I. Correlational Research Strategy

A. Relationships

No attempt to manipulate or control variables

Measuring 2 or more variables as they exist naturally

Goal: Establish that a relationship exists between the variables & describe the nature of that relationship

Pearson’s product-moment correlation coefficient (r): -1 to 0 to +1 (direction & magnitude)

scatterplots: graphical displays of the relationship between two variables

Examples of different values for linear correlations: (a) shows a strong positive relationship, approximately +0.90; (b) shows a relatively weak negative correlation, approximately –0.40; (c) shows a perfect negative correlation, –1.00; (d) shows no linear trend, 0.00.

B. Predictions

Using regression analysis, we can predict values on y, given values on x

The distance between the actual data point (Y) and the predicted point on the line (Ŷ) is defined as Y – Ŷ. The goal of regression is to find the equation for the line that minimized these distances.

C. Interpreting Correlational Research

Caution:

Correlation does not imply causation. Why not?

Because the following 2 problems exist in correlational research:

(1) Third-variable problem

(2) The directionality problem

Argument: The more golf the members of a country play, the more peaceful the nation is. Therefore, playing golf causes peace. http://partners.nytimes.com/library/magazine/home/20000604mag-idealab.html

The Third-Variable Problem

The Directionality Problem

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