23 June,2023
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Suppose you are conducting an experiment to observe the impact of different fertilizers on plant growth. In this case, the primary variables for your experiment would be the types of fertilizers and the rate of plant growth. But variables like moisture, temperature, and sunlight can also have an influence on the results you derive. Though the later variables are not directly involved with the experiment, there can be changes in results if they are not kept constant. Because these variables have the tendency to control or, rather say, influence the experiment results (even though when they are not primary variables), they are termed the control variables.
It is understandable if you are still confused. Read this blog to the end, and surely you’ll have no more doubts.
Anything kept constant or constrained in a research study is referred to as a control variable. Despite not being relevant to the study’s goals, this variable is controlled because it might have an impact on the results.
The room temperature in an experiment is an example of a variable that can be controlled directly. Variables can also be controlled indirectly using techniques like randomization or statistical control, such as accounting for participant characteristics like age in statistical tests. Research biases like omitted variable bias can be avoided by including control variables in your analysis.
Regression analysis and analysis of covariance frequently combine data from control variables with data from independent and dependent variables. This enables the researchers to distinguish the effects of the control variable from the correlation between the variables of interest.
Whenever you are using a control variable in your research, make sure to identify them separately, have a record of their values, and also include their details in the write-up.
Control variables reduce the impact of confounding and other extraneous variables, improving the internal validity of a study. By doing this, you can prevent research bias and demonstrate a correlational or causal relationship between your variables of interest.
All variables that can affect the outcomes, except the independent and dependent variables, should be under control. You might not be able to prove that important factors had no impact on your results if you don’t control them. Alternative explanations for your results, or uncontrolled factors, have an impact on the validity of your claims.
For example, if you consider the example mentioned earlier – the temperature, moisture, and sunlight are the control variables. You know very well that these factors have an impact on plant growth. Now when you are testing the impact of fertilizers on plant growth and do not keep these variables constant and under control, it is nearly not impossible to get accurate observations on the working of the fertilizers. It will become very difficult to infer whether the plant growth rate is only because of the fertilizers or due to favorable conditions of the control variables.
You can improve the internal validity of your research by controlling variables. The degree of certainty that there is a causal link between the therapy and the difference in results is known as internal validity. In other words, what is the likelihood that the differences you notice are the result of your experiment? Are the findings accurate? Or may different results be attributable to different causes?
To understand control variables in a better way, it is also important for you to understand these two variables –
Can’t figure out whether a variable is primary or controlled? Getting the right results in your research study will be impossible unless you figure out what control variables are. So, before you begin your research, go through these examples and get your doubts clarified.
In an experimental study, an independent variable is one that you change or alter to examine its effects. It is named “independent” because it is unaffected by any other study variables.
Other names for independent variables include:
These phrases are particularly useful in statistics, where you evaluate how well a change in one independent variable can account for or anticipate changes in another.
A dependent variable is one that is altered as a result of the modification of an independent variable. Your independent variable “depends” on the outcome you’re interested in measuring.
Dependent variables are also referred to as:
After changing the independent variable, you record the dependent variable. By performing statistical studies, you can utilize this measurement data to determine whether and how much your independent variable affects the dependent variable.
It might be challenging to distinguish between independent and dependent variables when planning a complicated study or reading a complex academic research paper.
It’s crucial to pay attention to the research design because a dependent variable from one study can be the independent variable in another.
Here are some pointers for determining each type of variable.
In the case of an independent variable, make sure to check whether –
In the case of dependent variables, check whether –
Here are a few examples of independent and dependent variables
In this type of research, a researcher usually wants to know how an independent variable affects a dependent variable in an experiment. You can utilize control variables to make sure that your experiment’s alteration was the only factor in your results.
Here’s an example –
Again let’s consider the previously mentioned example. In this case, consider that you are applying fertilizers to one group of plants to see the impact on their growth. This means that automatically you’ll also have a group of plants that are not under the influence of the fertilizer.
‘In this case, the independent variable would be whether or not the fertilizer is added to the plants, and the dependent variable would be their rate of growth.
To make sure any growth is occurring due to the fertilizer, you have to make sure to control the other variables that might impact growth in a plant, like moisture, sunlight, and temperature.
A researcher cannot control the independent variable in an observational study or other types of non-experimental research (typically because of logistical or ethical issues). Instead, correlations between the primary variables of interest are inferred by measuring and accounting for control variables.
In this case, we cannot really consider the previously mentioned example, and hence let’s consider another instance –
Say the relationship between the factors of income and happiness is something you want to look into. In this case, obviously, you hypothesise that income level predicts happiness, but if you think practically, it is quite not possible to manipulate the variable of income. Instead, you gather information about income and happiness via a survey containing Likert scale questions.
Here you have to measure the following control variables to take into consideration additional variables that might have an impact on the outcomes:
The methods of controlling a variable are mentioned below. Remember that some of these techniques can be used in case of observational study and quasi-experimental designs –
Participants in experimental investigations with numerous groups should be divided into various conditions at random. You can avoid systematic discrepancies across groups by balancing the features of the groups through random assignment.
The participant variables that would normally differ between groups and distort your results are controlled by this assignment strategy.
