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Hayes, Timothy – Journal of Educational and Behavioral Statistics, 2019
Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner…
Descriptors: Computer Software Evaluation, Computer Software Reviews, Hierarchical Linear Modeling, Data Analysis
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
Hao, Jiangang; Ho, Tin Kam – Journal of Educational and Behavioral Statistics, 2019
Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review…
Descriptors: Artificial Intelligence, Statistical Inference, Data Analysis, Programming Languages
Dai, Shenghai; Svetina, Dubravka; Wang, Xiaolin – Journal of Educational and Behavioral Statistics, 2017
There is an increasing interest in reporting test subscores for diagnostic purposes. In this article, we review nine popular R packages (subscore, mirt, TAM, sirt, CDM, NPCD, lavaan, sem, and OpenMX) that are capable of implementing subscore-reporting methods within one or more frameworks including classical test theory, multidimensional item…
Descriptors: Diagnostic Tests, Scores, Computer Software, Item Response Theory
Thoemmes, Felix; Liao, Wang; Jin, Ze – Journal of Educational and Behavioral Statistics, 2017
This article describes the analysis of regression-discontinuity designs (RDDs) using the R packages rdd, rdrobust, and rddtools. We discuss similarities and differences between these packages and provide directions on how to use them effectively. We use real data from the Carolina Abecedarian Project to show how an analysis of an RDD can be…
Descriptors: Regression (Statistics), Research Design, Robustness (Statistics), Computer Software
Keller, Bryan; Tipton, Elizabeth – Journal of Educational and Behavioral Statistics, 2016
In this article, we review four software packages for implementing propensity score analysis in R: "Matching, MatchIt, PSAgraphics," and "twang." After briefly discussing essential elements for propensity score analysis, we apply each package to a data set from the Early Childhood Longitudinal Study in order to estimate the…
Descriptors: Computer Software, Probability, Statistical Analysis, Longitudinal Studies
Slater, Stefan; Joksimovic, Srecko; Kovanovic, Vitomir; Baker, Ryan S.; Gasevic, Dragan – Journal of Educational and Behavioral Statistics, 2017
In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will…
Descriptors: Data Analysis, Data Processing, Computer Uses in Education, Educational Research
von Davier, Matthias; Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2007
Reporting methods used in large-scale assessments such as the National Assessment of Educational Progress (NAEP) rely on latent regression models. To fit the latent regression model using the maximum likelihood estimation technique, multivariate integrals must be evaluated. In the computer program MGROUP used by the Educational Testing Service for…
Descriptors: Simulation, Computer Software, Sampling, Data Analysis
Muthen, Bengt; Masyn, Katherine – Journal of Educational and Behavioral Statistics, 2005
This article proposes a general latent variable approach to discrete-time survival analysis of nonrepeatable events such as onset of drug use. It is shown how the survival analysis can be formulated as a generalized latent class analysis of event history indicators. The latent class analysis can use covariates and can be combined with the joint…
Descriptors: Drug Use, Maximum Likelihood Statistics, Computer Software, Aggression

de Leeuw, Jan; Kreft, Ita G. G. – Journal of Educational and Behavioral Statistics, 1995
Practical problems with multilevel techniques are discussed. These problems relate to terminology, computer programs employing different algorithms, and interpretations of the coefficients in either one or two steps. The usefulness of hierarchical linear models (HLMs) in common situations in educational research is explored. While elegant, HLMs…
Descriptors: Algorithms, Computer Software, Definitions, Educational Research

Cohen, Steve; And Others – Journal of Educational and Behavioral Statistics, 1996
A detailed multisite evaluation of instructional software, the ConStatS package, designed to help students conceptualize introductory probability and statistics, yielded patterns of error on several assessment items. Results from 739 college students demonstrated 10 misconceptions that may be among the most difficult concepts to teach. (SLD)
Descriptors: College Students, Computer Assisted Instruction, Computer Software Evaluation, Educational Assessment
Zwick, Rebecca; Sklar, Jeffrey C. – Journal of Educational and Behavioral Statistics, 2005
Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that…
Descriptors: Error of Measurement, Regression (Statistics), Computer Software, Predictor Variables