordering or ranking.

Discrete and Continuous Variables

Quantitative variables are again of two types: discrete and continuous.

Variables such as some children in a household or the number of defective items in a box are discrete variables since the possible scores are discrete on the scale.

For example, a household could have three or five children, but not 4.52 children.

Other variables, such as ‘time required to complete an MCQ test’ and ‘waiting time in a queue in front of a bank counter,’ are continuous variables.

The time required in the above examples is a continuous variable, which could be, for example, 1.65 minutes or 1.6584795214 minutes.

Of course, the practicalities of measurement preclude most measured variables from being continuous.

Discrete Variable

A discrete variable, restricted to certain values, usually (but not necessarily) consists of whole numbers, such as the family size and a number of defective items in a box. They are often the results of enumeration or counting.

A few more examples are;

  • The number of accidents in the twelve months.
  • The number of mobile cards sold in a store within seven days.
  • The number of patients admitted to a hospital over a specified period.
  • The number of new branches of a bank opened annually during 2001- 2007.
  • The number of weekly visits made by health personnel in the last 12 months.

Continuous Variable

A continuous variable may take on an infinite number of intermediate values along a specified interval. Examples are:

  • The sugar level in the human body;
  • Blood pressure reading;
  • Temperature;
  • Height or weight of the human body;
  • Rate of bank interest;
  • Internal rate of return (IRR),
  • Earning ratio (ER);
  • Current ratio (CR)

No matter how close two observations might be, if the instrument of measurement is precise enough, a third observation can be found, falling between the first two.

A continuous variable generally results from measurement and can assume countless values in the specified range.

Dependent Variables and Independent Variable

In many research settings, two specific classes of variables need to be distinguished from one another: independent variable and dependent variable.

Many research studies aim to reveal and understand the causes of underlying phenomena or problems with the ultimate goal of establishing a causal relationship between them.

Look at the following statements:

  • Low intake of food causes underweight.
  • Smoking enhances the risk of lung cancer.
  • Level of education influences job satisfaction .
  • Advertisement helps in sales promotion .
  • The drug causes improvement of health problems.
  • Nursing intervention causes more rapid recovery.
  • Previous job experiences determine the initial salary.
  • Blueberries slow down aging.
  • The dividend per share determines share prices.

In each of the above queries, we have two independent and dependent variables. In the first example, ‘low intake of food’ is believed to have caused the ‘problem of being underweight.’

It is thus the so-called independent variable. Underweight is the dependent variable because we believe this ‘problem’ (the problem of being underweight) has been caused by ‘the low intake of food’ (the factor).

Similarly, smoking, dividend, and advertisement are all independent variables, and lung cancer, job satisfaction, and sales are dependent variables.

In general, an independent variable is manipulated by the experimenter or researcher, and its effects on the dependent variable are measured.

Independent Variable

The variable that is used to describe or measure the factor that is assumed to cause or at least to influence the problem or outcome is called an independent variable.

The definition implies that the experimenter uses the independent variable to describe or explain its influence or effect of it on the dependent variable.

Variability in the dependent variable is presumed to depend on variability in the independent variable.

Depending on the context, an independent variable is sometimes called a predictor variable, regressor, controlled variable, manipulated variable, explanatory variable, exposure variable (as used in reliability theory), risk factor (as used in medical statistics ), feature (as used in machine learning and pattern recognition) or input variable.

The explanatory variable is preferred by some authors over the independent variable when the quantities treated as independent variables may not be statistically independent or independently manipulable by the researcher.

If the independent variable is referred to as an explanatory variable, then the term response variable is preferred by some authors for the dependent variable.

Dependent Variable

The variable used to describe or measure the problem or outcome under study is called a dependent variable.

In a causal relationship, the cause is the independent variable, and the effect is the dependent variable. If we hypothesize that smoking causes lung cancer, ‘smoking’ is the independent variable and cancer the dependent variable.

A business researcher may find it useful to include the dividend in determining the share prices. Here dividend is the independent variable, while the share price is the dependent variable.

The dependent variable usually is the variable the researcher is interested in understanding, explaining, or predicting.

In lung cancer research, the carcinoma is of real interest to the researcher, not smoking behavior per se. The independent variable is the presumed cause of, antecedent to, or influence on the dependent variable.

Depending on the context, a dependent variable is sometimes called a response variable, regressand, predicted variable, measured variable, explained variable, experimental variable, responding variable, outcome variable, output variable, or label.

An explained variable is preferred by some authors over the dependent variable when the quantities treated as dependent variables may not be statistically dependent.

If the dependent variable is referred to as an explained variable, then the term predictor variable is preferred by some authors for the independent variable.

Levels of an Independent Variable

If an experimenter compares an experimental treatment with a control treatment, then the independent variable (a type of treatment) has two levels: experimental and control.

If an experiment were to compare five types of diets, then the independent variables (types of diet) would have five levels.

In general, the number of levels of an independent variable is the number of experimental conditions.

Background Variable

In almost every study, we collect information such as age, sex, educational attainment, socioeconomic status, marital status, religion, place of birth, and the like. These variables are referred to as background variables.

These variables are often related to many independent variables, so they indirectly influence the problem. Hence they are called background variables.

The background variables should be measured if they are important to the study. However, we should try to keep the number of background variables as few as possible in the interest of the economy.

Moderating Variable

In any statement of relationships of variables, it is normally hypothesized that in some way, the independent variable ’causes’ the dependent variable to occur.

In simple relationships, all other variables are extraneous and are ignored.

In actual study situations, such a simple one-to-one relationship needs to be revised to take other variables into account to explain the relationship better.

This emphasizes the need to consider a second independent variable that is expected to have a significant contributory or contingent effect on the originally stated dependent-independent relationship.

Such a variable is termed a moderating variable.

Suppose you are studying the impact of field-based and classroom-based training on the work performance of health and family planning workers. You consider the type of training as the independent variable.

If you are focusing on the relationship between the age of the trainees and work performance, you might use ‘type of training’ as a moderating variable.

Extraneous Variable

Most studies concern the identification of a single independent variable and measuring its effect on the dependent variable.

But still, several variables might conceivably affect our hypothesized independent-dependent variable relationship, thereby distorting the study. These variables are referred to as extraneous variables.

Extraneous variables are not necessarily part of the study. They exert a confounding effect on the dependent-independent relationship and thus need to be eliminated or controlled for.

An example may illustrate the concept of extraneous variables. Suppose we are interested in examining the relationship between the work status of mothers and breastfeeding duration.

It is not unreasonable in this instance to presume that the level of education of mothers as it influences work status might have an impact on breastfeeding duration too.

