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Lee, Yi-Hsuan; Haberman, Shelby J. – Journal of Educational Measurement, 2021
For assessments that use different forms in different administrations, equating methods are applied to ensure comparability of scores over time. Ideally, a score scale is well maintained throughout the life of a testing program. In reality, instability of a score scale can result from a variety of causes, some are expected while others may be…
Descriptors: Scores, Regression (Statistics), Demography, Data
van Rijn, Peter W.; Sinharay, Sandip; Haberman, Shelby J.; Johnson, Matthew S. – Large-scale Assessments in Education, 2016
Latent regression models are used for score-reporting purposes in large-scale educational survey assessments such as the National Assessment of Educational Progress (NAEP) and Trends in International Mathematics and Science Study (TIMSS). One component of these models is based on item response theory. While there exists some research on assessment…
Descriptors: Goodness of Fit, Item Response Theory, Regression (Statistics), National Competency Tests
Haberman, Shelby J. – ETS Research Report Series, 2013
A general program for item-response analysis is described that uses the stabilized Newton-Raphson algorithm. This program is written to be compliant with Fortran 2003 standards and is sufficiently general to handle independent variables, multidimensional ability parameters, and matrix sampling. The ability variables may be either polytomous or…
Descriptors: Predictor Variables, Mathematics, Item Response Theory, Probability
Haberman, Shelby J.; Sinharay, Sandip; Lee, Yi-Hsuan – Educational Testing Service, 2011
Providing information to test takers and test score users about the abilities of test takers at different score levels has been a persistent problem in educational and psychological measurement (Carroll, 1993). Scale anchoring (Beaton & Allen, 1992), a technique that describes what students at different points on a score scale know and can do,…
Descriptors: Statistical Analysis, Scores, Regression (Statistics), Item Response Theory
Haberman, Shelby J.; Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2010
Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…
Descriptors: Scoring, Regression (Statistics), Essays, Computer Software
Haberman, Shelby J. – ETS Research Report Series, 2009
A regression procedure is developed to link simultaneously a very large number of item response theory (IRT) parameter estimates obtained from a large number of test forms, where each form has been separately calibrated and where forms can be linked on a pairwise basis by means of common items. An application is made to forms in which a…
Descriptors: Regression (Statistics), Item Response Theory, Models, Equated Scores
Haberman, Shelby J.; Sinharay, Sandip – ETS Research Report Series, 2008
Sample-size requirements were considered for automated essay scoring in cases in which the automated essay score estimates the score provided by a human rater. Analysis considered both cases in which an essay prompt is examined in isolation and those in which a family of essay prompts is studied. In typical cases in which content analysis is not…
Descriptors: Sample Size, Scoring, Essays, Automation
Haberman, Shelby J. – ETS Research Report Series, 2008
Outliers in assessments are often treated as a nuisance for data analysis; however, they can also assist in quality assurance. Their frequency can suggest problems with form codes, scanning accuracy, ability of examinees to enter responses as they intend, or exposure of items.
Descriptors: Educational Assessment, Quality Assurance, Scores, Regression (Statistics)
Haberman, Shelby J. – Journal of Educational and Behavioral Statistics, 2008
In educational tests, subscores are often generated from a portion of the items in a larger test. Guidelines based on mean squared error are proposed to indicate whether subscores are worth reporting. Alternatives considered are direct reports of subscores, estimates of subscores based on total score, combined estimates based on subscores and…
Descriptors: Testing Programs, Regression (Statistics), Scores, Student Evaluation
Haberman, Shelby J.; Qian, Jiahe – Journal of Educational and Behavioral Statistics, 2007
Statistical prediction problems often involve both a direct estimate of a true score and covariates of this true score. Given the criterion of mean squared error, this study determines the best linear predictor of the true score given the direct estimate and the covariates. Results yield an extension of Kelley's formula for estimation of the true…
Descriptors: Prediction, Regression (Statistics), True Scores, Correlation
Haberman, Shelby J. – ETS Research Report Series, 2008
In educational testing, subscores may be provided based on a portion of the items from a larger test. One consideration in evaluation of such subscores is their ability to predict a criterion score. Two limitations on prediction exist. The first, which is well known, is that the coefficient of determination for linear prediction of the criterion…
Descriptors: Scores, Validity, Educational Testing, Correlation
Haberman, Shelby J. – ETS Research Report Series, 2005
In educational tests, subscores are often generated from a portion of the items in a larger test. Guidelines based on mean-squared error are proposed to indicate whether subscores are worth reporting. Alternatives considered are direct reports of subscores, estimates of subscores based on total score, combined estimates based on subscores and…
Descriptors: Scores, Test Items, Error of Measurement, Computation