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C. J. Van Lissa; M. Garnier-Villarreal; D. Anadria – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there…
Descriptors: Multivariate Analysis, Structural Equation Models, Open Source Technology, Computation
Dan Wei; Peida Zhan; Hongyun Liu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
In latent growth curve modeling (LGCM), overall fit indices have garnered increased disputation for model selection, and model fit evaluation based on the mean structure has becoming popularity. The present study developed a versatile fit index, named Weighted Root Mean Squared Errors (WRMSE), based on individual case residuals (ICRs) with the aim…
Descriptors: Structural Equation Models, Goodness of Fit, Error of Measurement, Computation
Chunhua Cao; Benjamin Lugu; Jujia Li – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality, sample size, model size, the magnitude of misspecified path effect, and the choice or prior on the performance of the fit indices was also…
Descriptors: Structural Equation Models, Bayesian Statistics, Measurement, Error of Measurement
Nese, Joseph F. T.; Stevens, Joseph J.; Schulte, Ann C.; Tindal, Gerald; Elliott, Stephen N. – Journal of Special Education, 2017
Our purpose was to examine different approaches to modeling the time-varying nature of exceptionality classification. Using longitudinal data from one state's mathematics achievement test for 28,829 students in Grades 3 to 8, we describe the reclassification rate within special education and between general and special education, and compare four…
Descriptors: Classification, Achievement Gains, Special Needs Students, Mathematics Achievement
Ardasheva, Yuliya; Tretter, Thomas R. – Modern Language Journal, 2013
As the school-aged English language learner (ELL) population continues to grow in the United States and other English-speaking countries, psychometrically sound instruments to measure their language learning strategies (LLS) become ever more critical. This study adapted and validated an adult-oriented measure of LLS (50-item "Strategy…
Descriptors: Second Language Learning, Second Language Instruction, Learning Strategies, Measures (Individuals)
Marsh, Herbert W.; Ludtke, Oliver; Muthen, Bengt; Asparouhov, Tihomir; Morin, Alexandre J. S.; Trautwein, Ulrich; Nagengast, Benjamin – Psychological Assessment, 2010
NEO instruments are widely used to assess Big Five personality factors, but confirmatory factor analyses (CFAs) conducted at the item level do not support their a priori structure due, in part, to the overly restrictive CFA assumptions. We demonstrate that exploratory structural equation modeling (ESEM), an integration of CFA and exploratory…
Descriptors: Structural Equation Models, Factor Structure, Personality Traits, Factor Analysis
Bandalos, Deborah L. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This study examined the efficacy of 4 different parceling methods for modeling categorical data with 2, 3, and 4 categories and with normal, moderately nonnormal, and severely nonnormal distributions. The parceling methods investigated were isolated parceling in which items were parceled with other items sharing the same source of variance, and…
Descriptors: Structural Equation Models, Computation, Goodness of Fit, Classification