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Introduction to Mediation, Moderation, and Conditional Process Analysis
中介、調節和條件過程分析簡介

A Regression-Based Approach
基於回歸的方法

Andrew F. Hayes

“This decidedly readable, informative book is perfectly suited for a range of audiences, from the novice graduate student not quite ready for SEM to the advanced statistics instructor. Even the seasoned quantitative methodologist will benefit from Hayes’s years of accumulated wisdom as he expertly navigates this burgeoning-and at times inconsistent-literature. This book is particularly well suited for graduate-level courses. Hayes brings conditional process analysis to life with such passion that even the most ‘stat-o-phobic’ will become convinced that they too can master SPSS (or SAS) process. The thoughtful use of real-life examples, accompanied by SPSS and SAS syntax and output, makes the book highly accessible.”
"這本可讀性極佳、資訊豐富的書完全適合各種讀者,從尚未準備好學習 SEM 的研究生到進階統計學講師。即使是經驗豐富的定量方法學家,也能從 Hayes 多年累積的智慧中獲益良多,因為他能專業地瀏覽這本數量激增、有時甚至不一致的文獻。這本書特別適合研究生程度的課程。Hayes 以充沛的熱情將條件過程分析帶入生活,即使是最有「統計恐懼症」的人也會深信他們也能掌握 SPSS (或 SAS) 過程。真實例子的貼心使用,搭配 SPSS 和 SAS 的語法和輸出,讓這本書非常容易上手"。

—Shelley Brown, PhD, Department of Psychology, Carleton University, Canada
-Shelley Brown 博士,加拿大卡爾頓大學心理學系

“A welcome contribution. This book’s accessible language and diverse set of examples will appeal to a wide variety of substantive researchers looking to explore how or why, and under what conditions, relationships among variables exist. Hayes has a unique ability to effectively communicate technical material to nontechnical audiences. He facilitates application of several cutting-edge statistical models by providing practical, well-oiled machinery for conducting the analyses in practice. I can use this book to enhance my graduate-level mediation class by extending the course to include more coverage on differentiating mediation versus moderation and on conditional process models that simultaneously evaluate both effects together.”
"值得歡迎的貢獻。這本書以通俗易懂的語言和多樣化的範例,吸引了許多希望探索變數之間的關係如何或為何存在,以及在何種條件下存在的實務研究人員。Hayes 擁有獨特的能力,能有效地將技術資料傳達給非技術讀者。他提供了在實務中進行分析的實用、完善的機器,有助於幾個尖端統計模型的應用。我可以利用這本書來加強我的研究所級調解課程,將課程延長,以涵蓋更多關於區分調解與調制以及同時評估兩種效果的條件過程模型的內容"。

—Amanda Jane Fairchild, PhD, Department of Psychology, University of South Carolina
-Amanda Jane Fairchild,南卡羅來納大學心理學系博士

“Mediation and moderation are two of the most widely used statistical tools in the social sciences. Students and experienced researchers have been waiting for a clear, engaging, and comprehensive book on these topics for years, but the wait has been worth it-this book is an absolute winner. With his usual clarity, Hayes has written what will become the default resource on mediation and moderation for many years to come.”
"中介和調節是社會科學中使用最廣泛的兩種統計工具。多年來,學生和有經驗的研究人員一直在等待一本關於這些主題的清晰、引人入勝且全面的書籍,但等待是值得的--這本書是絕對的贏家。Hayes 以其一貫的清晰度,寫出了這本將在未來多年內成為中介和調節的預設資源"。

-Andy Field, PhD, School of Psychology, University of Sussex, United Kingdom
-Andy Field,英國薩塞克斯大學心理學院博士

