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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010200263 | QA278.2 A38 2004 | Open Access Book | Book | Searching... |
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Summary
Summary
Does the stability of personality vary by gender or ethnicity? Does a particular therapy work better to treat clients with one type of personality disorder than those with another? Providing a solution to thorny problems such as these, Aguinis shows readers how to better assess whether the relationship between two variables is moderated by group membership through the use of a statistical technique, moderated multiple regression (MMR). Clearly written, the book requires only basic knowledge of inferential statistics. It helps students, researchers, and practitioners determine whether a particular intervention is likely to yield dissimilar outcomes for members of various groups. Associated computer programs and data sets are available at the companion website ( www.guilford.com/aguinis-materials ).
Author Notes
Herman Aguinis is Associate Professor and Director of the Management Programs at the University of Colorado at Denver.
Reviews 1
Choice Review
Aguinis (Univ. of Colorado, Denver) provides a comprehensive, clear treatment of moderated regression analysis involving categorical variables based on social science. Ten chapters show differences between moderated and mediated variables, and between mediated and moderated relationships; offer basic statistical assumptions underlying the moderated multiple regression procedure (MMR); describe various computer packages for MMR analysis; and include the Web address for data sets used. Other chapters discuss assumption of the homogeneity of error variance, a key element of MMR; focus on the statistical power of MMR procedures, and the various factors affecting such a power and minimizing adverse effects of such power factors; and present computerized tools for determining the power of MMR tests (with the suggestion that determination be made prior to final decisions on null hypothesis). Later chapters treat complex MMR models and additional issues related to interpretation of results based on analysis; and provide an integrated summary of guidelines and recommendations for use and interpretation of MMR to estimate moderating effects of categorical variables. Only essential mathematical concepts are used; readers need to know basic concepts of descriptive/inferential statistics leading to multiple regression. This is an excellent resource with an extensive reference list, author and subject indexes, and five appendixes on MMR models. ^BSumming Up: Highly recommended. Upper-division undergraduates through professionals. D. V. Chopra Wichita State University
Table of Contents
| 1 What Is a Moderator Variable and Why Should We Care? |
| Why Should We Study Moderator Variables? |
| Distinction between Moderator and Mediator Variables |
| Importance of A Priori Rationale in Investigating Moderating Effects |
| Conclusions |
| 2 Moderated Multiple Regression |
| What Is MMR? |
| Endorsement of MMR as an Appropriate Technique |
| Pervasive Use of MMR in the Social Sciences: Literature Review |
| Conclusions |
| 3 Performing and Interpreting Moderated Multiple Regression Analysis Using Computer Programs |
| Research Scenario |
| Data Set |
| Conducting an MMR Analysis Using Computer Programs: Two Steps |
| Output Interpretation |
| Conclusions |
| 4 Homogeneity of Error Variance Assumption |
| What Is the Homogeneity of Error Variance Assumption? |
| Two Distinct Assumptions: Homoscedasticity and Homogeneity of Error Variance |
| Is It a Big Deal to Violate the Assumption? |
| Violation of the Assumption in Published Research |
| How to Check If the Homogeneity Assumption Is Violated |
| What to Do When the Homogeneity of Error Variance Assumption Is Violated |
| ALTMMR: Computer Program to Check Assumption Compliance and Compute Alternative Statistics If Needed |
| Conclusions |
| 5 MMR's Low-Power Problem |
| Statistical Inferences and Power |
| Controversy Over Null Hypothesis Significance Testing |
| Factors Affecting the Power of All Inferential Tests |
| Factors Affecting the Power of MMR |
| Effect Sizes and Power in Published Research |
| Implications of Small Observed Effect Sizes for Social Science Research |
| Conclusions |
| 6 Light at the End of the Tunnel: How to Solve the Low-Power Problem |
| How to Minimize the Impact of Factors Affecting the Power of All Inferential Tests |
| How to Minimize the Impact of Factors Affecting the Power of MMR |
| Conclusions |
| 7 Computing Statistical Power |
