Observed Score Linear Equating with Covariates

Bränberg, Kenny; Wiberg, Marie

doi:10.1111/j.1745-3984.2011.00153.x

Cited by 28 publications

(60 citation statements)

References 17 publications

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“…One advantage of the PEG method is that it has the power of including as many variables as available. This feature makes PEG distinct from other linking methods that utilize regression methods with limited background variables to link test scores (e.g., Brancerg & Wiberg, ; Wiberg & Branberg, ),…”

Section: Summary and Discussionmentioning

confidence: 99%

A Simulation Study to Compare Nonequivalent Groups With Anchor Test Equating and Pseudo‐Equivalent Group Linking

Guo

2018

ETS Research Report Series

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In this paper we compare the newly developed pseudo‐equivalent groups (PEG) linking method with the linking methods based on the traditional nonequivalent groups with anchor test (NEAT) design and illustrate how to use the PEG methods under imperfect equating conditions. To do this, we proposed a new method that combines the features of PEG linking and NEAT equating (referred as PEGAT) and compared it with NEAT and PEG. PEG mainly uses test takers' background variables to create PEG and then links scores on different forms whereas NEAT equating adjusts group differences in ability through the anchor test scores. The proposed method, PEGAT, uses background variables and anchor scores to adjust group ability differences. Using simulated data, these 3 linking methods were compared in 2 equating scenarios: small and large group difference in ability. The simulation design was based on real data on a test in operation. The test scores and the background variables were assumed to have a multivariate multinomial distribution. A log linear model was used to manipulate and produce simulated data. For PEG and PEGAT linking, 3 different sets of background variables were manipulated to study the impact of correlation strength of background variables to the total scores. Our results showed that NEAT linking outperformed PEG linking when the group ability difference was large, but that NEAT linking could be improved by incorporating the PEG adjustment procedure based on background variables. When the groups were similar in ability, PEG linking produced comparable results to NEAT. This finding justifies the use of PEG linking when a good anchor test is not available as well as the use of PEGAT when a good anchor is available but needs to be strengthened by background variables.

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Section: Summary and Discussionmentioning

confidence: 99%

A Simulation Study to Compare Nonequivalent Groups With Anchor Test Equating and Pseudo‐Equivalent Group Linking

Guo

2018

ETS Research Report Series

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“…Applications which incorporate other sources of information could also be explored. For example, recent research has demonstrated the use of collateral information, including information from external variables and prior equating functions, to improve estimation of the equating function (e.g., Branberg & Wiberg, ; Kim, Livingston, & Lewis, ; Livingston & Kim, ). Such collateral information may also be used to improve estimation of specific components of the general linear function.…”

Section: Discussionmentioning

confidence: 99%

A General Linear Method for Equating With Small Samples

Albano

2015

J Educational Measurement

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Research on equating with small samples has shown that methods with stronger assumptions and fewer statistical estimates can lead to decreased error in the estimated equating function. This article introduces a new approach to linear observed‐score equating, one which provides flexible control over how form difficulty is assumed versus estimated to change across the score scale. A general linear method is presented as an extension of traditional linear methods. The general method is then compared to other linear and nonlinear methods in terms of accuracy in estimating a criterion equating function. Results from two parametric bootstrapping studies based on real data demonstrate the usefulness of the general linear method.

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“…Nevertheless, the use of covariates seems to be a rather unexplored topic in the equating literature. To the best of our knowledge, only in a recent paper, Branberg and Wiberg (2011) proposed the use of covariates in observed score linear equating, where the mean of the score distributions depends on covariates. These authors have shown how covariates can be used to decrease the standard error of equating.…”

Section: Introductionmentioning

confidence: 98%

“…Our model allows the use of additional information in the estimation of the equating transformation, something that, as was mentioned before, has not been widely explored in currently available methods. A major feature of this approach is that not only the location, as it is the case of the model proposed by Branberg and Wiberg (2011), but also the complete shape of the scores distribution may change as a function of the covariates, which in turn implies that the complete shape of the equating function is also allowed to change according to the covariate values.…”

Section: Introductionmentioning

confidence: 99%