Statistical issues -- Missing data techniques and low response rates : the role of systematic nonresponse parameters / Daniel A. Newman -- The partial revival of a dead horse? : comparing classical test theory and item response theory / Michael J. Zickar and Alison A. Broadfoot -- Four common misconceptions in exploratory factor analysis / Deborah L. Bandalos and Meggen R. Boehm-Kaufman -- Dr. StrangeLOVE, or : how I learned to stop worrying and love omitted variables / Adam W. Meade, Tara S. Behrend, and Charles E. Lance -- The truth(s) on testing for mediation in the social and organizational sciences / James M. LeBreton, Jane Wu, and Mark N. Bing -- Seven deadly myths of testing moderation in organizational research / Jeffrey R. Edwards -- Alternative model specifications in structural equation modeling : facts, fictions, and truth / Robert J. Vandenberg and Darrin M. Grelle -- On the practice of allowing correlated residuals among indicators in structural equation models / Ronald S. Landis, Bryan D. Edwards, and Jose M. Cortina -- Methodological issues -- Qualitative research : the redheaded stepchild in organizational and social science research? / Lillian T. Eby, Carrie S. Hurst, and Marcus M. Butts -- Do samples really matter that much? / Scott Highhouse and Jennifer Z. Gillespie -- Sample size rules of thumb : evaluating three common practices / Herman Aguinis and Erika E. Harden -- When small effect sizes tell a big story, and when large effect sizes don't / Jose M. Cortina and Ronald S. Landis -- So why ask me? : are self-report data really that bad? / David Chan -- If it ain't trait it must be method : (mis)application of the multitrait-multimethod design in organizational research / Charles E. Lance [and others] -- Chopped liver? OK. Chopped data? Not OK / Marcus M. Butts and Thomas W.H. Ng.
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SUMMARY OR ABSTRACT
Text of Note
This book provides an up-to-date review of commonly undertaken methodological and statistical practices that are sustained, in part, upon sound rationale and justification and, in part, upon unfounded lore. Some examples of these 'methodological urban legends' as we refer to them in this book are characterized by manuscript critiques such as: 'your self-report measures suffer from common method bias'; 'your item-to-subject ratios are too low'; 'you can't generalize these findings to the real world'; and, 'your effect sizes are too low'. Historically, there is a kernel of truth to most of these legends, but in many cases that truth has been long forgotten, ignored or embellished beyond recognition. This book examines several such legends. Each chapter is organized to address: what is the legend that 'we (almost) all know to be true'; what is the 'kernel of truth' to each legend; what are the myths that have developed around this kernel of truth; and, what should the state of the practice be. This book meets an important need for the accumulation and integration of these methodological and statistical practices.