我们的网站为什么显示成这样？

可能因为您的浏览器不支持样式，您可以更新您的浏览器到最新版本，以获取对此功能的支持，访问下面的网站，获取关于浏览器的信息：

|本期目录/Table of Contents|

 贝叶斯多组比较——渐近测量不变性(PDF)

《心理学探新》[ISSN:1003-5184/CN:36-1228/B]

期数:
 2021年01期
页码:
 69-75
栏目:
 
出版日期:
 2021-02-20

文章信息/Info

Title:
 Bayesian Multiple-group Analysis:Approximate Measurement Invariance
文章编号:
1003-5184(2021)01-0069-07
作者:
 宋琼雅 张沥今 潘俊豪
 (中山大学心理学系,广州 510006)
Author(s):
 Song Qiongya Zhang Lijin Pan Junhao
 (Department of Psychology,Sun Yat-sen University,Guangzhou 510006)
关键词:
 贝叶斯方法;  多组验证性因子分析;  渐近测量不变性
Keywords:
 Bayesian estimation;  multiple-group structural equation modeling;  approximate measurement invariance
分类号:
 B841.2
DOI:
 -
文献标识码:
 A
摘要:
 在行为科学研究领域中,检验测量工具的测量不变性是进行群体差异比较的前提。目前,多组验证性因子分析(多组CFA)方法被广泛用于检验测量不变性,但是它对跨组等值的限制过于严格,在实际应用中常常存在大量局限。贝叶斯渐近测量不变性方法基于贝叶斯思想的优良特性,放宽了传统多组CFA方法对跨组差异的严格限制,避免了传统方法的问题,具有较高的应用价值。文章详细介绍了贝叶斯渐近测量不变性方法的原理及优势,同时通过实例展示了渐近测量不变性方法在Mplus软件中的具体分析过程。
Abstract:
 In the behavioral science,the comparison of multiple groups under the latent variable framework is popular.Measurement invariance(MI)is a pre-requisite for such multiple-group comparison.Multiple-group Confirmatory Factor Analysis(Multiple-group CFA)is the most commonly used approach for testing measurement invariance.In traditional multi-group CFA,strict invariant constraints are imposed on measurement parameters across groups.However,due to the complexity of modeling multi-group data,these strict constraints are unrealistic in real data analysis and can easily lead to poor model fitting.In fact,scalar invariance is almost unachievable in practice.The Bayesian approximate MI proposed by Muthén and Asparouhov(2013)compensates for these limitations to some extent by providing a zero-mean,small-variance prior for the differences in measurement parameters.It allows for small differences between groups and avoids the problems caused by strict restrictions in classical method,such as poor model fitting,awkward model modifications and higher Type I error rate.These strengths make this new approach a more suitable method in the practical research.This paper introduced the principles and advantages of the approximate MI approach by comparing it with the classical multi-group CFA method.Besides,a real data set was analyzed to demonstrate the validity and application of this approach using Mplus.

