Diana Zhu
Department of Psychology
The Ohio State University
Title: Hierarchical Clustering for Measurement Invariance with Many Groups
Abstract:
When dealing with violation of Measurement Invariance (MI) across many groups, one solution is to find clusters of groups where MI holds and compare the measured constructs within the clusters (Davidov et al., 2014). In this talk, a common unsupervised learning algorithms, Hierarchical Clustering, is proposed to organize and view the information from measurement models within the factor analytics framework across many groups.
Hierarchical clustering is a connectivity-based clustering analysis that requires pairwise distance metrics of the groups to form a hierarchy of clusters of groups. There are two reasons why one might consider clustering methods. (1) They are simple exploratory methods and can be easily implemented without use of specialized software programs. They are widely available in software implementation and can be used as a second stage analysis in a two-stage process, in which the first stage was obtained either from an EFA- or CFA -based factor analysis. (2) Clustering methods are appropriate when one assumes MI violation have discrete patterns across groups rather than a continuous/random pattern. If the assumption does not hold, they may still suggest interpretations for how to interpret the MI violations.
An analysis of 17 groups for a collaboration project investigating survey translation methods will be presented. This research is part of my dissertation work, and a report of the presented analysis will be submitted to Journal of Applied Psychology.
Discussant: Kathryn Hoisington-Shaw