A Test of the Adequacy of Curvilinear Score Equating Models

Marco, Gary L.; Petersen, Nancy S.; Stewart, Elizabeth

doi:10.1016/b978-0-12-742780-5.50018-4

Cited by 44 publications

(61 citation statements)

References 6 publications

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“…Marco (1981) discussed the efforts of test disclosure on score equating in a volume on coaching, disclosure, and ethnic bias. Marco et al (1983b) published the curvilinear equating analogue to their linear equating chapter that appeared in Holland and Rubin (1982) in a volume on latent trait theory and computer-adaptive testing. Cook and Eignor (1983) addressed the practical considerations associated with using IRT to equate or link test scores in a volume on IRT.…”

Section: Books and Chaptersmentioning

confidence: 99%

Contributions to Score Linking Theory and Practice

Dorans

Puhan

2017

Methodology of Educational Measurement and Assessment

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Section: Books and Chaptersmentioning

confidence: 99%

Contributions to Score Linking Theory and Practice

Dorans

Puhan

2017

Methodology of Educational Measurement and Assessment

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“…Sinharay (2008) provided a summary of these studies. Marco, Petersen, and Stewart (1983) and Livingston, Dorans, and Wright (1990) compared the PSE and CE methods (and several others) using data from the SAT ® -I examination. Harris and Kolen (1990) compared the PSE and CE methods using data from a certification test.…”

Section: Tablementioning

confidence: 99%

The Missing Data Assumptions of the Nonequivalent Groups With Anchor Test (Neat) Design and Their Implications for Test Equating

Sinharay

Holland

2009

ETS Research Report Series

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The nonequivalent groups with anchor test (NEAT) design involves missing data that are missing by design. Three popular equating methods that can be used with a NEAT design are the poststratification equating method, the chain equipercentile equating method, and the itemresponse-theory observed-score-equating method. These three methods each make different assumptions about the missing data in the NEAT design. Though studies have compared the equating performance of the three methods under the NEAT design, none has examined the missing data assumptions and their implications for such comparisons. The missing data assumptions can affect equating studies because it is necessary to fill in the missing data or their distribution in some way in order to have a true, or criterion, equating function to compare the accuracy and bias of the different methods. If the missing data or their distribution are filled in using missing data assumptions that correspond to a given method, that may favor that method in any comparison with the others. This paper first describes the missing data assumptions of the three equating methods and then performs a fair comparison of the 3 methods using data from 3 different operational tests. For each data set, we examine how the 3 equating methods perform when the missing data satisfy the assumptions made by only 1 of these equating methods. The chain equating method is somewhat more satisfactory overall than the other methods in our fair comparison of the methods; hence, we recommend that equating practitioners seriously consider the chain equating method when using the NEAT design. In addition, we conclude that the results from the different equating methods will tend to agree with each other when proper equating conditions are in place. Moreover, to uncover problems that might not reveal themselves otherwise, it is important for operational testing programs to apply multiple equating methods and study the differences among their results.

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“…Angoff (1984Angoff ( /1971 gives advice on designing anchor tests. For a comparison of a variety of methods for treating the NEAT design, see Kolen and Brennan (1995), Marco, Petersen, and Stewart (1983), and Petersen, Marco, and Stewart (1982).…”

Section: Introductionmentioning

confidence: 99%

A Unified Approach to Irt Scale Linking and Scale Transformations

Davier

2004

ETS Research Report Series

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This paper examines item response theory (IRT) scale transformations and IRT scale linking methods used in the Non-Equivalent Groups with Anchor Test (NEAT) design to equate two tests, X and Y. It proposes a unifying approach to the commonly used IRT linking methods: mean-mean, mean-var linking, concurrent calibration, Stocking and Lord and Haebara characteristic curves approaches, and fixed-item parameters scale linkage.The main idea is to view any linking procedure as a restriction on the item parameter space. Then a rewriting of the log-likelihood function together with an appropriately implemented maximization procedure of the log-likelihood function under linear (or nonlinear restrictions) will accomplish the linking. The proposed method is general enough to cover both the dichotomous item response models (the one parameter logistic (1PL) model, 2PL, and 3PL) and the polytomous unidimensional IRT models like the generalized partial credit model.

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A Test of the Adequacy of Curvilinear Score Equating Models

Cited by 44 publications

References 6 publications

Contributions to Score Linking Theory and Practice

Contributions to Score Linking Theory and Practice

The Missing Data Assumptions of the Nonequivalent Groups With Anchor Test (Neat) Design and Their Implications for Test Equating

A Unified Approach to Irt Scale Linking and Scale Transformations

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