Abstract 摘要

“Hayes provides an accessible, thorough introduction to the analysis of models containing mediators, moderators, or both. The text is easy to follow and written at a level appropriate for an introductory graduate course on mediation and moderation analysis. The book is also an extremely useful resource for applied researchers interested in analyzing conditional process models. One strength is the inclusion of numerous examples using real data, with step-by-step instructions for analysis of the data and interpretation of the results. This book’s largest contribution to the field is its replacement of the confusing terminology of mediated moderation and moderated mediation with the clearer and broader term conditional process model.” -Matthew Fritz, PhD, Department of Psychology, Virginia Polytechnic Institute and State University
"Hayes對包含中介者、調節者或兩者的模型分析進行了通俗易懂、深入淺出的介紹。這本書的文字通俗易懂,其寫作水準適合中介和調節分析的研究生入門課程。對於有興趣分析條件過程模型的應用研究人員來說,這本書也是非常有用的資源。本書的優點之一是包含大量使用真實資料的範例,並逐步說明如何分析資料和詮釋結果。這本書對這個領域最大的貢獻,就是用更清晰、更廣泛的名詞條件過程模型,取代了令人困惑的中介調節和中介調節的術語"。-弗吉尼亞理工學院暨州立大學心理學系博士 Matthew Fritz

This engaging book explains the fundamentals of mediation and moderation analysis and their integration as “conditional process analysis.” Procedures are described for testing hypotheses about the mechanisms by which causal effects operate, the conditions under which they occur, and the moderation of mechanisms. Relying on the principles of ordinary least squares regression, Andrew Hayes carefully explains the estimation and interpretation of direct and indirect effects, probing and visualization of interactions, and testing of questions about moderated mediation. Examples using data from published studies illustrate how to conduct and report the analyses described in the book. Of special value, the book introduces and documents PROCESS, a macro for SPSS and SAS that does all the computations described in the book. The author’s website (www.afhayes.com) offers free downloads of PROCESS plus data files for the book’s examples.
這本引人入勝的書籍闡述了中介分析和調節分析的基本原理,並將它們整合為「條件過程分析」。書中描述了測試因果效應運作機制、發生條件和機制調節的假設程序。Andrew Hayes 依據普通最小二乘迴歸的原則,仔細解釋了直接與間接效應的估計與詮釋、互動作用的探測與可視化,以及有關調節中介的測試問題。使用已發表研究資料的範例說明了如何進行和報告書中所描述的分析。特別值得一提的是,本書介紹並記錄了 PROCESS 這個 SPSS 和 SAS 的巨集,它可以完成書中描述的所有計算。作者的網站(www.afhayes.com)提供 PROCESS 和書中範例的資料檔案免費下載。

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Public Seminars 公開研討會

On-Site Seminars 現場研討會

Integrating Mediation and Moderation Analysis: Fundamentals using PROCESS
整合中介與調節分析:使用 PROCESS 的基本原理

A short seminar by Andrew Hayes, Ph.D.
Andrew Hayes 博士的簡短研討會
Conditional Process Analysis, also known as the analysis of moderated mediation, is the integration of mediation and moderation analysis and used when one’s analytical goal is to describe and understand the conditional nature of the mechanism or mechanisms by which a variable transmits its effect on another (see Hayes, 2013). After a brief introduction to principles of mediation and moderation analysis, this half-day seminar introduces the fundamentals of conditional process analysis and its implementation using the PROCESS tool for SPSS or SAS. Using OLS regression-based path analysis, it covers the estimation of various classes of models which allow indirect and/or direct effects to be moderated, the estimation of conditional indirect effects, testing a moderated mediation hypothesis, and how to compare conditional indirect effects.
條件過程分析(Conditional Process Analysis),也稱為調節中介分析,是中介分析和調節分析的整合,當分析目標是描述和理解一個變數傳遞其對另一個變數影響的機制的條件性時使用(見 Hayes, 2013)。在簡短介紹中介和調節分析的原則之後,這個為期半天的研討會將介紹條件過程分析的基本原理,以及使用 SPSS 或 SAS 的 PROCESS 工具來實作。使用以 OLS 迴歸為基礎的路徑分析,它涵蓋了允許間接和/或直接效應被調節的各類模型的估算、條件間接效應的估算、測試調節中介假設,以及如何比較條件間接效應。

WHO SHOULD ATTEND? 誰應該參加?