| Usefulness of Computing Statistical Power |
| Empirically Based Programs |
| Theory-Based Program |
| Relative Impact of the Factors Affecting Power |
| Conclusions |
| 8 Complex MMR Models |
| MMR Analyses Including a Moderator Variable with More Than Two Levels |
| Linear Interactions and Non-linear Effects: Friends or Foes? |
| Testing and Interpreting Three-Way and Higher-Order Interaction Effects |
| Conclusions |
| 9 Further Issues in the Interpretation of Moderating Effects |
| Is the Moderating Effect Practically Significant? |
| The Signed Coefficient Rule for Interpreting Moderating Effects |
| The Importance on Identifying Criterion and Predictor A Priori |
| Conclusions |
| 10 Summary and Conclusions |
| Moderators and Social Science Theory and Practice |
| Use of Moderated Multiple Regression |
| Homogeneity of Error Variance Assumption |
| Low Statistical Power and Proposed Remedies |
| Complex MMR Models |
| Assessing Practical Significance |
| Conclusions |
| Appendix A Computation of Bartlett's (1937) \ital\M\ital\ Statistic |
| Appendix B Computation of James's (1951) \ital\J\ital\ Statistic |
| Appendix C Computation of Alexander's (Alexander & Govern, 1994) \ital\A\ital\ Statistic |
| Appendix D Computation of Modified \ital\f\ital\\superscript\2\superscript\ |
| Appendix E Theory-Based Power Approximation |
| References |
| Name Index |
| Subject Index |
| 1 What Is a Moderator Variable and Why Should We Care? |
| Why Should We Study Moderator Variables? |
| Distinction between Moderator and Mediator Variables |
| Importance of A Priori Rationale in Investigating Moderating Effects |
| Conclusions |
| 2 Moderated Multiple Regression |
| What Is MMR? |
| Endorsement of MMR as an Appropriate Technique |
| Pervasive Use of MMR in the Social Sciences: Literature Review |
| Conclusions |
| 3 Performing and Interpreting Moderated Multiple Regression Analysis Using Computer Programs |
| Research Scenario |
| Data Set |
| Conducting an MMR Analysis Using Computer Programs: Two Steps |
| Output Interpretation |
| Conclusions |
| 4 Homogeneity of Error Variance Assumption |
| What Is the Homogeneity of Error Variance Assumption? |
| Two Distinct Assumptions: Homoscedasticity and Homogeneity of Error Variance |
| Is It a Big Deal to Violate the Assumption? |
| Violation of the Assumption in Published Research |
| How to Check If the Homogeneity Assumption Is Violated |
| What to Do When the Homogeneity of Error Variance Assumption Is Violated |
| ALTMMR: Computer Program to Check Assumption Compliance and Compute Alternative Statistics If Needed |
| Conclusions |
| 5 MMR's Low-Power Problem |
| Statistical Inferences and Power |
| Controversy Over Null Hypothesis Significance Testing |
| Factors Affecting the Power of All Inferential Tests |
| Factors Affecting the Power of MMR |
| Effect Sizes and Power in Published Research |
| Implications of Small Observed Effect Sizes for Social Science Research |
| Conclusions |
| 6 Light at the End of the Tunnel: How to Solve the Low-Power Problem |
| How to Minimize the Impact of Factors Affecting the Power of All Inferential Tests |
| How to Minimize the Impact of Factors Affecting the Power of MMR |
| Conclusions |
| 7 Computing Statistical Power |
| Usefulness of Computing Statistical Power |
| Empirically Based Programs |
| Theory-Based Program |
| Relative Impact of the Factors Affecting Power |
| Conclusions |
| 8 Complex MMR Models |
| MMR Analyses Including a Moderator Variable with More Than Two Levels |
| Linear Interactions and Non-linear Effects: Friends or Foes? |
| Testing and Interpreting Three-Way and Higher-Order Interaction Effects |
| Conclusions |
| 9 Further Issues in the Interpretation of Moderating Effects |
| Is the Moderating Effect Practically Significant? |
| The Signed Coefficient Rule for Interpreting Moderating Effects |
| The Importance on Identifying Criterion and Predictor A Priori |
| Conclusions |
| 10 Summary and Conclusions |
| Moderators and Social Science Theory and Practice |
| Use of Moderated Multiple Regression |
| Homogeneity of Error Variance Assumption |
| Low Statistical Power and Proposed Remedies |
| Complex MMR Models |
| Assessing Practical Significance |
| Conclusions |
| Appendix A Computation of Bartlett's (1937) \ital\M\ital\ Statistic |
| Appendix B Computation of James's (1951) \ital\J\ital\ Statistic |
| Appendix C Computation of Alexander's (Alexander & Govern, 1994) \ital\A\ital\ Statistic |
| Appendix D Computation of Modified \ital\f\ital\\superscript\2\superscript\ |
| Appendix E Theory-Based Power Approximation |
| References |
| Name Index |
| Subject Index |
