The Quantitative Methods in Education Empirical Research in China: A Review on Five Years’ Application

doi:10.16382/j.cnki.1000-5560.2020.09.003

Abstract

Abstract: Since the “National Educational Empirical Research Forum” was first held in 2015, the application of quantitative methods in the field of education science in China has been popular. However, due to the limitation of research level and research environment, investigating the methodology of quantitative methods and applying quantitative methods in quantitative or mixed research are still a weak point in educational research in China. The academic community has not formed a comprehensive and objective understanding of the application of quantitative methods in education empirical research. Even though some scholars review the application of quantitative methods in education empirical research in China, they only describe and summarize the statistical data, and there is limited analysis on the specific problems with the application. Moreover, there is no correction for the misuse of quantitative methods, which cannot help the applicators effectively. Therefore, efforts to analyze the practical application of quantitative methods in China and give more specific suggestions on its shortcomings are of great significance for the development of quantitative methods in China. This paper examined the articles published in 11 comprehensive education journals included in the Chinese Social Science Citation Index (CSSCI) from 2015 to 2019 as the objects. It summarized the application of quantitative methods in educational empirical research in China in the past five years, and provided suggested solutions to some specific issues. Also, it presented the misuse of some widely used quantitative methods, and made the correct application suggestions; analyzed the future trend of applying quantitative methods in educational empirical research in China.

Key words: quantitative research methods, application status, misuse, trend

Lyu Jing. The Quantitative Methods in Education Empirical Research in China: A Review on Five Years’ Application[J]. Journal of East China Normal University(Educational Sciences), 2020, 38(9): 36-55.

References

邓猛, 潘剑芳. (2002). 论教育研究中的混合方法设计. 教育研究与实验, (03),56-61
付瑛, 周谊. (2004). 教育研究方法中定性研究与定量研究的比较. 中华医学教育探索杂志,3(02),9-11+21
国务院. (2017). 新一代人工智能发展规划. 取自中华人民共和国中央人民政府网站(2017年7月20日): http://www.gov.cn/home/2017-07/20/content_5212053.htm.
高潇怡. (2010). 论混合方法在高等教育研究中的具体应用. 国内高等教育教学研究动态, (03),9
何哲. (2015). 大数据战略上升为国家战略. 取自人民网(2015年11月8日): http://politics.people.com.cn/n/2015/1108/c1001-27790239.html.
黎荷芳. (2001). 浅析教育量化研究存在的问题及其正确应用. 吉林教育科学, (06),36-39
刘建设. (1999). 对我国教育量化研究的若干思考. 北京邮电大学学报(社会科学版), (02),22-25
卢克. (2016). 多层次模型(郑冰岛译). 上海: 上海人民出版社.
李宪印, 张宝芳, 姜丽萍. (2019). 大学生创新行为的构成因素及其实证研究. 教育研究, (04),91-100
诺克, 杨. (2012). 社会网络分析(李兰译). 上海: 格致出版社.
佟庆伟. (2004). 论量化研究方法在教育科研中的应用. 教育探索, (11),24-25
田虎伟. (2007a). 混和方法研究——美国教育研究方法的一种新范式. 比较教育研究,28(01),12-17
唐涌. (2015). 混合方法研究——美国教育研究方法论的新取向. 外国教育研究, (02),12-21
吴战杰, 秦健. (2003). Agent技术及其在网络教育中的应用研究. 电化教育研究, (03),32-36
谢美华. (2005). 浅评定量研究方法及其在我国教育研究中的应用. 南昌: 江西师范大学硕士学位论文.
向蓉. (2019). 研究方法的发展及其在基础教育研究中的应用. 现代教育科学, (07),33-39
易红梅, 何婧, 张林秀. (2019). 有条件的现金转移支持承诺对贫困学生高中完成情况的影响研究. 北京大学教育评论,17(02),149-166
余胜泉. (2018). 人工智能+教育. 北京: 北京师范大学出版社.
袁振国. (2017). 实证研究是教育学走向科学的必要途径. 华东师范大学学报(教育科学版),35(03),4-17+168
张东辉. (2013). 美国教育研究方法论的最新进展:混合法研究的兴起与应用. 教育研究与实验, (04),9-12
张绘. (2012). 混合研究方法的形成、研究设计与应用价值——对“第三种教育研究范式”的探析. 复旦教育论坛, (05),53-59
朱军文, 马银琦. (2020). 教育实证研究这五年: 特征、趋势及展望. 华东师范大学学报(教育科学版), (9),16-35
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. United Kingdom: Wiley Press.
Cheung, M. W-L. (2015). Meta-Analysis: A Structural Equation Modeling Approach （pp. 1- 152）. New York: Wiley Press.
Creswell, J. W. (2015). A Concise Introduction to Mixed Methods Research. Sydney: SAGE Publications.
Creswell, J. W., & Clark, P. V. L. (2018). Designing and Conducting Mixed Methods Research. Sydney: SAGE Publications.
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72-91
Gilbert, N. (2020). Agent-Based Models, Second Edition. London: SAGE Publications.
Greene, J. C. (2012). Engaging critical issues in social inquiry by mixing methods. American Behavioral Scientist, 56, 755-773
Hennig, C., Meila, M., Murtagh, F., & Rocci, R. (2016). Handbook of Cluster Analysis. Boca Raton: CRC Press.
Lv, J., & Maeda, Y. (2020). Evaluation of the efficacy of meta-analytic structural equation modeling with missing correlations. Structural Equation Modeling: A Multidisciplinary Journal, 27(3), 414-437
Lv, J., Liu, Q., Zeng, X., OEI, T. P. S., Liu, Y., Xu, K., Sun, W., Hou, H., & Liu, J. (2020). The effect of four immeasurable meditations on depressive symptoms: a systematic review and meta-analysis. Clinical Psychology Review, 76, 101814
MacKinnon, D. P., & Valente, M. J. (2014). Mediation from multilevel to structural equation modeling. Annals of Nutrition and Metabolism, 65(2–3), 198-204
Mcintyre, L. J. (2003). Need to Know: Social Science Research Methods. McGram Hill: München.
Moeyaert, M. (2019). Quantitative synthesis of research evidence: multilevel meta-analysis. Behavioral Disorders, 44(4), 241-256
Muthén, B., du Toit, S.H.C., & Spisic, D. (1997). Robust Inference Using Weighted Least Squares and Quadratic Estimating Equations in Latent Variable Modeling with Categorical and Continuous Outcomes. Unpublished technical report.
Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2016). Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychological Methods, 21(2), 189-205
Rabe-Hesketh, S., Skrondal, A., & Zheng, X. (2007). Multilevel Structural Equation Modeling. In S. Lee (Ed.), Handbook of Latent Variable and Related Models (pp. 209–227). Amsterdam: Elsevier.
Ryu, E. (2014). Model fit evaluation in multilevel structural equation models. Frontiers in Psychology, 5, 81
Shumway, R. H., & David, S. S. (2011). Time Series Analysis and Its Applications with R Examples, Third Edition. Switzerland: Springer Science+Business Media.
Silva, B. C., Bosancianu, C. M., & Littvay, L. (2019). Multilevel Structural Equation Modeling. Germany: SAGE Publications.
Teddlie, C., & Tashakkori, A. (2012). Common “core” characteristics of mixed methods research: a review of critical issues and call for greater convergence. American Behavioral Scientist, 56, 774-788
van den Noortgate, W., & Onghena, P. (2003). Multilevel meta-analysis: a comparison with traditional meta-analytical procedures. Educational and Psychological Measurement, 65(5), 765-790
van den Noortgate, W., & Onghena, P. (2008). A multilevel meta-analysis of single-subject experimental design studies. Evidence-Based Communication Assessment and Intervention, 2(3), 142-151
van der Linden, W. J. (2016). Handbook of Item Response Theory: Volume 1, Models. Boca Raton: CRC Press.
van der Linden, W. J. (2017). Handbook of Item Response Theory: Volume 2, Statistical Tools. Boca Raton: CRC Press.
Yue, C., & Xu, X. (2020). Review of quantitative methods used in Chinese educational research, 1978-2018. ECNU Review of Education, 2(4), 515-543

