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Youmi Suk – Journal of Educational and Behavioral Statistics, 2024
Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level…
Descriptors: Artificial Intelligence, Causal Models, Statistical Inference, Maximum Likelihood Statistics
Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2023
In order to evaluate the effect of a policy or treatment with pre- and post-treatment outcomes, we propose an approach based on a transition model, which may be applied with multivariate outcomes and accounts for unobserved heterogeneity. This model is based on potential versions of discrete latent variables representing the individual…
Descriptors: Causal Models, Multivariate Analysis, Markov Processes, Human Capital
Su, Kun; Henson, Robert A. – Journal of Educational and Behavioral Statistics, 2023
This article provides a process to carefully evaluate the suitability of a content domain for which diagnostic classification models (DCMs) could be applicable and then optimized steps for constructing a test blueprint for applying DCMs and a real-life example illustrating this process. The content domains were carefully evaluated using a set of…
Descriptors: Classification, Models, Science Tests, Physics
Wu, Edward; Gagnon-Bartsch, Johann A. – Journal of Educational and Behavioral Statistics, 2021
In paired experiments, participants are grouped into pairs with similar characteristics, and one observation from each pair is randomly assigned to treatment. The resulting treatment and control groups should be well-balanced; however, there may still be small chance imbalances. Building on work for completely randomized experiments, we propose a…
Descriptors: Experiments, Groups, Research Design, Statistical Analysis
Liao, Xiangyi; Bolt, Daniel M. – Journal of Educational and Behavioral Statistics, 2021
Four-parameter models have received increasing psychometric attention in recent years, as a reduced upper asymptote for item characteristic curves can be appealing for measurement applications such as adaptive testing and person-fit assessment. However, applications can be challenging due to the large number of parameters in the model. In this…
Descriptors: Test Items, Models, Mathematics Tests, Item Response Theory
Reardon, Sean F.; Kalogrides, Demetra; Ho, Andrew D. – Journal of Educational and Behavioral Statistics, 2021
Linking score scales across different tests is considered speculative and fraught, even at the aggregate level. We introduce and illustrate validation methods for aggregate linkages, using the challenge of linking U.S. school district average test scores across states as a motivating example. We show that aggregate linkages can be validated both…
Descriptors: Equated Scores, Validity, Methods, School Districts
Gao, Xuliang; Ma, Wenchao; Wang, Daxun; Cai, Yan; Tu, Dongbo – Journal of Educational and Behavioral Statistics, 2021
This article proposes a class of cognitive diagnosis models (CDMs) for polytomously scored items with different link functions. Many existing polytomous CDMs can be considered as special cases of the proposed class of polytomous CDMs. Simulation studies were carried out to investigate the feasibility of the proposed CDMs and the performance of…
Descriptors: Cognitive Measurement, Models, Test Items, Scoring
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
Loeb, Susanna; Christian, Michael S.; Hough, Heather; Meyer, Robert H.; Rice, Andrew B.; West, Martin R. – Journal of Educational and Behavioral Statistics, 2019
Measures of school-level growth in student outcomes are common tools for assessing the impacts of schools. The vast majority of these measures use standardized tests as the outcome of interest, even though emerging evidence demonstrates the importance of social-emotional learning (SEL). In this article, we present results from using the first…
Descriptors: Social Development, Emotional Development, Student Surveys, Institutional Characteristics
Guo, Hongwen; Zhang, Mo; Deane, Paul; Bennett, Randy E. – Journal of Educational and Behavioral Statistics, 2019
We used an unobtrusive approach, keystroke logging, to examine students' cognitive states during essay writing. Based on data contained in the logs, we classified writing process data into three states: text production, long pause, and editing. We used semi-Markov processes to model the sequences of writing states and compared the state transition…
Descriptors: Writing Processes, Cognitive Processes, Essays, Keyboarding (Data Entry)
Jiang, Yu; Zhang, Jiahui; Xin, Tao – Journal of Educational and Behavioral Statistics, 2019
This article is an overview of the National Assessment of Education Quality (NAEQ) of China in reading, mathematics, sciences, arts, physical education, and moral education at Grades 4 and 8. After a review of the background and history of NAEQ, we present the assessment framework with students' holistic development at the core and the design for…
Descriptors: Foreign Countries, Educational Quality, Educational Improvement, National Competency Tests
Matta, Tyler H.; Soland, James – Journal of Educational and Behavioral Statistics, 2019
The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in English learner (EL) policy. This article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously, treating student-specific random…
Descriptors: English Language Learners, Language Proficiency, Classification, Language Fluency
Ma, Wenchao; de la Torre, Jimmy – Journal of Educational and Behavioral Statistics, 2019
Solving a constructed-response item usually requires successfully performing a sequence of tasks. Each task could involve different attributes, and those required attributes may be "condensed" in various ways to produce the responses. The sequential generalized deterministic input noisy "and" gate model is a general cognitive…
Descriptors: Test Items, Cognitive Measurement, Models, Hypothesis Testing
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
Koch, Tobias; Schultze, Martin; Burrus, Jeremy; Roberts, Richard D.; Eid, Michael – Journal of Educational and Behavioral Statistics, 2015
The numerous advantages of structural equation modeling (SEM) for the analysis of multitrait-multimethod (MTMM) data are well known. MTMM-SEMs allow researchers to explicitly model the measurement error, to examine the true convergent and discriminant validity of the given measures, and to relate external variables to the latent trait as well as…
Descriptors: Structural Equation Models, Hierarchical Linear Modeling, Factor Analysis, Multitrait Multimethod Techniques
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