Control variables play a pivotal role in scientific methodology, ensuring the integrity of research outcomes. These are specific types of variables that researchers keep constant throughout an experiment to minimize external influence and ensure that the results accurately reflect the relationship between independent vs. dependent variables. The following article will explain everything you need to know about control variables and how they can help to avoid research bias.
Control Variables – In a Nutshell
Control variables are circumstances in an experiment, which are held constant and not altered throughout the study.
Definition: Control variables
Control variables are factors in a study that are kept steady to avoid them influencing the dependent variable. This way, they limit the impact of possible extraneous variables and confounding variables, which increases internal validity. They help to create replicable, verifiable data (i.e. in statistics) from direct experimentation, observation, and sampling by setting hard limits. They also allow us to bypass ‘false’ causatives in observational studies. As a single study may use many different types, they can be categorical variables as well as quantitative variables.
Types of control variables
There are two types of control variables, experimental and non-experimental ones.
Experimental
Experimental control values let us isolate the dependent and independent variables in closed experimental conditions. They are called the ‘lab’ variety.
Example
In research, to see if adding a different mineral to soil stimulates houseplant growth, set control variables must detail how much sunlight, water, and air each plant should receive. As these factors are recognized as growth causatives (i.e. positive and negative confounders), quantitive nominal amounts may be required.
To do so, the scientists may read past papers, search through data about chronic conditions and rainfall, and examine similar nearby plants in the wild. Afterwards, they may agree that the plants should all receive 8.0 hours of daily light and 500 ml of daily water in a closed, single-fan-circulated shed. As a result, they may reach a consensus on their chosen brand of compost, pot diameter and volume, and the species and variety of plant seed.
Due to control variables, the experiment’s active and passive constants and constraints may now be ready and the effects of the independent variable (i.e. mineral type) on the dependent variable (i.e. plant growth rate) may now be safely observable.
Non-experimental
Control variables in non-experimental research are similar. However, they’re tailored much more towards validating observations of environmental conditions, natural phenomena, particularly human behaviour.
Non-experimental variables are helpful when potential confounding causative factors (i.e. income, age, gender) may not be removed entirely from samples for ethical, legal, or practical reasons. Instead, they monitor or neutralize data on known causatives.
Example
Suppose there is no lab to conduct the study in, one generous scientist suggests they may borrow their garden and spare (uniform) compost bags as a ‘real’ test bed. However, a few non-experimental design changes are needed first so that this new, natural approach works.
It is impossible to control the rainfall and sunlight each day outdoors. However, they may be tracked instead by converting the set of experimental variables into monitored, non-experimental categories. Statistical reasoning (i.e. mean value calculation) allows the scientists to work out expected ‘real’ growth ranges for the plants they study, limiting bias.
How to control variables
Three advanced techniques that use control variables help remove bias from sample sets – if applied correctly. Here is how they work.
Random assignment
It is simply impossible to consider and even find all possible confounding variables and other uncontrolled variables in an experiment. This is why researchers often assign subjects to their groups randomly, spreading the possible factors equally.
Example
The unfortunate scientists double-check their equipment to find they have accidentally purchased three different varieties of tomato seed packet plant – ‘Medium-Mato’, ‘Mini-Mato’, and ‘Mega-Mato’ (x 100). Luckily, the scientists also found past papers indicating these three varieties map comfortably onto a standard natural distribution. This means that even a not scientifically bred tomato plant could theoretically produce all three sizes of tomatoes.
The researchers quickly pour the seeds into a container and shake it thoroughly for ten minutes before selecting exactly 100 samples (c.33%). Here, randomization guards against confounding influence.
Modern statistical studies may use a digital database to calculate random samples rather than a simple glass jar. Demographic weighting (i.e. stratification) might also be applied to better model divided random populations.
Standardized procedures
Experiments always have a standardized procedure to follow, so not even time can impact the results. A steady sequence of tasks also ensures that other chores of the experiment are also always conducted in the same way.
Example
In the plant growth experiment, a standardized procedure can be the exact time the plants are watered. When the experiment is continued over a period of time, the lighting should also be considered. One option would be to ensure constant artificial light on the same level according to one day cycle or another option would be to use the natural daylight, which fluctuates over the course of a year.
Statistical controls
Sometimes, removing all traces of extraneous influence is impossible. By applying modelling , weighting, and averaging based on what’s known about the factors you’re trying to account for, a more realistic statistical picture may emerge. This means, that the predictor variable, also called an independent variable, is complemented by the control variables. Suitable statistical techniques are a simple linear regression analysis and ANOVA.
Control variables vs. Control groups
Control variables shouldn’t be confused with control groups! Control groups are governed by control variables, allowing the creation of a ‘neutral’ subsample.
| Control Variables: | Control Groups: |
| Set a distinct rule or base value for a variable causative factor. | Are single-study groups that create a frame of reference for other samples. |
| Remain completely consistent across time. | Are controlled by variables. |
| Can affect sets or a singular value. | Do not directly affect statistical results. |
| Are not created from a sampled population. | Are always created from a population of samples. |
| May change over time. |
Other types of variables
Other types of variables in experiments are:
- independent variables
- dependent variables
- Qualitative variables
- Categorical variables
- continuous variables
- interval variables and ratio variables
- binary variables
- mediator variable
- moderator variable
FAQs
A control variable is a scientific safeguard that details a factor in a study that should (ideally) be kept the same. It can also set out what factors should be accounted for and excluded from causative arguments.
Control variables add immense statistical power and validity. They are an easy-to-use, effective way to guard against confounding factors that might warp our understanding of a complex topic.
Variables can be controlled through randomized assignment of research subjects into the test group, spreading possible influences evenly. Another method can be standardized procedures, ensuring the continuity of the impact. And if these are not enough, statistical methods such as regression analysis and ANOVA can be helpful.
