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The Effects Of Estimator Choice And Weighting Strategies On Confirmatory Factor Analysis With Stratified Samples
Author(s) -
Bradley J. Brummel,
Fritz Drasgow
Publication year - 2010
Publication title -
applied multivariate research
Language(s) - English
Resource type - Journals
ISSN - 1918-1108
DOI - 10.22329/amr.v13i2.3019
Subject(s) - statistics , weighting , estimator , lisrel , mathematics , econometrics , maximum likelihood , stratified sampling , population , confirmatory factor analysis , estimation , estimation theory , restricted maximum likelihood , standard error , simple random sample , structural equation modeling , economics , demography , medicine , management , sociology , radiology
Survey researchers often design stratified sampling strategies to target specific subpopulations within the larger population. This stratification can influence the population parameter estimates from these samples because they are not simple random samples of the population. There are three typical estimation options that account for the effects of this stratification in latent variable models: unweighted maximum likelihood, weighted maximum likelihood, and pseudo-maximum likelihood estimation. This paper examines the effects of these procedures on parameter estimates, standard errors, and fit statistics in Lisrel 8.7 (Jöreskog & Sörbom, 2004) and Mplus 3.0 (Muthén & Muthén, 2004). Options using several estimation methods will be compared to pseudo-maximum likelihood estimation. Results indicated the choice of estimation technique does not have a substantial effect on confirmatory factor analysis parameter estimates in large samples. However, standard errors of those parameter estimates and RMSEA values for assessing of model fit can be substantially affected by estimation technique.

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