心理测量与评价方向博士（北师大心理学院与美国伊利诺伊大学香槟分校心理系联合培养），教授，博士生导师，方法组组长，美国明尼苏达大学心理系、华盛顿大学教育学院访问学者。从事心理测量前沿理论与方法研究，核心方向包括计算机化自适应测验与分类测验、测量模型参数估计、大规模教育测评技术等方面的教研工作。主持国家自然科学基金青年项目和面上项目、人社部国家外国专家项目等多项课题，已在Psychometrika、Psychol. Methods、Br J Math Stat Psychol (BJMSP)、Behav Res Methods、J Educ Behav Stat、J Educ Meas (JEM)、Appl Psych Meas、Educ Meas-Issues Pra和心理学报等主流权威期刊发表论文70余篇，出版教材1部，参编著作3部。现任Psychometrika和Chin Engl J Educ Meas Eval副主编、BJMSP顾问编委、《心理学探新》编委、中国教育技术协会教育测量与评价专业委员会副主任和中国心理学会网络心理专业委员会委员，曾任JEM副主编和美国教育研究协会（AERA）“量化方法与统计理论”模块联合主席。多年来，为国家监测中出现的“统计测量难题”提供解决方案，围绕“跨年等值”、“标准设定”、“计算机测评”和“题库建设”等核心关键技术开展深入研究与实践。

招生要求：欢迎心理学、统计学、计算机科学、数学等相关专业背景，且对心理测量与评价方向感兴趣的优秀学生报考。希望你热爱科研，踏实认真，有较强的自主学习能力和科研主动性。

部分研究成果：

[1] Zhang, L. F., Jeon, M., & Chen, P.* (2026). Columnwise neural imputation for incomplete ordinal psychometric data. Psychological Methods. doi:10.1037/met0000867.

[2] Song, Z. L., Zeng, Q., & Chen, P.* (2026). Detecting differential item functioning with unknown subgroups and anchor items in cognitive diagnostic assessment. British Journal of Mathematical and Statistical Psychology. doi:10.1111/bmsp.70073.

[3] Li, J., & Chen, P.* (2026, accepted). Improving Q-matrix validation using Theil index. Journal of Educational and Behavioral Statistics.

[4] Yuan, L., Huang, Y. S., & Chen, P.* (2026). Online calibration for multidimensional CAT with polytomously scored items: A neural network-based approach. Journal of Educational and Behavioral Statistics, 51(1), 141–174.

[5] Zeng, Q., He, Y. S., Quan, J. X., Yang, Y., Li, J., & Chen, P.* (2026). Career maturity and career adaptability among vocational high school students: A moderated mediation model of future time perspective and future work self. International Journal for Educational and Vocational Guidance, 26(1), 177–196.

[6] Zeng, Q., Yang, Z. Y., Chen, Z. Q., & Chen, P.* (2025). The relationships between nature connectedness, nature contact, and positive psychological outcomes: A meta-analysis. Journal of Environmental Psychology, 105, Article 102675, 1–18.

[7] Zeng, Q., Song, Z. L., Yang, Z. Y., Wang, S. Y., & Chen, P.* (2025). Career construction networks among junior and senior high school students. Journal of Career Assessment, 33(3), 446–469.

[8] Xue, M. F., & Chen, P.* (2025). Comparing and combining IRTree models and anchoring vignettes in addressing response styles. Journal of Educational Measurement, 62(2), 225–247.

[9] Zeng, Q., Yang, Z. L., Xiao, T., Luo, H. J., & Chen, P.* (2025). Different parental rearing behaviors and depressive symptoms of adolescents: Roles of psychological insecurity and core self-evaluations. School Psychology International, 46(2), 172–196.

[10] Zhang, L. F., & Chen, P.* (2024). A neural network paradigm for modeling psychometric data and estimating IRT model parameters: Cross estimation network. Behavior Research Methods, 56(7), 7026–7058.

[11] Li, J., & Chen, P.* (2024). A new Q-matrix validation methods based on signal detection theory. British Journal of Mathematical and Statistical Psychology, 78(2), 522–554.

[12] Huang, Y. S., Ren, H., & Chen, P.* (2023). Item selection methods with exposure and time control for computerized classification test. British Journal of Mathematical and Statistical Psychology, 76(1), 52–68.

[13] Yuan, L., Huang, Y. S., Li, S. H., & Chen, P.* (2023). Online Calibration in Multidimensional Computerized Adaptive Testing with Polytomously Scored Items. Journal of Educational Measurement, 60(3), 476–500.

[14] Yuan, L., Liu, Y. L., Chen, P.*, & Xin, T.* (2022). Development of a new learning progression verification method based on the hierarchical diagnostic classification model: Taking grade 5 students’ fractional operations as an example. Educational Measurement: Issues and Practice, 41(3), 69–82.

[15] Chen, P.*, & Wang, C. (2021). Using EM algorithm for finite mixtures and reformed supplemented EM for MIRT calibration. Psychometrika, 86(1), 299–326.

[16] He, Y. H.*, Chen, P.*, & Li, Y. (2021). Maximum information per time unit designs for continuous online item calibration. British Journal of Mathematical and Statistical Psychology, 74(S1), 24–51.

[17] Wang, C., Chen, P., & Huebner, A. (2021). Stopping rules for multi-category computerized classification testing. British Journal of Mathematical and Statistical Psychology, 74(2), 184–202.

[18] He, Y. H., & Chen, P.* (2020). Optimal online calibration designs for itemreplenishment in adaptive testing, Psychometrika, 85(1), 35–55.

[19] Chen, P., Engel, S., & Wang, C.* (2020). The multivariate adaptive design for efficient estimation of the time-course of perceptual adaptation. Behavior Research Methods, 52(3), 1073–1090.

