Información General

• Autores: Charles E. Lance (coord.), Robert J. Vandenberg (coord.)
• Editores: Taylor & Francis Group, Routledge
• Año de publicación: 2015
• País: Reino Unido
• Idioma: inglés
• ISBN: 9780415838986
• Texto completo no disponible (Saber más ...)

Resumen

• This book provides an up-to-date review of commonly undertaken methodological and statistical practices that are based partially in sound scientific rationale and partially in unfounded lore. Some examples of these “methodological urban legends” are characterized by manuscript critiques such as: (a) “your self-report measures suffer from common method bias”; (b) “your item-to-subject ratios are too low”; (c) “you can’t generalize these findings to the real world”; or (d) “your effect sizes are too low.” What do these critiques mean, and what is their historical basis? More Statistical and Methodological Myths and Urban Legends catalogs several of these quirky practices and outlines proper research techniques. Topics covered include sample size requirements, missing data bias in correlation matrices, negative wording in survey research, and much more.

Otros catálogos

• Red de Bibliotecas Universitarias (REBIUN)

Listado de artículos

• • Part I. General issues

    • Is Ours a Hard Science (And Do We Care)?

      Ronald S. Landis, Jose M. Cortina

      págs. 9-35

      

    • Publication Bias: Understanding the Myths Concerning Threats to the Advancement of Science

      George C. Banks, Sven Kepes, Michael A. McDaniel

      págs. 36-64

      

  • Part II. Design issues

    • Red-Headed No More: Tipping Points in Qualitative Research in Management

      Anne D. Smith, Laura T. Madden, Donde P. Ashmos

      págs. 67-84

      

    • Two Waves of Measurement Do Not a Longitudinal Study Make

      Robert E. Ployhart, William I. MacKenzie Jr.

      págs. 85-99

      

    • The Problem of Generational Change: Why Cross-Sectional Designs Are Inadequate for Investigating Generational Differences

      Brittany Gentile, Lauren A. Wood, Jean M. Twenge, Brian J. Hoffman, W. Keith Campbell

      págs. 100-111

      

    • Negatively Worded Items Negatively Impact Survey Research

      Dev K. Dalal, Nathan T. Carter

      págs. 112-132

      

    • Missing Data Bias: Exactly How Bad Is Pairwise Deletion?

      Daniel A. Newman, Jonathan M. Cottrell

      págs. 133-161

      

    • Size Matters... Just Not in the Way that You Think: Myths Surrounding Sample Size Requirements for Statistical Analyses

      Scott Tonidandel, Eleanor B. Williams, James M. LeBreton

      págs. 162-183

      

  • Part III. Analytical issues

    • Weight a Minute... What You See in a Weighted Composite Is Probably Not What You Get!

      Frederick L. Oswald, Dan J. Putka, Jisoo Ock

      págs. 187-205

      

    • Debunking Myths and Urban Legends about How to Identify Influential Outliers

      Herman Aguinis, Harry Joo

      págs. 206-223

      

    • Pulling the Sobel Test Up By Its Bootstraps

      Joel Koopman, Michael J. A. Howe, John R. Hollenbeck

      págs. 224-243

      

  • Part IV. Inferential issues

    • "The" Reliability of Job Performance Ratins Equals 0.52

      Dan J. Putka, Brian J. Hoffman

      págs. 247-275

      

    • Use of "Independent" Meausres Does Not Solve the Shared Method Bias Problem

      Charles E. Lance, Allison B. Siminovsky

      págs. 276-291

      

    • The Not-So-Direct Cross-Level Direct Effect

      Alexander C. LoPilato, Robert J. Vandenberg

      págs. 292-310

      

    • Aggregation Aggravation: The Fallacy of the Wrong Level Revisited

      David J. Woehr, Andrew C. Loignon, Paul Schmidt

      págs. 311-326

      

    • The Practical Importance of Meaurement Invariance

      Neal Schmitt, Abdifatah A. Ali

      págs. 327-346