Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in Organizational and Social Sciences

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2008-10-03
  • Publisher: Routledge

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The objective of this book is to provide an up-to-date review of commonly undertaken methodological and statistical practices that are sustained, in part, upon sound rationale and justification and, in part, upon unfounded lore. The practices themselves are not necessarily intrinscially faulty. Rather, it is often the reasoning why or rationalization used to justify the practices that is questionable. In this book, a group of scholars look at statistical and urban myths and legends and suggest what the state of practice should be. This book meets an important need and will be of interest to researchers, students and scholars in the fields of organizational and social sciences. Book jacket.

Table of Contents

Prefacep. xv
About the Editorsp. xvii
Acknowledgmentsp. xix
Introductionp. 1
Statistical Issues
Missing Data Techniques and Low Response Rates: The Role of Systematic Nonresponse Parametersp. 7
Organization of the Chapterp. 8
Levels, Problems, and Mechanisms of Missing Datap. 8
Three Levels of Missing Datap. 9
Two Problems Caused by Missing Data (External Validity and Statistical Power)p. 9
Missingness Mechanisms (MCAR, MAR, and MNAR)p. 9
Missing Data Treatmentsp. 11
A Fundamental Principle of Missing Data Analysisp. 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 Nonresponsep. 17
Missing Data Legendsp. 21
"Low Response Rates Invalidate Results"p. 21
"When in Doubt, Use Listwise or Pairwise Deletion"p. 24
Applicationsp. 26
Longitudinal Modelingp. 26
Within-Group Agreement Estimationp. 27
Meta-analysisp. 27
Social Network Analysisp. 28
Moderated Regressionp. 29
Conclusionsp. 29
Future Research on d[subscript miss] and f[superscript 2 subscript miss]p. 30
Missing Data Techniquesp. 31
Referencesp. 31
Appendixp. 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 Theoryp. 37
Basic Statement of the Two Theoriesp. 38
Classical Test Theoryp. 38
Item Response Theoryp. 40
Criticisms and Limitations of CTTp. 44
Lack of Population Invariancep. 44
Person and Item Parameters on Different Scalesp. 45
Correlations Between Item Parametersp. 46
Reliability as a Monolithic Conceptp. 47
Criticisms and Limitations of IRTp. 48
Large Sample Sizesp. 48
Strong Assumptionsp. 49
Complicated Programsp. 50
Times to Use CTTp. 50
Small Sample Sizesp. 50
Multidimensional Data?p. 51
CTT Supports Other Methodologiesp. 52
Times to Use IRTp. 53
Focus on Particular Range of Constructp. 53
Conduct Goodness-of-Fit Studiesp. 53
IRT Supports Many Psychometric Toolsp. 55
Conclusionsp. 56
Referencesp. 57
Four Common Misconceptions in Exploratory Factor Analysisp. 61
The Choice Between Component and Common Factor Analysis Is Inconsequentialp. 62
The Component Versus Common Factor Debate: Methodological Argumentsp. 66
The Component Versus Common Factor Debate: Philosophical Argumentsp. 68
Differences in Results from Component and Common Factor Analysisp. 69
Orthogonal Rotation Results in Better Simple Structure Than Oblique Rotationp. 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 Guidelinesp. 76
The "Eigenvalues Greater Than One" Rule Is the Best Way of Choosing the Number of Factorsp. 79
Discussionp. 83
Referencesp. 85
Dr. StrangeLOVE, or: How I Learned to Stop Worrying and Love Omitted Variablesp. 89
Theoretical and Mathematical Definition of the Omitted Variables Problemp. 91
Violated Assumptionsp. 96
More Complex Modelsp. 97
Path Coefficient Bias Versus Significance Testingp. 100
Minimizing the Risk of LOVEp. 102
Experimental Controlp. 102
More Inclusive Modelsp. 103
Use Previous Research to Justify Assumptionsp. 103
Consideration of Research Purposep. 104
Referencesp. 105
The Truth(s) on Testing for Mediation in the Social and Organizational Sciencesp. 107
Baron and Kenny's (1986) Four-Step Test of Mediationp. 110
Condition/Step 1p. 111
Condition/Step 2p. 111
Condition/Step 3p. 111
Condition/Step 4p. 112
The Urban Legend: Baron and Kenny's Four-Step Test Is an Optimal and Sufficient Test for Mediation Hypothesesp. 113
The Kernel of Truth About the Urban Legendsp. 113
Debunking the Legendsp. 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 Hypothesesp. 120
Fulfilling the Conditions Articulated in the Baron and Kenny (1986) Four-Step Test Is Sufficient for Drawing Conclusions About Mediated Relationshipsp. 122
Suggestions for Testing Mediation Hypothesesp. 124
Structural Equation Modeling (SEM) as an Analytic Frameworkp. 124
Summary of Tests of Mediationp. 127
A Heuristic Framework for Classifying Mediation Modelsp. 129
Summaryp. 135
Conclusionp. 136
Author Notep. 136
Referencesp. 137
Seven Deadly Myths of Testing Moderation in Organizational Researchp. 143
The Seven Mythsp. 144
Product Terms Create Multicollinearity Problemsp. 144
Coefficients on First-Order Terms Are Meaninglessp. 146
Measurement Error Poses Little Concern When First-Order Terms Are Reliablep. 148
Product Terms Should Be Tested Hierarchicallyp. 150
Curvilinearity Can Be Disregarded When Testing Moderationp. 151