[1]	Yicheng Jiao. The Impact of Higher Education Expansion on Undergraduate Enrollment Quality in China: Based on the Data of NCSS [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(8): 22-33.
[2]	Wei Shen, Tianci Sun. The Historical Context and Multiple Perspectives of Research on Chinese Teaching-research Officers [J]. Journal of East China Normal University(Educational Sciences), 2021, 39(5): 116-129.
[3]	Huijuan Chen, Tao Xin. The Evolution and Trend of China’s Basic Education Quality Monitoring and Evaluation System [J]. Journal of East China Normal University(Educational Sciences), 2021, 39(4): 42-52.
[4]	Rong Yao. Farewell to Autonomy: American University Governance in the Age of Compliance [J]. Journal of East China Normal University(Educational Sciences), 2021, 39(2): 75-88.
[5]	Zhu Junwen, Ma Yinqi. Empirical Education Research in China(2015—2019): Characteristics, Trends and Prospects [J]. Journal of East China Normal University(Educational Sciences), 2020, 38(9): 16-35.
[6]	Xu Guangtao, Xu Zuodong, Li Yingming, Huang Jianlan, Li Yao. Trend in Science of Learning in Latest Two Decades: Based on a Textual Analysis of Report Series of How People Learn [J]. Journal of East China Normal University(Educational Sciences), 2020, 38(3): 80-92.
[7]	JIANG Cheng, ZHANG Sisi. A Trend Study on College Graduates Grass-root Employment: 2003-2017 [J]. Journal of East China Normal University(Educationa, 2018, 36(5): 60-70+167.
[8]	WANG Zhan-Ren. Evolution and Development Trend of Chinese Entrepreneurship Education [J]. Journal of East China Normal University(Educationa, 2016, 34(2): 30-38.
[9]	LIU Tao. The Status Quo and Trends of Studies on Thoughts of Education in China: a Bibliometric Analysis of Core Periodical Literature [J]. Journal of East China Normal University(Educationa, 2015, 33(2): 10-16.
[10]	HAN Kui-Kui, HU Wei-Ping, WANG Bi-Mei. Research Progress and Trend of International Research on Science Teaching Psychology [J]. Journal of East China Normal University(Educationa, 2014, 32(4): 63-70.