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Yuqi Gu; Elena A. Erosheva; Gongjun Xu; David B. Dunson – Grantee Submission, 2023
Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying,…
Descriptors: Multivariate Analysis, Item Response Theory, Bayesian Statistics, Models
Gongjun Xu; Zhuoran Shang – Grantee Submission, 2018
This article focuses on a family of restricted latent structure models with wide applications in psychological and educational assessment, where the model parameters are restricted via a latent structure matrix to reflect prespecified assumptions on the latent attributes. Such a latent matrix is often provided by experts and assumed to be correct…
Descriptors: Psychological Evaluation, Educational Assessment, Item Response Theory, Models
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Li, Zhen; Cai, Li – Grantee Submission, 2017
In standard item response theory (IRT) applications, the latent variable is typically assumed to be normally distributed. If the normality assumption is violated, the item parameter estimates can become biased. Summed score likelihood based statistics may be useful for testing latent variable distribution fit. We develop Satorra-Bentler type…
Descriptors: Scores, Goodness of Fit, Statistical Distributions, Item Response Theory
Falk, Carl F.; Cai, Li – Grantee Submission, 2016
We present a logistic function of a monotonic polynomial with a lower asymptote, allowing additional flexibility beyond the three-parameter logistic model. We develop a maximum marginal likelihood based approach to estimate the item parameters. The new item response model is demonstrated on math assessment data from a state, and a computationally…
Descriptors: Item Response Theory, Guessing (Tests), Mathematics Tests, Simulation
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Herrmann-Abell, Cari F.; DeBoer, George E. – Grantee Submission, 2016
Understanding students' misconceptions and how they change is an essential part of supporting students in their science learning. This paper presents results from distractor-driven multiple-choice assessments that target students' misconceptions about energy. Over 20,000 elementary, middle and high school students from across the U.S. participated…
Descriptors: Item Response Theory, Probability, Elementary School Students, Middle School Students
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Hardcastle, Joseph; Herrmann-Abell, Cari F.; DeBoer, George E. – Grantee Submission, 2017
Can student performance on computer-based tests (CBT) and paper-and-pencil tests (PPT) be considered equivalent measures of student knowledge? States and school districts are grappling with this question, and although studies addressing this question are growing, additional research is needed. We report on the performance of students who took…
Descriptors: Academic Achievement, Computer Assisted Testing, Comparative Analysis, Student Evaluation