When studying for a test, there are a lot of crucial variables that impact how productive we feel. How motivated are we? How much sleep have we had? Is there something else on our minds? Is technology giving us a hard time? Each variable needs to be in check, or it can have a knock-on effect on other factors. If we don't get enough sleep, we don't feel motivated. If technology gives us a hard time, we may perform poorly on tests. You can probably see where we're going with these examples. These concepts are exactly what psychology investigates, how and if changes in one variable the independent variable affect another one, i.e. the dependent variable. We'll discuss these in more detail so that you can differentiate and provide examples of the different types of variables.
- First, we will learn the variable meaning and how variables in research are used.
- Next, we will discuss what independent variables are and cover an example.
- We will then explore dependent variables with an example.
- After that, we will talk about other types of variables.
- Finally, we will explore what the operationalisation of variables means.
Variable: Meaning
If you've ever conducted an experiment or even planned to, chances are you've almost definitely encountered many parameters you needed to alter, measure, or control. We refer to these factors as variables, but how do we define them?
A variable is a factor of interest to the researcher. It is required to have a unit of measurement and is usually something that is manipulated (or it naturally changes) or measured.
While the idea of a variable sounds easy enough, there are numerous types of variables and ways to incorporate them into research. Let's keep going to find out more.
Variables in Research
When you think of a research project, you might think of different types; some involve collecting primary, secondary, or both types of data.
Primary data is where the researcher collects data themself, and secondary data is data collected not from participants themselves, e.g. previously published findings, diary entries, etc.
The likelihood of having no variables in primary research is close to impossible. So, what exactly do all these variables designate in experimental research?
Experimental research takes an empirical approach to investigate a hypothesis and involves manipulating a variable and measuring how it affects another.
Fig. 1 - When choosing a topic for an experiment, it is important to understand which variables can alter the results and how.
Experimental research thus focuses on testing and analysing two variables: the independent variable (IV) and the dependent variable (DV).
Independent Variable (IV)
As we have already learned, the purpose of experimental research is to support or disprove a hypothesis. We use this type of research to understand the cause-and-effect relationship by measuring the outcome of a manipulated factor/variable (experimental method).
The independent variable (IV) is a factor that the experimenter manipulates to see if it affects the dependent variable (DV).
The independent variable is what the researcher predicts as a cause of a phenomenon.
Sounds confusing, doesn't it? Let's look at an example to gain some more clarity.
Let's say you want to study the impact of social media on self-esteem. Here, think about how this cause-and-effect relationship can be studied. A simple way would be to measure the number of hours someone spends on social media and compare it with their self-esteem score.
Now, think about what you can manipulate and what you cannot. There really isn't a way for anyone to manipulate self-esteem but can the hours spent on social media be manipulated? Yes, it can! Therefore, the number of hours spent on social media becomes your independent variable.
Dependent Variable (DV)
So we know that experimental research is one of the most effective ways to understand a cause-and-effect relationship consisting of two main variables - independent and dependent. Since we've learned what an independent variable is, let's now focus on the dependent variable.
The dependent variable (DV) is the factor that is affected when the independent variable (IV) is manipulated.
Keeping both definitions in mind, we can conclude that the IV is what the researcher suspects to be the cause of the phenomenon. At the same time, the DV is a variable/factor measured or tested in the experiment. With respect to the example given above, let's see what a dependent variable actually looks like in experimental research.
We decided that if you wanted to study the impact of social media on self-esteem, the dependent variable would be the hours spent on social media because that's something you can manipulate. Great, that's been identified! Now, what's left?
The measurement of self-esteem. And can that variable be manipulated? Can you increase it or decrease it as per your needs? No, you cannot; changes in the independent variable will only impact the changes in this variable. Therefore, we can say that the measurement of self-esteem is the dependent variable.
Let's look at the table below to understand the independent and dependent variables further and see how they can take form in different research scenarios.
Research scenario |
Example of IV | Example of DV |
| The number of hours spent studying. | Test scores. |
| The amount of water (ml). | Size plant grows (cm). |
| Treatment groups, i.e. treatment-drug therapy group and a placebo group. | Behavioural scores, e.g. measures of anxiety, depression, or aggressiveness. |
Types of Variables
While the independent and dependent variables are often considered the most important ones, this is not to say that there aren't other types of variables that can impact the research at hand. Let's discuss some of these below.
