Regression Analysis for Categorical Moderators (Methodology In The Social Sciences)

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出版者:The Guilford Press
作者:Herman Aguinis
出品人:
页数:202
译者:
出版时间:2003-12-23
价格:USD 34.00
装帧:Hardcover
isbn号码:9781572309692
丛书系列:
图书标签:
  • 社会学
  • 哲学
  • statistics
  • regression
  • Regression Analysis
  • Categorical Moderators
  • Methodology
  • Social Sciences
  • Statistics
  • Data Analysis
  • Quantitative Research
  • Moderation Analysis
  • Psychology
  • Sociology
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具体描述

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 RegressionWhat 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) M Statistic

Appendix B. Computation of James's (1951) J Statistic

Appendix C. Computation of Alexander's (Alexander & Govern, 1994) A Statistic

Appendix D. Computation of Modified f2

Appendix E. Theory-Based Power Approximation

References

Name Index

Subject Index

Review:

"A masterful presentation reflecting many years of research and study. It should prove to be valuable to any researcher who has even a basic understanding of statistical analysis."

-International Journal of Consumer Studies (Ronald E. Goldsmith, Florida State University in 29, 1, January 2005)

"This book presents a complete and current treatment of a topic of great importance to management and organizational studies researchers. Strengths of the book include the use of an integrative example with data that is available to readers, and the clear presentation style. The treatment of homogeneity of error variance and statistical power problems is especially impressive and provides readers with practical guidance for dealing with these issues. This book will be an excellent resource for any researcher who works with regression models."

-Larry J. Williams, PhD, Center for the Advancement of Research Methods and Analysis, School of Business, Virginia Commonwealth University

"Aguinis has provided an extraordinarily understandable guide to conducting tests of moderation by categorical variables. The book contains clear examples for running the analyses, checking assumptions, and interpreting the results. This book is an excellent resource for courses on regression analysis at both the undergraduate and graduate levels, and for individuals who need a refresher on moderator analysis."

-Lois Tetrick, PhD, Department of Psychology, George Mason University

"Aguinis has produced the most comprehensive single-source treatment on the topic of why and how to conduct moderated regression analysis for categorical moderators. The book presents very clear steps for how to test for moderators, but is more than a cookbook in that it also explores in detail the underlying assumptions; issues that will affect interpretation (e.g., homogeneity of variance and power); and solutions to frequently encountered problems. Examples from different types of research problems help clarify the analytical strategy, and presentation of the software for examining underlying issues is very valuable. Aguinis also provides excellent coverage of the literature surrounding the analytical strategy. This volume is an excellent reference for any researcher or student interested in studying interactions with categorical variables."

-Sheldon Zedeck, PhD, Department of Psychology, University of California, Berkeley

