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Showing 1 to 15 of 290 results Save | Export
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Kentaro Hayashi; Ke-Hai Yuan; Peter M. Bentler – Grantee Submission, 2025
Most existing studies on the relationship between factor analysis (FA) and principal component analysis (PCA) focus on approximating the common factors by the first few components via the closeness between their loadings. Based on a setup in Bentler and de Leeuw (Psychometrika 76:461-470, 2011), this study examines the relationship between FA…
Descriptors: Factor Analysis, Comparative Analysis, Correlation, Evaluation Criteria
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Liyang Sun; Eli Ben-Michael; Avi Feller – Grantee Submission, 2024
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data…
Descriptors: Evaluation Methods, Comparative Analysis, Computation, Data Analysis
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Lingbo Tong; Wen Qu; Zhiyong Zhang – Grantee Submission, 2025
Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…
Descriptors: Factor Analysis, Monte Carlo Methods, Statistical Analysis, Sample Size
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Jiaying Xiao; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to…
Descriptors: Error of Measurement, Models, Evaluation Methods, Item Analysis
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Charlotte Z. Mann; Adam C. Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2025
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…
Descriptors: Causal Models, Statistical Analysis, Privacy, Risk
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Saijun Zhao; Zhiyong Zhang; Hong Zhang – Grantee Submission, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
Nina K. Simms; Bryan J. Matlen; Benjamin D. Jee; Dedre Gentner – Grantee Submission, 2023
Visual comparisons are pervasive in science, technology, engineering, and mathematics (STEM) instruction and practice. In previous work, adults' visual comparisons of simple stimuli were faster and more accurate when the layout of a display facilitated alignment of corresponding elements--the "spatial alignment principle" (Matlen et al.,…
Descriptors: Spatial Ability, Biological Sciences, Visual Aids, Stimuli
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Christopher DeCamp; Christopher J. Lonigan – Grantee Submission, 2024
Discrepancies between teacher and parent reports of children's externalizing behaviors are well documented. However, less research has examined the associations these different ratings have with objective indicators of functioning in other domains. The goal of this study was to compare the strength of association of parent and teacher reports of…
Descriptors: Parent Attitudes, Teacher Attitudes, Preschool Children, Rating Scales
Jennifer Hill; George Perrett; Vincent Dorie – Grantee Submission, 2023
Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…
Descriptors: Causal Models, Statistical Inference, Artificial Intelligence, Data Analysis
Lauren Berkovits; Jan Blacher; Abbey Eisenhower; Stuart Daniel – Grantee Submission, 2023
Purpose: Comparative data of autism-sensitive standardized measures of emotion regulation and lability, describing percentage change over time for populations of young autistic children, are currently publicly unavailable. We propose publication of such data as a support for future therapeutic intervention studies. Methods: We generate and present…
Descriptors: Emotional Response, Check Lists, Autism Spectrum Disorders, Comparative Analysis
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Dae Woong Ham; Luke Miratrix – Grantee Submission, 2024
The consequence of a change in school leadership (e.g., principal turnover) on student achievement has important implications for education policy. The impact of such an event can be estimated via the popular Difference in Difference (DiD) estimator, where those schools with a turnover event are compared to a selected set of schools that did not…
Descriptors: Trend Analysis, Faculty Mobility, Academic Achievement, Principals
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Ke-Hai Yuan; Yongfei Fang – Grantee Submission, 2023
Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and…
Descriptors: Structural Equation Models, Regression (Statistics), Weighted Scores, Comparative Analysis
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Han Du; Brian Keller; Egamaria Alacam; Craig Enders – Grantee Submission, 2023
In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). A multilevel mediation model is used as an illustrative example to compare different types of DIC and WAIC. More specifically, the study compares the…
Descriptors: Bayesian Statistics, Models, Comparative Analysis, Probability
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Zhang, Haoran; Litman, Diane – Grantee Submission, 2021
Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES…
Descriptors: Essays, Grading, Writing Evaluation, Computational Linguistics
William E. Even; Takashi Yamashita; Phyllis A. Cummins – Grantee Submission, 2023
Using data from the Program for the International Assessment of Adult Competencies, this paper compares the earnings premium and employment share of jobs in Science, Technology, Engineering and Mathematics (STEM) across 11 member countries of the Organisation for Economic Co-operation and Development. The results reveal that the STEM wage premium…
Descriptors: STEM Careers, Wages, Comparative Analysis, Educational Attainment
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