Control Variables: A Comprehensive Guide

A control variable is something that remains constant or limited in a research project. Despite not being related to the study's objectives, this variable is controlled because it may influence the outcomes. The room temperature in an experiment is one example of a variable that may be directly controlled.

Variables can also be indirectly controlled by procedures such as randomization or statistical control, which include accounting for participant variables such as age in statistical tests. To avoid research biases such as omitted variable bias, include control variables in your analyses.

Regression and covariance analyses typically combine data from control variables with data from independent and dependent variables. This allows the researchers to separate the effects of the control variable from the relationship between the variables of interest.

Whenever you use a control variable in your research, make sure to identify them separately, keep track of their values, and include their information in the write-up.

Why Are Control Variables Important?

Control factors limit the impact of confounding and other extraneous variables, which improves a study's internal validity. This allows you to avoid research bias while yet demonstrating a correlational or causal relationship between your variables of interest.

Except for the dependent and independent variables in research, all variables that potentially influence the results should be controlled. If you do not control for significant factors, you may be unable to demonstrate that they had no impact on your results. Alternative explanations for your results, or uncontrolled variables, affect the validity of your statements.

Consider the example where the control variables are temperature, moisture, and sunlight. You're well aware that these factors influence plant development. When examining the impact of fertilizers on plant development without keeping these variables constant and under control, it is practically impossible to obtain precise observations on how the fertilizers perform. It will be difficult to determine whether the plant growth rate is attributable solely to fertilizers or to favourable control variables.

You can increase your research's internal validity by controlling factors. Internal validity refers to the degree of conviction that a causal link exists between the therapy and the difference in results. In other words, how likely is it that the differences you observe are the product of your experiment? Are the results accurate? Or could different outcomes be attributed to distinct causes?

Independent and Dependent Variables

To better comprehend control variables, you should also grasp these two variables:

Independent variables

In an experimental study, an independent variable is one that is changed or altered in order to investigate its consequences. It is called "independent" since it is unaffected by the other study factors.

Independent variables can also be referred to as explanatory factors, which help explain an event or outcome.

• Predictor variables (those that can predict a dependent variable's result).
• The variables on the right side of a regression equation are called right-side variables.

These statements are instrumental in statistics, where you may assess how effectively a change in one independent variable can explain or predict changes in another.

Dependent Variables

A dependent variable is one that changes as an independent variable is modified. Your independent variable "depends" on the outcome you're looking to measure. Dependent variables can also be referred to as response variables, outcome variables, or left-side variables in regression equations. After altering the independent variable, you should record the dependent variable. By conducting statistical analyses, you may establish whether and how much your independent variable influences the dependent variable.

How to Identify Independent vs Dependent Variables?

It might be challenging to discern between independent and dependent variables while organizing a complicated study or reading a complex academic research report. It is critical to consider the research design since a dependent variable in one study may be the independent variable in another.

Here are some tips for determining the various types of variables.

When analyzing independent variables, consider if they were manipulated, controlled, or subject-grouped by the researcher.

• Does one variable appear earlier than the other?
• Does the study aim to identify the relationship between variables?

When analyzing dependent variables, consider whether they were manipulated, controlled, or grouped by the researcher.

• Does one variable appear earlier than the other?
• Does the study aim to identify the relationship between variables?

Here are some instances of dependent and independent variables.

1. Compare paper towels to discover which has the highest moisture absorption capacity. The type of paper towel you used served as the experiment's independent variable. The dependent variable would be the amount of liquid absorbed by the paper towels.
2. Conduct an experiment to see if a substance is important for a dog's diet. The independent variable is whether the chemical is present or not. The dependent variable is the dog's condition, namely whether it is alive and capable of reproducing. If it is discovered that the chemical is required for healthy nutrition, the amount needed can be calculated in the following experiment. In this scenario, the amount of chemicals would be the independent variable, with the dog's health as the dependent variable.

Control Variables in Experimental Research

In this form of research, the researcher typically aims to determine how an independent variable influences a dependent variable in an experiment. You can use control variables to ensure that the adjustment in your experiment was the only thing influencing your results. Consider the previously described example. Consider administering fertilizers to one group of plants to see how they grow. This means you'll automatically have a group of plants that aren't affected by the fertilizer. In this scenario, the independent variable is whether or not fertilizer is applied to the plants, whereas the dependent variable is the rate of growth. To ensure that any growth is due to fertilizer, you must regulate the other variables that may affect plant growth, such as moisture, sunlight, and temperature.

Control Variables in Non-Experimental Research

In an observational study or other non-experimental research, the independent variable cannot be controlled (usually due to logistical or ethical concerns). Instead, correlations between primary variables of interest are estimated by measuring and accounting for control variables.

In this scenario, we cannot properly consider the previously described example, so let's consider another one - Say you're interested in the relationship between income and happiness. In this example, obviously, you hypothesize that income level affects happiness, but practically speaking, it is impossible to change the income variable. Instead, you collect information about income and satisfaction through a survey that includes Likert scale questions.

To account for extra variables that may influence the outcomes, measure the following control variables:

• Age, health, marital status, gender, and geographic location.

How Do You Control a Variable?

The methods for controlling a variable are described below. Keep in mind that several of these strategies can be employed in observational studies and quasi-experimental designs.

Random Assignment

In experiments with many groups, participants should be randomly assigned to different circumstances. You can avoid systematic differences across groups by balancing the features of the groups via random assignment. This assignment approach controls the participant variables that may otherwise vary between groups and distort your results.

Here is an example –

To find volunteers for your experiment, you use a variety of approaches, including social media ads, word-of-mouth marketing, and campus posters. The majority of participants (more than 50%) learned about the study through campus brochures, while almost 40% joined up through Facebook marketing. It's worth noting that participants who learned about the study via Facebook tend to use more screens, which may impair their attention throughout the study. To maintain fairness, participants are randomly allocated to either the control or experimental groups, independent of their characteristics.

Another easy example or random assignment is to label equal-sized balls with the names of your 50-person study group. The balls are then placed in a well-mixed urn (a typical ball and urn experiment). You draw 25 balls and place the first 25 in the experimental group. All other participants are assigned to the control group. When you are not given a random assignment, you will use your knowledge, experience, and judgment to split the participants into experimental groups.

Standardized Procedures

In an experiment, it is critical to follow the same protocols in each group. To isolate the independent variable's impact on the dependent variable (the findings), the groups should only differ in how they manipulate the independent variable. To keep variables constant at a predetermined level, you might establish and adhere to a method for each participant session. For example, in a lab environment, all participants should be given the same instructions and have the same amount of time to complete an experimental task.

For example, take the circumstance of growing plants with the help of fertilizers: All of the plants included in the study follow the same set of rules. For example, plants receive direct sunshine from 9:00 AM to 12:00 PM, are housed in a temperature-controlled area, and have a moisture-controlled atmosphere. The control group of plants receives the specific fertilizer as part of the experiment, whilst the experimental group does not.

Conclusion

When doing research and experiments, understanding and implementing control variables is paramount to ensure the reliability and validity of study outcomes. This practice is crucial in both experimental and non-experimental research settings, where the manipulation of independent variables and the observation of their impact on dependent variables necessitate careful consideration of potential confounding factors. To learn more about control variables and how they work, read our blogs on “Do My Assignment”.

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