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使用多维计算机自适应测试提高人格特质分数:以 NEO PI-R 为例。

Improving personality facet scores with multidimensional computer adaptive testing: an illustration with the NEO PI-R.

机构信息

Master International A/S, Alleroed, Denmark.

出版信息

Assessment. 2013 Feb;20(1):3-13. doi: 10.1177/1073191112437756. Epub 2012 Feb 21.

Abstract

Narrowly defined personality facet scores are commonly reported and used for making decisions in clinical and organizational settings. Although these facets are typically related, scoring is usually carried out for a single facet at a time. This method can be ineffective and time consuming when personality tests contain many highly correlated facets. This article investigates the possibility of increasing the precision of the NEO PI-R facet scores by scoring items with multidimensional item response theory and by efficiently administering and scoring items with multidimensional computer adaptive testing (MCAT). The increase in the precision of personality facet scores is obtained from exploiting the correlations between the facets. Results indicate that the NEO PI-R could be substantially shorter without attenuating precision when the MCAT methodology is used. Furthermore, the study shows that the MCAT methodology is particularly appropriate for constructs that have many highly correlated facets.

摘要

狭义定义的人格特质分数通常用于临床和组织环境中的决策。虽然这些特质通常是相关的,但评分通常一次只针对一个特质进行。当人格测试包含许多高度相关的特质时,这种方法可能效率低下且耗时。本文研究了通过多维项目反应理论对项目进行评分以及通过多维计算机自适应测试 (MCAT) 高效管理和评分来提高 NEO PI-R 特质分数精度的可能性。通过利用特质之间的相关性,可以提高人格特质分数的精度。结果表明,当使用 MCAT 方法时,NEO PI-R 可以大大缩短而不会降低精度。此外,该研究表明,MCAT 方法特别适用于具有许多高度相关特质的结构。

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