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A control variable is a variable or an element which is held constant throughout an experiment or a research in order to assess the relationship between multiple variables. Since it remains constant, i.e in an unchanging state, it enables researchers and scientists to test and better understand the relationship between other variables.
A control variable is the factor that ensures that the test results can be compared fairly and that they aren’t skewed.
Researchers and scientists often mould control variable data along with independent and dependent variable data in regression analysis and analysis of covariance. This allows them to separate the control variable’s effects from the relationship between the variables of interest. There are multiple ways of controlling variables. They may be controlled directly or indirectly.
To control variables directly, all you need to do is hold them constant throughout a research or experiment (for instance, keeping the temperature constant). To control them indirectly, you can use methods like statistical control.
Control variables are also called constant or controlled variables.Â
A controlled variable in an experiment is the one that the researcher holds constant or controls.
Control variables are the variables or elements that researchers strive to keep constant throughout their research so that they would not influence the outcomes.
In a typical research design, the effect of an independent variable on a dependent variable is measured. For this to happen properly, it is crucial to control other extraneous or standardized variable
In experiments, a researcher or a scientist aims to understand the effect that an independent variable has on a dependent variable. Control variables help ensure that the experiment results are fair, unskewed, and not caused by your experimental manipulation.
In non-experimental research, there’s no way researchers can manipulate the independent variable. In such research studies, control variables help infer relationships between the main variables of interest.
By now, you must have understood how important it is to control variables that can impact the results of a research or experiment, in addition to the independent and dependent variables.
If you don’t control variables, you may not be able to figure out or prove that they didn’t impact your results. You would never be sure of whether your results are an effect of your independent variable or not. There’ll be no explanations of your faulty results then.
Controlling variables is important because even the slightest of variations in the research study could influence the results..
Another advantage of control variables is that they make it easier and more convenient to reproduce a research study and establish the relationship between the independent and dependent variables.
For instance, say you are trying to determine whether a particular soil quality has an effect on plant growth. The independent variable is the soil quality, while the dependent variable is the rate of growth of the plant. If you don’t control the quality of the soil, you may end up skewing your results.
Does soil quality affect plant growth?
Controlled variables
Does caffeine improve memory recall?
Controlled variables
Do people with a fear of water perceive water images faster than other people?
Controlled variables
There are several ways to control extraneous variables in experimental designs, quasi-experimental designs, observational designs, and research studies.
Here’s the best way to do so:
This is most relevant for experimental studies with multiple groups. It helps control participant variables that are likely to differ between groups and skew your results. In this method, participants are randomly assigned to the different conditions of groups to eliminate any systematic differences between them.
It’s important to use the same procedures across all groups in your experiment or research study. To control variables, consider holding them constant at a fixed level and do this for all participant sessions.
Experimentation is not as simple as changing one factor and recording the outcome. In reality, every possible research has numerous different factors that can influence the results. Control variables are a way of keeping some of these factors constant so that comparison can happen in an unbiased and transparent manner.
In many experiments, even the smallest fluctuations in some factors can lead to inaccurate findings and skewed results. Voxco’s experimental research toolkit aims to give researchers more control and hold over the variables in order to help them obtain desired results.
A control variable isn’t the same as a control group. Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.
A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).
Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.
A control variable is an element that is kept the same throughout the experiment in order to assess the relationship between multiple variables.
A controlled variable in an experiment is the one that the researcher holds constant or controls.Â
It is also known as a constant or control variable.Â
The controlled variable is not part of an experiment. It is not an independent or dependent variable. However, a controlled variable is important because it can affect the experiment’s result.
When we follow the control variable definition, it is easy to notice the elements of the experiment that can be put under control.Â
In an experiment to observe the growth of a plant, the temperature can be classified as a control variable if it is controlled during an experiment. Other examples of control variables could be the amount of light, duration of the experiment, amount of water, and pot of the plant.
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