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More statistical and methodological myths and urban legends
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | More statistical and methodological myths and urban legends/ edited by Charles E. Lance and Robert J. Vandenberg. |
| 개인저자 | Lance, Charles E.,1954- editor, Vandenberg, Robert J.,editor, |
| 형태사항 | 1 online resource (x, 357 pages): illustrations. |
| 기타형태 저록 | Print version: More statistical and methodological myths and urban legends. New York, NY : Routledge, 2015 9780415838986 9780415838993 |
| ISBN | 1135039437 9781135039431 9780203775851 0203775856 |
| 서지주기 | Includes bibliographical references and index. |
| 내용주기 | Cover; Title; Copyright; CONTENTS; List of Contributors; Introduction; PART I General Issues; 1 Is Ours a Hard Science (and Do We Care)?; 2 Publication Bias: Understanding the Myths Concerning Threats to the Advancement of Science; PART II Design Issues; 3 Red-Headed No More: Tipping Points in Qualitative Research in Management; 4 Two Waves of Measurement Do Not a Longitudinal Study Make; 5 The Problem of Generational Change: Why Cross-Sectional Designs Are Inadequate for Investigating Generational Differences; 6 Negatively Worded Items Negatively Impact Survey Research. 7 Missing Data Bias: Exactly How Bad Is Pairwise Deletion?8 Size Matters ... Just Not in the Way that You Think: Myths Surrounding Sample Size Requirements for Statistical Analyses; PART III Analytical Issues; 9 Weight a Minute ... What You See in a Weighted Composite Is Probably Not What You Get!; 10 Debunking Myths and Urban Legends about How to Identify Influential Outliers; 11 Pulling the Sobel Test Up By Its Bootstraps; PART IV Inferential Issues; 12 "The" Reliability of Job Performance Ratings Equals 0.52. 13 Use of "Independent" Measures Does Not Solve the Shared Method Bias Problem14 The Not-So-Direct Cross-Level Direct Effect; 15 Aggregation Aggravation: The Fallacy of the Wrong Level Revisited; 16 The Practical Importance of Measurement Invariance; Index. |
| 요약 | 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 Metho. |
| 통일서명 | Statistical and methodological myths and urban legends. |
| 일반주제명 | Organization --Research --Methodology. Organization --Research --Statistical methods. Social sciences --Statistical methods. Social sciences --Research --Statistical methods. SOCIAL SCIENCE --Essays. SOCIAL SCIENCE --Reference. Organization --Research --Methodology. Organization --Research --Statistical methods. Social sciences --Research --Statistical methods. Social sciences --Statistical methods. |
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