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Items parameters of the space-relations subtest using item response theory.

ABSTRACT: This article describes the item parameters analysis result of the space-relations subtest measurement. This subtest is part of the differential aptitude test (DAT) instrument. The item parameters are characteristic psychometric refer quality of the item. The Item parameters than analyzed in this instrument are item fit model, item difficulty, item discrimination, pseudo-guessing, item information curves, and test information function. The data was collected through documentation technique from the space-relation test conducted at Biro Psikologi (Psychology Bureau) UNY, amounting to 1046 students from Yogyakarta, Indonesia. Data were analyzed using item response analysis with the assistance of BILOG program.

SUBMITTER: Setiawati FA

PROVIDER: S-EPMC6141381 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Items parameters of the space-relations subtest using item response theory.

Setiawati Farida Agus FA Izzaty Rita Eka RE Hidayat Veny V

Data in brief 20180626

This article describes the item parameters analysis result of the space-relations subtest measurement. This subtest is part of the differential aptitude test (DAT) instrument. The item parameters are characteristic psychometric refer quality of the item. The Item parameters than analyzed in this instrument are item fit model, item difficulty, item discrimination, pseudo-guessing, item information curves, and test information function. The data was collected through documentation technique from t ...[more]

PMID: 30229051

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Project description:Allostatic load is commonly operationalized using a sum-score of high-risk biomarkers. However, this method implies that biomarkers contribute equally to allostatic load, as each is given equal weight. Our goal in this methodological paper is to evaluate this, and complementarily, to identify biomarkers that are most informative and least informative for developing an allostatic load index. Item response theory models provide an alternate approach to calculating the allostatic load score, by treating individual biomarkers (e.g. “items”) as indicators of a latent allostatic load construct. Item response theory scores account for the data-driven discriminating power of each biomarker, and an individual’s pattern of biomarker responses. To demonstrate feasibility of this approach, we used data from the 2015–2016 National Health Examination and Nutrition Survey (NHANES; N = 3751), with twelve allostatic load biomarkers representing immune response, metabolic function and cardiovascular health. Item response theory models revealed that body-mass-index and C-reactive protein were the most informative biomarkers for allostatic load. Both higher allostatic load sum-score and allostatic load item response theory score were associated with lower socio-economic status (p = 0.008; p<0.001, respectively). Further, both formulations of allostatic load were positively associated with a nine-item depression screener (p<0.001 for both), but only the item response theory score was also positively associated with the impact of depressive symptoms on daily life (p = 0.045). Item response theory scores may be more finely tuned to tease out effects, compared to sum-scores, and also provide more flexibility when there are missing biomarker measurements. Supplemental R code for our approach are included. Highlights • Methodological paper to introduce item response theory for calculating allostatic load.• Biomarker data from NHANES 2015–2016 representative of United States adults.• Body-mass-index and C-reactive protein most informative for allostatic load.• Item response theory captures more variability in allostatic load compared to sum-scores.• Future work - item response theory can standardize allostatic load across datasets.

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Items parameters of the space-relations subtest using item response theory.

Publications

Items parameters of the space-relations subtest using item response theory.

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