Product Terms Can Be Treated as Causal Variablesp. 156
Testing Moderation in Structural Equation Modeling Is Impracticalp. 158
Myths Beyond Moderationp. 159
Conclusionp. 160
Referencesp. 160
Alternative Model Specifications in Structural Equation Modeling: Facts, Fictions, and Truthp. 165
The Core of the Issuep. 167
AMS Strategiesp. 170
Equivalent Modelsp. 170
Nested Modelsp. 174
Nonnested Alternative Modelsp. 177
Summaryp. 179
AMS in Practicep. 181
Summaryp. 186
Referencesp. 187
On the Practice of Allowing Correlated Residuals Among Indicators in Structural Equation Modelsp. 193
Unraveling the Urban Legendp. 195
Extent of the Problemp. 195
Originsp. 196
A Brief Review of Structural Equation Modelingp. 197
Indicator Residualsp. 199
Model Fitp. 200
An Examplep. 202
Why Correlated IRs Improve Fitp. 204
Problems With Correlated Residualsp. 207
Recommendationsp. 209
Summary and Conclusionsp. 211
Referencesp. 212
Methodological Issues
Qualitative Research: The Redheaded Stepchild in Organizational and Social Science Research?p. 219
Definitional Issuesp. 221
Philosophical Differences in Qualitative and Quantiative Researchp. 222
Quantitative and Qualitative Conceptualizations of Validityp. 223
Caveats and Assumptionsp. 225
Beliefs Associated With Qualitative Researchp. 225
Qualitative Research Does Not Utilize the Scientific Methodp. 225
Qualitative Research Lacks Methodological Rigorp. 226
Qualitative Research Contributes Little to the Advancement of Knowledgep. 228
Evaluating the Beliefs Associated With Qualitative Researchp. 229
Qualitative Research Does Not Utilize the Scientific Methodp. 234
Qualitative Research Is Methodologically Weakp. 236
Qualitative Research Has Weak Internal Validityp. 236
Qualitative Research Has Weak Construct Validityp. 237
Qualitative Research Has Weak External Validityp. 238
Qualitative Research Contributes Little to the Advancement of Knowledgep. 239
The Future of Qualitative Research in the Social and Organizational Sciencesp. 240
Concluding Thoughtsp. 241
Author Notep. 242
Referencesp. 242
Do Samples Really Matter That Much?p. 247
Kernel of Truthp. 248
Backgroundp. 251
History of the Concernp. 251
The Research Basep. 253
Why Do Samples Seem to Matter So Much?p. 255
People Confuse Random Sampling With Random Assignmentp. 255
People Focus on the Wrong Thingsp. 257
People Rely on Superficial Similaritiesp. 259
Concluding Thoughtsp. 260
Author Notep. 262
Referencesp. 262
Sample Size Rules of Thumb: Evaluating Three Common Practicesp. 267
Determine Whether Sample Size Is Appropriate by Conducting a Power Analysis Using Cohen's Definitions of Small, Medium, and Large Effect Sizep. 269
Discussionp. 271
Increase the A Priori Type I Error Rate to .10 Because of Your Small Sample Sizep. 273
Discussionp. 275
Sample Size Should Include at Least 5 Observations per Estimated Parameter in Covariance Structure Analysesp. 277
Discussionp. 279
Discussionp. 280
Author Notep. 283
Referencesp. 284
When Small Effect Sizes Tell a Big Story, and When Large Effect Sizes Don'tp. 287
Effect Size Definedp. 289
The Urban Legendp. 290
The Kernel of Truthp. 291
Quine and Ontological Relativismp. 292
Contextualizationp. 295
Inauspicious Designsp. 296
Phenomena With Obscured Consequencesp. 299
Phenomena That Challenge Fundamental Assumptionsp. 300
The Flip Side: Trivial "Large" Effectsp. 302
Conclusionp. 305
Referencesp. 306
So Why Ask Me? Are Self-Report Data Really That Bad?p. 309
The Urban Legend of Self-Report Data and Its Historical Rootsp. 310
Construct Validity of Self-Report Datap. 313
Interpreting the Correlations in Self-Report Datap. 316
Social Desirability Responding in Self-Report Datap. 319
Value of Data Collected From Non-Self-Report Measuresp. 325
Conclusion and Moving Forwardp. 330
Referencesp. 332
If It Ain't Trait It Must Be Method: (Mis)application of the Multitrait-Multimethod Design in Organizational Researchp. 337
Backgroundp. 338
Literature Reviewp. 342
Range of Traits Studiedp. 342
Range of Methods Studiedp. 343
Not All "Measurement Methods" Are Created Equalp. 344
The Case of Multisource Performance Appraisalp. 345
The Case of AC Construct Validityp. 347
Other Casesp. 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
Referencesp. 353
Chopped Liver? OK. Chopped Data? Not OKp. 361
Urban Legends Regarding Chopped Datap. 362
Urban Legends Associated With the Occurrence of Chopped Datap. 363
Urban Legends Associated With Chopped Data Techniquesp. 364
Urban Legends Associated With Chopped Data Justificationsp. 365
Literature Reviewp. 366
Chopped Data Through the Yearsp. 367
Prevalence of Chopped Datap. 370
The Occurrence of Chopped Data Over Timep. 371
Chopped Data Across Disciplinesp. 372
Types of Chopped Data Approachesp. 372
Evaluating Justifications for Using Chopped Datap. 374
Insufficient or Faulty Justifications (Myths)p. 374
Legitimate Justifications (Truths)p. 376
Advantages of, Disadvantages of, and Recommendations for Using Chopped Datap. 377
(Perceived) Advantages of Chopping Datap. 378
Disadvantages of Chopping Datap. 378
Recommendations When Faced With Chopping Datap. 382
Conclusionp. 383
Referencesp. 383
Subject Indexp. 387
Author Indexp. 401
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