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Heteroscedasticity Test in Regression Analysis

Heteroscedasticity Test in Regression Analysis

Homoscedasticity is a nasty word that means that the variable’s residual (error) exhibits consistent variance across different levels of the variable. There are good reasons for desiring this. For more information, see Hair et al. 2010 chapter 2. 🙂 A simple way to determine if a relationship is homoscedastic is to do a simple scatter plot with the variable on the y-axis and the variable’s residual on the x-axis. To see a step by step guide on how to do this, watch the video tutorial. If the plot comes up with a consistent pattern – as in the figure below, then we are good – we have homoscedasticity! If there is not a consistent pattern, then the relationship is considered heteroskedastic. This can be fixed by transforming the data or by splitting the data by subgroups (such as two groups for gender). You can read more about transformations in Hair et al. 2010 ch. 4.

Schools of thought on homoscedasticity are still out. Some suggest that evidence of heteroskedasticity is not a problem (and is actually desirable and expected in moderated models), and so we shouldn’t worry about testing for homoscedasticity. I never conduct this test unless specifically requested to by a reviewer.

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