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When performing research (market, social, or academic) variables are used to examine and describe the subject of the research – people, place, ideas, etc. However, there are many variables, and choosing the right variable can lead to precise statistical analysis.
But what are these variables and how do they help in research? In this blog, we will discuss the different types of variables.
In research, a variable is any kind of attribute or characteristic that you are trying to measure, whether that be regarding a place, a thing, a person, or even a phenomenon.
For instance, a psychologist may examine the impact of sibling rivalry on self-confidence growing up. In this example, sibling rivalry is the variable.
Research and statistical tests are often only made possible through the clear identification and manipulation of different variables to extract useful insights. You need to determine which types of variables you are using in the research to choose an appropriate statistical test.
We will look at different kinds of variables and understand how some of them relate to others.
Data can be defined as a specific measurement of a variable. Data can be categorized into the following two types of variables:
This type of data takes numerical values that represent some kind of measurement.
This data type includes non-numerical values and represents groupings, such as rankings, classifications, and binary outcomes.
Quantitative variables represent amounts and therefore take the form of numerical values. This kind of data has a numeric significance and can be used in calculations. Some common examples of quantitative data are the data collected on variables such as weight and height.
There are two main types of quantitative variables, namely discrete and continuous variables.
Also known as Integer Variables, Discrete Variables represent the counts of individual values of items. They are countable in a finite period of time. For example, the number of employees that are working for an organization at a specific point in time.
Continuous variables are also known as ratio variables. They represent the measurements of values that are non-finite and are therefore continuous. For example, age is a continuous variable as it is measured in units that are continually changing. Tomorrow, you will be a little older than you are today
Categorical variables represent groups. Categorical data is qualitative in nature, however, it can sometimes include numerical values as long as they don’t exhibit quantitative characteristics. With categorical data, each observation can only be placed in one category, hence each category is mutually exclusive.
There are three main types of categorical variables, and they are as follows:
A binary variable is a variable that has only two possible values. For example, when a basketball game is played, there are only two outcomes; win or lose.
Nominal variables take qualitative values that represent different categories. These categories do not have an intrinsic order. For example, the names of different dog breeds.
An ordinal variable is a type of categorical variable for which the possible values are ordered. For example, responses to a Likert scale on a survey.
[Related Read: Quantitative Data Vs Categorical Data]
Experiments are designed to test or evaluate a hypothesis or a theory. This is usually done by testing the effect of one variable on another.
Experiments require two main types of variables, namely the independent variable and the dependent variable.
The independent variable is the variable that is manipulated and is assumed to have a direct effect on the dependent variable, the variable being measured and tested.
Experiments even have controlled variables. These are the variables that researchers keep constant or limited while conducting a research study.
To understand these variables better, let’s consider the following example:
Let’s say you’re conducting an experiment to understand the effects of temperature on the action of an enzyme.
In this investigation, your independent variable is temperature as it is going to be manipulated to identify its effects on the activity of an enzyme, which is your dependent variable. Your controlled variable, in this case, can be the enzyme concentration and pH level.
[Related Read: Introduction to Independent Variables & Dependent Variables]
Although we’ve gone over the most major types of variables used to conduct research, here are a few more useful variables:
A confounding variable can be defined as an “extra” variable that influences both, your independent and dependent variables.
If left unaccounted for, confounding variables can ruin the results of an experiment. This type of variable can make the research appear biased or suggest a relationship between the variables when it doesn’t exist.
A latent variable is a variable that cannot be observed, however, its effects can be seen on observable variables.
For example, the quality of a person’s life cannot be measured. Therefore, to examine one’s quality of life observable variables such as physical & mental health, education, leisure, social belonging, etc., are examined.
A composite variable can be defined as a variable that is a combination of multiple variables.
This type of variable is created when you analyze the research data.
A lurking variable is a variable that could affect the variables within a study. They are different from confounding variables as they are “hidden” and not considered a part of the study. A lurking variable can hide the true relationship between variables or identify a false one.
For example, a doctor examines the impact of the keto diet on a person’s weight loss. However, smoking, stress, or sleep deprivation can also cause weight loss. In this case, these are lurking variables.
Variables in research can be measured, manipulated, or controlled as per the requirement of the research. The types of variables used in the research can lead to the successful completion or failure of the research. It is important to understand the variables present in the research and also the ones which stay hidden.
This wraps up the discussion about the types of variables.
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