Education is treated here as an extraneous variable. In any attempt to eliminate or control the effect of this variable, we may consider this variable a confounding variable.

An appropriate way of dealing with confounding variables is to follow the stratification procedure, which involves a separate analysis of the different levels of lies in confounding variables.

For this purpose, one can construct two cross­tables for illiterate mothers and the other for literate mothers.

Suppose we find a similar association between work status and duration of breast­feeding in both the groups of mothers. In that case, we conclude that mothers’ educational level is not a confounding variable.

Intervening Variable

Often an apparent relationship between two variables is caused by a third variable.

For example, variables X and Y may be highly correlated, but only because X causes the third variable, Z, which in turn causes Y. In this case, Z is the intervening variable.

An intervening variable theoretically affects the observed phenomena but cannot be seen, measured, or manipulated directly; its effects can only be inferred from the effects of the independent and moderating variables on the observed phenomena.

We might view motivation or counseling as the intervening variable in the work-status and breastfeeding relationship.

Thus, motive, job satisfaction, responsibility, behavior, and justice are some of the examples of intervening variables.

Suppressor Variable

In many cases, we have good reasons to believe that the variables of interest have a relationship, but our data fail to establish any such relationship. Some hidden factors may suppress the true relationship between the two original variables.

Such a factor is referred to as a suppressor variable because it suppresses the relationship between the other two variables.

The suppressor variable suppresses the relationship by being positively correlated with one of the variables in the relationship and negatively correlated with the other. The true relationship between the two variables will reappear when the suppressor variable is controlled for.

Thus, for example, low age may pull education up but income down. In contrast, a high age may pull income up but education down, effectively canceling the relationship between education and income unless age is controlled for.

4 Relationships Between Variables

Variables: Definition, Examples, Types Of Variables In Research

In dealing with relationships between variables in research, we observe a variety of dimensions in these relationships.

Positive and Negative Relationship

Symmetrical relationship, causal relationship, linear and non-linear relationship.

Two or more variables may have a positive, negative, or no relationship. In the case of two variables, a positive relationship is one in which both variables vary in the same direction.

However, they are said to have a negative relationship when they vary in opposite directions.

When a change in the other variable does not accompany the change or movement of one variable, we say that the variables in question are unrelated.

For example, if an increase in wage rate accompanies one’s job experience, the relationship between job experience and the wage rate is positive.

If an increase in an individual’s education level decreases his desire for additional children, the relationship is negative or inverse.

If the level of education does not have any bearing on the desire, we say that the variables’ desire for additional children and ‘education’ are unrelated.

Strength of Relationship

Once it has been established that two variables are related, we want to ascertain how strongly they are related.

A common statistic to measure the strength of a relationship is the so-called correlation coefficient symbolized by r. r is a unit-free measure, lying between -1 and +1 inclusive, with zero signifying no linear relationship.

As far as the prediction of one variable from the knowledge of the other variable is concerned, a value of r= +1 means a 100% accuracy in predicting a positive relationship between the two variables, and a value of r = -1 means a 100% accuracy in predicting a negative relationship between the two variables.

So far, we have discussed only symmetrical relationships in which a change in the other variable accompanies a change in either variable.

This relationship does not indicate which variable is the independent variable and which variable is the dependent variable.

In other words, you can label either of the variables as the independent variable.

Such a relationship is a symmetrical  relationship. In an asymmetrical relationship, a change in variable X (say) is accompanied by a change in variable Y, but not vice versa.

The amount of rainfall, for example, will increase productivity , but productivity will not affect the rainfall. This is an asymmetrical relationship.

Similarly, the relationship between smoking and lung cancer would be asymmetrical because smoking could cause cancer, but lung cancer could not cause smoking.

Indicating a relationship between two variables does not automatically ensure that changes in one variable cause changes in another.

It is, however, very difficult to establish the existence of causality between variables. While no one can ever be certain that variable A causes variable B , one can gather some evidence that increases our belief that A leads to B.

In an attempt to do so, we seek the following evidence:

  • Is there a relationship between A and B?  When such evidence exists, it indicates a possible causal link between the variables.
  • Is the relationship asymmetrical so that a change in A results in B but not vice-versa? In other words, does A occur before B? If we find that B occurs before A, we can have little confidence that A causes.
  • Does a change in A result in a change in B regardless of the actions of other factors? Or, is it possible to eliminate other possible causes of B? Can one determine that C, D, and E (say) do not co-vary with B in a way that suggests possible causal connections?

A linear relationship is a straight-line relationship between two variables, where the variables vary at the same rate regardless of whether the values are low, high, or intermediate.

This is in contrast with the non-linear (or curvilinear) relationships, where the rate at which one variable changes in value may differ for different values of the second variable.

Whether a variable is linearly related to the other variable or not can simply be ascertained by plotting the K values against X values.

If the values, when plotted, appear to lie on a straight line, the existence of a linear relationship between X and Y is suggested.

Height and weight almost always have an approximately linear relationship, while age and fertility rates have a non-linear relationship.

Frequently Asked Questions about Variable

What is a variable within the context of a research investigation.

A variable, within the context of a research investigation, refers to concepts that vary. It can be any property, characteristic, number, or quantity that can increase or decrease over time or take on different values.

How is a variable used in research?

In research, a variable is any property or characteristic that can take on different values. Experiments often manipulate variables to compare outcomes. For instance, an experimenter might compare the effectiveness of different types of fertilizers, where the variable is the ‘type of fertilizers.’

What distinguishes qualitative variables from quantitative variables?

Qualitative variables express a qualitative attribute, such as hair color or religion, and do not imply a meaningful numerical ordering. Quantitative variables, on the other hand, are measured in terms of numbers, like a person’s age or family size.

How do discrete and continuous variables differ in terms of quantitative variables?

Discrete variables are restricted to certain values, often whole numbers, resulting from enumeration or counting, like the number of children in a household. Continuous variables can take on an infinite number of intermediate values along a specified interval, such as the time required to complete a test.

What are the roles of independent and dependent variables in research?

In research, the independent variable is manipulated by the researcher to observe its effects on the dependent variable. The independent variable is the presumed cause or influence, while the dependent variable is the outcome or effect that is being measured.

What is a background variable in a study?

Background variables are information collected in a study, such as age, sex, or educational attainment. These variables are often related to many independent variables and indirectly influence the main problem or outcome, hence they are termed background variables.

How does a suppressor variable affect the relationship between two other variables?

A suppressor variable can suppress or hide the true relationship between two other variables. It does this by being positively correlated with one of the variables and negatively correlated with the other. When the suppressor variable is controlled for, the true relationship between the two original variables can be observed.

