Kim Eun Sook, Dedrick Robert F, Cao Chunhua, Ferron John M
a University of South Florida.
Multivariate Behav Res. 2016 Nov-Dec;51(6):881-898. doi: 10.1080/00273171.2016.1228042. Epub 2016 Oct 18.
We provide reporting guidelines for multilevel factor analysis (MFA) and use these guidelines to systematically review 72 MFA applications in journals across a range of disciplines (e.g., education, health/nursing, management, and psychology) published between 1994 and 2014. Results are organized in terms of the (a) characteristics of the MFA application (e.g., construct measured), (b) purpose (e.g., measurement validation), (c) data source (e.g., number of cases at Level 1 and Level 2), (d) statistical approach (e.g., maximum likelihood), and (e) results reported (e.g., intraclass correlations for indicators and latent variables, standardized factor loadings, fit indices). Results from this review have implications for applied researchers interested in expanding their approaches to psychometric analyses and construct validation within a multilevel framework and for methodologists using Monte Carlo methods to explore technical and methodological issues grounded in realistic research design conditions.
我们提供了多层次因素分析(MFA)的报告指南,并使用这些指南对1994年至2014年间发表在一系列学科(如教育、健康/护理、管理和心理学)期刊上的72项MFA应用进行系统综述。结果按照以下方面进行组织:(a)MFA应用的特征(如测量的构念),(b)目的(如测量验证),(c)数据源(如一级和二级的案例数量),(d)统计方法(如最大似然法),以及(e)报告的结果(如指标和潜在变量的组内相关系数、标准化因子载荷、拟合指数)。本综述的结果对有兴趣在多层次框架内扩展其心理测量分析方法和构念验证方法的应用研究人员,以及使用蒙特卡罗方法探索基于现实研究设计条件的技术和方法问题的方法学家具有启示意义。