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Tests of Associations
Tests of association are based on the correlation among two or more variables.  A simple (zero-order) correlation can vary between -1 and +1 with a correlation of 0 meaning there is no relationship among two variables.  A negative correlation means that as values on one variable rise, those on the other fall.  With a positive correlation, increases on one variable are associated with increases on the other variable.  Usually, measurement error results in correlations that are imperfect (i.e., not -1 or +1), but correlations of 0.8 or above, can be considered high.  Correlations of 0.2 are relatively weak, those of 0.4-0.6 are moderate.

When squared, a correlation tells you how much of the variability in the two variables is shared.  With a correlation of 1.0, there is perfect overlap.  With a correlation of 0.6, 36% of the variance is shared (overlaps).  The higher the correlation, the more that knowing the score on one variable tells you about scores on the other variable.

An Important Note on Causation
Do not mistake this for causation.  When one variable "causes" another, then they will be highly correlated.  However, variables can correlate for many reasons, sometimes because a third variable is operating.  And, even when highly correlated, we do not know the direction of the relationship.  Simple correlations give us important information, but more complex research designs are needed to imply causation.

There are many extensions of the correlational approach that are used in research.  One common approach is referred to as regression analysis.  In this approach, the correlations among a set of predictor variables and an outcome variable can be computed.  For example, we can look at the independent contribution of a series of variables (e.g., time with the company, satisfaction with compensation and benefits, ratings of "my manager", etc.) to an outcome variable (e.g., "employee satisfaction").  This approach can help us to better understand the relationship among different variables and give us some indication of which ones are more strongly related to our desired outcomes.

This type of approach can be further extended to develop highly sophisticated procedures for statistical modeling.  Statistical modeling can help us to understand the complex interplay that underlies business relationships.  Click on the link below to learn more about statistical modeling.

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