Now that you are familiar with variables; explore complete guideline on research and research methodology concepts .

  • Research Hypothesis: Elements, Format, Types
  • Standard Error of Measurement
  • Business Research: Definition, Examples
  • Research: Definition, Characteristics, Goals, Approaches
  • Simple Category Scale: Definition, Example
  • How to Write an Evaluation Report
  • How to Select an Appropriate Evaluator
  • Content Analysis Method in Research
  • Research Objectives: Meaning, Types
  • Research Proposal: Components, Structure, Sample, Example
  • Data Analysis and Interpretation
  • Causal Research: Definition, Examples, Types
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  • Self-Administered Questionnaire Method: Definition, Advantages, Disadvantages
  • What is Statistics? Science of Analyzing & Interpreting Data

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2.2: Concepts, Constructs, and Variables

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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.

clipboard_e3c11ed02287e51de02928c4dd14dea17.png

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 .

clipboard_ec4455df573382437125e02822d3e7aa4.png

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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An explanatory variable that can affect other (dependent) variables but cannot be affected by them. For example, one's schooling may affect one's subsequent career, but the reverse is unlikely to be true. See also regression.

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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 .

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

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

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

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

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

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

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

What about correlational research?

When you do correlational research , the terms ‘dependent’ and ‘independent’ don’t apply, because you are not trying to establish a cause-and-effect relationship.

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

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

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

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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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."

background variable in research example

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

background variable in research example

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 variable. 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."

Guides, Examples and Vocabularies

Background information and background variables.

You can choose the best options for your research from the examples below, or formulate your own appropriate background information (qualitative data) or background variables (quantitative data). It is not always necessary to include all the background information listed here, and other background information that has been customised for each specific study is often needed. Categorising background information is one way to follow the GDPR principles of data minimisation.

1. Respondent's gender

2. Respondent's gender

  • Other or unspecified

3. Respondent's gender

  • Back to top

There are many ways to ask about the respondent’s age. Response alternatives can be modified to fit the needs of a specific study. Ranges of age groups can be almost anything. In addition to the examples below, categorisation based on year of birth is also an option.

1. Respondent’s age (in years, open-ended variable or question)

2. Respondent’s age group

  • 15–19 years
  • 20–24 years
  • 25–29 years
  • 30–34 years
  • 35–39 years
  • 40–44 years
  • 45–49 years
  • 50–54 years
  • 55–59 years
  • 60–64 years
  • 65 years or over

3. Respondent’s age group

  • 80 yrs. or over

4. Respondent’s age group

  • 15–24 years
  • 25–34 years
  • 35–49 years
  • 50–64 years
  • 65–74 years
  • 75 years or over

5. Respondent’s age group (young people and young adults)

  • Under 20 years
  • 20–22 years
  • 23–25 years
  • 26–28 years
  • 29 years or over

6. Respondent’s age group (young people and young adults)

  • 15–17 years
  • 18–20 years
  • 21–23 years
  • 24–26 years
  • 27–29 years
  • 30 years or over

7. Respondent’s age group (children, young people and young adults)

  • 10–14 years
  • 20–25 years
  • 26–30 years

8. Respondent’s age group (elderly)

  • 75–79 years
  • 80–84 years
  • 85–89 years
  • 90 years or over

Location of residence, country of birth

1. Do you live in: (mutually exclusive response options)

  • Big city centre
  • Big city suburb
  • Other type of town
  • Population centre in a rural area
  • Sparsely populated rural area

2. Do you live in: (mutually exclusive response options)

  • Countryside

3. Municipality type (mutually exclusive response options)

  • Rural municipality
  • Urban municipality

4. Respondent’s area of residence (mutually exclusive response options)

  • Capital area (Helsinki, Vantaa, Espoo)
  • Other city/town

5. Respondent’s major region of residence (mutually exclusive response options, NUTS2)

  • Eastern Finland (South Savo, North Savo, North Karelia, Kainuu)
  • Southern Finland (Uusimaa, Itä-Uusimaa, Varsinais-Suomi, Kanta-Häme, Päijät-Häme, Kymenlaakso, South Karelia)
  • Western Finland (Satakunta, Pirkanmaa, Central Finland, South Ostrobothnia, Ostrobothnia)
  • Northern Finland (Central Ostrobothnia, North Ostrobothnia, Lapland)
  • Åland Islands (Ahvenanmaa)

6. Respondent’s region of residence (mutually exclusive response options, NUTS3)

  • Varsinais-Suomi
  • Päijät-Häme
  • Kymenlaakso
  • South Karelia
  • North Karelia
  • Central Finland
  • South Ostrobothnia
  • Ostrobothnia
  • Central Ostrobothnia
  • North Ostrobothnia
  • Åland Islands

7. Were you born in Finland? (mutually exclusive response options)

8. Respondent’s country of birth (mutually exclusive response options)

9. Respondent’s country of birth (mutually exclusive response options)

  • Other Nordic country
  • Other European country
  • Other country outside of Europe

10. What country or region were you born in? (mutually exclusive response options)

  • Other EU country
  • Elsewhere in Europe
  • Middle East
  • Latin America
  • The USA or Canada
  • Oceania (Australia and Pacific Ocean islands)

11. What is your mother tongue?

Situation in life, economic activity, occupational and employment status

1. Which of the following best describes your current situation? If you are temporarily away from paid work due to maternity, paternity or other leave, unpaid leave, or other similar reason, respond according to your usual employment status.

  • Employee in full-time work
  • Employee in part-time work
  • Entrepreneur, self-employed, own account worker
  • Conscript or in civilian service
  • Pensioner/retired
  • Housewife/house husband, caretaker
  • Student or at school

2. Which of the following best describes your current employment status?

  • Managerial/professional employee
  • Intermediate level employee (clerical, technical, admin.)

