Collin V Terri, Violato Claudio, Hecker Kent
Department of Surgery, University of Pittsburgh Medical Center, 200 Lothrop St. Room F677PUH, Pittsburgh, PA 15213, USA.
Adv Health Sci Educ Theory Pract. 2009 Aug;14(3):355-66. doi: 10.1007/s10459-008-9121-7. Epub 2008 May 15.
To develop and test a latent variable path model of general achievement, aptitude for medicine and competence in medicine employing data from the Medical College Admission Test (MCAT), pre-medical undergraduate grade point average (UGPA) and demographic characteristics for competence in pre-clinical and measures of competence (United States Licensure Examination {USMLE} Steps 1, 2, and 3). Data were gathered on 839,710 participants from 1991 to 2000 on demographic and school variables, UGPA, MCAT subtest scores and Steps 1, 2, and 3 of the United Stated Licensure Examination (USMLE). However, subsets of the total 839,710 participants included in the database were used for various analyses and the testing of a latent variable path model (LVPA). A number of preliminary descriptive and inferential techniques were used to confirm previous hypotheses and stated relationships amongst the variables of interest to the present study. Through development and testing of a latent variable path model, three latent variables measured by UGPA (general achievement), subscales of the MCAT (aptitude for medicine), and Steps 1, 2, and 3 of the USMLE (competence in medicine) were identified which resulted in a comparative fit index = .932 of the model to a large sample (n = 20,714). In a confirmatory latent variable path model we were able to identify theoretical constructs, aptitude for medicine, general achievement, and competence in medicine and their interrelationships. These are distinct but interrelated latent variables.
利用医学院入学考试(MCAT)的数据、医学预科本科平均绩点(UGPA)以及人口统计学特征,来开发和测试一个关于一般学业成就、医学天赋和医学能力的潜在变量路径模型,该模型用于衡量临床前能力和能力测评(美国医师执照考试{USMLE}第1、2和3步)。收集了1991年至2000年839,710名参与者的人口统计学和学校变量、UGPA、MCAT子测试分数以及美国医师执照考试(USMLE)第1、2和3步的数据。然而,数据库中839,710名参与者的子集被用于各种分析和潜在变量路径模型(LVPA)的测试。使用了一些初步的描述性和推断性技术来证实先前的假设以及本研究感兴趣的变量之间既定的关系。通过开发和测试一个潜在变量路径模型,确定了由UGPA(一般学业成就)、MCAT子量表(医学天赋)以及USMLE第1、2和3步(医学能力)衡量的三个潜在变量,这使得该模型与一个大样本(n = 20,714)的比较拟合指数为0.932。在一个验证性潜在变量路径模型中,我们能够识别理论结构、医学天赋、一般学业成就和医学能力及其相互关系。这些是不同但相互关联的潜在变量。