Medical interview score data from PostCC-OSCE and programs for an extended many-facet IRT model

Objective structured clinical examinations (OSCEs) are widely used performance assessments for medical and dental students. A common limitation of OSCEs is that the evaluation results depend on the characteristics of raters and the scoring rubric. To overcome this limitation, item response theory (IRT) models such as the many-facet models have been proposed to estimate examinee abilities while accounting for the characteristics of raters and evaluation items in a rubric. However, conventional IRT models have two impractical assumptions: constant rater severity across all evaluation items in a rubric and an equal interval rating scale among evaluation items, which can decrease model fitting and ability measurement accuracy.
To resolve this problem, we propose a new IRT model that relaxes these assumptions. We demonstrate the effectiveness of the proposed model by applying it to actual data collected from a medical interview test conducted at Tokyo Medical and Dental University as part of..., , , # Medical Interview Score Data from PostCC-OSCE and Programs for an Extended Many-Facet IRT Model

[https://doi.org/10.5061/dryad.tmpg4f56q](https://doi.org/10.5061/dryad.tmpg4f56q)

This dataset includes the actual score data collected from a medical interview test conducted at Tokyo Medical and Dental University as part of a PostCC OSCE, as well as the program for estimating parameters of the new item response theory model.

## **Structure of data and related programs:**

This dataset includes the following data:

* **dat.csv:** The actual OSCE score data.

It also includes the following related programs:

* **main.R:** The main program to estimate the proposed IRT model parameters.
* **model.stan:** The Stan code for the parameter estimation of the proposed model.

## Description of the data

This is the actual score data collected from a medical interview test. The data consist of rating scores assigned by five raters to 30 videos recorded as material for reconfirmation of medical i...

客观结构化临床考试（Objective Structured Clinical Examinations，OSCE）是广泛应用于医学生与口腔医学生的绩效评估手段。OSCE的一个普遍局限在于，其评估结果依赖于评分者特征与评分量表的设定。为克服这一局限，学界已提出项目反应理论（Item Response Theory，IRT）模型（如多面性模型），在考量评分者与量表中评估条目特征的同时，对考生的能力进行估计。然而，传统IRT模型存在两处不符合实际的假设：一是评分者在量表所有评估条目上的严苛程度恒定，二是各评估条目采用等距评分尺度，这会降低模型拟合效果与能力测量的准确性。
为解决这一问题，本文提出一种放宽上述两项假设的新型IRT模型。我们将该模型应用于东京医科齿科大学（Tokyo Medical and Dental University）开展的医学面试测试的实测数据，以此验证模型的有效性，该测试作为PostCC-OSCE的组成部分之一——# 面向扩展多面IRT模型的PostCC-OSCE医学面试评分数据与配套程序
[https://doi.org/10.5061/dryad.tmpg4f56q](https://doi.org/10.5061/dryad.tmpg4f56q)

本数据集包含东京医科齿科大学作为PostCC-OSCE组成部分开展的医学面试测试的实测评分数据，以及用于估计新型项目反应理论模型参数的配套程序。

## 数据与相关程序结构：
本数据集包含以下数据：
* **dat.csv**：实测OSCE评分数据。

本数据集还包含以下相关程序：
* **main.R**：用于估计所提IRT模型参数的主程序。
* **model.stan**：用于所提模型参数估计的Stan代码。

## 数据说明
本数据为医学面试测试的实测评分数据，由5名评分者对30段作为复核材料的医学访谈视频给出的评分组成……