参考文献/References

 李锡钦.(2011).结构方程模型:贝叶斯方法(蔡敬衡,潘俊豪,周影辉 译).北京:高等教育出版社.
李娜.(2009).大学生职业决策困难问卷修编及其特点研究(硕士论文).西南大学.
吴艳,温忠麟.(2011).结构方程建模中的题目打包策略.心理科学进展,19(12),1859-1867.
叶悦妹,戴晓阳.(2008).大学生社会支持评定量表的编制.中国临床心理学杂志,16(5),465-468.
张沥今,陆嘉琦,魏夏琰,潘俊豪.(2019).贝叶斯结构方程模型及其研究现状.心理科学进展,27(11),1812-1825.
Asparouhov,T.,& Muthén,B.(2014).Multiple-group factor analysis alignment.Structural Equation Modeling:A Multidisciplinary Journal,21(4),495-508.
Cieciuch,J.,Davidov,E.,Algesheimer,René.,& Schmidt,P.(2017).Testing for approximate measurement invariance of human values in the European social survey.Sociological Methods & Research,47(4),665-686.
Fong,T.,& Ho,R.(2014).Testing gender invariance of the hospital anxiety and depression scale using the classical approach and bayesian approach.Quality of Life Research,23(5),1421-1426.
Jak,S.,Oort,F.J.,& Dolan,C.V.(2013).A test for cluster bias:Detecting violations of measurement invariance across clusters in multilevel data.Structural Equation Modeling:A Multidisciplinary Journal,20(2),265-282.
Kim,E.S.,Cao,C.,Wang,Y.,& Nguyen,D.T.(2017).Measurement invariance testing with many groups:A comparison of five approaches.Structural Equation Modeling:A Multidisciplinary Journal,24(4),524-544.
Little,T.D.,& Card,N.A.(2013).Longitudinal structural equation modeling.The Guilford Press.
Lu,Z.H.,Chow,S.M.,& Loken,E.(2016).Bayesian factor analysis as a variable-selection problem:Alternative priors and consequences.Multivariate Behavioral Research,51(4),519-539.
Marsh,H.W.,Guo,J.,Parker,P.D.,Nagengast,B.,Asparouhov,T.,Muthén,B.,et al.(2018).What to do when scalar invariance fails:The extended alignment method for multi-group factor analysis comparison of latent means across many groups.Psychological Methods,23(3),524-545.
Maslowsky,J.,Jager,J.,& Hemken,D.(2015).Estimating and interpreting latent variable interactions:A tutorial for applying the latent moderated structural equations method.International Journal of Behavioral Development,39(1),87-96.
Meghan,K.C.,& Zhang,Z.Y.(2018).Fit for a Bayesian:An evaluation of PPpand DIC for structural equation modeling.Structural Equation Modeling:A Multidisciplinary Journal,25(4),1-12.
Muthén,B.,& Asparouhov,T.(2012).Bayesian structural equation modeling:A more flexible representation of substantive theory.Psychological Methods,17(3),313-335.
Muthén,B.,& Asparouhov,T.(2013).BSEM measurement invariance analysis:Mplus Web Note17.http://www.statmodel.com/examples/webnotes/webnote17.pdf.
Pan,J.,Ip,E.H.,& Dubé,L.(2017).An alternative to post hoc model modification in confirmatory factor analysis:The Bayesian lasso.Psychological Methods,22(4),687-704.
Raju,N.S.,Laffitte,L.J.,& Byrne,B.M.(2002).Measurement equivalence:A comparison of methods based on confirmatory factor analysis and item response theory.Journal of Applied Psychology,87(3),517-529.
Rutkowski,L.,& Svetina,D.(2014).Assessing the hypothesis of measurement invariance in the context of large-scale international surveys.Educational & Psychological Measurement,74(1),31-57.
Schmitt,N.,& Kuljanin,G.(2008).Measurement invariance:Review of practice and implications.Human Resource Management Review,18(4),210-222.
Schwartz,S.H.(1992).“Universals in the content and structure of values:Theory and empirical tests in 20 countries.”In P.Z.Mark(Ed.),Advancesin experimental social psychology(pp.1-65).New York:Academic Press.
Shorey,R.C.,Allan,N.P.,Cohen,J.R.,Fite,P.J.,Stuart,G.L.,& Temple,J.R.(2019).Testing the factor structure and measurement invariance of the conflict in Adolescent Dating Relationship Inventory.Psychological Assessment,31(3),410.
Spiegelhalter,D.J.,Best,N.G.,Carlin,B.P.,& Van Der Linde,A.(2002).Bayesian measures of model complexity and fit.Journal of the Royal Statistical Society:Series B(Statistical Methodology),64(4),583-639.
van de Schoot,R.,Kluytmans,A.,Tummers,L.,Lugtig,P.,Hox,J.,& Muthén,B.(2013).Facing off with scylla and charybdis:A comparison of scalar,partial,and the novel possibility of approximate measurement invariance.Frontiers in Psychology,4,770.
van de Schoot,R.,Winter,S.D.,Ryan,O.,Zondervan-Zwijnenburg,M.,& Depaoli,S.(2017).A systematic review of Bayesian articles in psychology:The last 25 years.Psychological Methods,22(2),217-239.
West,S.G.,Finch,J.F.,& Curran,P.J.(1995).Structural equation models with nonnormal variables:Problems and remedies.In R.H.Hoyle(Ed.),Structural equation modeling:Concepts,issues,and applications(pp.56-75).Thousand Oaks,CA:Sage Publications,Inc.
Yuan,Y.,& MacKinnon,D.P.(2009).Bayesian mediation analysis.Psychological Methods,14(4),301-322.

备注/Memo

备注/Memo:
 基金项目:国家自然科学基金项目(31871128),教育部人文社会科学研究规划基金项目(18YJA190013)。
通讯作者:潘俊豪,E-mail:panjunh@mail.sysu.edu.cn。
更新日期/Last Update:  2021-02-20