More Statistical and Methodological Myths and Urban Legends [Hardcover]

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 cant 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 Legendscatalogs 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.

Part I: General Issues  1.  Is Ours a Hard Science (And Do We Care)? Ronald S. Landis and Jos? M. Cortina  2. Publication Bias: Understanding the Myths Concerning Threats to the Advancement of Science George C. Banks, Sven Kepes, and Michael A. McDaniel  Part II: Design Issues  3. Red-Headed No More: Tipping Points in Qualitative Research in Management Anne D. Smith, Laura T. Madden, and Donde Ashmos Plowman  4. Two Waves of Measurement Do Not a Longitudinal Study Make Robert E. Ployhart, and William I. MacKenzie Jr.  5. 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, and W. Keith Campbell  6. Negatively Worded Items Negatively Impact Survey Research Dev K. Dalal and Nathan T. Carter  7. Missing Data Bias: Exactly How BadlÕ