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Benjamin Lu; Eli Ben-Michael; Avi Feller; Luke Miratrix – Journal of Educational and Behavioral Statistics, 2023
In multisite trials, learning about treatment effect variation across sites is critical for understanding where and for whom a program works. Unadjusted comparisons, however, capture "compositional" differences in the distributions of unit-level features as well as "contextual" differences in site-level features, including…
Descriptors: Statistical Analysis, Statistical Distributions, Program Implementation, Comparative Analysis
Shear, Benjamin R.; Reardon, Sean F. – Journal of Educational and Behavioral Statistics, 2021
This article describes an extension to the use of heteroskedastic ordered probit (HETOP) models to estimate latent distributional parameters from grouped, ordered-categorical data by pooling across multiple waves of data. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in…
Descriptors: Statistical Analysis, Computation, Regression (Statistics), Sample Size
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Kleinke, Kristian – Journal of Educational and Behavioral Statistics, 2017
Predictive mean matching (PMM) is a standard technique for the imputation of incomplete continuous data. PMM imputes an actual observed value, whose predicted value is among a set of k = 1 values (the so-called donor pool), which are closest to the one predicted for the missing case. PMM is usually better able to preserve the original distribution…
Descriptors: Statistical Analysis, Statistical Distributions, Robustness (Statistics), Sample Size
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Schweig, Jonathan – Journal of Educational and Behavioral Statistics, 2014
Measures of classroom environments have become central to policy efforts that assess school and teacher quality. This has sparked a wide interest in using multilevel factor analysis to test measurement hypotheses about classroom-level variables. One approach partitions the total covariance matrix and tests models separately on the…
Descriptors: Factor Analysis, Robustness (Statistics), Measurement, Classroom Environment
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Jozwiak, Katarzyna; Moerbeek, Mirjam – Journal of Educational and Behavioral Statistics, 2012
Studies on event occurrence aim to investigate if and when subjects experience a particular event. The timing of events may be measured continuously using thin precise units or discretely using time periods. The latter metric of time is often used in social science research and the generalized linear model (GLM) is an appropriate model for data…
Descriptors: Statistical Analysis, Time, Sample Size, Social Science Research
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Zeng, Lingjia; Cope, Ronald T. – Journal of Educational and Behavioral Statistics, 1995
Large-sample standard errors of linear equating for the counterbalanced design are derived using the general delta method. Computer simulations found that standard errors derived without the normality assumption were more accurate than those derived with the normality assumption in a large sample with moderately skewed score distributions. (SLD)
Descriptors: Computer Simulation, Error of Measurement, Research Design, Sample Size
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von Davier, Alina A.; Kong, Nan – Journal of Educational and Behavioral Statistics, 2005
This article describes a new, unified framework for linear equating in a non-equivalent groups anchor test (NEAT) design. The authors focus on three methods for linear equating in the NEAT design--Tucker, Levine observed-score, and chain--and develop a common parameterization that shows that each particular equating method is a special case of the…
Descriptors: Equations (Mathematics), Sample Size, Statistical Distributions, Error of Measurement
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Yuan, Ke-Hai; Maxwell, Scott – Journal of Educational and Behavioral Statistics, 2005
Retrospective or post hoc power analysis is recommended by reviewers and editors of many journals. Little literature has been found that gave a serious study of the post hoc power. When the sample size is large, the observed effect size is a good estimator of the true power. This article studies whether such a power estimator provides valuable…
Descriptors: Effect Size, Computation, Monte Carlo Methods, Bias