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| Preface | |
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| About the Editors | |
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| Acknowledgments | |
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| Introduction | |
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| Statistical Issues | |
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| Missing Data Techniques and Low Response Rates: The Role of Systematic Nonresponse Parameters | |
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| Organization of the Chapter | |
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| Levels, Problems, and Mechanisms of Missing Data | |
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| Three Levels of Missing Data | |
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| Two Problems Caused by Missing Data (External Validity and Statistical Power) | |
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| Missingness Mechanisms (MCAR, MAR, and MNAR) | |
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| Missing Data Treatments | |
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| A Fundamental Principle of Missing Data Analysis | |
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| Missing Data Techniques (Listwise and Pairwise Deletion, ML, and MI) | |
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| Systematic Nonresponse Parameters (d[subscript miss] and f[superscript 2 subscript miss]) | |
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| Theory of Survey Nonresponse | |
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| Missing Data Legends | |
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| "Low Response Rates Invalidate Results" | |
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| "When in Doubt, Use Listwise or Pairwise Deletion" | |
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| Applications | |
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| Longitudinal Modeling | |
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| Within-Group Agreement Estimation | |
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| Meta-analysis | |
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| Social Network Analysis | |
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| Moderated Regression | |
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| Conclusions | |
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| Future Research on d[subscript miss] and f[superscript 2 subscript miss] | |
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| Missing Data Techniques | |
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| References | |
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| Appendix | |
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| Derivation of Response Rate Bias for the Correlation (Used to Generate Figure 1.1c) | |
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| The Partial Revival of a Dead Horse? Comparing Classical Test Theory and Item Response Theory | |
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| Basic Statement of the Two Theories | |
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| Classical Test Theory | |
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| Item Response Theory | |
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| Criticisms and Limitations of CTT | |
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| Lack of Population Invariance | |
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| Person and Item Parameters on Different Scales | |
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| Correlations Between Item Parameters | |
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| Reliability as a Monolithic Concept | |
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| Criticisms and Limitations of IRT | |
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| Large Sample Sizes | |
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| Strong Assumptions | |
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| Complicated Programs | |
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| Times to Use CTT | |
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| Small Sample Sizes | |
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| Multidimensional Data? | |
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| CTT Supports Other Methodologies | |
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| Times to Use IRT | |
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| Focus on Particular Range of Construct | |
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| Conduct Goodness-of-Fit Studies | |
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| IRT Supports Many Psychometric Tools | |
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| Conclusions | |
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| References | |
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| Four Common Misconceptions in Exploratory Factor Analysis | |
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| The Choice Between Component and Common Factor Analysis Is Inconsequential | |
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| The Component Versus Common Factor Debate: Methodological Arguments | |
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| The Component Versus Common Factor Debate: Philosophical Arguments | |
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| Differences in Results from Component and Common Factor Analysis | |
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| Orthogonal Rotation Results in Better Simple Structure Than Oblique Rotation | |
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| Oblique or Orthogonal Rotation? | |
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| Do Orthogonal Rotations Result in Better Simple Structure? | |
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| The Minimum Sample Size Needed for Factor Analysis Is... (Insert Your Favorite Guideline) | |
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| New Sample Size Guidelines | |
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| The "Eigenvalues Greater Than One" Rule Is the Best Way of Choosing the Number of Factors | |
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| Discussion | |
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| References | |
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| Dr. StrangeLOVE, or: How I Learned to Stop Worrying and Love Omitted Variables | |
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| Theoretical and Mathematical Definition of the Omitted Variables Problem | |
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| Violated Assumptions | |
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| More Complex Models | |
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| Path Coefficient Bias Versus Significance Testing | |
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| Minimizing the Risk of LOVE | |
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| Experimental Control | |
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| More Inclusive Models | |
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| Use Previous Research to Justify Assumptions | |
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| Consideration of Research Purpose | |
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| References | |
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| The Truth(s) on Testing for Mediation in the Social and Organizational Sciences | |
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| Baron and Kenny's (1986) Four-Step Test of Mediation | |
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| Condition/Step 1 | |
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| Condition/Step 2 | |
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| Condition/Step 3 | |
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| Condition/Step 4 | |
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| The Urban Legend: Baron and Kenny's Four-Step Test Is an Optimal and Sufficient Test for Mediation Hypotheses | |
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| The Kernel of Truth About the Urban Legends | |
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| Debunking the Legends | |
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| A Test of a Mediation Hypothesis Should Consist of the Four Steps Articulated by Baron and Kenny (1986) | |
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| Baron and Kenny's (1986) Four-Step Procedure Is the Optimal Test of Mediation Hypotheses | |
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| Fulfilling the Conditions Articulated in the Baron and Kenny (1986) Four-Step Test Is Sufficient for Drawing Conclusions About Mediated Relationships | |
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| Suggestions for Testing Mediation Hypotheses | |
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| Structural Equation Modeling (SEM) as an Analytic Framework | |
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| Summary of Tests of Mediation | |
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| A Heuristic Framework for Classifying Mediation Models | |
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| Summary | |
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| Conclusion | |
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| Author Note | |
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| References | |
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| Seven Deadly Myths of Testing Moderation in Organizational Research | |
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| The Seven Myths | |
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| Product Terms Create Multicollinearity Problems | |
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| Coefficients on First-Order Terms Are Meaningless | |
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| Measurement Error Poses Little Concern When First-Order Terms Are Reliable | |
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| Product Terms Should Be Tested Hierarchically | |
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| Curvilinearity Can Be Disregarded When Testing Moderation | |
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| Product Terms Can Be Treated as Causal Variables | |
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| Testing Moderation in Structural Equation Modeling Is Impractical | |
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| Myths Beyond Moderation | |
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| Conclusion | |
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| References | |
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| Alternative Model Specifications in Structural Equation Modeling: Facts, Fictions, and Truth | |
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| The Core of the Issue | |
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| AMS Strategies | |
