Introduction
The results of scientific studies involve multiple factors and variables. These are the most influential factors in an experiment that derives desired results or faulty outcomes. These include the well-known types: independent variables and dependent variables. The link between dependent and independent variables shows the research outcome in any experiment. However, the two types of variables do not affect the result alone, as there is another significant type: a control variable. This is the most influential variable in any study, as it can lead to bias.
A control variable does not participate in the research aims but can potentially affect your study results. Since dependent variables and independent variables are not difficult to understand, understanding them is crucial in every case. In this post, we will provide you with an introductory guide covering all aspects of them in research, from examples to control methods. If you want to know what they are in research and how you can control them, keep reading this blog for an in-depth understanding of control variables.
Understanding What is a Control Variable?
To define them, we can say that variables that are kept constant throughout the research to avoid biased results are called control variables. Although these are not in any interest of the scientific study or its aims, they can greatly affect your analysis.
This is the simplest definition to comprehend the key concept. While conducting research, they are either set directly or through indirect methods such as random control or statistical methods. This way, it is easier to find out the relationship between dependent and independent variables. Also, there is less risk of faulty answers when extraneous variables are restrained.
Influence of Variable in Research
Except for the primary variables, control variables or omitted variables may affect your study results. As they tend to manipulate the research answer, a study can be declared useless if a control variable or other confounding variable has its impact. Your research can be nullified if you fail to demonstrate the core connection or relationship effect of independent and dependent variables.
Importance of a Control Variable
They are crucially important in any research conducted. By controlling a variable, the researcher can demonstrate the correlation between dependent and independent variables by intensifying their internal validity. If you do not control them, your claims can be terminated and regarded as alternative explanations for your experiment. Uncontrolled variables also produce faulty results and void important ideas you want to deliver.
Therefore, it is important to exclude extraneous variables and constraint variables that can negatively impact the result. As it may portray, the primary or important factors do not affect your findings. Now, you must have a clear concept of what the control variable means and what it does.
Difference Between Independent vs. Dependent Variables
In easy terms, we can consider that the things or factors we change during the experiment are known as independent variables. The things that we measure are referred to as the dependent variable.
For example, if we say people love eating ice cream, then there are two main factors involved in the observation. Firstly, the happiness level that is to be measured is a dependent variable. However, the quantity of ice cream people have to be happy is an independent variable.
How Are Control Variables Different From Control Groups?
The difference between the control group and the control variable is pretty evident. The controlling variable is kept constant to analyse the effect of the independent variable on the dependent variable. Therefore, the results acquired are fair and accurate.
Whereas a control group in the experiment does not have an independent variable. It is the participant who is not involved in the experimental change and serves as a baseline to review the results of the experimental group.
Clear Your Concept with Examples of Control Variables
To ensure you understand them, control groups and factors in detail, a practical guide is necessary. Here are a few examples that will make the idea clearer to you.
Think of a cooking contest where two chefs are participating and trying out a new spice. One of the two chefs agreed to add the spice to the dish they were preparing, while the other chef did not. The contest is to prepare the best dish using ingredients that taste the same or better than each other to analyse individual task performance. Now we will look into the example and know what category each thing has fallen into:
- The chef who agreed to put the spice in the dish is referred to as an experimental group.
- The chef who followed the usual recipe of the dish served as the control group.
- The new spice acts as the independent variable.
- The taste remains the dependent variable.
Since the rest of the ingredients are the same, portion size, cooking time, and temperature have also been kept similar, so all these factors are counted as control variables.
Let us look at another example to understand it thoroughly.
Consider a plant that is sown in the soil using a different fertiliser and the other is sown in another. Since the growth needs to be measured when a special fertiliser is used, then growth remains the dependent variable of this experiment while the fertiliser becomes the independent variable. However, other factors such as sunlight, watering frequency, soil, and temperature remain the same in both cases. Hence, they are regarded as control variables.
Types of Research and Control Variables
The two types of research, when using them, have different impacts. Such them in experimental studies differ from those of non-experimental studies. Find out the explanation below.
Experimental Studies
Control variables and extraneous variables can be kept constant and limited throughout the experimental studies. Whereas, independent variables and dependent variables are manipulated to analyse their effect on one another. Such that, you manipulate factors that can influence your experiment results and evaluate what difference it makes to the other variable.
Non-Experimental Studies
In non-experimental studies, it is tough to manipulate independent variables, as it is considered unethical in observational studies. However, variables of interest are considered and inferred to evaluate the relationship effect between variables. They are not in researchers’ hands in this case but they can retain their level by deriving the correlation analysis between variables.
Methods to Control a Variable in a Scientific Study
As of now, we have discussed the importance and influential aspects of them. Hence, researchers must take into account that controlling a variable has its significance and there are different techniques involved in controlling variables. The three methods that are usually used when studying a topic are stated further. Find out about them by reading the following brief.
Randomisation
Random assignment or randomisation, is the most commonly used method for variable controlling, in experimental studies. Researchers apply this method where several groups are involved and each group’s participants have different characteristics. To create a balance among their properties, randomisation is a useful controlling method.
Standardisation
The standard procedure or standardisation, refers to the same implementation for all groups involved in a study. There is a standard, same process to carry out research for all participants and groups rather than studying each group individually. The researcher must use the standardisation method and not other methods for every group.
Statistical Method
Since every study is not experimental, you cannot control variables and their influence on the result. In non-experimental research, it is difficult to control the variables as you cannot set them at a constant level. However, you can utilise statistical control or limit bias in regression estimate results. You can separate the effect of the relation between independent and dependent variables by modelling control variable data. The statistical approach includes weighting, calculating averages, and ages in some cases.
Conclusion
To conclude, the control variable's meaning is simple, but the concept is technical to understand. Researchers must have experienced once in their study projects that they can significantly neglect their findings by valuing dominating factors. However, by understanding the controlling methods and variable behaviours, you can easily navigate the relationship between independent and dependent variables and enhance their validation. To avoid errors and unnecessary conclusions, you should learn to restrict them to derive the optimum results as per your research idea.
