This volume covers the commonly ignored topic of heteroskedasticity (unequal error variances) in regression analyses and provides a practical guide for how to proceed in terms of testing and correction. Emphasizing how to apply diagnostic tests and corrections for heteroskedasticity in actual data analyses, the book offers three approaches for dealing with heteroskedasticity:

variance-stabilizing transformations of the dependent variable; calculating robust standard errors, or heteroskedasticity-consistent standard errors; and; generalized least squares estimation coefficients and standard errors.

The detection and correction of heteroskedasticity is illustrated with three examples that vary in terms of sample size and the types of units analyzed (individuals, households, U.S. states). Intended as a supplementary text for graduate-level courses and a primer for quantitative researchers, the book fills the gap between the limited coverage of heteroskedasticity provided in applied regression textbooks and the more theoretical statistical treatment in advanced econometrics textbooks.

Detecting and Diagnosing Heteroskedasticity

The starting place for assessing if heteroskedasticity might be present is to think about the nature of your data and variables to determine if you should a priori suspect that there might be heteroskedasticity. Are the data in your analysis characterized by any of the three common situations in which heteroskedasticity is likely that I discussed in Chapter 1? Specifically, are you using an aggregated or geographic unit of analysis in which the dependent variable is a summary statistic calculated from the cases within that unit of analysis? Does your model involve a comparison among social groups or social categories? Or is the distribution of your dependent variable highly skewed or unusual in some fashion? If the answer to either of ...

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