3. What is your current educational situation? (young people and young adults)

  • Primary or lower secondary school
  • Vocational upper secondary school
  • General upper secondary school
  • University of applied sciences
  • Folk high school
  • Other education
  • I’m not a student or at school

Level of education

1. What is your level of education? (choose the highest level attained)

  • Primary education
  • Vocational school or course
  • General upper secondary education (matriculation examination)
  • Vocational college (post-secondary)
  • University of applied sciences degree (Bachelor's)
  • University of applied sciences degree (Master's)
  • University degree (Bachelor's)
  • University degree (Master's)

Field of education

  • General programmes
  • Humanities and arts
  • Social sciences
  • Business, administration and law
  • Engineering, manufacturing and construction
  • Agriculture, forestry and fishing
  • Health and welfare

Branch of science

  • Natural sciences
  • Engineering and technology
  • Medical and health sciences
  • Agriculture and forestry

Occupational group, status in employment

1. If you are in employment, which of the following occupational groups would you say you belong to? (ISCO08 classification)

  • Armed forces
  • Professionals
  • Technicians and associate professionals
  • Clerical support workers
  • Service and sales workers
  • Skilled agricultural, forestry and fishery workers
  • Craft and related trades workers
  • Plant and machine operators, and assemblers
  • Elementary occupations

Sector of employment, industry of employment

1. What type of sector do you work in?

  • Public sector
  • Private sector
  • Not-for-profit sector
  • I am not in paid employment

2. Respondent’s industry of employment

  • Industry, manufacturing, construction
  • Private services
  • Public services
  • Third sector organisations

Income, social class

The income data of the entire population at the time of the research should be considered when categorising income.

1. What is your total monthly income before taxes?

  • 0–1,100 euros
  • 1,101–1,400 euros
  • 1,401–1,800 euros
  • 1,801–2,200 euros
  • 2,201–2,600 euros
  • 2,601–3,200 euros
  • 3,201–3,800 euros
  • 3,801–4,600 euros
  • 4,601–5,700 euros
  • 5,701 euros or over

2. What is your gross annual income?

  • 0–13,500 euros
  • 13,501–16,800 euros
  • 16,801–21,000 euros
  • 21,001–26,000 euros
  • 26,001–31,700 euros
  • 31,701–38,400 euros
  • 38,401–46,100 euros
  • 46,101–54,800 euros
  • 54,801–68,500 euros
  • 68,501 euros or over

3. How would you describe the sufficiency of your or your household’s income?

  • I/we get by comfortably with the current income
  • I/we get by with the current income
  • It is hard to get by with the current income
  • It is very hard to get by with the current income

4. How would you describe your or your household’s financial circumstances at the moment?

  • I am/we are affluent
  • I/we manage quite well
  • I/we manage, if I/we shop with care
  • Sometimes I/we have to compromise
  • I/we have to compromise with almost everything

5. What is your or your household’s financial situation?

  • Income is not sufficient to get by with
  • Income is sufficient to get by with
  • Income is more than sufficient to get by with

6. Which social class would you say you belong to?

  • Working class
  • Lower middle class
  • Middle class
  • Upper middle class
  • Upper class
  • Do not consider myself as belonging to any class

Religious affiliation, degree of religiosity

1. Do you belong to a church or other religious denomination?

  • Evangelical Lutheran Church of Finland
  • Finnish Orthodox Church
  • Other Christian church or community
  • Other religious denomination
  • Don't belong to any church or religious community

2. Do you belong to a church or other religious denomination?

3. How religious would you say you are?

  • Not at all religious
  • Not very religious
  • Somewhat religious
  • Very religious

4. How religious are you?

  • Extremely religious
  • Neither religious nor non-religious
  • Somewhat non-religious
  • Very non-religious
  • Extremely non-religious

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  • Indian Dermatol Online J
  • v.10(1); Jan-Feb 2019

Types of Variables, Descriptive Statistics, and Sample Size

Feroze kaliyadan.

Department of Dermatology, King Faisal University, Al Hofuf, Saudi Arabia

Vinay Kulkarni

1 Department of Dermatology, Prayas Amrita Clinic, Pune, Maharashtra, India

This short “snippet” covers three important aspects related to statistics – the concept of variables , the importance, and practical aspects related to descriptive statistics and issues related to sampling – types of sampling and sample size estimation.

What is a variable?[ 1 , 2 ] To put it in very simple terms, a variable is an entity whose value varies. A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same. The importance of variables is that they help in operationalization of concepts for data collection. For example, if you want to do an experiment based on the severity of urticaria, one option would be to measure the severity using a scale to grade severity of itching. This becomes an operational variable. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance. Variables can be classified into various ways as discussed below.

Quantitative vs qualitative

A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type)

A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks).

Quantitative variables can be either discrete or continuous

Discrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria).

Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test. If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. The mid-way value would be 11.5 min which makes sense. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense.

Under the umbrella of qualitative variables, you can have nominal/categorical variables and ordinal variables

Nominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis).

Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching).

Dependent and independent variables

In the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study. For example, in a clinical trial on psoriasis, the PASI (psoriasis area severity index) would possibly be one dependent variable. The independent variable (sometime also called explanatory variable) is something which is not affected by the experiment itself but which can be manipulated to affect the dependent variable. Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment.

Descriptive Statistics

Statistics can be broadly divided into descriptive statistics and inferential statistics.[ 3 , 4 ] Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary. When we describe the population using tools such as frequency distribution tables, percentages, and other measures of central tendency like the mean, for example, we are talking about descriptive statistics. When we use a specific statistical test (e.g., Mann–Whitney U-test) to compare the mean scores and express it in terms of statistical significance, we are talking about inferential statistics. Descriptive statistics can help in summarizing data in the form of simple quantitative measures such as percentages or means or in the form of visual summaries such as histograms and box plots.

Descriptive statistics can be used to describe a single variable (univariate analysis) or more than one variable (bivariate/multivariate analysis). In the case of more than one variable, descriptive statistics can help summarize relationships between variables using tools such as scatter plots.

Descriptive statistics can be broadly put under two categories:

  • Sorting/grouping and illustration/visual displays
  • Summary statistics.

Sorting and grouping

Sorting and grouping is most commonly done using frequency distribution tables. For continuous variables, it is generally better to use groups in the frequency table. Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”).

Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description.

Suppose the weight in kilograms of a group of 10 patients is as follows:

56, 34, 48, 43, 87, 78, 54, 62, 61, 59

The “stem” records the value of the “ten's” place (or higher) and the “leaf” records the value in the “one's” place [ Table 1 ].

Stem and leaf plot

Illustration/visual display of data

The most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). Composite bar charts can be used to compare variables. For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1 .

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Object name is IDOJ-10-82-g001.jpg

Composite bar chart

A pie chart helps show how a total quantity is divided among its constituent variables. Scatter diagrams can be used to illustrate the relationship between two variables. For example, global scores given for improvement in a condition like acne by the patient and the doctor [ Figure 2 ].

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Object name is IDOJ-10-82-g002.jpg

Scatter diagram

Summary statistics

The main tools used for summary statistics are broadly grouped into measures of central tendency (such as mean, median, and mode) and measures of dispersion or variation (such as range, standard deviation, and variance).

Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order:

30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86

Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data.

The mean is basically the sum of all the values divided by the total number. In this case, we get a value of 45.

