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Identifying a parsimonious model for predicting academic achievement in undergraduate medical education: A confirmatory factor analysis

机译:确定用于预测医学本科学习成绩的简约模型:一个验证性因素分析

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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: X2 (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.
机译:目的:本研究旨在为入学考试和过程的有效性提供证据,并确定可预测学生在医学院学习成绩的简约模型。方法:在五年制医学研究中对入学数据和评估分数进行的心理计量学研究巴基斯坦阿加汗大学医学院,使用验证性因子分析(CFA)和结构化方程模型(SEM)。样本包括2003年,2004年和2005年招收的276名医学生。结果:SEM支持使用最大可能性(ML)估计(n = 112)预测口头推理,科学知识和临床知识之间的协方差,以预测医学院的学习成绩。拟合指数:X2(21)= 59.70,p = <。0001; CFI = .873; RMSEA = 0.129; SRMR = 0.093。结论:这项研究表明,除了生物学和化学以外,传统上这些生物学和化学被用作进入巴基斯坦医学院校的主要标准。数学已被证明是医学院取得更高成就的更好的预测指标。

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