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Goodman, Joshua T.; Dallas, Andrew D.; Fan, Fen – Applied Measurement in Education, 2020
Recent research has suggested that re-setting the standard for each administration of a small sample examination, in addition to the high cost, does not adequately maintain similar performance expectations year after year. Small-sample equating methods have shown promise with samples between 20 and 30. For groups that have fewer than 20 students,…
Descriptors: Equated Scores, Sample Size, Sampling, Weighted Scores
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Kopp, Jason P.; Jones, Andrew T. – Applied Measurement in Education, 2020
Traditional psychometric guidelines suggest that at least several hundred respondents are needed to obtain accurate parameter estimates under the Rasch model. However, recent research indicates that Rasch equating results in accurate parameter estimates with sample sizes as small as 25. Item parameter drift under the Rasch model has been…
Descriptors: Item Response Theory, Psychometrics, Sample Size, Sampling
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Diao, Hongyu; Keller, Lisa – Applied Measurement in Education, 2020
Examinees who attempt the same test multiple times are often referred to as "repeaters." Previous studies suggested that repeaters should be excluded from the total sample before equating because repeater groups are distinguishable from non-repeater groups. In addition, repeaters might memorize anchor items, causing item drift under a…
Descriptors: Licensing Examinations (Professions), College Entrance Examinations, Repetition, Testing Problems
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Michaelides, Michalis P.; Haertel, Edward H. – Applied Measurement in Education, 2014
The standard error of equating quantifies the variability in the estimation of an equating function. Because common items for deriving equated scores are treated as fixed, the only source of variability typically considered arises from the estimation of common-item parameters from responses of samples of examinees. Use of alternative, equally…
Descriptors: Equated Scores, Test Items, Sampling, Statistical Inference
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Phillips, Gary W. – Applied Measurement in Education, 2015
This article proposes that sampling design effects have potentially huge unrecognized impacts on the results reported by large-scale district and state assessments in the United States. When design effects are unrecognized and unaccounted for they lead to underestimating the sampling error in item and test statistics. Underestimating the sampling…
Descriptors: State Programs, Sampling, Research Design, Error of Measurement