[20] He, Y. H., Chen, P.*, Li, Y. (2020). New efficient and practicable designs for calibrating items online. Applied Psychological Measurement, 44(1), 3–16.

[21] Wang, C., Chen, P., & Jiang, S. (2020). Item calibration methods with multiple subscale multistage testing. Journal of Educational Measurement, 57(1), 3–28.

[22] He, Y. H., Chen, P.*, Li, Y., & Zhang, S. M. (2017). A new online calibration method based on Lord’s bias-correction. Applied Psychological Measurement, 41(6), 456–471.

[23] Chen, P.*(2017). A comparative study of online item calibration methods in multidimensional computerized adaptive testing.Journal of Educational and Behavioral Statistics, 42(5), 559–590.

[24] Chen, P.*, Wang, C., Xin, T., & Chang, H. (2017). Developing new online calibration methods for multidimensional computerized adaptive testing. British Journal of Mathematical and Statistical Psychology, 70(1), 81–117.

[25] Chen, P.*, & Wang, C. (2016). A new online calibration method for multidimensional computerized adaptive testing.Psychometrika, 81(3), 674–701.

[26] Chen, P.*, & Xin, T. (2014). Online calibration with cognitive diagnostic assessment. In Cheng, Y., & Chang, H. (eds), Advancing Methodologies to Support Both Summative and Formative Assessments (pp. 287–313). Charlotte, NC: Information Age Publishing Inc.

[27] Chen, P.*, Xin, T., Wang, C., & Chang, H. (2012). Online calibration methods for the DINA model with independent attributes in CD-CAT. Psychometrika, 77(2), 201–222.

[28] 陈平. (2026). 自适应测验与自适应诊断评估. 北京师范大学出版社.

[29] 陈平. (2026). 神经网络在题库建设参数估计中的应用. 中国考试, 7, 43–53.

[30] 陆翔宇, 陈平*. (2025). 交互式问题解决测验中学习效应的分析：过程数据测量模型的拓展与应用. 心理学报, 57(9), 1677–1688.

[31] 陈平, 李潇*, 任赫, 辛涛. (2023). 改良单组设计下的跨年等值影响因素研究. 心理科学, 46(4), 960–970.

[32] 陈平*, 黄颖诗, 代艺. (2023). 测验模式效应：来源、检测与应用. 心理科学进展, 31(10), 1966–1980.

[33] 陈平*, 任赫. (2023). 计算机化自适应测验. 王孟成, 刘拓 (主编), 心理与行为定量研究手册 (pp. 102–134), 重庆大学出版社.

[34] 任赫, 黄颖诗, 陈平*. (2022). 计算机化分类测验终止规则的类别、特点及应用. 心理科学进展, 30(5), 1168–1182.

[35] 陈平. (2022). 浅谈标准设定中的关键技术：来自我国大规模测评项目的经验. 中国考试, 8, 48–56.

[36] 任赫, 陈平*. (2021). 两种新的多维计算机化分类测验终止规则. 心理学报, 53(9), 1044–1058.

[37] 薛明锋, 陈平*, 刘拓, 甄锋泉. (2021). 在GLMM框架下统一GT和IRT. 心理科学, 44(2), 449–456.

[38] 陈冠宇, 陈平*. (2019). 解释性项目反应理论模型：理论与应用. 心理科学进展, 27(5), 937–950.

[39] 聂旭刚, 陈平*, 张缨斌, 何引红. (2018). 题目位置效应的概念及检测. 心理科学进展, 26(2), 368–380.

[40] 陈平*. (2016). 两种新的计算机化自适应测验在线标定方法. 心理学报, 48(9), 1184–1198.

[41] 詹沛达, 陈平*, 边玉芳*. (2016). 使用验证性补偿多维IRT模型进行认知诊断评估. 心理学报, 48(10), 1347–1356.

[42] 林喆, 陈平, 辛涛*. (2015). 允许CAT题目检查的区块题目袋方法. 心理学报, 49 (7), 1188–1198.

[43] 陈平*, 张佳慧,辛涛. (2013). 在线标定技术在计算机化自适应测验中的应用. 心理科学进展, 21(10), 1883–1892.

[44] 陈平, 辛涛*. (2013). Bookmark标准设定中的分界分数估计方法比较. 北京师范大学学报 (自然科学版), 49(1), 105–110.

[45] 陈平*. (2012). 认知诊断计算机化自适应测验中题目参数的在线标定及其设计. 涂冬波, 蔡艳, 丁树良 (主编), 认知诊断理论、方法与应用 (pp. 189–204), 北京师范大学出版社.

[46] 陈平, 辛涛*. (2011). 认知诊断计算机化自适应测验中的项目增补. 心理学报, 43(7), 836–850.

[47] 陈平, 李珍, 辛涛*. (2011). 认知诊断计算机化自适应测验的题库使用均匀性初探. 心理与行为研究, 9(2), 125–132.

[48] 陈平, 李珍, 辛涛*, 高慧健. (2011). 标准参照测验决策一致性指标研究的总结与展望. 心理发展与教育, 27(2), 210–216.

[49] 陈平, 丁树良*. (2008). 允许检查并修改答案的计算机化自适应测验. 心理学报, 40(6), 737–747.

[50] 陈平, 丁树良*, 林海菁, 周婕. (2006). 等级反应模型下计算机化自适应测验选题策略. 心理学报, 38(3), 461–467.

Google Scholar: https://scholar.google.com/citations?user=NA9RIDkAAAAJ&hl=en

ResearchGate: https://www.researchgate.net/profile/Ping-Chen-36?ev=hdr_xprf