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Vidotto, Davide; Vermunt, Jeroen K.; van Deun, Katrijn – Journal of Educational and Behavioral Statistics, 2018
With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex…
Descriptors: Bayesian Statistics, Multivariate Analysis, Data, Hierarchical Linear Modeling
McCoach, D. Betsy; Rifenbark, Graham G.; Newton, Sarah D.; Li, Xiaoran; Kooken, Janice; Yomtov, Dani; Gambino, Anthony J.; Bellara, Aarti – Journal of Educational and Behavioral Statistics, 2018
This study compared five common multilevel software packages via Monte Carlo simulation: HLM 7, M"plus" 7.4, R (lme4 V1.1-12), Stata 14.1, and SAS 9.4 to determine how the programs differ in estimation accuracy and speed, as well as convergence, when modeling multiple randomly varying slopes of different magnitudes. Simulated data…
Descriptors: Hierarchical Linear Modeling, Computer Software, Comparative Analysis, Monte Carlo Methods
Ker, H. W. – Universal Journal of Educational Research, 2014
Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…
Descriptors: Effective Schools Research, Hierarchical Linear Modeling, Regression (Statistics), Comparative Analysis
Castellano, Katherine E.; Rabe-Hesketh, Sophia; Skrondal, Anders – Journal of Educational and Behavioral Statistics, 2014
Investigations of the effects of schools (or teachers) on student achievement focus on either (1) individual school effects, such as value-added analyses, or (2) school-type effects, such as comparisons of charter and public schools. Controlling for school composition by including student covariates is critical for valid estimation of either kind…
Descriptors: Hierarchical Linear Modeling, Context Effect, Economics, Educational Research
Jacob, Robin T.; Goddard, Roger D.; Kim, Eun Sook – Educational Evaluation and Policy Analysis, 2014
It is often difficult and costly to obtain individual-level student achievement data, yet, researchers are frequently reluctant to use school-level achievement data that are widely available from state websites. We argue that public-use aggregate school-level achievement data are, in fact, sufficient to address a wide range of evaluation questions…
Descriptors: Academic Achievement, Data, Information Utilization, Educational Assessment
Rhoads, Christopher – Journal of Research on Educational Effectiveness, 2016
Experimental evaluations that involve the educational system usually involve a hierarchical structure (students are nested within classrooms that are nested within schools, etc.). Concerns about contamination, where research subjects receive certain features of an intervention intended for subjects in a different experimental group, have often led…
Descriptors: Educational Experiments, Error of Measurement, Research Design, Statistical Analysis
Azigwe, John Bosco – Journal of Education and Practice, 2016
International surveys of student achievement are becoming increasingly popular with governments around the world, as they try to measure the performance of their country's education system. The main reason for this trend is the shared opinion that countries will need to be able to compete in the "knowledge economy" to assure the economic…
Descriptors: Achievement Tests, Foreign Countries, International Assessment, Secondary School Students
Canivez, Gary L.; Kush, Joseph C. – Journal of Psychoeducational Assessment, 2013
Weiss, Keith, Zhu, and Chen (2013a) and Weiss, Keith, Zhu, and Chen (2013b), this issue, report examinations of the factor structure of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) and Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV), respectively; comparing Wechsler Hierarchical Model (W-HM) and…
Descriptors: Intelligence Tests, Factor Structure, Comparative Analysis, Arithmetic
Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen – American Journal of Evaluation, 2016
Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…
Descriptors: Intervention, Multivariate Analysis, Mixed Methods Research, Models