确定用于预测本科医学教育学业成绩的简约模型：验证性因素分析。
Identifying a parsimonious model for predicting academic achievement in undergraduate medical education: A confirmatory factor analysis.
作者信息
Ali Syeda Kauser, Baig Lubna Ansari, Violato Claudio, Zahid Onaiza
机构信息
Syeda Kauser Ali, Associate Professor, Department for Educational Development, Aga Khan University, PO Box 3500, Stadium Road, Karachi-74800, Pakistan.
Lubna Baig, Pro-VC and Dean, APPNA Institute of Public Health Jinnah Sindh Medical University, Rafiqui Shaheed Road Karachi, Pakistan.
出版信息
Pak J Med Sci. 2017 Jul-Aug;33(4):903-908. doi: 10.12669/pjms.334.12610.
OBJECTIVES
This study was conducted to adduce evidence of validity for admissions tests and processes and for identifying a parsimonious model that predicts students' academic achievement in Medical College.
METHODS
Psychometric study done on admission data and assessment scores for five years of medical studies at Aga Khan University Medical College, Pakistan using confirmatory factor analysis (CFA) and structured equation modeling (SEM). Sample included 276 medical students admitted in 2003, 2004 and 2005.
RESULTS
The SEM supported the existence of covariance between verbal reasoning, science and clinical knowledge for predicting achievement in medical school employing Maximum Likelihood (ML) estimations (n=112). Fit indices: χ (21) = 59.70, p =<.0001; CFI=.873; RMSEA = 0.129; SRMR = 0.093.
CONCLUSIONS
This study shows that in addition to biology and chemistry which have been traditionally used as major criteria for admission to medical colleges in Pakistan; mathematics has proven to be a better predictor for higher achievements in medical college.
目的
本研究旨在为医学院入学考试及流程提供效度证据，并确定一个能预测医学院学生学业成绩的简约模型。
方法
对巴基斯坦阿迦汗大学医学院五年医学学习的入学数据和评估分数进行心理测量学研究，采用验证性因素分析（CFA）和结构方程模型（SEM）。样本包括2003年、2004年和2005年录取的276名医学生。
结果
结构方程模型支持言语推理、科学和临床知识之间存在协方差，以采用最大似然估计法（ML）预测医学院的学业成绩（n = 112）。拟合指数：χ（21）= 59.70，p = <.0001；CFI =.873；RMSEA = 0.129；SRMR = 0.093。
结论
本研究表明，除了传统上作为巴基斯坦医学院入学主要标准的生物学和化学外，数学已被证明是医学院更高学业成绩的更好预测指标。
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