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Huang, Francis L. – Journal of Educational and Behavioral Statistics, 2022
The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked…
Descriptors: Multivariate Analysis, Computation, Correlation, Error of Measurement
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Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2022
This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features…
Descriptors: Comparative Analysis, Statistical Analysis, Sample Size, Measurement Techniques
Oranje, Andreas; Kolstad, Andrew – Journal of Educational and Behavioral Statistics, 2019
The design and psychometric methodology of the National Assessment of Educational Progress (NAEP) is constantly evolving to meet the changing interests and demands stemming from a rapidly shifting educational landscape. NAEP has been built on strong research foundations that include conducting extensive evaluations and comparisons before new…
Descriptors: National Competency Tests, Psychometrics, Statistical Analysis, Computation
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Longford, Nicholas Tibor – Journal of Educational and Behavioral Statistics, 2016
We address the problem of selecting the best of a set of units based on a criterion variable, when its value is recorded for every unit subject to estimation, measurement, or another source of error. The solution is constructed in a decision-theoretical framework, incorporating the consequences (ramifications) of the various kinds of error that…
Descriptors: Decision Making, Classification, Guidelines, Undergraduate Students
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Grabovsky, Irina; Wainer, Howard – Journal of Educational and Behavioral Statistics, 2017
In this essay, we describe the construction and use of the Cut-Score Operating Function in aiding standard setting decisions. The Cut-Score Operating Function shows the relation between the cut-score chosen and the consequent error rate. It allows error rates to be defined by multiple loss functions and will show the behavior of each loss…
Descriptors: Cutting Scores, Standard Setting (Scoring), Decision Making, Error Patterns
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Martin, Michael O.; Mullis, Ina V. S. – Journal of Educational and Behavioral Statistics, 2019
International large-scale assessments of student achievement such as International Association for the Evaluation of Educational Achievement's Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study and Organization for Economic Cooperation and Development's Program for International…
Descriptors: Achievement Tests, International Assessment, Mathematics Tests, Science Achievement
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Xi, Nuo; Browne, Michael W. – Journal of Educational and Behavioral Statistics, 2014
A promising "underlying bivariate normal" approach was proposed by Jöreskog and Moustaki for use in the factor analysis of ordinal data. This was a limited information approach that involved the maximization of a composite likelihood function. Its advantage over full-information maximum likelihood was that very much less computation was…
Descriptors: Factor Analysis, Maximum Likelihood Statistics, Data, Computation
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Thissen, David – Journal of Educational and Behavioral Statistics, 2016
David Thissen, a professor in the Department of Psychology and Neuroscience, Quantitative Program at the University of North Carolina, has consulted and served on technical advisory committees for assessment programs that use item response theory (IRT) over the past couple decades. He has come to the conclusion that there are usually two purposes…
Descriptors: Item Response Theory, Test Construction, Testing Problems, Student Evaluation
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Lee, Taehun; Cai, Li – Journal of Educational and Behavioral Statistics, 2012
Model-based multiple imputation has become an indispensable method in the educational and behavioral sciences. Mean and covariance structure models are often fitted to multiply imputed data sets. However, the presence of multiple random imputations complicates model fit testing, which is an important aspect of mean and covariance structure…
Descriptors: Statistical Inference, Structural Equation Models, Goodness of Fit, Statistical Analysis
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Liu, Xiaofeng Steven – Journal of Educational and Behavioral Statistics, 2010
This article provides a way to determine adequate sample size for the confidence interval of covariate-adjusted mean difference in randomized experiments. The standard error of adjusted mean difference depends on covariate variance and balance, which are two unknown quantities at the stage of planning sample size. If covariate observations are…
Descriptors: Sample Size, Computation, Statistical Analysis, Error of Measurement
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Aloe, Ariel M.; Becker, Betsy Jane – Journal of Educational and Behavioral Statistics, 2012
A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…
Descriptors: Meta Analysis, Effect Size, Multiple Regression Analysis, Models
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Longford, Nicholas T. – Journal of Educational and Behavioral Statistics, 2009
We derive an estimator of the standardized value which, under the standard assumptions of normality and homoscedasticity, is more efficient than the established (asymptotically efficient) estimator and discuss its gains for small samples. (Contains 1 table and 3 figures.)
Descriptors: Efficiency, Computation, Statistics, Sample Size
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Camilli, Gregory – Journal of Educational and Behavioral Statistics, 2006
A simple errors-in-variables regression model is given in this article for illustrating the method of marginal maximum likelihood (MML). Given suitable estimates of reliability, error variables, as nuisance variables, can be integrated out of likelihood equations. Given the closed form expression of the resulting marginal likelihood, the effects…
Descriptors: Maximum Likelihood Statistics, Regression (Statistics), Reliability, Error of Measurement
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Wilson, Mark; Hoskens, Machteld – Journal of Educational and Behavioral Statistics, 2001
Introduces the Rater Bundle Model, an item response model for repeated ratings of student work. Applies the model to real and simulated data to illustrate the approach, which was motivated by the observation that when repeated ratings occur, the assumption of conditional independence is violated, and current item response models can then…
Descriptors: Error of Measurement, Evaluators, Item Response Theory, Models
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Williams, Valerie S. L.; Jones, Lyle V.; Tukey, John W. – Journal of Educational and Behavioral Statistics, 1999
Illustrates and compares three alternative procedures to adjust significance levels for multiplicity: (1) the traditional Bonferroni technique; (2) a sequential Bonferroni technique; and (3) a sequential approach to control the false discovery rate proposed by Y. Benjamini and Y. Hochberg (1995). Explains advantages of the Benjamini and Hochberg…
Descriptors: Academic Achievement, Comparative Analysis, Error of Measurement, Statistical Significance
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