Customer Reviews

Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences)

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This book is an ideal, comprehensive short reference for regression diagnostics that has most or all of the techniques in one place. John Fox is the current master guru of regression, and his writings are very authoritative. Very useful desk reference for the practicing statistician, but perhaps not totally accessible to the beginning learner.

Multiple regression is a powerful and useful statistical technique. It and its derivative techniques are workhorses in the social sciences. Much of my own quantitative research is based on the use of regression. There are real strengths of this method: the results have pretty straightforward interpretation; you can control for a wide variety of variables; software (such as SPSS) makes it easy as pie to run.

However, there are problems with multiple regression that the user needs to be aware of. For example, if two independent variables are highly intercorrelated, you may get--as one result--strange results. High intercorrelations are indicators of the dread problem of multicollinearity. Hence, when running a regression, one would want to test for this effect. Thankfully, programs like SPSS allow one to test for multicollinearity in a variety of ways. Just so, outliers (extreme scores) can throw off results. The researcher can check to see if there are such outliers. Other problems for regression discussed in this slender volume: non-normality, heteroscedasticity, and nonlinearity. The good thing is that contemporary statistical software allows one to check these out.

So, a good resource for the person wanting to run multiple regression while making sure that the data do not confound their results.