深入解析类别变量在回归分析中的调控作用:一种方法论的探索 在社会科学研究领域,我们经常面临着解释变量之间复杂关系的挑战,而这些关系并非总是独立存在的,很多时候会受到其他因素的影响,这些影响因素的性质和作用方式对我们理解研究现象至关重要。特别是当这些影响因素本身是以类别形式存在时,传统的回归分析方法可能难以全面捕捉其精妙的调节作用。本书《类别调节变量的回归分析:社会科学研究方法论》正是为了应对这一挑战而生,它为研究者提供了一套严谨且实用的方法论工具,以系统地探索和量化类别变量在回归分析中的调控效应。 本书的核心在于揭示和阐释如何将类别变量纳入回归模型,使其不仅仅作为独立的预测因子,更能作为调节变量,改变其他自变量与因变量之间的关系强度或方向。这种“交互作用”的分析,对于理解社会现象的异质性、情境依赖性以及不同人群或群体行为的差异至关重要。例如,在教育研究中,学生本身的学习能力(自变量)固然会影响其学业成绩(因变量),但这种影响在高收入家庭(类别调节变量)和低收入家庭(类别调节变量)中可能存在显著差异。本书将教你如何精确地捕捉并量化这种家庭经济背景对学习能力与学业成绩之间关系的调节效应。 本书内容丰富,涵盖了从基础概念到高级应用的完整体系。首先,它会带领读者回顾回归分析的基本原理,包括简单回归和多元回归,确保读者对模型构建、参数估计、假设检验等核心概念有扎实的掌握。在此基础上,本书将重点引入类别变量的概念及其在研究设计中的意义。研究者将学习如何识别和定义类别调节变量,例如性别(男/女)、种族(白人/黑人/亚裔)、教育程度(高中/本科/研究生)、地区(城市/乡村)等,以及如何将其有效地编码为模型可以理解的形式,例如使用哑变量(dummy variables)进行编码。 随后,本书将深入探讨如何在回归模型中引入类别调节变量,并阐述其理论基础。核心在于理解“交互项”的概念。本书会详细解释如何通过将自变量与类别调节变量进行交互项的乘积,来构建包含调节效应的回归模型。例如,一个模型可能包含“学习能力”变量、“家庭收入水平”(编码后的哑变量)以及“学习能力”与“家庭收入水平”的交互项。通过对这个交互项系数的显著性检验,研究者可以判断家庭收入水平是否显著调节了学习能力对学业成绩的影响。 为了让读者更好地理解模型构建的逻辑,本书会提供详尽的统计推断过程。它将详细讲解交互项系数的解释,包括其显著性、效应大小以及如何进行后验检验(post-hoc tests)来进一步探索不同类别下的具体效应差异。例如,如果交互项显著,研究者可能需要进行事后比较,以确定在“高收入家庭”和“低收入家庭”中,学习能力对学业成绩的影响是更大、更小,还是方向相反。本书将提供清晰的步骤和案例,指导读者如何进行这些必要的检验,并准确解读结果。 本书不仅关注统计模型本身,更注重其在实际研究中的应用。它将展示如何将这些方法论应用于社会科学的各个分支,包括心理学、社会学、政治学、教育学、传播学等。通过大量真实世界的研究案例,本书将帮助读者理解这些方法论如何被用来回答各种复杂的社会学研究问题。例如,本书可能会分析: 心理学研究:探索社会支持(类别调节变量)是否调节了压力(自变量)对抑郁症状(因变量)的影响。 社会学研究:检验种族(类别调节变量)是否调节了经济状况(自变量)对犯罪率(因变量)的影响。 政治学研究:分析投票意愿(自变量)与候选人形象(类别调节变量)之间的关系是否受到选民年龄段(类别调节变量)的调节,进而影响投票行为(因变量)。 教育学研究:研究教学方法(类别调节变量)是否调节了学生学习风格(自变量)对学习成果(因变量)的影响。 传播学研究:探讨媒体类型(类别调节变量)是否调节了信息来源的可信度(自变量)对公众态度(因变量)的影响。 除了基本的类别调节变量分析,本书还会探讨一些更高级的主题,以满足研究者更广泛的需求。这可能包括: 多重类别调节变量:当存在两个或多个类别调节变量时,如何构建和解释更复杂的模型。例如,不仅考虑家庭收入水平,还要同时考虑学生所在的学校类型(公立/私立)是否共同调节学习能力对学业成绩的影响。 连续变量与类别变量的混合调节:在某些研究中,调节变量可能既包含类别变量,也包含连续变量。本书会指导如何在这种混合情境下构建模型。 模型诊断与稳健性检验:在任何回归分析中,模型诊断都是至关重要的。本书将提供关于如何检查模型假设、识别异常值、处理多重共线性以及进行稳健性检验的方法,以确保研究结果的可靠性。 软件实现:本书将提供使用主流统计软件(如 R, Stata, SPSS 等)实现这些分析的具体步骤和代码示例,使读者能够快速将理论付诸实践。 本书的独特之处在于其深度和广度。它不仅仅停留在介绍统计公式,而是深入挖掘这些方法论背后的逻辑和假设,并强调其在社会科学研究中的实际意义。作者通过清晰的语言、循序渐进的讲解和丰富的案例,力求使复杂的统计概念变得易于理解和掌握。无论是初涉统计建模的研究生,还是希望深化自己统计分析技能的资深研究者,都能从本书中获得宝贵的知识和启发。 通过掌握本书所介绍的类别调节变量的回归分析方法,研究者将能够更准确地描绘和解释社会现象的复杂性,揭示变量之间看似简单关系下的深层机制,从而推动社会科学研究的理论进步和实践应用。本书将成为每一位致力于严谨、深入进行社会科学研究者的必备参考。它为研究者提供了一个强大的框架,用以探寻“为什么”和“在什么条件下”这些看似普遍的关系会发生变化,从而获得对人类行为和复杂社会系统更深刻的洞察。

作者简介

Herman Aguinis, PhD, is Associate Professor and Director of the Management Programs at the University of Colorado at Denver. He has held visiting appointments at China Agricultural University, City University of Hong Kong, the University of Science of Malaysia, and the University of Santiago de Compostela, in Spain. He has published over 40 articles in refereed journals and delivered over 100 presentations in the United States and abroad on the topics of research methods and statistics, personnel selection, and social power and influence in organizations. He is currently Associate Editor of Organizational Research Methods and serves on the editorial boards of several journals, including Journal of Applied Psychology and Journal of International Business Studies. He has been elected Chair of the Research Methods Division of the Academy of Management for 2003-2004.

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