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Geiser, Christian; Eid, Michael; Nussbeck, Fridtjof W. – Psychological Methods, 2008
In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006, see EJ751121) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an…
Descriptors: Factor Structure, Factor Analysis, Structural Equation Models, Questionnaires
Eid, Michael; Nussbeck, Fridtjof W.; Geiser, Christian; Cole, David A.; Gollwitzer, Mario; Lischetzke, Tanja – Psychological Methods, 2008
The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods…
Descriptors: Structural Equation Models, Multitrait Multimethod Techniques, Statistical Analysis, Error of Measurement
Hayashi, Kentaro; Bentler, Peter M.; Yuan, Ke-Hai – Structural Equation Modeling: A Multidisciplinary Journal, 2007
In the exploratory factor analysis, when the number of factors exceeds the true number of factors, the likelihood ratio test statistic no longer follows the chi-square distribution due to a problem of rank deficiency and nonidentifiability of model parameters. As a result, decisions regarding the number of factors may be incorrect. Several…
Descriptors: Researchers, Factor Analysis, Factor Structure, Structural Equation Models
Glockner-Rist, Angelika; Hoijtink, Herbert – Structural Equation Modeling: A Multidisciplinary Journal, 2003
Both structural equation modeling (SEM) and item response theory (IRT) can be used for factor analysis of dichotomous item responses. In this case, the measurement models of both approaches are formally equivalent. They were refined within and across different disciplines, and make complementary contributions to central measurement problems…
Descriptors: Social Science Research, Measurement Techniques, Social Sciences, Item Response Theory

Moore, Alan D. – Remedial and Special Education, 1995
This article suggests the use of structural equation modeling in special education research, to analyze multivariate data from both nonexperimental and experimental research. It combines a structural model linking latent variables and a measurement model linking observed variables with latent variables. (Author/DB)
Descriptors: Data Analysis, Disabilities, Educational Research, Elementary Secondary Education

McArdle, J. J.; Epstein, David – Child Development, 1987
Uses structural equation modeling to combine traditional ideas from repeated-measures ANOVA with some traditional ideas from longitudinal factor analysis. The model describes a latent growth curve model that permits the estimation of parameters representing individual and group dynamics. (Author/RH)
Descriptors: Analysis of Variance, Children, Cognitive Development, Comparative Analysis

McCoach, D. Betsy; Siegle, Del – Roeper Review, 2002
Using structured equation modeling techniques, this study examined factor structure differences in academic self-perceptions of 210 gifted high school students and a general population student group. Although there were large mean differences between gifted students and the general population students on the academic self-perceptions scale, the…
Descriptors: Factor Analysis, Gifted, Grade Point Average, High Schools

Wong, Ngai-Ying; Lin, Wen-Ying; Watkins, David – Educational Psychology, 1996
Analyzes responses to a questionnaire from 10 samples of primary and secondary school students from Nigeria, Zimbabwe, Malaysia, Beijing, Hong Kong, and Canada. The questionnaire covered learning strategies, approaches, and motivation. These data were then analyzed using six different structural equation models. Includes discussion of the models…
Descriptors: Educational Experience, Factor Analysis, Foreign Countries, Learning Experience