Center for Research and Innovation in Medical Education, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.
Med Teach. 2009 Jun;31(6):e226-32. doi: 10.1080/01421590802516756.
The validation of educational instruments, in particular the employment of factor analysis, can be improved in many instances.
To demonstrate the superiority of a sophisticated method of factor analysis, implying an integration of recommendations described in the factor analysis literature, over often employed limited applications of factor analysis. We demonstrate the essential steps, focusing on the Postgraduate Hospital Educational Environment Measure (PHEEM).
The PHEEM was completed by 279 clerks. We performed Principal Component Analysis (PCA) with varimax rotation. A combination of three psychometric criteria was applied: scree plot, eigenvalues >1.5 and a minimum percentage of additionally explained variance of approximately 5%. Furthermore, four interpretability criteria were used. Confirmatory factor analysis was performed to verify the original scale structure.
Our method yielded three interpretable and practically useful dimensions: learning content and coaching, beneficial affective climate and external regulation. Additionally, combining several criteria reduced the risk of overfactoring and underfactoring. Furthermore, the resulting dimensions corresponded with three learning functions essential to high-quality learning, thus strengthening our findings. Confirmatory factor analysis disproved the original scale structure.
Our sophisticated approach yielded several advantages over methods applied in previous validation studies. Therefore, we recommend this method in validation studies to achieve best practice.
教育工具的验证,特别是因子分析的应用,可以在很多情况下得到改进。
展示一种复杂的因子分析方法的优越性,该方法综合了因子分析文献中描述的建议,优于通常应用的有限因子分析。我们以研究生医院教育环境量表(PHEEM)为例,演示基本步骤。
279 名办事员完成了 PHEEM。我们进行了主成分分析(PCA),采用方差极大旋转。应用了三种心理计量学标准的组合:碎石图、特征值>1.5 和附加方差解释率约为 5%。此外,还使用了四个可解释性标准。进行验证性因子分析以验证原始量表结构。
我们的方法产生了三个可理解和实用的维度:学习内容和指导、有益的情感氛围和外部调节。此外,结合多个标准降低了过度和不足因子分析的风险。此外,所得维度与高质量学习的三个学习功能相对应,从而加强了我们的发现。验证性因子分析证明了原始量表结构不成立。
我们复杂的方法相对于以往验证研究中应用的方法具有多项优势。因此,我们建议在验证研究中采用这种方法以实现最佳实践。