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If you've ever conducted an experiment, you've almost definitely encountered a number of parameters that you needed to alter, measure, or control in order to successfully complete your research. Variables are these factors, and there are numerous types of them.
So, what exactly do all these variables designate in experimental research? Let's step right in.
Experimental research is research that takes an empirical approach to investigate a hypothesis.
A variable is a factor that gets measured.
Experimental research thus focuses on testing and analysing two variables: the independent variable (IV) and the dependent variable (DV). This article will use examples to explain what IV and DV are and why they are used in research.
As we have already learned, the purpose of experimental research is to confirm or reject a hypothesis. We use this type of research to understand the cause and effect relationship. This is done by measuring the outcome of a manipulated factor/variable (experimental method).
IV is a factor that the experimenter manipulates to see if it affects the DV. Thus, the IV is what the researcher suspects to be the phenomenon cause, while DV is a variable/factor measured or tested in the experiment.
It follows that the value of the DV can give us an insight into the effect of a relationship. In summary, experimental research involves manipulating hypothesis IV to determine causal relationships of a phenomenon and observing how this affects the DV.
When we identify variables in psychology, we must operationalise them. Operationalised variables specify how the variable is to be defined and measured.
For example, if we are studying the impact of social media on self-esteem, the IV would be operationalised as the number of hours spent on social media platforms such as Instagram or Facebook. The DV would be the self-esteem score as measured by the Rosenberg Self-Esteem Scale (RSES).
As mentioned earlier, the variables we manipulate are called IV. Using them in research is to observe how manipulating variables affect DV. However, other factors may affect the DV that the researcher is not interested in (potential confounding/extraneous variables). These tend to reduce the validity of the results and increase the likelihood that the null hypothesis will be rejected when it would otherwise be accepted. Therefore, researchers need to control these variables (hold them constant or exclude them from the research), hence the name control variables.
We will now discuss an example of a research scenario where researchers must manipulate and control variables.
One study examined whether caffeine affected participants’ ability to recall memories. The manipulated variable in this study was the amount of caffeine consumed before a memory recall test. In addition, there were potential control variables that needed to be accounted for/restricted before the study:
Let us now consider the different types of variables and their properties. We will also give examples so that we may understand them better.
A continuous variable is a variable that can potentially have an unlimited number of possible values and is usually determined by measuring or counting a variable. An example of a continuous variable is age.
Extraneous and confounding variables are factors other than the IV and DV that may affect the study’s outcome. The presence of such variables affects the validity of the results. This section will now discuss the definitions of extraneous and confounding variables with examples of how they affect the validity of the results and how the research combats the effects caused by these variables.
Extraneous variables are variables/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.
These variables may cause the effects of the independent variable on the DV to be underestimated or overestimated, reducing the power of the results.
For example, the noise level can be a potential extraneous variable when investigating studying time and test scores. 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 IV and DV exists.
Examples of different types of extraneous variables are:
A confounding variable is a factor that has not been considered because it is associated with both IV and DV. Confounding variables affect the DV and are also correlated or causally related to the IV.
The presence of confounding variables means the research design lacks internal validity because the study does not measure the causal relationship between IV and DV.
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 IV affects DV. It is a confounding variable.
As mentioned earlier, extraneous and confounding variables affect the validity of results, so researchers can take steps to minimise them. These are discussed in detail below.
Extraneous and confounding variables are controlled by:
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.
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.
For example, we might measure bullying by observing the frequency of kicking, name-calling, or derogatory language.
We will now discuss an example of a research scenario to explain the operationalisation of variables.
The following example uses the research scenario of investigating whether emotions influence problem-solving skills. The researcher would identify emotions as IV and problem-solving skills as DV. The operationalised definition of IV is ‘emotional intelligence as measured and assessed by the Emotional Intelligence Test’. Furthermore, the operationalised definition of DV would be ‘time required to solve a problem-solving test, such as a Sudoku puzzle’.
Clearly defining variables and how they are measured in research makes it easier for researchers to replicate the research and determine the reliability of the results.
It is easier to ensure that the variables being studied have high internal validity (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. This is an essential prerequisite for the research to be called empirical.
Research that is investigating ‘whether problem solving skills are affected by emotion’, would identify emotion as the IV and problem-solving skills as the DV.
A variable is something that is being measured.
A continuous variable is a variable that can potentially have an unlimited number of possible values and is usually determined by measuring or counting a variable. An example of a continuous variable is age.
The IV is a factor that the experimenter manipulates to identify if it affects the DV.
The DV is a variable/ factor measured or tested in the experiment and allows for inferences of whether it has a causal relationship with the hypothesised IV.
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