Performing the Kernel Method of Test Equating with the Package<b>kequate</b>

Andersson, Björn; Bränberg, Kenny; Wiberg, Marie

doi:10.18637/jss.v055.i06

Cited by 61 publications

(47 citation statements)

References 13 publications

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“…The R (R Development Core Team, ) package kequate (Andersson, Bränberg, & Wiberg, ) was used for the calculations.…”

Section: Methodsmentioning

confidence: 99%

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Improving the Bandwidth Selection in Kernel Equating

Andersson

Davier

2014

J Educational Measurement

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We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners for being too complex and that it does not offer sufficient smoothing in certain cases. In addition, the bandwidth parameters have been treated as constants in the derivation of the standard error of equating even when they were selected by considering the observed data. Here, the bandwidth selection is simplified, and modified standard errors of equating (SEEs) that reflect the bandwidth selection method are derived. The method is illustrated with real data examples and simulated data.

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“…The R (R Development Core Team, ) package kequate (Andersson, Bränberg, & Wiberg, ) was used for the calculations.…”

Section: Methodsmentioning

confidence: 99%

“…All the analyses of the simulated data are based on 500 independent replications. Summary statistics for the data sets used are provided in Tables 1 and 2. The R (R Development Core Team, 2013) package kequate (Andersson, Bränberg, & Wiberg, 2013) was used for the calculations.…”

Section: Methodsmentioning

confidence: 99%

Improving the Bandwidth Selection in Kernel Equating

Andersson

Davier

2014

J Educational Measurement

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“…Analysis of the data was done with SPSS and FACTOR (Lorenzo-Seva & Ferrando, 2006). The equate R package (Albano, 2016) was used for traditional equating methods analyses and the kequate R package (Andersson, Branberg & Wiberg, 2013) was used for kernel equating methods analyses (R Core Team, 2017).…”

Section: Equating Design and Data Analysismentioning

confidence: 99%

A Comparison of Traditional and Kernel Equating Methods

Arıkan

Gelbal

2018

International Journal of Assessment Tools in Education

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Abstract:In this study, the equated score results of the kernel equating (KE) method compared with the results of traditional equating methodsequipercentile and linear equating and 9th grade 2009 ÖBBS Form B of Social Sciences and 2009 ÖBBS Form D of Social Sciences was used under an equivalent groups (EG) design. Study sample consists of 16.249 students taking booklets B and another 16.327 students taking D in that test. The analysis of the test forms was carried out in four steps. First, descriptive statistics were calculated for the data and then it was checked whether the data obtained from the two booklets satisfy the equating conditions. In the second step, the booklets were equated according to methods. Lastly, the errors for each equating methods were calculated. Kernel equating results were nearly same to the results from the corresponding traditional equating methods. In Kernel equating, when parameter h was selected as optimal, equated scores provided almost identical results as traditional equipercentile equating. When it was selected large, this time the equated scores provided results almost identical to traditional linear equating. It is concluded that Kernel equating methods are relatively more the most appropriate equating method method than traditional equating methods. ARTICLE HISTORY

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“…In the simulation using the symmetric setting, the true score probabilities for the EG design were the fitted score probabilities from chapter 7 of von Davier et al (), and the true score probabilities for the NEAT design were the simeq data found in the kequate package Andersson, Bränberg, & Wiberg, ). In the skewed setting, the true score probabilities were the fitted score probabilities based on score samples created by multiplying beta random numbers (with shape parameters 5 and 2) by the maximum score value.…”

Section: Empirical Studymentioning

confidence: 99%

“…Let the p th moments of scores from X and

e_{X} (normalY)

be denoted by

μ_{p} (normalX) = \sum_{j = 1}^{J} {(x_{j})}^{p} r_{j}

and

μ_{p} (e_{X} (Y)) = \sum_{k = 1}^{K} {(e_{X} (y_{k}))}^{p} s_{k}

, then the PRE is defined as

PRE (p) = 100 \frac{μ_{p} (e_{X} (Y)) - μ_{p} (normalX)}{μ_{p} (normalX)}

(von Davier et al., ). The evaluation and simulations were performed using R (R Core Team, ) and the kernel equating package kequate (Andersson et al, ).…”

Section: Empirical Studymentioning

confidence: 99%

Optimal Bandwidth Selection in Observed‐Score Kernel Equating

Häggström

Wiberg

2014

J Educational Measurement

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The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent groups design and a nonequivalent group with anchor test design. The performance of the methods was evaluated through simulation studies using both symmetric and skewed score distributions. In addition, the bandwidth selection methods were applied to real data from a college admissions test. The results show that the traditional penalty method works well although double smoothing is a viable alternative because it performs reasonably well compared to the traditional method.

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Performing the Kernel Method of Test Equating with the Packagekequate

Cited by 61 publications

References 13 publications

Improving the Bandwidth Selection in Kernel Equating

Improving the Bandwidth Selection in Kernel Equating

A Comparison of Traditional and Kernel Equating Methods

Optimal Bandwidth Selection in Observed‐Score Kernel Equating

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