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Statistical and methodological myths and urban legends : doctrine, verity and fable in the organizational and social sciences / edited by Charles E. Lance & Robert J. Vandenberg

By: Contributor(s): Material type: TextTextPublication details: New York ; London : Routledge, 2009Description: xix, 412 s. : illISBN:
  • 9780805862379 (hardcover)
  • 0805862374 (hardcover)
  • 9780805862386 (pbk.)
  • 0805862382 (pbk.)
Subject(s): DDC classification:
  • 300.72 22
LOC classification:
  • HD30.4 .S727 2009
Other classification:
  • O:dd
Online resources:
Contents:
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 ... [et al.]. Chopped liver? -- OK. Chopped data? -- Not OK / Marcus M. Butts and Thomas W.H. Ng.
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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 ... [et al.]. Chopped liver? -- OK. Chopped data? -- Not OK / Marcus M. Butts and Thomas W.H. Ng.

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