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多站式面试中,应否根据面试官的严格或宽松程度调整考生分数?

Should candidate scores be adjusted for interviewer stringency or leniency in the multiple mini-interview?

机构信息

Sydney Medical School-Northern, University of Sydney, Sydney, New South Wales, Australia.

出版信息

Med Educ. 2010 Jul;44(7):690-8. doi: 10.1111/j.1365-2923.2010.03689.x.

Abstract

CONTEXT

There are significant levels of variation in candidate multiple mini-interview (MMI) scores caused by interviewer-related factors. Multi-facet Rasch modelling (MFRM) has the capability to both identify these sources of error and partially adjust for them within a measurement model that may be fairer to the candidate.

METHODS

Using facets software, a variance components analysis estimated sources of measurement error that were comparable with those produced by generalisability theory. Fair average scores for the effects of the stringency/leniency of interviewers and question difficulty were calculated and adjusted rankings of candidates were modelled.

RESULTS

The decisions of 207 interviewers had an acceptable fit to the MFRM model. For one candidate assessed by one interviewer on one MMI question, 19.1% of the variance reflected candidate ability, 8.9% reflected interviewer stringency/leniency, 5.1% reflected interviewer question-specific stringency/leniency and 2.6% reflected question difficulty. If adjustments were made to candidates' raw scores for interviewer stringency/leniency and question difficulty, 11.5% of candidates would see a significant change in their ranking for selection into the programme. Greater interviewer leniency was associated with the number of candidates interviewed.

CONCLUSIONS

Interviewers differ in their degree of stringency/leniency and this appears to be a stable characteristic. The MFRM provides a recommendable way of giving a candidate score which adjusts for the stringency/leniency of whichever interviewers the candidate sees and the difficulty of the questions the candidate is asked.

摘要

背景

由于面试官相关因素,候选人的多站迷你面试(MMI)分数存在显著的差异。多方面 Rasch 建模(MFRM)具有识别这些误差源的能力,并在可能对候选人更公平的测量模型中对其进行部分调整。

方法

使用 facet 软件,方差分量分析估计了与广义理论产生的误差源相当的测量误差源。计算了面试官严格/宽松程度和问题难度的公平平均分数,并对候选人的调整排名进行了建模。

结果

207 名面试官的决策与 MFRM 模型具有良好的拟合度。对于一名候选人由一名面试官在一个 MMI 问题上进行评估,19.1%的方差反映了候选人的能力,8.9%反映了面试官的严格/宽松程度,5.1%反映了面试官针对特定问题的严格/宽松程度,2.6%反映了问题的难度。如果对候选人的原始分数进行面试官严格/宽松程度和问题难度的调整,11.5%的候选人的排名将发生显著变化,从而影响其进入项目的选择。面试官的宽松程度与面试的候选人数量有关。

结论

面试官在严格/宽松程度上存在差异,而且这似乎是一个稳定的特征。MFRM 为候选人提供了一种值得推荐的评分方法,该方法可以调整候选人所遇到的面试官的严格/宽松程度以及候选人所回答的问题的难度。

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