二、数据分析实例
1. 一元线性回归
- Chang, D., Hwang, GJ., Chang, SC. et al. (2021). Promoting students’ cross-disciplinary performance and higher order thinking: a peer assessment-facilitated STEM approach in a mathematics course. Education Tech Research Dev 69, 3281–3306.
- Rosenzweig, E.Q., Chen, XY., Song, Y. et al. (2024).Beyond STEM attrition: changing career plans within STEM fields in college is associated with lower motivation, certainty, and satisfaction about one’s career. IJ STEM Ed 11, 15 . https://doi.org/10.1186/s40594-024-00475-6(使用线性回归对大学期间改变STEM领域的职业计划与学生对当前职业计划的动机（即感知的能力相关的信念和对该职业计划的任务价值观的看法）、对当前职业计划的满意度以及大学后追求这些职业计划的确定性有什么关系进行分析)
2. 多元线性回归
- Tsarava, K. (2022). A cognitive definition of computational thinking in primary education. Computers & Education, 179, 104425. （调查研究，分析了多个认知技能对CT能力的预测）
- Liu,Y., & Benoliel, P. (2022). “National context, school factors, and individual teacher characteristics: Which matters most for teacher collaboration?”. Teaching and Teacher Education,120, 103885. (使用2018 TALIS、2018 PISA以及2004 GLOBE的数据，通过多层线性模型，分析了教师特征、学校因素以及国家政策和文化变迁对教师合作的影响。)
- Chen, Y., Cao, L., Guo, L.， & Cheng，J.M. (2022). Driving is believing: Using telepresence robots to access makerspace for teachers in rural areas. British Journal of Educational Technology. (在实验干预之后，用问卷调查了乡村教师对远程机器人操作的感受，然后用多元线性回归分析考察了具身、社会存在、行为参与、情感参与对认知参与的预测关系。)
- Sun L, Hu L, Yang W, Zhou D, Wang X. (2021). STEM learning attitude predicts computational thinking skills among primary school students. Journal of Computer Assistted Learning. 37:346–358 （用阶层回归分析判断STEM的学习态度能否预测CT技能：第一阶段，性别和年级、数学和科学成绩作为预测变量；第二阶段，STEM的学习态度和三个因素作为预测变量，最后所有因素都作为预测变量进入。）
- Rozgonjuk, D., Kraav, T., Mikkor, K. et al. (2020). Mathematics anxiety among STEM and social sciences students: the roles of mathematics self-efficacy, and deep and surface approach to learning. International Journal of STEM Education 7, 46 .
- Förster, M. et al. (2022). Pre-class video watching fosters achievement and knowledge retention in a flipped classroom. Computers & Education, 179, 104399.
- Reeves,T.D., Hamilton, V., & Onder, Y.(2022). Which teacher induction practices work? Linking forms of induction to teacher practices, self-efficacy, and job satisfaction. Teaching and Teacher Education,109. (对美国参与2018TALIS调查的736名教师的数据进行量化分析，因变量有三个：教师实践、自我效能感以及工作满意度，自变量为入职培训，控制变量包括性别、年龄、学历、教学经验等，研究者运用R语言对这些变量进行三次多元线性回归分析——one for each dependent variable。）
- Lachner, A. et al. (2021). Fostering pre-service teachers' technological pedagogical content knowledge (TPACK): A quasi-experimental field study , Computers & Education,174. 浏览课程单元讨论