基础医学与临床 ›› 2020, Vol. 40 ›› Issue (2): 265-269.

• 医学教育 • 上一篇    下一篇

基于结构方程模型预测医学本科生的科研参与行为

潘倩*   

  1. 四川大学 华西基础医学与法医学院,四川 成都 610041
  • 收稿日期:2019-03-01 修回日期:2019-05-29 出版日期:2020-02-05 发布日期:2020-02-05
  • 通讯作者: *panqian1078@scu.edu.cn
  • 基金资助:
    四川大学新世纪高等教育教学改革工程(第七期)研究项目(SCUY7090)

Based on structural equation modeling to predict research participation behavior of medical undergraduates

PAN Qian*   

  1. West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
  • Received:2019-03-01 Revised:2019-05-29 Online:2020-02-05 Published:2020-02-05
  • Contact: *panqian1078@scu.edu.cn

摘要: 目的 建立一种通过学生个人特质预测其科研参与方式、体验与收获的方法,为个性化学习提供决策参考。方法 通过相关调查问卷收集数据并整理,对问卷进行信度、效度分析;随后从问卷各观察变量中提取个人特质、科研参与方式、体验与收获4个潜在变量,分别构建个人特质与后三者的结构方程模型(SEM);在SEM结果基础上分别建立个人特质与后三者的预测方程。结果 调查问卷有效率94.99%(417/439),α信度系数0.774,效度分析KMO值0.804,Bartlett球形度检验显著,提取公因子解释总方差53.94%;3个SEM均无负误差方差,卡方P>0.05,1<规范卡方<3,AGFI>0.9,GFI>0.9,RMSEA<0.05,NC>200。个人特质与科研参与方式、体验与收获的预测方程均为一次函数,其中自变量为个人特质,因变量分别为科研参与方式、体验与收获的各观察变量。结论 该调查问卷有效率高,信度、效度良好,所构建SEM无不合理参数且与数据适配度良好,基于SEM通过学生个人特质预测其科研参与方式、体验与收获的方法客观、可行。

关键词: 个性化学习, 结构方程模型, 预测, 本科生科研

Abstract: Objective To establish a method to predict style, experience and gains of research participation by undergraduates' personal traits, thus to provide decision-making reference for individualized learning. Methods Gather data through the relevant questionnaire, and analyze reliability and validity of the questionnaire; then extract 4 latent variables (personal traits, research participation style, research participation experience and research participation gains) from observed variables in the questionnaire, and build structural equation modeling (SEM) consisting of the above 4 latent variables; at last establish predicting equations of personal traits and style/experience/gains of research participation respectively based on SEM analysis results. Results With 94.99% (417/439) efficiency, the alpha reliability coefficient of the questionnaire is 0.774, KMO of the validity analysis is 0.804, Bartlett's test is significant and extracted common factors can explain 53.94% of the total variance; three SEMs do not show negative variance, and the three SEMs' Chi-square P>0.05, Normed Chi-square are all greater than 1 less than 3, AGFI and GFI are all greater than 0.9, RMSEA are all less than 0.05, and NC are all greater than 200; the three predicting equations are all linear functions (with the independent variable: personal traits and the dependent variable: observed variables of research participation style/experience/gains respectively). Conclusions This questionnaire is highly efficient with good reliability and validity. The constructed SEMs have no unreasonable parameters and fit the data well. Based on SME, the method to predict style, experience and gains of research participation through undergraduates' personal traits is objectively and feasible.

Key words: individualized learning, structural equation modeling, prediction, undergraduate research

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