PED lecture notes

Non experimental design correlational study partial

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Non-Experimental Design: Correlational Study Partial correlation. The calculation is complicated but the idea of partial correlation is simple. It is an estimate of the correlation between two variables in a population that is homogeneous on the variable (or variables) that is being controlled, whose effects are being removed, or variability on this variable is made into a constant. For example, a correlation between height and intelligence computed out of a sample that is heterogenous on age, say, ranging from 4 to 15, is a simple correlation which is high and positive. A partial correlation would be an average correlation between height and intelligence within each age group where age is a constant. This partial correlation which is likely zero more truly depicts the relationship between height and intelligence. Spurious effect. When two variables are correlated solely because they are both affected by the same cause, the correlation between these two variables is spurious. For example, the tobacco industry argues that the correlation between cigarette smoking and lung disease is not causal but spurious in that both these variables may be caused by a common third factor such as stress or an unhappy mental state. Another example will be the positive correlation between height and intelligence often observed in children. Here, the correlation is again spurious because both variables have the common cause of chronicle age. Mediating variable. The correlation between two variables can be the result of a mediating variable. For example, a strong correlation between SES and academic achievement is often observed and makes some people believe that there is a causal relationship between how rich the parents are and how well the kids do in school. However, such a relationship is now found to be mediated by a third variable, Achievement motivation. That is rich people's children are more motivated to study (by their parents' success) and this motivation leads to good academic performance. This later finding is achieved by correlating SES and Achievement while statistically partialling out Motivation. The correlation is almost zero. The important implication of this statistical insight is that the key lies in motivating the poor kids (providing them with role models) whereas giving them material incentives may not make them study. Suppressor variable. A special case when a partial correlation is larger than its zero- order correlation is called a suppressor variable effect. A suppressor variable is a variable that has a zero, or close to zero, correlation with the criterion or dependent variable but is correlated with the predictor or independent variable. When such suppressor variable is not taken into consideration, the correlation between the independent and dependent variable may be "suppressed" or reduced by this uncontrolled suppressor. For example, a paper-and- pencil pilot test as a predictor was found to predict little of the criterion, flying. The correlation was suppressed by a third variable, verbal ability, which has little to do with flying but a lot to do with test taking. When this suppressor variable was partialled out, the

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correlation between the pilot test and piloting increased significantly.
• Fall '11
• John Smith
• external validity

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