The Experimental Method:
(Independent & Dependent Variables)
Confounded Variables:
A confound occurs when you select an IV and another variable varies with it. What then is the cause of the effect on the DV?
Example:
J The Study: The Effects of Race on Guilty/Not Guilty Decisions in Jury Trials for DWI (Drinking While Intoxicated).
J Your Hypothesis: People will more easily vote to convict people of different races.
J Method: Set up mock trials with juries and defendants of similar or varying races.
L Confound: In the USA, unfortunately, income can vary according to race. Perhaps, people vote to convict folks of different incomes. If you just take a random sample of different ethnic groups - you might have a confounded study. See the graphic below.
Solution: Make sure all incomes and races are represented in your groups.
Quantitative vs. Categorical Variables
You might want to review Workshop #1 on various data types. Please remember that your independent or dependent variable might be of any type. Experimental conditions (IVs) might be from a numerical scale like the dosage of a drug used to treat depression. However, the IV could be from a categorical scale when you compare two chemically different drugs to treat depression. Be sure when using Quantitative Variables to pay attention to the difference between Discrete (limited to certain values) and Continuous Variables (falls on a continuum and is not limited to certain values).
Similarly, you could have a numerically scaled dependent variable like reaction time or a categorically scaled dependent variable like choice of favorite food.
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