My past several posts have detailed confounding variables, a problem you might encounter in research or quality improvement projects.
To recap, confounding variables are correlated predictors. Leaving a confounding variable out of a statistical model can make an included predictor look falsely insignificant or falsely significant. In other words, they can totally flip your statistical analysis results on its head!
To find lurking confounding variables, you must take the time to understand your data and the important variables that may influence a process. Background research and solid subject-area knowledge can help you navigate data difficulties. You should also measure and include everything that you think is important.
Of course, understanding and measuring everything of importance may not be possible due to time and cost constraints. Indeed, all of the relevant variables may not be known or even measurable. What to do?
There is a simple solution to this complex problem. You can wave the white flag and admit that you don’t know everything, or at least that you can’t measure everything that affects your response. You randomize!
Randomness plays several important roles in the design of experiments. In this case, we’re talking about random assignment, which is different than random selection.
- Random selection is how you draw the sample for your study. This allows you to make unbiased inferences about the population based on your sample.
- Random assignment is how you assign the sample to the control and treatment groups in your experiment. This allows you to make causal conclusions about the effect of one variable on another variable.
Random assignment might involve flipping a coin, drawing names out of a hat, or using random numbers. All subjects should have the same probability of being assigned to any group. This process helps assure that the groups are similar to each other when treatment begins. Therefore, any post-study differences between groups shouldn’t be due to prior differences.
Let’s work through an example and see how it combats confounding variables. Take the biomechanics study where we wanted to see if the jumping exercise (treatment group) produced greater bone density than the group that didn’t jump (control group). Further, let’s assume that greater physical activity is correlated with increased bone density but we didn’t measure it. We’ll compare 2 scenarios.
Scenario 1: We don’t use random assignment and, unbeknownst to us, the more physically active subjects end up in the treatment group. The treatment group starts out more active than the control group. Because activity increases bone density, the higher activity in the treatment group may account for the greater bone density compared to the less active control group. Because it is not in the model, activity is a confounding variable that makes the jumping exercise appear to be significant when it might not be.
Scenario 2: We use random assignment so the treatment and control groups start out with roughly equal levels of physical activity. Activity still affects bone density but it is equally spread across the groups. Indeed, the groups are roughly equal in all ways except for the jumping exercise in the treatment group. If the treatment group has a significantly higher bone density, it’s almost certainly due to the jumping exercise.
For both scenarios, the data and statistical results could be identical. However, the results for the second scenario are more valid thanks to the methodology.
Random assignment helps protect you from the perils of confounding variables and competing explanations. However, you can’t always implement random assignment. For the bone density study, we did randomly assign the subjects to the treatment or control group. However, when I used the data from that study to look for patterns amongst the subjects who developed knee pain, I couldn’t randomly assign them to higher and lower calcium intake groups! This highlights one of the pitfalls of ad hoc data analysis.
We’ve detailed the negative aspects of confounding variables here and in my last several posts. However, confounding variables have a potential upside. They don’t sound quite so threatening when you think of them as proxy variables, which we’ll cover in my next post.
Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group.
Study participants are randomly assigned to different groups, such as the experimental group, or treatment group. Random assignment might involve such tactics as flipping a coin, drawing names out of a hat, rolling dice, or assigning random numbers to participants.
It is important to note that random assignment differs from random selection. While random selection refers to how participants are randomly chosen to represent the larger population, random assignment refers to how those chosen participants are then assigned to experimental groups.
How Does Random Assignment Work in a Psychology Experiment?
To determine if changes in one variable lead to changes in another variable, psychologists must perform an experiment. Researchers often begin by forming a testable hypothesis predicting that one variable of interest will have some impact on another variable.
The variable that the experimenters will manipulate in the experiment is known as the independent variable while the variable that they will then measure is known as the dependent variable. While there are different ways to look at relationships between variables, and experiment is the best way to get a clear idea if there is a cause-and-effect relationship between two or more variables.
Once researchers have formulated a hypothesis, conducted background research, and chosen an experimental design, it is time to find participants for their experiment. How exactly do researchers decide who will be part of an experiment? As mentioned previously, this is often accomplished through something known as random selection.
In order to generalize the results of an experiment to a larger group, it is important to choose a sample that is representative of the qualities found in that population. For example, if the total population is 51 percent female and 49 percent male, then the sample should reflect those same percentages. Choosing a representative sample is often accomplished by randomly picking people from the population to be participants in a study. Random selection means that everyone in the group stands and equal chance of being chosen.
Once a pool of participants has been selected, it is time to assign them into groups. By randomly assigning the participants into groups, the experimenters can be sure that each group will be the same before the independent variable is applied.
Participants might be randomly assigned to the control group, which does not receive the treatment in question. Or they might be randomly assigned to the experimental group, which does receive the treatment. Random assignment increases the likelihood that the two groups are the same at the outset, that way any changes that result from the application of the independent variable can be assumed to be the result of the treatment of interest.
An Example of Random Assignment
Imagine that a researcher is interested in learning whether or not drinking caffeinated beverages prior to an exam will improve test performance. After randomly selecting a pool of participants, each person is randomly assigned to either the control group or the experimental group. The participants in the control group consume a placebo drink prior to the exam that does not contain any caffeine. Those in the experimental group, on the other hand, consume a caffeinated beverage before taking the test. Participants in both groups then take the test and the researcher compares the results to determine if the caffeinated beverage had any impact on test performance.
A Word From Verywell
Random assignment plays an important role in the psychology research process. Not only does this process help eliminate possible sources of bias, it also makes it easier to generalize the results of a population to a larger population.
Random assignment helps ensure that members of each group in the experiment are the same, which means that the groups are also likely more representative of what is present in the larger population. Through the use of this technique, psychology researchers are able to study complex phenomena and contribute to our understanding of the human mind and behavior.
Alferes, VR. Methods of Randomization in Experimental Design. Los Angeles: SAGE; 2012.
Nestor, PG & Schutt, RK. Research Methods in Psychology: Investigating Human Behavior. Los Angeles: SAGE; 2015.