Here is an example –
To recruit volunteers for your experiment, you utilize several methods, including social media ads, word-of-mouth marketing, and campus posters. The majority of the participants (over 50%) were informed about the study through campus brochures, while around 40% signed up via Facebook marketing. It’s noteworthy that those who found out about the study through Facebook tend to use more screens, which may affect their alertness during the study. To ensure impartiality, participants are randomly assigned to either the control or experimental group, regardless of their characteristics.
Another very simple example or just a random assignment is suppose you label equal-sized balls with the names of your 50-person study group. The balls are then put into a well-mixed urn (this is a traditional ball and urn experiment). You draw 25 balls, and the first 25 are placed in the experimental group. All others are placed in the control group. When you are not doing a random assignment, you will use your knowledge, experience and judgment factor to divide the people into experimental groups.
In an experiment, it’s crucial to follow the identical protocols in every group. To isolate the independent variable’s impact on the dependent variable (the findings), the groups should only differ in the manipulation of the independent variable.
You can use a procedure that you establish and follow for each participant session to keep variables constant at a set level. For instance, all participants in a lab setting should receive the same instructions and allot the same amount of time to complete an experimental task.
For example, once again, let us consider the situation of growing plants with the help of fertilizers –
The same set of rules are followed for all the plants that are part of the study. For instance –
Plants in the control group receive the particular fertilizer as part of the experiment, while the experimental group is given no such thing.
To eliminate their influence on other types of variables, you can statistically measure and account for superfluous factors.
In regression analysis and ANCOVAs, “controlling for a variable” refers to modeling control variable data alongside independent and dependent variable data. In this manner, you may separate the effects of the control variable from the correlation between the variables of interest.
For example, along with your control variables of age, married status, and health, you gather information on your major variables of interest, income, and happiness.
You include all of the control variables and the independent variable as predictors in a multiple linear regression analysis. The findings reveal how much happiness can be predicted by wealth while age, marital status, and physical health are held constant.
In a scientific experiment, a control group is a subset of participants who are not included in the main experiment and whose outcomes are unaffected by the independent variable under study. By isolating the effects of the independent variable on the experiment, this can help rule out other possible explanations for the findings of the experiment.
Positive and negative control groups are the two categories into which they might be divided.
The parameters of the experiment are set to ensure a good outcome in positive control groups. A successful control group can demonstrate that the experiment is running as intended.
Here’s an example –
Let’s imagine that you are researching bacterial drug susceptibility. To confirm that the growth medium is capable of maintaining any bacteria, you can utilize a positive control. You could cultivate bacteria that have the marker for drug resistance and can therefore endure in a drug-treated environment. If these bacteria multiply, you have a positive control that indicates other bacteria with drug resistance should be able to pass the test.
On the other hand, negative control groups are those where the experiment’s settings are designed to produce a poor result.
A straightforward illustration of a negative control group can be found in an experiment where the researcher examines whether a fertilizer affects plant growth. The set of plants cultivated without fertilizer but under identical circumstances as the experimental group would be the negative control group. The application of fertilizer would be the only distinction between the experimental group.
There could be different experimental groups with varying fertilizer concentrations, application techniques, etc. The null hypothesis is that there is no effect of fertilizer on plant growth.
Control Variable vs Control Group
A control group and a control variable are not the same thing. While an independent variable varies between the control and experimental groups, control variables are maintained constant or monitored across the course of a study for both the control and experimental groups.
A control group that does not receive the desired experimental treatment has its results compared to those of the experimental group. A control group typically receives either no treatment, a well-accepted standard treatment, or a fake treatment.
Everything about an experimental process, excluding the experimental treatment, should be the same for both the experimental and control groups.
For example, in the experiment of observing plant growth under the influence of fertilizers, the controlled variable that might impact the final results are the sunlight, moisture and temperature. These variables are kept constant for both the experimental group and the control group.
Now which is the control group?
The group of plants that are not treated with the fertilizer is the control group.
Control variables, also referred to as controlled variables, are characteristics that researchers maintain constant for each observation throughout an experiment. Although they are not the main focus of the study, maintaining their values enables it to identify the real connections between the independent and dependent variables. Control groups and control variables are distinct.
Control variables are important because they serve as a standard or point of reference by which other test findings are evaluated. Typically, controls are employed in corporate research, cosmetic and drug testing, as well as scientific trials.
While an independent variable differs between the control and experimental groups, control variables are maintained constant or assessed throughout a study for both control and experimental groups.
The main idea is that control variables should be correlated with the dependent variable as well as the important dependent variables (those you want to know their effects on). Variables should also give a clear, systematic explanation for why the dependent variable might appear to be predicted by the important independent variables being investigated.
Hi, I am Mark, a Literature writer by profession. Fueled by a lifelong passion for Literature, story, and creative expression, I went on to get a PhD in creative writing. Over all these years, my passion has helped me manage a publication of my write ups in prominent websites and e-magazines. I have also been working part-time as a writing expert for myassignmenthelp.com for 5+ years now. It’s fun to guide students on academic write ups and bag those top grades like a pro. Apart from my professional life, I am a big-time foodie and travel enthusiast in my personal life. So, when I am not working, I am probably travelling places to try regional delicacies and sharing my experiences with people through my blog.
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