Lance C.E., Vandenberg R.J. More Statistical and Methodological Myths and Urban Legends

NY: Routledge, 2015. — 357 p. — ISBN: 978-0-415-83898-6.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.Charles E. Lance and Robert J. VandenbergGeneral Issues
Ronald S. Landis and José M. Cortina
Is Ours a Hard Science (and Do We Care)?
George C. Banks, Sven Kepes and Michael A. McDaniel
Publication Bias: Understanding the Myths Concerning Threats to the Advancement of Science
Design Issues
Anne D. Smith, Laura T. Madden and Donde Ashmos Plowman
Red-Headed No More: Tipping Points in Qualitative Research in Management
Robert E. Ployhart and William I. MacKenzie Jr.
Two Waves of Measurement Do Not a Longitudinal Study Make
Brittany Gentile, Lauren A. Wood, Jean M. Twenge, Brian J. Hoffman and W. Keith Campbell
The Problem of Generational Change: Why Cross-Sectional Designs Are Inadequate for Investigating Generational Differences
Dev K. Dalal and Nathan T. Carter
Negatively Worded Items Negatively Impact Survey Research
Daniel A. Newman and Jonathan M. Cottrell
Missing Data Bias: Exactly How Bad Is Pairwise Deletion?
Scott Tonidandel, Eleanor B. Williams and James M. LeBreton
Size Matters...Just Not in the Way that You Think: Myths Surrounding Sample Size Requirements for Statistical Analyses
Analytical Issues
Frederick L. Oswald, Dan J. Putka and Jisoo Ock
Weight a Minute...What You See in a Weighted Composite Is Probably Not What You Get!
Herman Aguinis and Harry Joo
Debunking Myths and Urban Legends about How to Identify Infl uential Outliers
Joel Koopman, Michael Howe and John R. Hollenbeck
Pulling the Sobel Test Up By Its Bootstraps
Inferential Issues
Dan J. Putka and Brian J. Hoffman
“The” Reliability of Job Performance Ratings Equals 0.52
Charles E. Lance and Allison B. Siminovsky
Use of “Independent” Measures Does Not Solve the Shared Method Bias Problem
Alexander C. LoPilato and Robert J. Vandenberg
The Not-So-Direct Cross-Level Direct Effect
David J. Woehr, Andrew C. Loignon and Paul Schmidt
Aggregation Aggravation: The Fallacy of the Wrong Level Revisited
Neal Schmitt and Abdifatah A. Ali
The Practical Importance of Measurement Invariance