<b>equate</b>: An <i>R</i> Package for Observed-Score Linking and Equating

Albano, Anthony D.

doi:10.18637/jss.v074.i08

Cited by 119 publications

(106 citation statements)

References 20 publications

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“…Test pairs with correlation ≥0.6 were then equated using equipercentile equating with log linear smoothing 19 using the “equate” package in R 20 . 95% confidence intervals were calculated using 100 bootstrap samples 21 .…”

Section: Methodsmentioning

confidence: 99%

Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study

Monsell¹,

Dodge

Zhou

et al. 2016

Alzheimer Disease &Amp; Associated Disorders

125

118

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INTRODUCTION Four new nonproprietary tests were recommended for use in the National Alzheimer's Coordinating Center's Uniform Data Set Neuropsychological Battery. These tests are similar to previous tests but also allow for continuity of longitudinal data collection and wide dissemination among research collaborators. METHODS A Crosswalk Study was conducted in early 2014 to assess the correlation between each set of new and previous tests. Tests with good correlation were equated using equipercentile equating. The resulting conversion tables allow scores on the new tests to be converted to equivalent scores on the previous tests. RESULTS All pairs of tests had good correlation (ρ=0.68–0.78). Learning effects were detected for Logical Memory only. Confidence intervals were narrow at each point estimate, and prediction accuracy was high. DISCUSSION The recommended new tests are well correlated with the previous tests. The equipercentile equating method produced conversion tables that provide a useful reference for clinicians and researchers.

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Section: Methodsmentioning

confidence: 99%

Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study

Monsell¹,

Dodge

Zhou

et al. 2016

Alzheimer Disease &Amp; Associated Disorders

125

118

View full text Add to dashboard Cite

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“…The equipercentile linking was conducted using the LEGS program. [49] To reduce the impact of random sampling error, [47; 49; 50] equipercentile linking can be conducted in conjunction with a pre- or post-smoothing method. Pre-smoothing involves smoothing the observed score distributions from the two measures to be linked, prior to their linking; post-smoothing involves smoothing the equipercentile equating function that is a product of the unsmoothed observed score distributions.…”

Section: Methodsmentioning

confidence: 99%

Establishing a common metric for self-reported pain: linking BPI Pain Interference and SF-36 Bodily Pain Subscale scores to the PROMIS Pain Interference metric

et al. 2015

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Purpose The study purposes were to mathematically link scores of the Brief Pain Inventory Pain Interference (BPI-PI) subscale and the Short Form-36 Bodily Pain (SF36-BP) subscale (legacy pain interference measures) to the NIH Patient Reported Outcome Measurement Information System (PROMIS®) Pain Interference (PROMIS-PI) metric and evaluate results. Methods Linking was accomplished using both equipercentile and item response theory (IRT) methods. Item parameters for legacy items were estimated on the PROMIS-PI metric to allow for pattern scoring. Crosswalk tables also were developed that associated raw scores (summed or average) on legacy measures to PROMIS-PI scores. For each linking strategy, participants’ actual PROMIS-PI scores were compared to those predicted based on their legacy scores. To assess the impact of different sample sizes, we conducted random resampling with replacement across 10,000 replications with sample sizes of n=25, 50, and 75. Results Analyses supported the assumption that all three scales were measuring similar constructs. IRT methods produced marginally better results than equipercentile linking. Accuracy of the links was substantially affected by sample size. Conclusions The linking tools (crosswalks and item parameter estimates) developed in this study are robust methods for estimating the PROMIS-PI scores of samples based on legacy measures. We recommend using pattern scoring for users who have the necessary software and score crosswalks for those who do not.

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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%

“…In addition, the difference in difficulty between test forms varies in a constant amount through the entire score scale (Albano, 2016). Concerning X new form and Y reference (old) form, , gives means of the forms, and gives standard deviations of the forms.…”

Section: Introductionmentioning

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

Cited by 119 publications

References 20 publications

Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study

Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study

Establishing a common metric for self-reported pain: linking BPI Pain Interference and SF-36 Bodily Pain Subscale scores to the PROMIS Pain Interference metric

A Comparison of Traditional and Kernel Equating Methods

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