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Bolin, Jocelyn H.; Finch, W. Holmes; Stenger, Rachel – Educational and Psychological Measurement, 2019
Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the…
Descriptors: Hierarchical Linear Modeling, Computation, Statistical Analysis, Maximum Likelihood Statistics
Lee, Soo; Suh, Youngsuk – Journal of Educational Measurement, 2018
Lord's Wald test for differential item functioning (DIF) has not been studied extensively in the context of the multidimensional item response theory (MIRT) framework. In this article, Lord's Wald test was implemented using two estimation approaches, marginal maximum likelihood estimation and Bayesian Markov chain Monte Carlo estimation, to detect…
Descriptors: Item Response Theory, Sample Size, Models, Error of Measurement
Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2016
We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…
Descriptors: Causal Models, Markov Processes, Longitudinal Studies, Probability
Roberts, James S.; Thompson, Vanessa M. – Applied Psychological Measurement, 2011
A marginal maximum a posteriori (MMAP) procedure was implemented to estimate item parameters in the generalized graded unfolding model (GGUM). Estimates from the MMAP method were compared with those derived from marginal maximum likelihood (MML) and Markov chain Monte Carlo (MCMC) procedures in a recovery simulation that varied sample size,…
Descriptors: Statistical Analysis, Markov Processes, Computation, Monte Carlo Methods
Depaoli, Sarah – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…
Descriptors: Markov Processes, Factor Analysis, Statistical Bias, Evaluation Research
Deping, Li; Oranje, Andreas – ETS Research Report Series, 2006
A hierarchical latent regression model is suggested to estimate nested and nonnested relationships in complex samples such as found in the National Assessment of Educational Progress (NAEP). The proposed model aims at improving both parameters and variance estimates via a two-level hierarchical linear model. This model falls naturally within the…
Descriptors: Hierarchical Linear Modeling, Computation, Measurement, Regression (Statistics)