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Craig K. Enders – Grantee Submission, 2023
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of "Psychological Methods." Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of…
Descriptors: Data, Research, Theories, Regression (Statistics)
Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.; Lee, Daniel; Goodrich, Ben; Betancourt, Michael; Brubaker, Marcus A.; Guo, Jiqiang; Li, Peter; Riddell, Allen – Grantee Submission, 2017
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the…
Descriptors: Programming Languages, Probability, Bayesian Statistics, Monte Carlo Methods
Aybek, Eren Can; Demirtasli, R. Nukhet – International Journal of Research in Education and Science, 2017
This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Test Items
Chiu, Chia-Yi; Köhn, Hans-Friedrich; Wu, Huey-Min – International Journal of Testing, 2016
The Reduced Reparameterized Unified Model (Reduced RUM) is a diagnostic classification model for educational assessment that has received considerable attention among psychometricians. However, the computational options for researchers and practitioners who wish to use the Reduced RUM in their work, but do not feel comfortable writing their own…
Descriptors: Educational Diagnosis, Classification, Models, Educational Assessment
Xi, Nuo; Browne, Michael W. – Journal of Educational and Behavioral Statistics, 2014
A promising "underlying bivariate normal" approach was proposed by Jöreskog and Moustaki for use in the factor analysis of ordinal data. This was a limited information approach that involved the maximization of a composite likelihood function. Its advantage over full-information maximum likelihood was that very much less computation was…
Descriptors: Factor Analysis, Maximum Likelihood Statistics, Data, Computation
Broatch, Jennifer; Lohr, Sharon – Journal of Educational and Behavioral Statistics, 2012
Measuring teacher effectiveness is challenging since no direct estimate exists; teacher effectiveness can be measured only indirectly through student responses. Traditional value-added assessment (VAA) models generally attempt to estimate the value that an individual teacher adds to students' knowledge as measured by scores on successive…
Descriptors: Teacher Effectiveness, Models, Maximum Likelihood Statistics, Computation
Bryant, Fred B.; Satorra, Albert – Structural Equation Modeling: A Multidisciplinary Journal, 2012
We highlight critical conceptual and statistical issues and how to resolve them in conducting Satorra-Bentler (SB) scaled difference chi-square tests. Concerning the original (Satorra & Bentler, 2001) and new (Satorra & Bentler, 2010) scaled difference tests, a fundamental difference exists in how to compute properly a model's scaling correction…
Descriptors: Statistical Analysis, Structural Equation Models, Goodness of Fit, Least Squares Statistics
Jeon, Minjeong; Rabe-Hesketh, Sophia – Journal of Educational and Behavioral Statistics, 2012
In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever setting some of the parameters of the model to known constants turns the model into a standard model. An important class of models that can be…
Descriptors: Maximum Likelihood Statistics, Computation, Models, Factor Structure
Schlomer, Gabriel L.; Bauman, Sheri; Card, Noel A. – Journal of Counseling Psychology, 2010
This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common…
Descriptors: Maximum Likelihood Statistics, Counseling Psychology, Researchers, Data Collection
Enders, Craig K. – Guilford Press, 2010
Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Maximum Likelihood Statistics

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