NotesFAQContact Us
Collection
Advanced
Search Tips
Source
Journal of Educational and…14
Audience
Laws, Policies, & Programs
Assessments and Surveys
Program for International…1
What Works Clearinghouse Rating
Showing all 14 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Clemens Draxler; Andreas Kurz; Can Gürer; Jan Philipp Nolte – Journal of Educational and Behavioral Statistics, 2024
A modified and improved inductive inferential approach to evaluate item discriminations in a conditional maximum likelihood and Rasch modeling framework is suggested. The new approach involves the derivation of four hypothesis tests. It implies a linear restriction of the assumed set of probability distributions in the classical approach that…
Descriptors: Inferences, Test Items, Item Analysis, Maximum Likelihood Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Lee, Daniel Y.; Harring, Jeffrey R. – Journal of Educational and Behavioral Statistics, 2023
A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation-maximization algorithm, (c) multiple imputation, (d) a two-stage multiple…
Descriptors: Monte Carlo Methods, Research Problems, Statistical Inference, Bayesian Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Yuan, Ke-Hai; Kano, Yutaka – Journal of Educational and Behavioral Statistics, 2018
Meta-analysis plays a key role in combining studies to obtain more reliable results. In social, behavioral, and health sciences, measurement units are typically not well defined. More meaningful results can be obtained by standardizing the variables and via the analysis of the correlation matrix. Structural equation modeling (SEM) with the…
Descriptors: Meta Analysis, Structural Equation Models, Maximum Likelihood Statistics, Least Squares Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Monroe, Scott – Journal of Educational and Behavioral Statistics, 2019
In item response theory (IRT) modeling, the Fisher information matrix is used for numerous inferential procedures such as estimating parameter standard errors, constructing test statistics, and facilitating test scoring. In principal, these procedures may be carried out using either the expected information or the observed information. However, in…
Descriptors: Item Response Theory, Error of Measurement, Scoring, Inferences
Lockwood, J. R.; Castellano, Katherine E.; Shear, Benjamin R. – Journal of Educational and Behavioral Statistics, 2018
This article proposes a flexible extension of the Fay--Herriot model for making inferences from coarsened, group-level achievement data, for example, school-level data consisting of numbers of students falling into various ordinal performance categories. The model builds on the heteroskedastic ordered probit (HETOP) framework advocated by Reardon,…
Descriptors: Bayesian Statistics, Mathematical Models, Statistical Inference, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
Liu, Yang; Yang, Ji Seung – Journal of Educational and Behavioral Statistics, 2018
The uncertainty arising from item parameter estimation is often not negligible and must be accounted for when calculating latent variable (LV) scores in item response theory (IRT). It is particularly so when the calibration sample size is limited and/or the calibration IRT model is complex. In the current work, we treat two-stage IRT scoring as a…
Descriptors: Intervals, Scores, Item Response Theory, Bayesian Statistics
Casabianca, Jodi M.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2015
Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation…
Descriptors: Item Response Theory, Maximum Likelihood Statistics, Computation, Comparative Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Pokropek, Artur – Journal of Educational and Behavioral Statistics, 2016
A response model that is able to detect guessing behaviors and produce unbiased estimates in low-stake conditions using timing information is proposed. The model is a special case of the grade of membership model in which responses are modeled as partial members of a class that is affected by motivation and a class that responds only according to…
Descriptors: Reaction Time, Models, Guessing (Tests), Computation
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Choi, Jaehwa; Kim, Sunhee; Chen, Jinsong; Dannels, Sharon – Journal of Educational and Behavioral Statistics, 2011
The purpose of this study is to compare the maximum likelihood (ML) and Bayesian estimation methods for polychoric correlation (PCC) under diverse conditions using a Monte Carlo simulation. Two new Bayesian estimates, maximum a posteriori (MAP) and expected a posteriori (EAP), are compared to ML, the classic solution, to estimate PCC. Different…
Descriptors: Computation, Maximum Likelihood Statistics, Bayesian Statistics, Correlation
Peer reviewed Peer reviewed
Direct linkDirect link
Verkuilen, Jay; Smithson, Michael – Journal of Educational and Behavioral Statistics, 2012
Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite…
Descriptors: Responses, Regression (Statistics), Statistical Analysis, Models
Peer reviewed Peer reviewed
Direct linkDirect link
de la Torre, Jimmy – Journal of Educational and Behavioral Statistics, 2009
Cognitive and skills diagnosis models are psychometric models that have immense potential to provide rich information relevant for instruction and learning. However, wider applications of these models have been hampered by their novelty and the lack of commercially available software that can be used to analyze data from this psychometric…
Descriptors: Models, Computation, Psychometrics, Item Response Theory
Peer reviewed Peer reviewed
Direct linkDirect link
Lee, Sik-Yum; Song, Xin-Yuan – Journal of Educational and Behavioral Statistics, 2005
In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects…
Descriptors: Mathematics, Sampling, Structural Equation Models, Bayesian Statistics
Peer reviewed Peer reviewed
Draper, David – Journal of Educational and Behavioral Statistics, 1995
The use of hierarchical models in social science research is discussed, with emphasis on causal inference and consideration of the limitations of hierarchical models. The increased use of Gibbs sampling and other Markov-chain Monte Carlo methods in the application of hierarchical models is recommended. (SLD)
Descriptors: Causal Models, Comparative Analysis, Markov Processes, Maximum Likelihood Statistics