Recovery of weak factor loadings in confirmatory factor analysis under conditions of model misspecification

被引：0
作者：
Carmen Ximénez
机构：
[1] Universidad Autonóma de Madrid,Departamento de Psicología Social y Metodología
来源：
Behavior Research Methods | 2009年 / 41卷
关键词：
Model Error; Exploratory Factor Analysis; Structural Error; Weak Factor; Monte Carlo Simulation Study;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This article presents the results of two Monte Carlo simulation studies of the recovery of weak factor loadings, in the context of confirmatory factor analysis, for models that do not exactly hold in the population. This issue has not been examined in previous research. Model error was introduced using a procedure that allows for specifying a covariance structure with a specified discrepancy in the population. The effects of sample size, estimation method (maximum likelihood vs. unweighted least squares), and factor correlation were also considered. The first simulation study examined recovery for models correctly specified with the known number of factors, and the second investigated recovery for models incorrectly specified by underfactoring. The results showed that recovery was not affected by model discrepancy for the correctly specified models but was affected for the incorrectly specified models. Recovery improved in both studies when factors were correlated, and unweighted least squares performed better than maximum likelihood in recovering the weak factor loadings.
引用
收藏
页码：1038 / 1052
页数：14
相关论文
共 33 条
[1]  
Anderson J. C.(1984)The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis Psychometrika 49 155-173
[2]  
Gerbing D. W.(2003)Recovery of weak common factors by maximum likelihood and ordinary least squares estimation Multivariate Behavioral Research 38 25-56
[3]  
Briggs N. E.(1992)Constructing a covariance ma trix that yields a specified minimizer and a specified minimum discrepancy function value Psychometrika 57 357-369
[4]  
MacCallum R. C.(2005)Sensitivity of fit indexes to misspecified structural or measurement model components: Rationale of two-index strategy revisited Structural Equation Modeling 12 343-367
[5]  
Cudeck R.(1996)The effects of underextraction in factor and component analyses Educational & Psychological Measurement 56 907-929
[6]  
Browne M. W.(1982)The behavior of number-of-factors rules with simulated data Multivariate Behavioral Research 17 193-219
[7]  
Fan X.(1999)Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives Structural Equation Modeling 6 1-55
[8]  
Sivo S. A.(1967)Some contributions to maximum likelihood factor analysis Psychometrika 32 443-482
[9]  
Fava J. L.(1989)Influence of sample size, estimation method, and model specification on goodness-of-fit assessments in structural equation models Journal of Applied Psychology 74 625-635
[10]  
Velicer W. F.(2003)2001 presidential address: Working with imperfect models Multivariate Behavioral Research 38 113-139
1234