This seminar will be helpful for researchers in any field-including psychology, sociology, education, business, human development, political science, public health, and communication-who want to learn how to conduct a conditional process analysis using SPSS and SAS. Participants should have a basic working knowledge of the principles and practice of multiple regression and elementary statistical inference. No knowledge of matrix algebra is required or assumed.
本研討會對任何領域的研究人員都有幫助,包括心理學、社會學、教育、商業、人類發展、政治科學、公共衛生和通訊領域,這些領域的研究人員想要學習如何使用 SPSS 和 SAS 進行條件過程分析。參加者應具備多元回归原理與實務的基本工作知識,以及基本的統計推論。不需要或假定需要矩陣代數的知識。

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$ 250.00 $ 250.00 $250.00\$ 250.00 的費用包含所有研討會資料。

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Model Templates for PROCESS for SPSS and SAS ©2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 ©2013 Andrew F. Hayes, http://www.afhayes.com/

Model 1 型號 1

Conceptual Diagram 概念圖
Statistical Diagram 統計圖表
Conditional effect of X X XX on Y = b 1 + b 3 M Y = b 1 + b 3 M Y=b_(1)+b_(3)MY=b_{1}+b_{3} M
X X XX Y = b 1 + b 3 M Y = b 1 + b 3 M Y=b_(1)+b_(3)MY=b_{1}+b_{3} M 的條件效應
Created with 以下列方式建立
Model Templates for PROCESS for SPSS and SAS (C2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 (C2013 Andrew F. Hayes, http://www.afhayes.com/)

Model 2 型號 2

Conditional effect of X X XX on Y = b 1 + b 4 M + b 5 W Y = b 1 + b 4 M + b 5 W Y=b_(1)+b_(4)M+b_(5)WY=b_{1}+b_{4} M+b_{5} W
X X XX Y = b 1 + b 4 M + b 5 W Y = b 1 + b 4 M + b 5 W Y=b_(1)+b_(4)M+b_(5)WY=b_{1}+b_{4} M+b_{5} W 的條件效應
Created with 以下列方式建立
Model Templates for PROCESS for SPSS and SAS ©2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 ©2013 Andrew F. Hayes, http://www.afhayes.com/

Model 3 型號 3

Conceptual Diagram 概念圖
Statistical Diagram 統計圖表
Conditional effect of X X XX on Y = b 1 + b 4 M + b 5 W + b 7 M W Y = b 1 + b 4 M + b 5 W + b 7 M W Y=b_(1)+b_(4)M+b_(5)W+b_(7)MWY=b_{1}+b_{4} M+b_{5} W+b_{7} M W
X X XX Y = b 1 + b 4 M + b 5 W + b 7 M W Y = b 1 + b 4 M + b 5 W + b 7 M W Y=b_(1)+b_(4)M+b_(5)W+b_(7)MWY=b_{1}+b_{4} M+b_{5} W+b_{7} M W 的條件效應
Created with 以下列方式建立
Model Templates for PROCESS for SPSS and SAS ©2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 ©2013 Andrew F. Hayes, http://www.afhayes.com/

Model 4 型號 4

Conceptual Diagram 概念圖

Statistical Diagram 統計圖表
Indirect effect of X X XX on Y Y YY through M i = a i b i M i = a i b i M_(i)=a_(i)b_(i)M_{i}=a_{i} b_{i}
X X XX 透過 M i = a i b i M i = a i b i M_(i)=a_(i)b_(i)M_{i}=a_{i} b_{i} Y Y YY 產生間接影響

Direct effect of X X XX on Y = c Y = c Y=c^(')Y=c^{\prime}
X X XX Y = c Y = c Y=c^(')Y=c^{\prime} 的直接影響

*Model 4 allows up to 10 mediators operating in parallel
*Model 4 最多允許 10 個調解器並行運作
Created with 以下列方式建立
Model Templates for PROCESS for SPSS and SAS (C2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 (C2013 Andrew F. Hayes, http://www.afhayes.com/)

Model 5 型號 5

Conceptual Diagram 概念圖

Statistical Diagram 統計圖表
Indirect effect of X X XX on Y Y YY through M i = a i b i M i = a i b i M_(i)=a_(i)b_(i)M_{i}=a_{i} b_{i}
X X XX 透過 M i = a i b i M i = a i b i M_(i)=a_(i)b_(i)M_{i}=a_{i} b_{i} Y Y YY 產生間接影響

