Abstract/Details

An evaluation of estimation methods in confirmatory factor analytic models with ordered categorical data in LISREL


2010 2010

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Abstract (summary)

Robust estimation methods for SEM models that include ordered categorical data are currently available to researchers in LISREL software However; little research has evaluated the efficiency of these methods. In this research, robust ML and robust DWLS estimation methods were examined, as well as two other commonly applied SEM estimators, WLS and normal theory ML. The effects of sample size, item distributional conditions, severity of non-normality of the data, and model size on resulting parameter estimates, standard error estimates, and model test and fit statistics for each estimation method were evaluated. Results indicated that both robust ML and robust DWLS performed much better on each of the study outcome variables regardless of condition than the other two estimators. Results also revealed that although the robust methods led to consistently unbiased parameter estimates, generally, robust ML resulted in more accurate standard error estimates, less biased chi square statistics, and lower Type I error rates than robust DWLS.

Indexing (details)


Subject
Quantitative psychology
Classification
0632: Quantitative psychology
Identifier / keyword
Psychology; Categorical data; Confirmatory factor analysis
Title
An evaluation of estimation methods in confirmatory factor analytic models with ordered categorical data in LISREL
Author
Trierweiler, Tammy
Number of pages
243
Publication year
2010
Degree date
2010
School code
0072
Source
DAI-B 71/08, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781124103877
Advisor
Lewis, Charles
University/institution
Fordham University
University location
United States -- New York
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3416004
ProQuest document ID
734812306
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/734812306
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