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Direct linkLi, Wei; Dong, Nianbo; Maynard, Rebecca A. – Journal of Educational and Behavioral Statistics, 2020
Cost-effectiveness analysis is a widely used educational evaluation tool. The randomized controlled trials that aim to evaluate the cost-effectiveness of the treatment are commonly referred to as randomized cost-effectiveness trials (RCETs). This study provides methods of power analysis for two-level multisite RCETs. Power computations take…
Descriptors: Statistical Analysis, Cost Effectiveness, Randomized Controlled Trials, Educational Research
Kim, Minjung; Hsu, Hsien-Yuan – Journal of Educational and Behavioral Statistics, 2019
Given the natural hierarchical structure in school-setting data, multilevel modeling (MLM) has been widely employed in education research using a number of different statistical software packages. The purpose of this article is to review a recent feature of Stat-JR, the statistical analysis assistants (SAAs) embedded in Stat-JR (Version 1.0.5),…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Computer Software, Computer Software Evaluation
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
Download full textAndrew Gelman; Daniel Lee; Jiqiang Guo – Journal of Educational and Behavioral Statistics, 2015
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…
Descriptors: Programming Languages, Bayesian Statistics, Inferences, Monte Carlo Methods
McNeish, Daniel M. – Journal of Educational and Behavioral Statistics, 2016
Mixed-effects models (MEMs) and latent growth models (LGMs) are often considered interchangeable save the discipline-specific nomenclature. Software implementations of these models, however, are not interchangeable, particularly with small sample sizes. Restricted maximum likelihood estimation that mitigates small sample bias in MEMs has not been…
Descriptors: Models, Statistical Analysis, Hierarchical Linear Modeling, Sample Size
Leckie, George; French, Robert; Charlton, Chris; Browne, William – Journal of Educational and Behavioral Statistics, 2014
Applications of multilevel models to continuous outcomes nearly always assume constant residual variance and constant random effects variances and covariances. However, modeling heterogeneity of variance can prove a useful indicator of model misspecification, and in some educational and behavioral studies, it may even be of direct substantive…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Predictor Variables, Computer Software
