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Sainan Xu; Jing Lu; Jiwei Zhang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational…
Descriptors: Educational Assessment, Bayesian Statistics, Statistical Inference, Item Response Theory
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Lyu, Weicong; Kim, Jee-Seon; Suk, Youmi – Journal of Educational and Behavioral Statistics, 2023
This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and…
Descriptors: Hierarchical Linear Modeling, Bayesian Statistics, Causal Models, Statistical Inference
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Suk, Youmi; Kim, Jee-Seon; Kang, Hyunseung – Journal of Educational and Behavioral Statistics, 2021
There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…
Descriptors: Artificial Intelligence, Statistical Analysis, Statistical Inference, Classification
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Suleman, Qaiser; Hussain, Ishtiaq – Journal of Education and Practice, 2016
The purpose of the research paper was to investigate the effect of eclectic learning approach on the academic achievement and retention of students in English at elementary level. A sample of forty students of 8th grade randomly selected from Government Boys High School Khurram District Karak was used. It was an experimental study and that's why…
Descriptors: Elementary School Students, Academic Achievement, School Holding Power, Pretests Posttests
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Zhang, Zhiyong; Lai, Keke; Lu, Zhenqiu; Tong, Xin – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the "t" distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian…
Descriptors: Structural Equation Models, Bayesian Statistics, Statistical Inference, Statistical Distributions
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Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2010
Pretest-posttest experimental designs often are used in randomized control trials (RCTs) in the education field to improve the precision of the estimated treatment effects. For logistic reasons, however, pretest data often are collected after random assignment, so that including them in the analysis could bias the posttest impact estimates. Thus,…
Descriptors: Pretests Posttests, Scores, Intervention, Scientific Methodology
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Olsen, Robert B.; Unlu, Fatih; Price, Cristofer; Jaciw, Andrew P. – National Center for Education Evaluation and Regional Assistance, 2011
This report examines the differences in impact estimates and standard errors that arise when these are derived using state achievement tests only (as pre-tests and post-tests), study-administered tests only, or some combination of state- and study-administered tests. State tests may yield different evaluation results relative to a test that is…
Descriptors: Achievement Tests, Standardized Tests, State Standards, Reading Achievement
Wilde, Elizabeth Ty; Hollister, Robinson – Institute for Research on Poverty, 2002
In this study we test the performance of some nonexperimental estimators of impacts applied to an educational intervention--reduction in class size--where achievement test scores were the outcome. We compare the nonexperimental estimates of the impacts to "true impact" estimates provided by a random-assignment design used to assess the…
Descriptors: Computation, Outcome Measures, Achievement Tests, Scores