Control variables play a pivotal role in scientific methodologies, ensuring the integrity of research outcomes. These are specific 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.
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Control Variables – In a Nutshell
- Control variables let us control an experiment’s conditions via set criteria by improving statistical repeatability, validity, and applicability.
- Control variables protect the independent and dependent variables of the study from undue bias (e.g. confounding causative variables).
- How and which variables are selected depends on the topic and mode of study.
- Control variables are expressed as simple quantities (numerical) or qualitative statements.
- Experimental variables govern ‘test lab’ conditions via stabilization.
- Non-experimental variables help eliminate bias in active (wild) observation, where complete control is impossible.
Definition: Control variables
Control variables are essential empirical tools. They help us create replicable, verifiable data (i.e. statistics) from direct experimentation, observation, and sampling by setting hard limits. They also allow us to bypass ‘false’ causatives in observational studies.
Variables may be expressed as a qualitative or quantitative statement and may be limited (i.e. within a set range) or total. Integers (i.e. whole numbers) are usual for statistical controls. A single study may use many different control variables.
The importance of control variables
Control variables are crucial, as they greatly enhance an experiment’s internal validity. When you’re assessing a study’s statistical power, internal validity plays an important role.
Why? Quality, empirical statistics must reflect ‘control’ conditions relevant to that topic (i.e. model reality) to their absolute best. Improving internal validity means a study becomes more reliable, repeatable, broadly applicable, and likely to survive intense peer review.
Using control variables in experiments
Experimental control values let us isolate the dependent and independent variables in a closed environment. They’re 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.
Using control variables in non-experimental studies
Non-experimental control variables are similar. However, they’re tailored much more towards validating observations of natural phenomena, particularly human behavior.
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 a freak lightning strike incinerates the carefully planned plant lab. The paper is due for submission in two weeks. There’s no money left. How will the researchers create useable statistics now?
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’s 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’s how they work.
Control variable methodology – Random assignment
If you have a set prone to outliers or clusters with wildly different behavior, you may want to make sure ‘lucky’ or ‘unlucky’ samples don’t skew your findings.
Researchers may use variables to ‘scramble’ set populations containing biased sub-sets, offsetting sampling bias. Random assignment ensures that sub-samples, with a balanced demographic ratio, occur. Pure luck determines which sample points are selected.
Here’s another example.
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). A conundrum!
Luckily, the scientists also find past papers indicating these three varieties map comfortably onto a standard natural distribution. 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.
Control variable methodology – Standardized procedures
Do you have a repetitive daily routine that governs your time? Experiments often do. The exact time and way something must be delivered may have surprising, unforeseen effects on a subject.
It’s therefore critical that all manual test procedures remain uniform. We may set variables as timed and listed instructions to ensure this happens. They may also exclude any eccentric behavior that might skew the tests.
Let’s look at our final practical example.
Example
Forward-thinking researchers set an additional control rule that outside plants should be watered at 10 AM and 5 PM if it hasn’t rained for two consecutive days. They also specify that no researchers should substitute cups of tea for rain-trough water as an exclusionary control.
Control variable methodology – Statistical controls
If everything else fails? You can apply statistical control methods to limit bias via final analysis.
Sometimes, removing all traces of extraneous influence is impossible. By applying modeling, weighting, and averaging based on what’s known about the factors you’re trying to account for, a more realistic statistical picture may emerge.
Applying multiple linear regression may help. By using averaged predictors as hypotheticals to weight and limit your values, trends and correlations may be better isolated.
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’ sub-sample.
| 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. | may change over time. |
| aren't created from a sampled population. | don't directly affect statistical results - outside of analysis. |
| are always created from a population of samples. |
FAQs
A scientific safeguard that details a factor in a study that should (ideally) be kept the same. Control variables can also set out what factors should be accounted for and excluded from causative arguments.
Control variables add immense statistical power and validity. They’re an easy-to-use, effective way to guard against confounding factors that might warp our understanding of a complex topic.
A good set of variables may create an unchanging, dependable ‘test chamber’. Within, researchers may modify an independent variable to see how it affects a dependent one without risk.