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| Equivalent Models | |
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| Nested Models | |
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| Nonnested Alternative Models | |
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| Summary | |
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| AMS in Practice | |
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| Summary | |
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| References | |
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| On the Practice of Allowing Correlated Residuals Among Indicators in Structural Equation Models | |
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| Unraveling the Urban Legend | |
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| Extent of the Problem | |
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| Origins | |
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| A Brief Review of Structural Equation Modeling | |
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| Indicator Residuals | |
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| Model Fit | |
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| An Example | |
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| Why Correlated IRs Improve Fit | |
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| Problems With Correlated Residuals | |
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| Recommendations | |
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| Summary and Conclusions | |
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| References | |
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| Methodological Issues | |
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| Qualitative Research: The Redheaded Stepchild in Organizational and Social Science Research? | |
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| Definitional Issues | |
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| Philosophical Differences in Qualitative and Quantiative Research | |
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| Quantitative and Qualitative Conceptualizations of Validity | |
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| Caveats and Assumptions | |
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| Beliefs Associated With Qualitative Research | |
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| Qualitative Research Does Not Utilize the Scientific Method | |
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| Qualitative Research Lacks Methodological Rigor | |
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| Qualitative Research Contributes Little to the Advancement of Knowledge | |
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| Evaluating the Beliefs Associated With Qualitative Research | |
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| Qualitative Research Does Not Utilize the Scientific Method | |
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| Qualitative Research Is Methodologically Weak | |
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| Qualitative Research Has Weak Internal Validity | |
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| Qualitative Research Has Weak Construct Validity | |
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| Qualitative Research Has Weak External Validity | |
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| Qualitative Research Contributes Little to the Advancement of Knowledge | |
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| The Future of Qualitative Research in the Social and Organizational Sciences | |
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| Concluding Thoughts | |
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| Author Note | |
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| References | |
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| Do Samples Really Matter That Much? | |
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| Kernel of Truth | |
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| Background | |
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| History of the Concern | |
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| The Research Base | |
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| Why Do Samples Seem to Matter So Much? | |
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| People Confuse Random Sampling With Random Assignment | |
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| People Focus on the Wrong Things | |
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| People Rely on Superficial Similarities | |
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| Concluding Thoughts | |
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| Author Note | |
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| References | |
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| Sample Size Rules of Thumb: Evaluating Three Common Practices | |
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| Determine Whether Sample Size Is Appropriate by Conducting a Power Analysis Using Cohen's Definitions of Small, Medium, and Large Effect Size | |
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| Discussion | |
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| Increase the A Priori Type I Error Rate to .10 Because of Your Small Sample Size | |
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| Discussion | |
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| Sample Size Should Include at Least 5 Observations per Estimated Parameter in Covariance Structure Analyses | |
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| Discussion | |
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| Discussion | |
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| Author Note | |
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| References | |
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| When Small Effect Sizes Tell a Big Story, and When Large Effect Sizes Don't | |
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| Effect Size Defined | |
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| The Urban Legend | |
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| The Kernel of Truth | |
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| Quine and Ontological Relativism | |
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| Contextualization | |
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| Inauspicious Designs | |
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| Phenomena With Obscured Consequences | |
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| Phenomena That Challenge Fundamental Assumptions | |
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| The Flip Side: Trivial "Large" Effects | |
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| Conclusion | |
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| References | |
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| So Why Ask Me? Are Self-Report Data Really That Bad? | |
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| The Urban Legend of Self-Report Data and Its Historical Roots | |
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| Construct Validity of Self-Report Data | |
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| Interpreting the Correlations in Self-Report Data | |
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| Social Desirability Responding in Self-Report Data | |
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| Value of Data Collected From Non-Self-Report Measures | |
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| Conclusion and Moving Forward | |
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| References | |
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| If It Ain't Trait It Must Be Method: (Mis)application of the Multitrait-Multimethod Design in Organizational Research | |
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| Background | |
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| Literature Review | |
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| Range of Traits Studied | |
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| Range of Methods Studied | |
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| Not All "Measurement Methods" Are Created Equal | |
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| The Case of Multisource Performance Appraisal | |
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| The Case of AC Construct Validity | |
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| Other Cases | |
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| So, Are Any "Method" Facets Really Method Facets? | |
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| Discriminating Method From Substance, or "If It Looks Like a Method and Quacks Like a Method..." | |
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| References | |
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| Chopped Liver? OK. Chopped Data? Not OK | |
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| Urban Legends Regarding Chopped Data | |
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| Urban Legends Associated With the Occurrence of Chopped Data | |
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| Urban Legends Associated With Chopped Data Techniques | |
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| Urban Legends Associated With Chopped Data Justifications | |
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| Literature Review | |
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| Chopped Data Through the Years | |
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| Prevalence of Chopped Data | |
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| The Occurrence of Chopped Data Over Time | |
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| Chopped Data Across Disciplines | |
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| Types of Chopped Data Approaches | |
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| Evaluating Justifications for Using Chopped Data | |
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| Insufficient or Faulty Justifications (Myths) | |
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| Legitimate Justifications (Truths) | |
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| Advantages of, Disadvantages of, and Recommendations for Using Chopped Data | |
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| (Perceived) Advantages of Chopping Data | |
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| Disadvantages of Chopping Data | |
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| Recommendations When Faced With Chopping Data | |
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| Conclusion | |
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| References | |
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| Subject Index | |
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| Author Index | |