The problem is that some extreme values (outliers), like “'86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50 th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8 th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median.

The mode is the most common data point. In our example, this would be 38. The mode as in our case may not necessarily be in the center of the distribution.

The median is the best measure of central tendency from among the mean, median, and mode. In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3 , a right skewed distribution is seen (direction of skew is based on the tail); data values' distribution is longer on the right-hand (positive) side than on the left-hand side. The mean is typically greater than the median in such cases.

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Object name is IDOJ-10-82-g003.jpg

Location of mode, median, and mean

Measures of dispersion

The range gives the spread between the lowest and highest values. In our previous example, this will be 86-30 = 56.

A more valuable measure is the interquartile range. A quartile is one of the values which break the distribution into four equal parts. The 25 th percentile is the data point which divides the group between the first one-fourth and the last three-fourth of the data. The first one-fourth will form the first quartile. The 75 th percentile is the data point which divides the distribution into a first three-fourth and last one-fourth (the last one-fourth being the fourth quartile). The range between the 25 th percentile and 75 th percentile is called the interquartile range.

Variance is also a measure of dispersion. The larger the variance, the further the individual units are from the mean. Let us consider the same example we used for calculating the mean. The mean was 45.

For the first value (30), the deviation from the mean will be 15; for the last value (86), the deviation will be 41. Similarly we can calculate the deviations for all values in a sample. Adding these deviations and averaging will give a clue to the total dispersion, but the problem is that since the deviations are a mix of negative and positive values, the final total becomes zero. To calculate the variance, this problem is overcome by adding squares of the deviations. So variance would be the sum of squares of the variation divided by the total number in the population (for a sample we use “n − 1”). To get a more realistic value of the average dispersion, we take the square root of the variance, which is called the “standard deviation.”

The box plot

The box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ].

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The concept of skewness and kurtosis

Skewness is a measure of the symmetry of distribution. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. When this is not the case, it is said to be skewed. Kurtosis is a representation of outliers. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. There are formulas to calculate both skewness and kurtosis [Figures ​ [Figures5 5 – 8 ].

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Positive skew

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High kurtosis (positive kurtosis – also called leptokurtic)

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Negative skew

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Low kurtosis (negative kurtosis – also called “Platykurtic”)

Sample Size

In an ideal study, we should be able to include all units of a particular population under study, something that is referred to as a census.[ 5 , 6 ] This would remove the chances of sampling error (difference between the outcome characteristics in a random sample when compared with the true population values – something that is virtually unavoidable when you take a random sample). However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error.

We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. Examples of probability sampling include methods such as simple random sampling (each member in a population has an equal chance of being selected), stratified random sampling (in nonhomogeneous populations, the population is divided into subgroups – followed be random sampling in each subgroup), systematic (sampling is based on a systematic technique – e.g., every third person is selected for a survey), and cluster sampling (similar to stratified sampling except that the clusters here are preexisting clusters unlike stratified sampling where the researcher decides on the stratification criteria), whereas nonprobability sampling, where every unit in the population does not have an equal chance of inclusion into the sample, includes methods such as convenience sampling (e.g., sample selected based on ease of access) and purposive sampling (where only people who meet specific criteria are included in the sample).

An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis. A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources.

We will first go through the sample size calculation for a hypothesis-based design (like a randomized control trial).

The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance (standard deviation for quantitative data), and expected proportions in the case of qualitative data. This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size). It is also important to know whether the data follow a normal distribution or not.

Two essential aspects we must understand are the concept of Type I and Type II errors. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error. When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test. While there are no absolute rules, the minimal levels accepted are 0.05 for α (corresponding to a significance level of 5%) and 0.20 for β (corresponding to a minimum recommended power of “1 − 0.20,” or 80%).

Effect size and minimal clinically relevant difference

For a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant (for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.). While we will not go into details of the formula for sample size calculation, some important points are as follows:

In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size. What this means in effect is that reducing the effect size will lead to an increase in the required sample size.

Reducing the level of significance (alpha) or increasing power (1-β) will lead to an increase in the calculated sample size.

An increase in variance of the outcome leads to an increase in the calculated sample size.

A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. This includes an idea about total population size (this generally does not make a major difference when population size is above 20,000, so in situations where population size is not known we can assume a population of 20,000 or more). The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates.

Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size. In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this.

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How do different background variables predict learning outcomes?

  • Published: 15 October 2015
  • Volume 27 , pages 31–50, ( 2017 )

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  • Manne Kallio 1 &
  • Mika Metsärinne 2  

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This article is a part of a research project aimed to find out how different background variables are related to learning outcomes in school subject Sloyd as found in the national evaluation of the Finnish National Board of Education. Results from this larger research project were previously published in this journal, where pupils’ readiness for Self-Regulated Learning were reported. Since then, pupils’ experiences of classroom techniques, attitudes towards the subject, leisure time interests and learning within the two domains of the subject (Technical Domain and Textile Domain, as the subject is usually divided in Finland) have been studied and results have been published in different journals. In this article, a new Structural Equation Model concludes the previous results. The new model highlights two paths of how the use of different learning orientations can predict successful learning outcomes. These paths can be followed using two pedagogical models: the Exploratory Production Approach and the Domain Specific Approach. Experiencing Learner-Centred Learning predicts positive attitudes and success in Exploratory Production activities. Experiencing Collaborative Learning predicts success in domain specific learning outcomes. Success in Exploratory Production predicts successful learning outcomes in both domains. The conclusion is that regulatory knowledge, “why”, is related to production activities and it is processed prior to domain specific knowledge, “what” and “how”. To develop the subject and pedagogy for schools and teacher training, it is not important to follow an approach defined by the domains (technical or textile). It is more important to teach pupils how to manage in the modern technological world and to understand why they need to be able to improve their life-world through Exploratory Production activities.

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Kallio, M., Metsärinne, M. How do different background variables predict learning outcomes?. Int J Technol Des Educ 27 , 31–50 (2017). https://doi.org/10.1007/s10798-015-9339-7

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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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background variable in research example

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.

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What is a control variable?

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

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

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

  • Temperature
  • Time of day
  • Noise or distractions

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

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

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

Other types of variables

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

  • Moderating variables
  • Mediating variables
  • Confounding variables
  • Latent variables

Let’s jump into it…

What is a moderating variable?

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

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

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

What is a mediating variable?

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

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

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

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

What is a confounding variable?

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

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

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

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

What is a latent variable?

