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Kim, Hyung Jin; Brennan, Robert L.; Lee, Won-Chan – Journal of Educational Measurement, 2020
In equating, smoothing techniques are frequently used to diminish sampling error. There are typically two types of smoothing: presmoothing and postsmoothing. For polynomial log-linear presmoothing, an optimum smoothing degree can be determined statistically based on the Akaike information criterion or Chi-square difference criterion. For…
Descriptors: Equated Scores, Sampling, Error of Measurement, Statistical Analysis
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Lee, Guemin; Fitzpatrick, Anne R. – Journal of Educational Measurement, 2003
Studied three procedures for estimating the standard errors of school passing rates using a generalizability theory model and considered the effects of student sample size. Results show that procedures differ in terms of assumptions about the populations from which students were sampled, and student sample size was found to have a large effect on…
Descriptors: Error of Measurement, Estimation (Mathematics), Generalizability Theory, Sampling
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Subkoviak, Michael J.; Levin, Joel R. – Journal of Educational Measurement, 1977
Measurement error in dependent variables reduces the power of statistical tests to detect mean differences of specified magnitude. Procedures for determining power and sample size that consider the reliability of the dependent variable are discussed and illustrated. Methods for estimating reliability coefficients used in these procedures are…
Descriptors: Error of Measurement, Hypothesis Testing, Power (Statistics), Sampling
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Whitely, Susan E. – Journal of Educational Measurement, 1977
A debate concerning specific issues and the general usefulness of the Rasch latent trait test model is continued. Methods of estimation, necessary sample size, and the applicability of the model are discussed. (JKS)
Descriptors: Error of Measurement, Item Analysis, Mathematical Models, Measurement
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Wright, Benjamin D. – Journal of Educational Measurement, 1977
Statements made in a previous article of this journal concerning the Rasch latent trait test model are questioned. Methods of estimation, necessary sample sizes, several formuli, and the general usefulness of the Rasch model are discussed. (JKS)
Descriptors: Computers, Error of Measurement, Item Analysis, Mathematical Models
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Haladyna, Thomas M.; Roid, Gale H. – Journal of Educational Measurement, 1983
The present study showed that Rasch-based adaptive tests--when item domains were finite and specifiable--had greater precision in domain score estimation than test forms created by random sampling of items. Results were replicated across four data sources representing a variety of criterion-referenced, domain-based tests varying in length.…
Descriptors: Adaptive Testing, Criterion Referenced Tests, Error of Measurement, Estimation (Mathematics)
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Willms, J. Douglas; Raudenbush, Stephen W. – Journal of Educational Measurement, 1989
A general longitudinal model is presented for estimating school effects and their stability. The model, capable of separating true changes from sampling and measurement error, controls statistically for effects of factors exogenous to the school system. The model is illustrated with data from large cohorts of students in Scotland. (SLD)
Descriptors: Elementary Secondary Education, Equations (Mathematics), Error of Measurement, Estimation (Mathematics)
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Shavelson, Richard J.; And Others – Journal of Educational Measurement, 1993
Evidence is presented on the generalizability and convergent validity of performance assessments using data from six studies of student achievement that sampled a wide range of measurement facets and methods. Results at individual and school levels indicate that task-sampling variability is the major source of measurement error. (SLD)
Descriptors: Academic Achievement, Educational Assessment, Error of Measurement, Generalizability Theory