A type of variable is called the extraneous variable.
Extraneous variables are factors that are not the IV but may influence the results (DV).
When extraneous variables are present in the research design, IV and DV may be considered causally related, although this is not the case. Let's simplify this using an example.
Imagine you are investigating studying time and whether or not it has a relationship with test scores. Something you may not consider is the noise level, which can be a potential extraneous variable here.
The noise level could irritate some participants and cause poor performance. Therefore, because of the extraneous variable (noise level that is not controlled), we cannot conclusively say a relationship between the IV and DV independently exists. I.e., different results may be found, e.g. no relationship if noise levels are controlled.
Another type of variable is a confounding variable.
A confounding variable is a factor that has not been considered and is associated with both IV and DV.
You might now wonder how a variable can be related to both the independent and dependent variables.
Consider this example of a research scenario examining exercise and weight loss. The researchers identified IV as randomly dividing participants into two groups: the exercise group and the non-exercise group, and DV as changes in body mass index (BMI).
It is known that dietary change is a factor that affects weight changes. If the research design does not account for dietary changes, this may distort the observed results of how much the IV affects the DV. Therefore, it is a confounding variable.
Variables: Manipulating and Controlling Variables
We now know that the variables we manipulate are called IV. By using them in research, we can observe how these manipulations affect the DV, if at all. Further, we discussed other factors that can affect the DV that the researcher may not be interested in, i.e., confounding and extraneous variables. So, how do we combat this?
Researchers need to control these variables, i.e., hold them constant or exclude them from the research entirely, hence the name control variables.
Let's say you are trying to examine whether caffeine affects participants' ability to recall words. What are some factors that may need to be held constant here? Let's take this step by step.
When discussing human participants, age is an important factor. We know that age is a continuous variable, so one way to control this would be only to discuss age in one value, i.e., only years, months or days.
Next, the noise level in the room can impact whether or not the participants can recall words effectively. Imagine you're in a room, but you can hear construction work outside; surely that'll be disturbing, right? So, the noise level is another factor that needs to be controlled. This can be done by ensuring the participant wears noise-cancelling headphones or that the room the experiment is conducted in a sound-proof room.
Operationalisation of Variables
The golden standard for quality research in psychological research is to operationalise all variables examined in studies.
Operationalisation of variables means that the variables under study are clearly defined with information about how the study will measure them.
Fig. 2 - Operationalising variables helps current, and future researchers understand exactly how to conduct their research and remove any associated subjectivity.
This shows that when operationalising variables, researchers need to conceptualise the variables being measured by breaking down the elements of the variables to show how the researchers are measuring them.
We might measure bullying by observing the frequency of kicking, name-calling, or derogatory language.
Let's look at how operationalisation would look in a research experiment.
Let's say you want to investigate whether emotions influence problem-solving skills. Here, you would identify emotions as the IV and problem-solving skills as the DV.
After operationalising these two variables, they would be - 'emotional intelligence as measured and assessed by the Emotional Intelligence Test' (IV) and 'time required to solve a Sudoku puzzle' (DV).
When there is a clear-cut decision, you can maximise the time spent collecting accurate data rather than spend that valuable time on deciding what to do, what not to do and what the best way to approach a particular situation is.
Operationalisation is essential for a few reasons. Some of these are -
- Clearly defining variables and how they are measured in research makes it easier for researchers to replicate the study and determine the reliability of the results.
- It is easier to ensure that the studied variables have high internal validity, i.e., they measure what they are supposed to measure).
- Reduces the likelihood that subjectivity will influence the research.
- Ensures that the variables being studied are observable and measurable.
Variables - Key takeaways
- A variable is a factor that gets measured.
- The independent variable (IV) is a factor that the experimenter manipulates to see if it affects the dependent variable (DV).
- The dependent variable (DV) is the factor that is affected when the independent variable (IV) is manipulated.
- The types of variables are extraneous, confounding, and control variables.
- Operationalisation of variables means that the variables under study are clearly defined with information about how the study will measure them.
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