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

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

  • Emotional intelligence
  • Quality of life
  • Business confidence
  • Ease of use

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

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

Let’s recap

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

To recap, we’ve explored:

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

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

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Organizing Your Social Sciences Research Paper

  • Background Information
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Background information identifies and describes the history and nature of a well-defined research problem with reference to contextualizing existing literature. The background information should indicate the root of the problem being studied, appropriate context of the problem in relation to theory, research, and/or practice , its scope, and the extent to which previous studies have successfully investigated the problem, noting, in particular, where gaps exist that your study attempts to address. Background information does not replace the literature review section of a research paper; it is intended to place the research problem within a specific context and an established plan for its solution.

Fitterling, Lori. Researching and Writing an Effective Background Section of a Research Paper. Kansas City University of Medicine & Biosciences; Creating a Research Paper: How to Write the Background to a Study. DurousseauElectricalInstitute.com; Background Information: Definition of Background Information. Literary Devices Definition and Examples of Literary Terms.

Importance of Having Enough Background Information

Background information expands upon the key points stated in the beginning of your introduction but is not intended to be the main focus of the paper. It generally supports the question, what is the most important information the reader needs to understand before continuing to read the paper? Sufficient background information helps the reader determine if you have a basic understanding of the research problem being investigated and promotes confidence in the overall quality of your analysis and findings. This information provides the reader with the essential context needed to conceptualize the research problem and its significance before moving on to a more thorough analysis of prior research.

Forms of contextualization included in background information can include describing one or more of the following:

  • Cultural -- placed within the learned behavior of a specific group or groups of people.
  • Economic -- of or relating to systems of production and management of material wealth and/or business activities.
  • Gender -- located within the behavioral, cultural, or psychological traits typically associated with being self-identified as male, female, or other form of  gender expression.
  • Historical -- the time in which something takes place or was created and how the condition of time influences how you interpret it.
  • Interdisciplinary -- explanation of theories, concepts, ideas, or methodologies borrowed from other disciplines applied to the research problem rooted in a discipline other than the discipline where your paper resides.
  • Philosophical -- clarification of the essential nature of being or of phenomena as it relates to the research problem.
  • Physical/Spatial -- reflects the meaning of space around something and how that influences how it is understood.
  • Political -- concerns the environment in which something is produced indicating it's public purpose or agenda.
  • Social -- the environment of people that surrounds something's creation or intended audience, reflecting how the people associated with something use and interpret it.
  • Temporal -- reflects issues or events of, relating to, or limited by time. Concerns past, present, or future contextualization and not just a historical past.

Background information can also include summaries of important research studies . This can be a particularly important element of providing background information if an innovative or groundbreaking study about the research problem laid a foundation for further research or there was a key study that is essential to understanding your arguments. The priority is to summarize for the reader what is known about the research problem before you conduct the analysis of prior research. This is accomplished with a general summary of the foundational research literature [with citations] that document findings that inform your study's overall aims and objectives.

NOTE : Research studies cited as part of the background information of your introduction should not include very specific, lengthy explanations. This should be discussed in greater detail in your literature review section. If you find a study requiring lengthy explanation, consider moving it to the literature review section.

ANOTHER NOTE : In some cases, your paper's introduction only needs to introduce the research problem, explain its significance, and then describe a road map for how you are going to address the problem; the background information basically forms the introduction part of your literature review. That said, while providing background information is not required, including it in the introduction is a way to highlight important contextual information that could otherwise be hidden or overlooked by the reader if placed in the literature review section.

Background of the Problem Section: What do you Need to Consider? Anonymous. Harvard University; Hopkins, Will G. How to Write a Research Paper. SPORTSCIENCE, Perspectives/Research Resources. Department of Physiology and School of Physical Education, University of Otago, 1999; Green, L. H. How to Write the Background/Introduction Section. Physics 499 Powerpoint slides. University of Illinois; Pyrczak, Fred. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 8th edition. Glendale, CA: Pyrczak Publishing, 2014; Stevens, Kathleen C. “Can We Improve Reading by Teaching Background Information?.” Journal of Reading 25 (January 1982): 326-329; Woodall, W. Gill. Writing the Background and Significance Section. Senior Research Scientist and Professor of Communication. Center on Alcoholism, Substance Abuse, and Addictions. University of New Mexico.

Structure and Writing Style

Providing background information in the introduction of a research paper serves as a bridge that links the reader to the research problem . Precisely how long and in-depth this bridge should be is largely dependent upon how much information you think the reader will need to know in order to fully understand the problem being discussed and to appreciate why the issues you are investigating are important.

From another perspective, the length and detail of background information also depends on the degree to which you need to demonstrate to your professor how much you understand the research problem. Keep this in mind because providing pertinent background information can be an effective way to demonstrate that you have a clear grasp of key issues, debates, and concepts related to your overall study.

The structure and writing style of your background information can vary depending upon the complexity of your research and/or the nature of the assignment. However, in most cases it should be limited to only one to two paragraphs in your introduction.

Given this, here are some questions to consider while writing this part of your introduction :

  • Are there concepts, terms, theories, or ideas that may be unfamiliar to the reader and, thus, require additional explanation?
  • Are there historical elements that need to be explored in order to provide needed context, to highlight specific people, issues, or events, or to lay a foundation for understanding the emergence of a current issue or event?
  • Are there theories, concepts, or ideas borrowed from other disciplines or academic traditions that may be unfamiliar to the reader and therefore require further explanation?
  • Is there a key study or small set of studies that set the stage for understanding the topic and frames why it is important to conduct further research on the topic?
  • Y our study uses a method of analysis never applied before;
  • Your study investigates a very esoteric or complex research problem;
  • Your study introduces new or unique variables that need to be taken into account ; or,
  • Your study relies upon analyzing unique texts or documents, such as, archival materials or primary documents like diaries or personal letters that do not represent the established body of source literature on the topic?

Almost all introductions to a research problem require some contextualizing, but the scope and breadth of background information varies depending on your assumption about the reader's level of prior knowledge . However, despite this assessment, background information should be brief and succinct and sets the stage for the elaboration of critical points or in-depth discussion of key issues in the literature review section of your paper.

Writing Tip

Background Information vs. the Literature Review

Incorporating background information into the introduction is intended to provide the reader with critical information about the topic being studied, such as, highlighting and expanding upon foundational studies conducted in the past, describing important historical events that inform why and in what ways the research problem exists, defining key components of your study [concepts, people, places, phenomena] and/or placing the research problem within a particular context. Although introductory background information can often blend into the literature review portion of the paper, essential background information should not be considered a substitute for a comprehensive review and synthesis of relevant research literature.

Hart, Cris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage, 1998; Pyrczak, Fred. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 8th edition. Glendale, CA: Pyrczak Publishing, 2014.