Conditional direct effect of X X XX on Y = c 1 + c 3 W Y = c 1 + c 3 W Y=c_(1)^(')+c_(3)^(')WY=c_{1}{ }^{\prime}+c_{3}{ }^{\prime} W
X X XX Y = c 1 + c 3 W Y = c 1 + c 3 W Y=c_(1)^(')+c_(3)^(')WY=c_{1}{ }^{\prime}+c_{3}{ }^{\prime} W 的條件直接效應

*Model 5 allows up to 10 mediators operating in parallel
*5 型允許多達 10 個調解器並行運作
Model Templates for PROCESS for SPSS and SAS ©2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 ©2013 Andrew F. Hayes, http://www.afhayes.com/

Model 6 型號 6

(2 mediators) (2名調解員)

Conceptual Diagram 概念圖

Statistical Diagram 統計圖表
Indirect effect of X X XX on Y Y YY through M i M i M_(i)M_{i} only = a i b i = a i b i =a_(i)b_(i)=a_{i} b_{i}
X X XX Y Y YY 經由 M i M i M_(i)M_{i} 僅對 = a i b i = a i b i =a_(i)b_(i)=a_{i} b_{i} 的間接影響

Indirect effect of X X XX on Y Y YY through M 1 M 1 M_(1)M_{1} and M 2 M 2 M_(2)M_{2} in serial = a 1 d 21 b 2 = a 1 d 21 b 2 =a_(1)d_(21)b_(2)=a_{1} d_{21} b_{2} Direct effect of X X XX on Y = c Y = c Y=c^(')Y=c^{\prime}
X X XX Y Y YY 經由 M 1 M 1 M_(1)M_{1} M 2 M 2 M_(2)M_{2} 在序列 = a 1 d 21 b 2 = a 1 d 21 b 2 =a_(1)d_(21)b_(2)=a_{1} d_{21} b_{2} 中的間接影響 X X XX Y = c Y = c Y=c^(')Y=c^{\prime} 的直接影響
Model Templates for PROCESS for SPSS and SAS ©2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 ©2013 Andrew F. Hayes, http://www.afhayes.com/

Model 6 型號 6

(3 mediators) (3位調解員)

Conceptual Diagram 概念圖

Statistical Diagram 統計圖表
Indirect effect of X X XX on Y Y YY through M i M i M_(i)M_{i} only = a i b i = a i b i =a_(i)b_(i)=a_{i} b_{i}
X X XX Y Y YY 經由 M i M i M_(i)M_{i} 僅對 = a i b i = a i b i =a_(i)b_(i)=a_{i} b_{i} 的間接影響

Indirect effect of X X XX on Y Y YY through M 1 M 1 M_(1)M_{1} and M 2 M 2 M_(2)M_{2} in serial = a 1 d 21 b 2 = a 1 d 21 b 2 =a_(1)d_(21)b_(2)=a_{1} d_{21} b_{2}
X X XX Y Y YY 經由 M 1 M 1 M_(1)M_{1} M 2 M 2 M_(2)M_{2} 在序列 = a 1 d 21 b 2 = a 1 d 21 b 2 =a_(1)d_(21)b_(2)=a_{1} d_{21} b_{2} 中的間接影響

Indirect effect of X X XX on Y Y YY through M 1 M 1 M_(1)M_{1} and M 3 M 3 M_(3)M_{3} in serial = a 1 d 31 b 3 = a 1 d 31 b 3 =a_(1)d_(31)b_(3)=a_{1} d_{31} b_{3}
X X XX Y Y YY 經由 M 1 M 1 M_(1)M_{1} M 3 M 3 M_(3)M_{3} 在序列 = a 1 d 31 b 3 = a 1 d 31 b 3 =a_(1)d_(31)b_(3)=a_{1} d_{31} b_{3} 中的間接影響

Indirect effect of X X XX on Y Y YY through M 2 M 2 M_(2)M_{2} and M 3 M 3 M_(3)M_{3} in serial = a 2 d 32 b 3 = a 2 d 32 b 3 =a_(2)d_(32)b_(3)=a_{2} d_{32} b_{3}
X X XX Y Y YY 經由 M 2 M 2 M_(2)M_{2} M 3 M 3 M_(3)M_{3} 在序列 = a 2 d 32 b 3 = a 2 d 32 b 3 =a_(2)d_(32)b_(3)=a_{2} d_{32} b_{3} 中的間接影響

