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Joemari Olea; Kevin Carl Santos – Journal of Educational and Behavioral Statistics, 2024
Although the generalized deterministic inputs, noisy "and" gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that…
Descriptors: Cognitive Measurement, Models, Algorithms, Simulation
Pang, Bo; Nijkamp, Erik; Wu, Ying Nian – Journal of Educational and Behavioral Statistics, 2020
This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models…
Descriptors: Artificial Intelligence, Regression (Statistics), Models, Classification
Minchen, Nathan D.; de la Torre, Jimmy; Liu, Ying – Journal of Educational and Behavioral Statistics, 2017
Nondichotomous response models have been of greater interest in recent years due to the increasing use of different scoring methods and various performance measures. As an important alternative to dichotomous scoring, the use of continuous response formats has been found in the literature. To assess finer-grained skills or attributes and to…
Descriptors: Models, Psychometrics, Test Theory, Maximum Likelihood Statistics
Zhan, Peida; Jiao, Hong; Liao, Dandan; Li, Feiming – Journal of Educational and Behavioral Statistics, 2019
Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth…
Descriptors: Classification, Growth Models, Educational Diagnosis, Models
Moerbeek, Mirjam; Safarkhani, Maryam – Journal of Educational and Behavioral Statistics, 2018
Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health-care professionals. It is important that the random cross-classification is taken into account…
Descriptors: Randomized Controlled Trials, Classification, Research Methodology, Military Personnel
Longford, Nicholas Tibor – Journal of Educational and Behavioral Statistics, 2016
We address the problem of selecting the best of a set of units based on a criterion variable, when its value is recorded for every unit subject to estimation, measurement, or another source of error. The solution is constructed in a decision-theoretical framework, incorporating the consequences (ramifications) of the various kinds of error that…
Descriptors: Decision Making, Classification, Guidelines, Undergraduate Students
Ho, Andrew D.; Reardon, Sean F. – Journal of Educational and Behavioral Statistics, 2012
Test scores are commonly reported in a small number of ordered categories. Examples of such reporting include state accountability testing, Advanced Placement tests, and English proficiency tests. This article introduces and evaluates methods for estimating achievement gaps on a familiar standard-deviation-unit metric using data from these ordered…
Descriptors: Achievement Gap, Scores, Computation, Classification
Longford, Nicholas T. – Journal of Educational and Behavioral Statistics, 2014
A method for medical screening is adapted to differential item functioning (DIF). Its essential elements are explicit declarations of the level of DIF that is acceptable and of the loss function that quantifies the consequences of the two kinds of inappropriate classification of an item. Instead of a single level and a single function, sets of…
Descriptors: Test Items, Test Bias, Simulation, Hypothesis Testing
Palardy, Gregory J.; Vermunt, Jeroen K. – Journal of Educational and Behavioral Statistics, 2010
This article introduces a multilevel growth mixture model (MGMM) for classifying both the individuals and the groups they are nested in. Nine variations of the general model are described that differ in terms of categorical and continuous latent variable specification within and between groups. An application in the context of school effectiveness…
Descriptors: Models, Classification, Effective Schools Research, Mathematics Achievement
Monahan, Patrick O.; McHorney, Colleen A.; Stump, Timothy E.; Perkins, Anthony J. – Journal of Educational and Behavioral Statistics, 2007
Previous methodological and applied studies that used binary logistic regression (LR) for detection of differential item functioning (DIF) in dichotomously scored items either did not report an effect size or did not employ several useful measures of DIF magnitude derived from the LR model. Equations are provided for these effect size indices.…
Descriptors: Classification, Effect Size, Probability, Test Bias

Hill, Peter W.; Goldstein, Harvey – Journal of Educational and Behavioral Statistics, 1998
Presents a method for handling educational data in which students belong to more than one unit at a given level, but there is missing information on the identification of the units to which students belong. The method, which involves setting up a cross-classified model, is illustrated with longitudinal data on students' progress in English. (SLD)
Descriptors: Classification, Educational Research, Group Membership, Longitudinal Studies

Janssen, Rianne; Tuerlinckx, Francis; Meulders, Michel; De Boeck, Paul – Journal of Educational and Behavioral Statistics, 2000
Proposes a hierarchical item response theory (IRT) model for mastery classification in criterion-referenced measurement. In this model, items measuring the same criterion are grouped, and a difficulty and discrimination parameter for the criterion is estimated on the same scale as the person and item parameters. Illustrates the model with a test…
Descriptors: Classification, Criterion Referenced Tests, Elementary Education, Elementary School Students