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Preface | p. xv |
About the Editors | p. xvii |
Acknowledgments | p. xix |
Introduction | p. 1 |
Statistical Issues | |
Missing Data Techniques and Low Response Rates: The Role of Systematic Nonresponse Parameters | p. 7 |
Organization of the Chapter | p. 8 |
Levels, Problems, and Mechanisms of Missing Data | p. 8 |
Three Levels of Missing Data | p. 9 |
Two Problems Caused by Missing Data (External Validity and Statistical Power) | p. 9 |
Missingness Mechanisms (MCAR, MAR, and MNAR) | p. 9 |
Missing Data Treatments | p. 11 |
A Fundamental Principle of Missing Data Analysis | p. 11 |
Missing Data Techniques (Listwise and Pairwise Deletion, ML, and MI) | p. 13 |
Systematic Nonresponse Parameters (d[subscript miss] and f[superscript 2 subscript miss]) | p. 14 |
Theory of Survey Nonresponse | p. 17 |
Missing Data Legends | p. 21 |
"Low Response Rates Invalidate Results" | p. 21 |
"When in Doubt, Use Listwise or Pairwise Deletion" | p. 24 |
Applications | p. 26 |
Longitudinal Modeling | p. 26 |
Within-Group Agreement Estimation | p. 27 |
Meta-analysis | p. 27 |
Social Network Analysis | p. 28 |
Moderated Regression | p. 29 |
Conclusions | p. 29 |
Future Research on d[subscript miss] and f[superscript 2 subscript miss] | p. 30 |
Missing Data Techniques | p. 31 |
References | p. 31 |
Appendix | p. 35 |
Derivation of Response Rate Bias for the Correlation (Used to Generate Figure 1.1c) | p. 35 |
The Partial Revival of a Dead Horse? Comparing Classical Test Theory and Item Response Theory | p. 37 |
Basic Statement of the Two Theories | p. 38 |
Classical Test Theory | p. 38 |
Item Response Theory | p. 40 |
Criticisms and Limitations of CTT | p. 44 |
Lack of Population Invariance | p. 44 |
Person and Item Parameters on Different Scales | p. 45 |
Correlations Between Item Parameters | p. 46 |
Reliability as a Monolithic Concept | p. 47 |
Criticisms and Limitations of IRT | p. 48 |
Large Sample Sizes | p. 48 |
Strong Assumptions | p. 49 |
Complicated Programs | p. 50 |
Times to Use CTT | p. 50 |
Small Sample Sizes | p. 50 |
Multidimensional Data? | p. 51 |
CTT Supports Other Methodologies | p. 52 |
Times to Use IRT | p. 53 |
Focus on Particular Range of Construct | p. 53 |
Conduct Goodness-of-Fit Studies | p. 53 |
IRT Supports Many Psychometric Tools | p. 55 |
Conclusions | p. 56 |
References | p. 57 |
Four Common Misconceptions in Exploratory Factor Analysis | p. 61 |
The Choice Between Component and Common Factor Analysis Is Inconsequential | p. 62 |
The Component Versus Common Factor Debate: Methodological Arguments | p. 66 |
The Component Versus Common Factor Debate: Philosophical Arguments | p. 68 |
Differences in Results from Component and Common Factor Analysis | p. 69 |
Orthogonal Rotation Results in Better Simple Structure Than Oblique Rotation | p. 71 |
Oblique or Orthogonal Rotation? | p. 71 |
Do Orthogonal Rotations Result in Better Simple Structure? | p. 72 |
The Minimum Sample Size Needed for Factor Analysis Is... (Insert Your Favorite Guideline) | p. 74 |
New Sample Size Guidelines | p. 76 |
The "Eigenvalues Greater Than One" Rule Is the Best Way of Choosing the Number of Factors | p. 79 |
Discussion | p. 83 |
References | p. 85 |
Dr. StrangeLOVE, or: How I Learned to Stop Worrying and Love Omitted Variables | p. 89 |
Theoretical and Mathematical Definition of the Omitted Variables Problem | p. 91 |
Violated Assumptions | p. 96 |
More Complex Models | p. 97 |
Path Coefficient Bias Versus Significance Testing | p. 100 |
Minimizing the Risk of LOVE | p. 102 |
Experimental Control | p. 102 |
More Inclusive Models | p. 103 |
Use Previous Research to Justify Assumptions | p. 103 |
Consideration of Research Purpose | p. 104 |
References | p. 105 |
