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Matthew J. Madison; Seungwon Chung; Junok Kim; Laine P. Bradshaw – Grantee Submission, 2023
Recent developments have enabled the modeling of longitudinal assessment data in a diagnostic classification model (DCM) framework. These longitudinal DCMs were developed to provide measures of student growth on a discrete scale in the form of attribute mastery transitions, thereby supporting categorical and criterion-referenced interpretations of…
Descriptors: Models, Cognitive Measurement, Diagnostic Tests, Classification
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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
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Henson, Robert; DiBello, Lou; Stout, Bill – Measurement: Interdisciplinary Research and Perspectives, 2018
Diagnostic classification models (DCMs, also known as cognitive diagnosis models) hold the promise of providing detailed classroom information about the skills a student has or has not mastered. Specifically, DCMs are special cases of constrained latent class models where classes are defined based on mastery/nonmastery of a set of attributes (or…
Descriptors: Classification, Diagnostic Tests, Models, Mastery Learning
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Jarecki, Jana B.; Meder, Björn; Nelson, Jonathan D. – Cognitive Science, 2018
Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference…
Descriptors: Classification, Conditioning, Inferences, Novelty (Stimulus Dimension)
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Chan, Wendy – Journal of Educational and Behavioral Statistics, 2018
Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score-based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods…
Descriptors: Computation, Generalization, Probability, Sample Size
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Chan, Wendy – AERA Online Paper Repository, 2017
Policymakers are increasingly interested in the extent to which experimental results generalize from a sample to a population of inference. When the sample is not randomly selected, propensity score methods are used to reweight the sample. Subclassification by propensity score is commonly used in which the population is partitioned into strata…
Descriptors: Generalization, Classification, Randomized Controlled Trials, Inferences
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Wang, Wenyi; Song, Lihong; Chen, Ping; Meng, Yaru; Ding, Shuliang – Journal of Educational Measurement, 2015
Classification consistency and accuracy are viewed as important indicators for evaluating the reliability and validity of classification results in cognitive diagnostic assessment (CDA). Pattern-level classification consistency and accuracy indices were introduced by Cui, Gierl, and Chang. However, the indices at the attribute level have not yet…
Descriptors: Classification, Reliability, Accuracy, Cognitive Tests
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Lathrop, Quinn N.; Cheng, Ying – Journal of Educational Measurement, 2014
When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…
Descriptors: Cutting Scores, Classification, Computation, Nonparametric Statistics
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Lathrop, Quinn N.; Cheng, Ying – Applied Psychological Measurement, 2013
Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach,…
Descriptors: Item Response Theory, Accuracy, Classification, Computation
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Rutkowski, Leslie; Zhou, Yan – Journal of Educational Measurement, 2015
Given the importance of large-scale assessments to educational policy conversations, it is critical that subpopulation achievement is estimated reliably and with sufficient precision. Despite this importance, biased subpopulation estimates have been found to occur when variables in the conditioning model side of a latent regression model contain…
Descriptors: Error of Measurement, Error Correction, Regression (Statistics), Computation
Feng, Yuling – ProQuest LLC, 2013
Diagnostic classification models (DCMs) are structured latent class models widely discussed in the field of psychometrics. They model subjects' underlying attribute patterns and classify subjects into unobservable groups based on their mastery of attributes required to answer the items correctly. The effective implementation of DCMs depends…
Descriptors: Classification, Models, Psychometrics, Computation
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Cui, Ying; Gierl, Mark J.; Chang, Hua-Hua – Journal of Educational Measurement, 2012
This article introduces procedures for the computation and asymptotic statistical inference for classification consistency and accuracy indices specifically designed for cognitive diagnostic assessments. The new classification indices can be used as important indicators of the reliability and validity of classification results produced by…
Descriptors: Classification, Accuracy, Cognitive Tests, Diagnostic Tests
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Wang, Wen-Chung; Liu, Chen-Wei; Wu, Shiu-Lien – Applied Psychological Measurement, 2013
The random-threshold generalized unfolding model (RTGUM) was developed by treating the thresholds in the generalized unfolding model as random effects rather than fixed effects to account for the subjective nature of the selection of categories in Likert items. The parameters of the new model can be estimated with the JAGS (Just Another Gibbs…
Descriptors: Computer Assisted Testing, Adaptive Testing, Models, Bayesian Statistics
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Molenaar, Dylan; Dolan, Conor V.; de Boeck, Paul – Psychometrika, 2012
The Graded Response Model (GRM; Samejima, "Estimation of ability using a response pattern of graded scores," Psychometric Monograph No. 17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, [theta], to underlie the ordinal item scores (Takane & de Leeuw in…
Descriptors: Simulation, Regression (Statistics), Psychometrics, Item Response Theory
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Tueller, Stephen; Lubke, Gitta – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Structural equation mixture models (SEMMs) are latent class models that permit the estimation of a structural equation model within each class. Fitting SEMMs is illustrated using data from 1 wave of the Notre Dame Longitudinal Study of Aging. Based on the model used in the illustration, SEMM parameter estimation and correct class assignment are…
Descriptors: Structural Equation Models, Computation, Classification, Longitudinal Studies
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