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  • Extraneous Variables | Examples, Types & Controls

Extraneous Variables | Examples, Types & Controls

Published on April 2, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In an experiment , an extraneous variable is any variable that you’re not investigating that can potentially affect the outcomes of your research study.

If left uncontrolled, extraneous variables can lead to inaccurate conclusions about the relationship between independent and dependent variables . They can also introduce a variety of research biases to your work, particularly selection bias

Table of contents

Why do extraneous variables matter, extraneous vs. confounding variables, types and controls of extraneous variables, other interesting articles, frequently asked questions about extraneous variables.

Extraneous variables can threaten the internal validity of your study by providing alternative explanations for your results.

When not accounted for, this type of variable can also introduce many biases to your research, particularly types of selection bias such as:

  • Sampling bias or ascertainment bias : when some members of the intended population are less likely to be included than others.
  • Attrition bias : when participants who drop out of a study are systematically different from those who stay.
  • Survivorship bias : when researchers draw conclusions by only focusing on examples of successful individuals (the “survivors”) rather than the group as a whole.
  • Nonresponse bias : when people who don’t respond to a survey are different in significant ways from those who do.
  • Undercoverage bias : when some members of your population are not represented in the sample.

In an experiment , you manipulate an independent variable to study its effects on a dependent variable.

You recruit students from a university to participate in the study. You manipulate the independent variable by splitting participants into two groups:

  • Participants in the experimental group are asked to wear a lab coat during the study.
  • Participants in the control group are asked to wear a casual coat during the study.

When extraneous variables are uncontrolled, it’s hard to determine the exact effects of the independent variable on the dependent variable, because the effects of extraneous variables may mask them.

Uncontrolled extraneous variables can also make it seem as though there is a true effect of the independent variable in an experiment when there’s actually none.

  • Participant’s major (e.g., STEM or humanities)
  • Participant’s interest in science
  • Demographic variables such as gender or educational background
  • Time of day of testing
  • Experiment environment or setting

Controlling extraneous variables is an important aspect of experimental design . When you control an extraneous variable, you turn it into a control variable .

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background variable in research example

A confounding variable is a type of extraneous variable that is associated with both the independent and dependent variables.

  • An extraneous variable is anything that could influence the dependent variable.
  • A confounding variable influences the dependent variable, and also correlates with or causally affects the independent variable.

In a conceptual framework diagram, you can draw an arrow from a confounder to the independent variable as well as to the dependent variable. You can draw an arrow from extraneous variables to a dependent variable.

Extraneous vs confounding variables

People who work in labs would regularly wear lab coats and may have higher scientific knowledge in general. Therefore, it’s unlikely that your manipulation will increase scientific reasoning abilities for these participants.

Variables that only impact on scientific reasoning are extraneous variables. These include participants’ interests in science and undergraduate majors. While interest in science may affect scientific reasoning ability, it’s not necessarily related to wearing a lab coat.

Example of extraneous vs confounding variables

Demand characteristics

Demand characteristics are cues that encourage participants to conform to researchers’ behavioral expectations.

Sometimes, participants can infer the intentions behind a research study from the materials or experimental settings, and use these hints to act in ways that are consistent with study hypotheses. These demand characteristics can bias the study outcomes and reduce the external validity , or generalizability , of the results.

You can avoid demand characteristics by making it difficult for participants to guess the aim of your study. Ask participants to perform unrelated filler tasks or fill out plausibly relevant surveys to lead them away from the true nature of the study.

Experimenter effects

Experimenter effects are unintentional actions by researchers that can influence study outcomes.

There are two main types of experimenter effects:

  • Experimenters’ interactions with participants can unintentionally affect their behaviours.
  • Errors in measurement, observation, analysis, or interpretation may change the study results.

To avoid experimenter effects, you can implement masking (blinding) to hide the condition assignment from participants and experimenters. In a double-blind study, researchers won’t be able to bias participants towards acting in expected ways or selectively interpret results to suit their hypotheses .

Situational variables

Situational variables, such as lighting or temperature, can alter participants’ behaviors in study environments. These factors are sources of random error or random variation in your measurements.

To understand the true relationship between independent and dependent variables, you’ll need to reduce or eliminate the effect of situational factors on your study outcomes.

To avoid situational variables from influencing study outcomes, it’s best to hold variables constant throughout the study or statistically account for them in your analyses.

Participant variables

A participant variable is any characteristic or aspect of a participant’s background that could affect study results, even though it’s not the focus of an experiment.

Participant variables can include sex, gender identity, age, educational attainment, marital status, religious affiliation, etc.

Since these individual differences between participants may lead to different outcomes, it’s important to measure and analyze these variables.

To control participant variables, you should aim to use random assignment to divide your sample into control and experimental groups. Random assignment makes your groups comparable by evenly distributing participant characteristics between them.

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

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

Research bias

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

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

  • Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
  • Experimenter effects : unintentional actions by researchers that influence study outcomes.
  • Situational variables : environmental variables that alter participants’ behaviors.
  • Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

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Home » Background of The Study – Examples and Writing Guide

Background of The Study – Examples and Writing Guide

Table of Contents

Background of The Study

Background of The Study

Definition:

Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

  • Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
  • Provide context: Provide a brief overview of the research area and highlight its significance in the field.
  • Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
  • Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
  • Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
  • Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
  • Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
  • Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

  • Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
  • Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
  • I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
  • Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
  • Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
  • State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
  • Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

  • Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
  • Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
  • Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
  • Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
  • State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
  • Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

  • Provide a clear understanding of the research problem and its context.
  • Identify the gap in knowledge that the study intends to fill.
  • Establish the significance of the research problem and its potential contribution to the field.
  • Highlight the key concepts, theories, and research findings related to the problem.
  • Provide a rationale for the research questions or hypotheses and the research design.
  • Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

  • Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
  • Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
  • Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
  • Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
  • Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

  • Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
  • Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
  • Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
  • Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
  • Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

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What is the Background of a Study and How Should it be Written?

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The background of a study is one of the most important components of a research paper. The quality of the background determines whether the reader will be interested in the rest of the study. Thus, to ensure that the audience is invested in reading the entire research paper, it is important to write an appealing and effective background. So, what constitutes the background of a study, and how must it be written?

What is the background of a study?

The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the research and its importance in a clear and logical manner. At times, the background may even explore whether the study builds on or refutes findings from previous studies. Any relevant information that the readers need to know before delving into the paper should be made available to them in the background.

How is a background different from the introduction?

The introduction of your research paper is presented before the background. Let’s find out what factors differentiate the background from the introduction.