The Truth(s) on Testing for Mediation in the Social and Organizational Sciences | p. 107 |
Baron and Kenny's (1986) Four-Step Test of Mediation | p. 110 |
Condition/Step 1 | p. 111 |
Condition/Step 2 | p. 111 |
Condition/Step 3 | p. 111 |
Condition/Step 4 | p. 112 |
The Urban Legend: Baron and Kenny's Four-Step Test Is an Optimal and Sufficient Test for Mediation Hypotheses | p. 113 |
The Kernel of Truth About the Urban Legends | p. 113 |
Debunking the Legends | p. 116 |
A Test of a Mediation Hypothesis Should Consist of the Four Steps Articulated by Baron and Kenny (1986) | p. 116 |
Baron and Kenny's (1986) Four-Step Procedure Is the Optimal Test of Mediation Hypotheses | p. 120 |
Fulfilling the Conditions Articulated in the Baron and Kenny (1986) Four-Step Test Is Sufficient for Drawing Conclusions About Mediated Relationships | p. 122 |
Suggestions for Testing Mediation Hypotheses | p. 124 |
Structural Equation Modeling (SEM) as an Analytic Framework | p. 124 |
Summary of Tests of Mediation | p. 127 |
A Heuristic Framework for Classifying Mediation Models | p. 129 |
Summary | p. 135 |
Conclusion | p. 136 |
Author Note | p. 136 |
References | p. 137 |
Seven Deadly Myths of Testing Moderation in Organizational Research | p. 143 |
The Seven Myths | p. 144 |
Product Terms Create Multicollinearity Problems | p. 144 |
Coefficients on First-Order Terms Are Meaningless | p. 146 |
Measurement Error Poses Little Concern When First-Order Terms Are Reliable | p. 148 |
Product Terms Should Be Tested Hierarchically | p. 150 |
Curvilinearity Can Be Disregarded When Testing Moderation | p. 151 |
Product Terms Can Be Treated as Causal Variables | p. 156 |
Testing Moderation in Structural Equation Modeling Is Impractical | p. 158 |
Myths Beyond Moderation | p. 159 |
Conclusion | p. 160 |
References | p. 160 |
Alternative Model Specifications in Structural Equation Modeling: Facts, Fictions, and Truth | p. 165 |
The Core of the Issue | p. 167 |
AMS Strategies | p. 170 |
Equivalent Models | p. 170 |
Nested Models | p. 174 |
Nonnested Alternative Models | p. 177 |
Summary | p. 179 |
AMS in Practice | p. 181 |
Summary | p. 186 |
References | p. 187 |
On the Practice of Allowing Correlated Residuals Among Indicators in Structural Equation Models | p. 193 |
Unraveling the Urban Legend | p. 195 |
Extent of the Problem | p. 195 |
Origins | p. 196 |
A Brief Review of Structural Equation Modeling | p. 197 |
Indicator Residuals | p. 199 |
Model Fit | p. 200 |
An Example | p. 202 |
Why Correlated IRs Improve Fit | p. 204 |
Problems With Correlated Residuals | p. 207 |
Recommendations | p. 209 |
Summary and Conclusions | p. 211 |
References | p. 212 |
Methodological Issues | |
Qualitative Research: The Redheaded Stepchild in Organizational and Social Science Research? | p. 219 |
Definitional Issues | p. 221 |
Philosophical Differences in Qualitative and Quantiative Research | p. 222 |
Quantitative and Qualitative Conceptualizations of Validity | p. 223 |
Caveats and Assumptions | p. 225 |
Beliefs Associated With Qualitative Research | p. 225 |
Qualitative Research Does Not Utilize the Scientific Method | p. 225 |
Qualitative Research Lacks Methodological Rigor | p. 226 |
Qualitative Research Contributes Little to the Advancement of Knowledge | p. 228 |
Evaluating the Beliefs Associated With Qualitative Research | p. 229 |
Qualitative Research Does Not Utilize the Scientific Method | p. 234 |
Qualitative Research Is Methodologically Weak | p. 236 |
Qualitative Research Has Weak Internal Validity | p. 236 |
Qualitative Research Has Weak Construct Validity | p. 237 |
Qualitative Research Has Weak External Validity | p. 238 |
Qualitative Research Contributes Little to the Advancement of Knowledge | p. 239 |
The Future of Qualitative Research in the Social and Organizational Sciences | p. 240 |
