Regression analysis for categorical moderators

著者

• Aguinis, Herman
• Kenny, David A.

書誌事項

Regression analysis for categorical moderators

Herman Aguinis ; series editor's note by David A. Kenny

（Methodology in the social sciences）

Guilford Press, c2004

注記

Bibliography: p. 175-189

Includes indexes

内容説明・目次

内容説明

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).

目次

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

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詳細情報

• NII書誌ID(NCID): BA67732767
• ISBN:
  • 1572309695
• LCCN: 2003024141
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: New York
• ページ数/冊数: xxi, 202 p.
• 大きさ: 24 cm
• 分類:
  • LCC : QA278.2
  • DC22 : 519.5/36
• 件名:
  • LCSH : Regression analysis
  • LCSH : Social sciences -- Statistical methods -- Data processing
  • LCSH : Regression analysis -- Computer programs
• 親書誌ID:
  • BA3787031X