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San Martín, Ernesto; González, Jorge – Journal of Educational and Behavioral Statistics, 2022
The nonequivalent groups with anchor test (NEAT) design is widely used in test equating. Under this design, two groups of examinees are administered different test forms with each test form containing a subset of common items. Because test takers from different groups are assigned only one test form, missing score data emerge by design rendering…
Descriptors: Tests, Scores, Statistical Analysis, Models
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van der Linden, Wim J. – Journal of Educational and Behavioral Statistics, 2022
The current literature on test equating generally defines it as the process necessary to obtain score comparability between different test forms. The definition is in contrast with Lord's foundational paper which viewed equating as the process required to obtain comparability of measurement scale between forms. The distinction between the notions…
Descriptors: Equated Scores, Test Items, Scores, Probability
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Feinberg, Richard A.; von Davier, Matthias – Journal of Educational and Behavioral Statistics, 2020
The literature showing that subscores fail to add value is vast; yet despite their typical redundancy and the frequent presence of substantial statistical errors, many stakeholders remain convinced of their necessity. This article describes a method for identifying and reporting unexpectedly high or low subscores by comparing each examinee's…
Descriptors: Scores, Probability, Statistical Distributions, Ability
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Nguyen, Trang Quynh; Stuart, Elizabeth A. – Journal of Educational and Behavioral Statistics, 2020
We address measurement error bias in propensity score (PS) analysis due to covariates that are latent variables. In the setting where latent covariate X is measured via multiple error-prone items W, PS analysis using several proxies for X--the W items themselves, a summary score (mean/sum of the items), or the conventional factor score (i.e.,…
Descriptors: Error of Measurement, Statistical Bias, Error Correction, Probability
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Chan, Wendy – Journal of Educational and Behavioral Statistics, 2018
Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score-based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods…
Descriptors: Computation, Generalization, Probability, Sample Size
Feller, Avi; Mealli, Fabrizia; Miratrix, Luke – Journal of Educational and Behavioral Statistics, 2017
Researchers addressing posttreatment complications in randomized trials often turn to principal stratification to define relevant assumptions and quantities of interest. One approach for the subsequent estimation of causal effects in this framework is to use methods based on the "principal score," the conditional probability of belonging…
Descriptors: Scores, Probability, Computation, Program Evaluation
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Culpepper, Steven Andrew – Journal of Educational and Behavioral Statistics, 2017
In the absence of clear incentives, achievement tests may be subject to the effect of slipping where item response functions have upper asymptotes below one. Slipping reduces score precision for higher latent scores and distorts test developers' understandings of item and test information. A multidimensional four-parameter normal ogive model was…
Descriptors: Measurement, Achievement Tests, Item Response Theory, National Competency Tests
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Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2016
We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…
Descriptors: Causal Models, Markov Processes, Longitudinal Studies, Probability
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Ho, Andrew Dean – Journal of Educational and Behavioral Statistics, 2009
Problems of scale typically arise when comparing test score trends, gaps, and gap trends across different tests. To overcome some of these difficulties, test score distributions on the same score scale can be represented by nonparametric graphs or statistics that are invariant under monotone scale transformations. This article motivates and then…
Descriptors: Nonparametric Statistics, Comparative Analysis, Trend Analysis, Scores
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Lee, Won-Chan; Brennan, Robert L.; Kolen, Michael J. – Journal of Educational and Behavioral Statistics, 2006
Assuming errors of measurement are distributed binomially, this article reviews various procedures for constructing an interval for an individual's true number-correct score; presents two general interval estimation procedures for an individual's true scale score (i.e., normal approximation and endpoints conversion methods); compares various…
Descriptors: Probability, Intervals, Guidelines, Computer Simulation
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Gelman, Andrew – Journal of Educational and Behavioral Statistics, 1997
Several classroom demonstrations are described that have sparked student involvement in undergraduate courses in probability and statistics. These demonstrations involve experimentation using exams and statistical analysis and adjustment of exam scores. (Author/SLD)
Descriptors: Classroom Techniques, College Faculty, College Students, Higher Education
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Dunn, Michelle C.; Kadane, Joseph B.; Garrow, John R. – Journal of Educational and Behavioral Statistics, 2003
This article addresses the relationship between academic achievement and the student characteristics of absence and mobility. Mobility is a measure of how often a student changes schools. Absence is how often a student misses class. Standardized test scores are used as proxies for academic achievement. A model for the full joint distribution of…
Descriptors: Standardized Tests, Academic Achievement, Probability, Student Mobility