Concluding Thoughts | p. 241 |
Author Note | p. 242 |
References | p. 242 |
Do Samples Really Matter That Much? | p. 247 |
Kernel of Truth | p. 248 |
Background | p. 251 |
History of the Concern | p. 251 |
The Research Base | p. 253 |
Why Do Samples Seem to Matter So Much? | p. 255 |
People Confuse Random Sampling With Random Assignment | p. 255 |
People Focus on the Wrong Things | p. 257 |
People Rely on Superficial Similarities | p. 259 |
Concluding Thoughts | p. 260 |
Author Note | p. 262 |
References | p. 262 |
Sample Size Rules of Thumb: Evaluating Three Common Practices | p. 267 |
Determine Whether Sample Size Is Appropriate by Conducting a Power Analysis Using Cohen's Definitions of Small, Medium, and Large Effect Size | p. 269 |
Discussion | p. 271 |
Increase the A Priori Type I Error Rate to .10 Because of Your Small Sample Size | p. 273 |
Discussion | p. 275 |
Sample Size Should Include at Least 5 Observations per Estimated Parameter in Covariance Structure Analyses | p. 277 |
Discussion | p. 279 |
Discussion | p. 280 |
Author Note | p. 283 |
References | p. 284 |
When Small Effect Sizes Tell a Big Story, and When Large Effect Sizes Don't | p. 287 |
Effect Size Defined | p. 289 |
The Urban Legend | p. 290 |
The Kernel of Truth | p. 291 |
Quine and Ontological Relativism | p. 292 |
Contextualization | p. 295 |
Inauspicious Designs | p. 296 |
Phenomena With Obscured Consequences | p. 299 |
Phenomena That Challenge Fundamental Assumptions | p. 300 |
The Flip Side: Trivial "Large" Effects | p. 302 |
Conclusion | p. 305 |
References | p. 306 |
So Why Ask Me? Are Self-Report Data Really That Bad? | p. 309 |
The Urban Legend of Self-Report Data and Its Historical Roots | p. 310 |
Construct Validity of Self-Report Data | p. 313 |
Interpreting the Correlations in Self-Report Data | p. 316 |
Social Desirability Responding in Self-Report Data | p. 319 |
Value of Data Collected From Non-Self-Report Measures | p. 325 |
Conclusion and Moving Forward | p. 330 |
References | p. 332 |
If It Ain't Trait It Must Be Method: (Mis)application of the Multitrait-Multimethod Design in Organizational Research | p. 337 |
Background | p. 338 |
Literature Review | p. 342 |
Range of Traits Studied | p. 342 |
Range of Methods Studied | p. 343 |
Not All "Measurement Methods" Are Created Equal | p. 344 |
The Case of Multisource Performance Appraisal | p. 345 |
The Case of AC Construct Validity | p. 347 |
Other Cases | p. 349 |
So, Are Any "Method" Facets Really Method Facets? | p. 350 |
Discriminating Method From Substance, or "If It Looks Like a Method and Quacks Like a Method..." | p. 351 |
References | p. 353 |
Chopped Liver? OK. Chopped Data? Not OK | p. 361 |
Urban Legends Regarding Chopped Data | p. 362 |
Urban Legends Associated With the Occurrence of Chopped Data | p. 363 |
Urban Legends Associated With Chopped Data Techniques | p. 364 |
Urban Legends Associated With Chopped Data Justifications | p. 365 |
Literature Review | p. 366 |
Chopped Data Through the Years | p. 367 |
Prevalence of Chopped Data | p. 370 |
The Occurrence of Chopped Data Over Time | p. 371 |
Chopped Data Across Disciplines | p. 372 |
Types of Chopped Data Approaches | p. 372 |
Evaluating Justifications for Using Chopped Data | p. 374 |
Insufficient or Faulty Justifications (Myths) | p. 374 |
Legitimate Justifications (Truths) | p. 376 |
Advantages of, Disadvantages of, and Recommendations for Using Chopped Data | p. 377 |
(Perceived) Advantages of Chopping Data | p. 378 |
Disadvantages of Chopping Data | p. 378 |
Recommendations When Faced With Chopping Data | p. 382 |
Conclusion | p. 383 |
References | p. 383 |
Subject Index | p. 387 |
Author Index | p. 401 |
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