Example: Random assignmentIn your experiment, you recruit volunteers through social media ads, word of mouth, and flyers on campus. This method of assignment controls participant variables that might otherwise differ between groups and skew your results. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them. In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational or quasi-experimental designs. To account for other factors that are likely to influence the results, you also measure these control variables: Instead, you use a survey to collect data about income and happiness. You hypothesize that income level predicts happiness, but it’s not practically possible to manipulate the variable of income. Example: Non-experimental designYou want to investigate whether there’s a relationship between the variables of income and happiness. Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.
In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations). To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:Ĭontrol variables in non-experimental research The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness. You design an experiment with a control group that receives a placebo pill, and an experimental group that receives the supplement.
Example: ExperimentYou want to study the effectiveness of vitamin D supplements on improving alertness. Control variables help you ensure that your results are solely caused by your experimental manipulation. In an experiment, a researcher is interested in understanding the effect of an independent variable on a dependent variable. Uncontrolled variables are alternative explanations for your results. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. This helps you establish a correlational or causal relationship between your variables of interest.Īside from the independent and dependent variables, all variables that can impact the results should be controlled. Frequently asked questions about control variablesĬontrol variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables.Examples of control variables Research questionĭo people with a fear of spiders perceive spider images faster than other people?
Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests). It’s a variable that is not of interest to the study’s aims, but is controlled because it could influence the outcomes. non-probability samplingĬontrol Variables | What Are They & Why Do They Matter?Ī control variable is anything that is held constant or limited in a research study.
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