  • The introduction only contains preliminary data about the research topic and does not state the purpose of the study. On the contrary, the background clarifies the importance of the study in detail.
  • The introduction provides an overview of the research topic from a broader perspective, while the background provides a detailed understanding of the topic.
  • The introduction should end with the mention of the research questions, aims, and objectives of the study. In contrast, the background follows no such format and only provides essential context to the study.

How should one write the background of a research paper?

The length and detail presented in the background varies for different research papers, depending on the complexity and novelty of the research topic. At times, a simple background suffices, even if the study is complex. Before writing and adding details in the background, take a note of these additional points:

  • Start with a strong beginning: Begin the background by defining the research topic and then identify the target audience.
  • Cover key components: Explain all theories, concepts, terms, and ideas that may feel unfamiliar to the target audience thoroughly.
  • Take note of important prerequisites: Go through the relevant literature in detail. Take notes while reading and cite the sources.
  • Maintain a balance: Make sure that the background is focused on important details, but also appeals to a broader audience.
  • Include historical data: Current issues largely originate from historical events or findings. If the research borrows information from a historical context, add relevant data in the background.
  • Explain novelty: If the research study or methodology is unique or novel, provide an explanation that helps to understand the research better.
  • Increase engagement: To make the background engaging, build a story around the central theme of the research

Avoid these mistakes while writing the background:

  • Ambiguity: Don’t be ambiguous. While writing, assume that the reader does not understand any intricate detail about your research.
  • Unrelated themes: Steer clear from topics that are not related to the key aspects of your research topic.
  • Poor organization: Do not place information without a structure. Make sure that the background reads in a chronological manner and organize the sub-sections so that it flows well.

Writing the background for a research paper should not be a daunting task. But directions to go about it can always help. At Elsevier Author Services we provide essential insights on how to write a high quality, appealing, and logically structured paper for publication, beginning with a robust background. For further queries, contact our experts now!

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  1. Types of Variables in Research & Statistics

    Example (salt tolerance experiment) Independent variables (aka treatment variables) Variables you manipulate in order to affect the outcome of an experiment. The amount of salt added to each plant's water. Dependent variables (aka response variables) Variables that represent the outcome of the experiment.

  2. Variables: Definition, Examples, Types of Variables in Research

    Quantitative Variables. Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person's age. Age can take on different values because a person can be 20 years old, 35 years old, and so on.

  3. A Practical Guide to Writing Quantitative and Qualitative Research

    This statement is based on background research and current knowledge.8,9 The research hypothesis makes a specific prediction about a new phenomenon10 or a formal statement on the expected relationship between an independent variable and a dependent variable.3,11 It provides a tentative answer to the research question to be tested or explored.4

  4. Variables in Research

    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.

  5. Background Variable definition

    A background variable is a certain factor in a respondent's background or circumstance which may affect other variables. However, such factors will not necessarily be significantly affected by the other variables. For example, it could be that one background variable of a respondent (i.e., his religion) as it influences his food preference.

  6. 2.2: Concepts, Constructs, and Variables

    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 ...

  7. Background variable

    Quick Reference. An explanatory variable that can affect other (dependent) variables but cannot be affected by them. For example, one's schooling may affect one's subsequent career, but the reverse is unlikely to be true. See also regression. From: background variable in A Dictionary of Statistics ». Subjects: Science and technology ...

  8. Types of Variables in Research

    Examples. Discrete variables (aka integer variables) Counts of individual items or values. Number of students in a class. Number of different tree species in a forest. Continuous variables (aka ratio variables) Measurements of continuous or non-finite values. Distance.

  9. Types of Variables in Psychology Research

    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 ...

  10. Importance of Variables in Stating the Research Objectives

    In any body of research, the subject of study requires to be described and understood. For example, if we wish to study predictors of response to antidepressant drugs (ADs) in patients with major depressive disorder (MDD), we might select patient age, sex, age at onset of MDD, number of previous episodes of depression, duration of current depressive episode, presence of psychotic symptoms ...

  11. Independent and Dependent Variables

    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.

  12. Background Information and Background Variables

    Background Information and Background Variables. You can choose the best options for your research from the examples below, or formulate your own appropriate background information (qualitative data) or background variables (quantitative data). It is not always necessary to include all the background information listed here, and other ...

  13. Mapping background variables with sequential patterns in problem

    This study used process data collected in log files and survey data from the Programme for the International Assessment of Adult Competencies (PIAAC), with a focus on the United States sample, to (a) identify employment-related background variables that significantly related to PSTRE skills and problem-solving behaviors, and (b) extract robust ...

  14. Types of Variables, Descriptive Statistics, and Sample Size

    A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same.

  15. How do different background variables predict learning outcomes?

    This article is a part of a research project aimed to find out how different background variables are related to learning outcomes in school subject Sloyd as found in the national evaluation of the Finnish National Board of Education. Results from this larger research project were previously published in this journal, where pupils' readiness for Self-Regulated Learning were reported. Since ...

  16. Independent & Dependent Variables (With Examples)

    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 ...

  17. Guidelines and Examples for Reporting Sample Demographics

    Specifically, take the following into account: Report Sample Characteristics in More Detail, and Considering the Context: In selecting which demographics and other background variables to report, consider (a) the local context and the relevant social dimensions/categories in the local context (including how those dimensions/categories are ...

  18. Background Information

    Fitterling, Lori. Researching and Writing an Effective Background Section of a Research Paper. Kansas City University of Medicine & Biosciences; Creating a Research Paper: How to Write the Background to a Study. DurousseauElectricalInstitute.com; Background Information: Definition of Background Information. Literary Devices Definition and Examples of Literary Terms.

  19. What is the Background of a Study and How to Write It (Examples Included)

    The background of a study in a research paper helps to establish the research problem or gap in knowledge that the study aims to address, sets the stage for the research question and objectives, and highlights the significance of the research. The background of a study also includes a review of relevant literature, which helps researchers ...

  20. Extraneous Variables

    Example: Extraneous variables. In your experiment, these extraneous variables can affect the science knowledge scores: Participant's major (e.g., STEM or humanities) Participant's interest in science. Demographic variables such as gender or educational background. Time of day of testing.

  21. Background of The Study

    Here are the steps to write the background of the study in a research paper: Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation. Conduct a literature review: Conduct a thorough literature review to ...

  22. (PDF) Effects of Background Variables of Early Childhood Teachers on

    In studies where the scale structure was tested with data obtained from different samples (e.g. Park, Dimitrov, and Park 2018; Sharma, Forlin, and Loreman 2007), the Resources and Workload factors ...

  23. What is the Background of a Study and How Should it be Written?

    The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the ...