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Informationen zum Autor Robert Kaufman (PhD University of Wisconsin, 1981) is professor of sociology and the Chair of the Department of Sociology at Temple University. His substantive research focuses on economic structure and labor market inequality, especially with respect to race, ethnicity, and gender. He has also explored other realms of race-ethnic inequality, including research on wealth, home equity, residential segregation, traffic stops and treatment by police, and media portrayals of crime. More abstract statistical issues motivate some of his current work on evaluating different methods for correcting for heteroskedasticity using Monte Carlo simulations. Dr. Kaufman has published papers on quantitative methods in American Sociological Review, American Journal of Sociology, Sociological Methodology, Sociological Methods and Research , and Social Science Quarterly . He served on the editorial board of Sociological Methods and Research for 15 years and has taught graduate-level statistics courses nearly every year for the past 30 years. Klappentext Heteroskedasticity in Regression: Detection and Correction, by Robert Kaufman, 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 monograph offers three approaches for dealing with heteroskedasticity: (1) variance-stabilizing transformations of the dependent variable; (2) calculating robust standard errors, or heteroskedasticity-consistent standard errors; and (3) 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. Zusammenfassung This text covers the consequences of violating one of the key assumptions of Ordinary Least Squares regression (equal error variances)! diagnostic tools to assess the existence of the problem of heteroskedasticity! and statistical techniques to analyse the data correctly. Inhaltsverzeichnis Series Editor¿s Introduction About the Authors Acknowledgements 1. What Is Heteroskedasticity and Why Should We Care? 2. Detecting and Diagnosing Heteroskedasticity 3. Variance-Stabilizing Transformations To Correct For Heteroskedasticity 4. Heteroskedasticity Consistent (Robust) Standard Errors 5. (Estimated) Generalized Least Squares Regression Model For Heteroskedasticity 6. Choosing Among Correction Options References Appendix: Miscellaneous Derivations and Tables ...
