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Teaching confirmatory factor analysis to non-statisticians: a case study for estimating the composite reliability of psychometric instruments.
- Source :
-
Case Studies in Business, Industry & Government Statistics . 2014, Vol. 5 Issue 2, p88-101. 14p. - Publication Year :
- 2014
-
Abstract
- Texts and software that we are currently using for teaching multivariate analysis to non-statisticians lack in the delivery of confirmatory factor analysis (CFA). The purpose of this paper is to provide educators with a complement to these resources that includes CFA and its computation. We focus on how to use CFA to estimate a "composite reliability" of a psychometric instrument. This paper provides step-by-step guidance for introducing, via a case-study, the non-statistician to CFA. As a complement to our instruction about the more traditional SPSS, we successfully piloted the software R for estimating CFA on nine non-statisticians. This approach can be used with healthcare graduate students taking a multivariate course, as well as modified for community stakeholders of our Center for American Indian Community Health (e.g. community advisory boards, summer interns, & research team members).The placement of CFA at the end of the class is strategic and gives us an opportunity to do some innovative teaching: (1) build ideas for understanding the case study using previous course work (such as ANOVA); (2) incorporate multi-dimensional scaling (that students already learned) into the selection of a factor structure (new concept); (3) use interactive data from the students (active learning); (4) review matrix algebra and its importance to psychometric evaluation; (5) show students how to do the calculation on their own; and (6) give students access to an actual recent research project. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2152372X
- Volume :
- 5
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Case Studies in Business, Industry & Government Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 96692345