6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

September 17th, 2009 by

The assumptions of normality and constant variance in a linear model (both OLS regression and ANOVA) are quite robust to departures.  That means that even if the assumptions aren’t met perfectly, the resulting p-values will still be reasonable estimates.

But you need to check the assumptions anyway, because some departures are so far that the p-value become inaccurate.  And in many cases there are remedial measures you can take to turn non-normal residuals into normal ones.

But sometimes you can’t.

Sometimes it’s because the dependent variable just isn’t appropriate for a linear model.  The (more…)

5 Practical Issues to Consider in Choosing a Statistical Analysis

March 9th, 2009 by

There are 4 questions you must answer to choose an appropriate statistical analysis.

1. What is your Research Question?
2. What is the scale of measurement of the variables used to answer the research question?
3. What is the Design? (between subjects, within subjects, etc.)
4. Are there any data issues? (missing, censored, truncated, etc.)

If you have not already, read about these in more detail.