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Julia-Kim Walther; Martin Hecht; Benjamin Nagengast; Steffen Zitzmann – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A two-level data set can be structured in either long format (LF) or wide format (WF), and both have corresponding SEM approaches for estimating multilevel models. Intuitively, one might expect these approaches to perform similarly. However, the two data formats yield data matrices with different numbers of columns and rows, and their "cols :…
Descriptors: Data, Monte Carlo Methods, Statistical Distributions, Matrices
Lu, Rui; Keller, Bryan Sean – AERA Online Paper Repository, 2019
When estimating an average treatment effect with observational data, it's possible to get an unbiased estimate of the causal effect if all confounding variables are observed and reliably measured. In education, confounding variables are often latent constructs. Covariate selection methods used in causal inference applications assume that all…
Descriptors: Factor Analysis, Predictor Variables, Monte Carlo Methods, Comparative Analysis
Cao, Mengyang; Song, Q. Chelsea; Tay, Louis – International Journal of Testing, 2018
There is a growing use of noncognitive assessments around the world, and recent research has posited an ideal point response process underlying such measures. A critical issue is whether the typical use of dominance approaches (e.g., average scores, factor analysis, and the Samejima's graded response model) in scoring such measures is adequate.…
Descriptors: Comparative Analysis, Item Response Theory, Factor Analysis, Models
Rowtho, Vikash – Higher Education Studies, 2017
Undergraduate student dropout is gradually becoming a global problem and the 39 Small Islands Developing States (SIDS) are no exception to this trend. The purpose of this research was to develop a method that can be used for early detection of students who are at-risk of performing poorly in their undergraduate studies. A sample of 279 students…
Descriptors: Foreign Countries, Undergraduate Students, Identification, At Risk Students
Rocci, Roberto; Vichi, Maurizio – Psychometrika, 2005
A new methodology is proposed for the simultaneous reduction of units, variables, and occasions of a three-mode data set. Units are partitioned into a reduced number of classes, while, simultaneously, components for variables and occasions accounting for the largest common information for the classification are identified. The model is a…
Descriptors: Factor Analysis, Classification, Least Squares Statistics, Monte Carlo Methods
Ogasawara, Haruhiko – Psychometrika, 2004
Formulas for the asymptotic biases of the parameter estimates in structural equation models are provided in the case of the Wishart maximum likelihood estimation for normally and nonnormally distributed variables. When multivariate normality is satisfied, considerable simplification is obtained for the models of unstandardized variables. Formulas…
Descriptors: Evaluation Methods, Bias, Factor Analysis, Structural Equation Models

Broodbooks, Wendy J.; Elmore, Patricia B. – Educational and Psychological Measurement, 1987
The effects of sample size, number of variables, and population value of the congruence coefficient on the sampling distribution of the congruence coefficient were examined. Sample data were generated on the basis of the common factor model, and principal axes factor analyses were performed. (Author/LMO)
Descriptors: Factor Analysis, Mathematical Models, Monte Carlo Methods, Predictor Variables

Morris, John D. – American Educational Research Journal, 1979
Computer-based Monte Carlo methods compared the predictive accuracy upon replication of regression of five complete and four incomplete factor score estimation methods. Prediction on incomplete factor scores showed better double cross-validated prediction accuracy than on complete scores. The unique unit-weighted factor score was superior among…
Descriptors: Correlation, Factor Analysis, Monte Carlo Methods, Multiple Regression Analysis