二、数据分析实例
(一)测量模型
- Zhang, YM. (2022). Individual differences matter in the effect of teaching presence on perceived learning: From the social cognitive perspective of self-regulated learning. Computers & Education, 179, 104427. (对翻译和改编多个量表以及国内大学的量表,包括探究社区量表、学习感知量表等,均进行了验证性因子分析CFA,检验了χ2 /df ,RMSEA,GFI和CFI的值,显示有良好的效度。)
- Ferede, B., et al. ( 2022). A structural equation model for determinants of instructors’ educational ICT use in higher education in developing countries: Evidence from Ethiopia. Computers & Education,188. 104566. (依据RIPPLES理论,提出了高校教师ICT教育应用的影响因素模型,采用多个整合的量表,对来自6所公立高校的946名教师的问卷数据进行分析。首先进行了测量模型的验证,包括模型拟合度、因子信效度以及区分度。)
- Sing, C.C., Teo, T., Huang, F. et al. (2022). Secondary school students’ intentions to learn AI: testing moderation effects of readiness, social good and optimism. Education Tech Research Dev 70, 765–782 (2022). (基于计划行为、技术接受模型等提出了有关中学生学习AI的系列假设,在511名中学生中收集数据,首先进行了测量模型的检验,包括χ2/df、CFI、RMSEA、SRMR、CR、AVE等以及区分度。)
(二)结构模型
- 极大似然法(Maximum Likelihood):Zhang, YM. (2022). Individual differences matter in the effect of teaching presence on perceived learning: From the social cognitive perspective of self-regulated learning. Computers & Education, 179, 104427. (基于国内高校779名大学生的调查数据,主要分析了大学生在线学习中感知到的教学存在和感知到的学习结果之间的3个调节变量的调节作用,其中一个为调节的调节变量,同时控制性别、年龄、完成率等变量)
- 极大似然法(Maximum Likelihood):Huang, X. et al. (2022). Associations of different types of informal teacher learning with teachers’ technology integration intention. Computers & Education,190, 104604.(应用maximum likelihood (ML)和bootstrap analysis验证了1181名中小学教师的非正式教师学习的理论模型。)
- Bootstrap(5000次重复取样):Zhang, S., & Wong, G. K. W. (2024). Unravelling the underlying mechanism of computational thinking: The mediating role of attitudinal beliefs between personality and learning performance. Journal of Computer Assisted Learning, (调查了434名小学生CT测试成绩、编程态度、人格特征的关系,包括编程态度的中介效应)
- Srakaya, M. , Srakaya, D. A. , & Korkmaz, Z. . (2020). The impact of stem attitude and thinking style on computational thinking determined via structural equation modeling. Journal of Science Education and Technology, 29(1), 1-12. (基于703名中学生的调查数据,分析了学生STEM态度、思维风格对CT技能的影响。)
- 偏最小二乘法(PLS):Ung, L-L. et al. (2022). Computational thinking for teachers: Development of a localised E-learning system. Computers & Education,177. https://doi.org/10.1016/j.compedu.2021.104379(对369名中小学教师,使用李克特五点量表进行e-learning平台使用后的平台感知测量,应用SPSS AMOS对已有e-learning理论模型进行了验证。)
- Guo, X., Hao, X., Deng, W. et al. (2022).The relationship between epistemological beliefs, reflective thinking, and science identity: a structural equation modeling analysis. International Journal of STEM Education,9(提出的有关认识论信念、反思思维以及科学身份认同之间的假设模型,然后以两所公立学校的544名高中生为样本,运用结构方程模型检验了该模型中的直接影响关系和间接影响关系。)
扩展阅读
- 偏最小二乘法(PLS):Fabian,K., Smith, S., Taylor-Smith, E., & Meharg, D. (2022). Identifying factors influencing study skills engagement and participation for online learners in higher education during COVID-19.British Journal of Educational Technology,(依据Moore的互动距离理论,调查了178名英国大学计算机学院的学生,运用偏最小二乘PLS法,探索师生互动距离、生生互动距离、数字化学习资本、感知有用性对学生技能投入和协作学习活动参与的影响。)