Individual differences variables are characteristics that cannot be changed, they vary across the population. Examples of independent difference variable are race, gender, religious affiliation and age. For the researcher, it is a variable that they cannot assign an individual to, and therefore are called natural groups design. Research done with natural groups looks for relationships between this description and prediction of outcome. An example of this type of research is given by Shaughnessy, Zechmeister, & Zechmeister (2009), is that divorced individuals are more likely to receive psychiatric care, than married individuals. The description would be the divorced people, and the prediction is seeking psychiatric care. Manipulated independent variables are variables that are able to be altered, like foods eaten, bed time, a drug taken, or television show that is watched. No matter how many different foods someone eats, it will not change their age or religious affiliation.
We need to be cautious when drawing casual inferences from this type of data, for instance, in the above example regarding divorced individuals and psychiatric care, one may be tempted to infer that divorce causes psychiatric problems. The question is, how are we to know which came first? The divorce or the psychiatric illness?
The issue of causality is difficult to determine whenever we haven't actively manipulated a variable. Sometimes the direction of causality seems rather obvious. For example, we know that smoking is associated with lung cancer. It makes more sense to conclude that smoking causes lung cancer than to conclude that people who start smoking because they develop ...
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