Bibliographic record and links to related information available from the Library of Congress catalog.
**Note: ** Contents data are machine generated based on pre-publication information provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.

Contents 1 What Is a Moderator Variable and Why Should We Care? 1 Why Should We Study Moderator Variables? / 3 Distinction between Moderator and Mediator Variables / 5 Importance of A Priori Rationale in Investigating Moderating Effects / 7 ··· Conclusions / 8 ··· 2 Moderated Multiple Regression 10 What Is MMR? / 10 Endorsement of MMR as an Appropriate Technique / 16 Pervasive Use of MMR in the Social Sciences: Literature Review / 18 ··· Conclusions / 20 ··· 3 Performing and Interpreting Moderated Multiple Regression Analyses Using Computer Programs 22 Research Scenario / 23 Data Set / 24 Conducting an MMR Analysis Using Computer Programs: Two Steps / 26 Step 1: Computation of Product Term / 26 Step 2: Computation of Regression Equations / 28 Output Interpretation / 31 Interpretation of Model 1 / 31 Interpretation of Model 2 / 33 Additional Issues in Output Interpretation / 35 ··· Conclusions / 39 ··· 4 The Homogeneity of Error Variance Assumption 42 What Is the Homogeneity of Error Variance Assumption? / 42 Two Distinct Assumptions: Homoscedasticity and Homogeneity of Error Variance / 44 Is It a Big Deal to Violate the Assumption? / 47 Violation of the Homogeneity of Error Variance Assumption: Type I Error Rates / 48 Violation of the Homogeneity of Error Variance Assumption: Type II Error Rates / 50 Violation of the Assumption in Published Research / 51 How to Check Whether the Homogeneity Assumption Is Violated / 54 What to Do When the Homogeneity of Error Variance Assumption Is Violated / 56 ALTMMR: Computer Program to Check Assumption Compliance and Compute Alternative Statistics if Needed / 58 Program Description / 59 Input / 60 · Assessment of Variance Heterogeneity / 60 · Computation of Alternatives to MMR / 61 · Output / 61 ··· Conclusions / 63 ··· 5 MMR's Low-Power Problem 65 Statistical Inferences and Power / 65 Controversy over Null Hypothesis Significance Testing / 67 Purpose of NHST / 67 Meaning of NHST / 67 Controversy over NHST: A Human Factors Problem / 68 Factors Affecting the Power of All Inferential Tests / 69 Factors Affecting the Power of MMR / 70 Variable Distributions / 70 Predictor Variable Variance Reduction / 70 · Transformations of Skewed Criterion Scores / 71 Operationalization of Criterion and Predictor Variables / 72 Measurement Error / 73 · Scale Coarseness / 74 · Polichotomization of a Truly Continuous Variable / 75 Sample Size / 75 Total Sample Size / 75 · Sample Size across Moderator-Based Subgroups / 76 Characteristics of the Predictor Variables / 77 Correlation between the Predictor X and the Moderator Z / 77 · Correlation between the Predictor X and the Criterion Y / 77 Interactive Effects / 78 Effect Sizes and Power in Published Research / 78 Implications of Small Observed Effect Sizes for Social Science Research / 80 ··· Conclusions / 82 ··· 6 Light at the End of the Tunnel: How to Solve the Low-Power Problem 85 How to Minimize the Impact of Factors Affecting the Power of All Inferential Tests / 86 Sample Size / 86 Preset Type I Error Rate / 86 Moderating-Effect Size / 88 How to Minimize the Impact of Factors Affecting the Power of MMR / 88 Reduction of Variance in Predictor Variables / 88 Transformations of Skewed Criterion Scores / 89 Measurement Error / 90 Scale Coarseness / 90 Polichotomization of Truly Continuous Variables / 92 Total Sample Size / 93 Sample Size across Moderator-Based Subgroups / 93 Correlation between the Predictor X and the Moderator Z / 94 Correlation between the Predictor X and the Criterion Y / 94 ··· Conclusions / 94 ··· 7 Computing Statistical Power 97 Usefulness of Computing Statistical Power / 97 Empirically Based Programs / 100 Program POWER / 100 Specifications / 100 · Example / 101 · Limitations / 101 Program MMRPWR / 103 Specifications / 103 · Example / 104 · Limitations / 106 Theory-Based Program / 107 Program MMRPOWER / 107 Specifications / 108 · Example / 110 · Limitations / 110 Relative Impact of the Factors Affecting Power / 113 ··· Conclusions / 115 ··· 8 Complex MMR Models 117 MMR Analyses Including a Moderator Variable with More Than Two Levels / 118 Dummy Coding / 119 Unweighted Effect Coding / 121 Weighted Effect Coding / 122 Contrast Coding / 123 How to Choose a Coding Scheme: The Importance of Theory / 124 Computing Statistical Power / 125 Linear Interactions and Nonlinear Effects: Friends or Foes? / 126 Testing and Interpreting Three-way and Higher-Order Interaction Effects / 131 Examining "Targeted" Lower-Order Interactions / 134 ··· Conclusions / 135 ··· 9 Further Issues in the Interpretation of Moderating Effects 138 Is the Moderating Effect Practically Significant? / 139 Measures of Improved Fit / 139 Difference in Correlations across Moderator-Based Subgroups / 139 · Proportion of Variance Explained by the Moderating Effect as Indexed by R2 / 140 · Proportion of Variance Explained by the Moderating Effect as Indexed by f2 / 141 · Proportion of Variance Explained by the Moderating Effect as Indexed by Modified f2 / 141 Measures of Improved Prediction / 143 Differences in Unstandardized Regression Coefficients across Moderator-Based Subgroups / 143 · Standardized Effect of the Moderator on the Y on X Slope / 144 · Differential Impact of the Moderator at Various Values of the Predictor X / 146 The Signed Coefficient Rule for Interpreting Moderating Effects / 149 The Importance of Identifying Criterion and Predictor A Priori / 150 ··· Conclusions / 152 ··· 10 Summary and Conclusions 155 Moderators and Social Science Theory and Practice / 155 Use of Moderated Multiple Regression / 156 Homogeneity of Error Variance Assumption / 159 Low Statistical Power and Proposed Remedies / 159 More Complex MMR Models / 162 Assessing Practical Significance / 162 ··· Conclusions / 164 ··· Appendix A Computation of Bartlett's (1937) M Statistic 167 Appendix B Computation of James's (1951) J Statistic 168 Appendix C Computation of Alexander's (Alexander & Govern, 1994) A Statistic 170 Appendix D Computation of Modified f2 171 Appendix E Theory-Based Power Approximation 173 References 175 Name Index 000 Subject Index 000

Library of Congress Subject Headings for this publication: Regression analysis, Social sciences Statistical methods Data processing, Regression analysis Computer programs