A General Linear Method for Equating With Small Samples

Albano, Anthony D.

doi:10.1111/jedm.12062

Cited by 10 publications

(11 citation statements)

References 20 publications

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“…Recently, general linear methods for identity, mean, and linear equating have been shown to reduce bias in equating (Albano, 2015). This promising equating method has been used to account for differences in test difficulty owing to differing scale lengths, violations of assumptions, or differing numbers of test takers between forms.…”

Section: Resultsmentioning

confidence: 98%

Equating in small-scale language testing programs

LaFlair

Isbell

May³

et al. 2016

Language Testing

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Language programs need multiple test forms for secure administrations and effective placement decisions, but can they have confidence that scores on alternate test forms have the same meaning? In large-scale testing programs, various equating methods are available to ensure the comparability of forms. The choice of equating method is informed by estimates of quality, namely the method with the least error as defined by random error, systematic error, and total error. This study compared seven different equating methods to no equating -mean, linear Levine, linear Tucker, chained equipercentile, circle-arc, nominal weights mean, and synthetic. A nonequivalent groups anchor test (NEAT) design was used to compare two listening and reading test forms based on small samples (one with 173 test takers the other, 88) at a university's English for Academic Purposes (EAP) program. The equating methods were evaluated based on the amount of error they introduced and their practical effects on placement decisions. It was found that two types of error (systematic and total) could not be reliably computed owing to the lack of an adequate criterion; consequently, only random error was compared. Among the seven methods, the circle-arc method introduced the least random error as estimated by the standard error of equating (SEE). Classification decisions made using the seven methods differed from no equating; all methods indicated that fewer students were ready for university placement. Although interpretations regarding the best equating method could not be made, circle-arc equating reduced the amount of random error in scores, had reportedly low bias in

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Section: Resultsmentioning

confidence: 98%

Equating in small-scale language testing programs

LaFlair

Isbell

May³

et al. 2016

Language Testing

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“…In the equate package, these methods are currently Tucker, nominal-weights, Levine observed-score, and Braun/Holland. See Albano (2015) for examples. Composites of these methods can also be obtained.…”

Section: Methods For General Linear Functionsmentioning

confidence: 99%

“…β is a general centrality parameter that can be estimated using µ, x j or y k , other values, or weighted combinations of these values. Applications of the general linear function are discussed below and in Albano (2015).…”

Section: General Linear Functionsmentioning

confidence: 99%

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equate: An R Package for Observed-Score Linking and Equating

Albano¹

2016

J. Stat. Soft.

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119

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“…Equating methods are well assigned for this scenario. 16,17 Equating is a statistical method that determines the relationship between 2 or more scoring scales, to correct possible differences in tests’ difficulty. 18 When equivalent tests are compared, equating can correct variations in scores that go beyond what is expected, fitting properly for medical clerkships assessment.…”

Section: Introductionmentioning

confidence: 99%

Assessment in pediatrics clerkships: impact of strategies to solve item-sharing problems

Faria-Costa¹,

Severo

Ferreira

et al. 2018

Porto Biomedical Journal

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Introduction: Reusing multiple-choice questions in different examinations may lead to item sharing between students. Our aim was to analyze and solve an item-sharing problem during assessment of medical students in pediatrics clerkship. Methods: This is a 3 years prospective analysis of 5th year medical students, submitted to an examination at the end of their Pediatrics’ clerkship. In 2012, questions were reused across different clerkships. In 2013, no questions were reused in different clerkships. In 2014, no questions were reused and the review of the test was postponed to the end of the year, after all clerkships ended. Results: In 2012, the mean score increased 1.36 points (in a scale of 0–20) per clerkship rotation, with the last clerkship having a difference of 9.5 points regarding the first one ( P < .001). Fifty percent of this variation was due to the repetition of questions. In 2013, with a new question bank, the mean score increased 0.8 points per clerkship rotation, with a difference of 5.6 points between the last and the first rotations ( P < .024). Finally, in 2014 there was no significant variation between clerkships. Tests’ scores had a significant moderate correlation with students’ average course grade ( r = 0.39, r = 0.30, r = 0.48, for 2012, 2013, and 2014, respectively). The students’ average course grade, however, did not confound the increase in tests’ scores across different clerkships. Conclusion: The present work demonstrated an item-sharing problem among students during pediatric clerkships. An effective approach to correct this bias assessment was achieved by restricting the reuse of questions, by changing the time-point of test revision and by progressively adapting equating strategies.

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A General Linear Method for Equating With Small Samples

Cited by 10 publications

References 20 publications

Equating in small-scale language testing programs

Equating in small-scale language testing programs

equate: An R Package for Observed-Score Linking and Equating

Assessment in pediatrics clerkships: impact of strategies to solve item-sharing problems

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