Indirect effect of X X XX on Y Y YY through M 1 , M 2 M 1 , M 2 M_(1),M_(2)M_{1}, M_{2}, and M 3 M 3 M_(3)M_{3} in serial = a 1 d 21 d 32 b 3 = a 1 d 21 d 32 b 3 =a_(1)d_(21)d_(32)b_(3)=a_{1} d_{21} d_{32} b_{3}
X X XX Y Y YY M 1 , M 2 M 1 , M 2 M_(1),M_(2)M_{1}, M_{2} 的間接影響,以及 M 3 M 3 M_(3)M_{3} 在序列 = a 1 d 21 d 32 b 3 = a 1 d 21 d 32 b 3 =a_(1)d_(21)d_(32)b_(3)=a_{1} d_{21} d_{32} b_{3} 中的間接影響

Direct effect of X X XX on Y = c Y = c Y=c^(')Y=c^{\prime}
X X XX Y = c Y = c Y=c^(')Y=c^{\prime} 的直接影響
Model Templates for PROCESS for SPSS and SAS ©2013 Andrew F. Hayes, http://www.afhayes.com/
SPSS 和 SAS 的 PROCESS 模型範本 ©2013 Andrew F. Hayes, http://www.afhayes.com/

Model 6 型號 6

(4 mediators) (4位調解員)

Conceptual Diagram 概念圖

Statistical Diagram 統計圖表
Indirect effect of X X XX on Y Y YY through M i M i M_(i)M_{i} only = a i b i = a i b i =a_(i)b_(i)=a_{i} b_{i}
X X XX Y Y YY 經由 M i M i M_(i)M_{i} 僅對 = a i b i = a i b i =a_(i)b_(i)=a_{i} b_{i} 的間接影響

Indirect effect of X X XX on Y Y YY through M 1 M 1 M_(1)M_{1} and M 2 M 2 M_(2)M_{2} in serial = a 1 d 21 b 2 = a 1 d 21 b 2 =a_(1)d_(21)b_(2)=a_{1} d_{21} b_{2} Indirect effect of X X XX on Y Y YY through M 1 M 1 M_(1)M_{1} and M 3 M 3 M_(3)M_{3} in serial = a 1 d 31 b 3 = a 1 d 31 b 3 =a_(1)d_(31)b_(3)=a_{1} d_{31} b_{3} Indirect effect of X X XX on Y Y YY through M 1 M 1 M_(1)M_{1} and M 4 M 4 M_(4)M_{4} in serial = a 1 d 41 b 4 = a 1 d 41 b 4 =a_(1)d_(41)b_(4)=a_{1} d_{41} b_{4} Indirect effect of X X XX on Y Y YY through M 2 M 2 M_(2)M_{2} and M 3 M 3 M_(3)M_{3} in serial = a 2 d 32 b 3 = a 2 d 32 b 3 =a_(2)d_(32)b_(3)=a_{2} d_{32} b_{3} Indirect effect of X X XX on Y Y YY through M 2 M 2 M_(2)M_{2} and M 4 M 4 M_(4)M_{4} in serial = a 2 d 42 b 4 = a 2 d 42 b 4 =a_(2)d_(42)b_(4)=a_{2} d_{42} b_{4} Indirect effect of X X XX on Y Y YY through M 3 M 3 M_(3)M_{3} and M 4 M 4 M_(4)M_{4} in serial = a 3 d 43 b 4 = a 3 d 43 b 4 =a_(3)d_(43)b_(4)=a_{3} d_{43} b_{4} Indirect effect of X X XX on Y Y YY through M 1 , M 2 M 1 , M 2 M_(1),M_(2)M_{1}, M_{2}, and M 3 M 3 M_(3)M_{3} in serial = a 1 d 21 d 32 b 3 = a 1 d 21 d 32 b 3 =a_(1)d_(21)d_(32)b_(3)=a_{1} d_{21} d_{32} b_{3}