认知诊断评估中主效应DIF与交互式DIF检测方法开发: 基于递归分割视角

doi:10.3724/SP.J.1041.2026.0995

心理学报 ›› 2026, Vol. 58 ›› Issue (5): 995-1014.doi: 10.3724/SP.J.1041.2026.0995 cstr: 32110.14.2026.0995

• 研究报告 • 上一篇

认知诊断评估中主效应DIF与交互式DIF检测方法开发: 基于递归分割视角

刘凯¹^,², 郭治辰¹, 王琴¹, 汪大勋¹(), 蔡艳¹(), 涂冬波¹^,³^,⁴()

¹ 江西师范大学心理学院, 南昌 330022
² 辽宁师范大学心理学院, 大连 116029
³ 江西省哲学社会科学实验室—江西师范大学数据科学与智能化心理测评及服务实验室, 南昌 330022
⁴ 智能信息处理与情感计算江西省重点实验室, 南昌 330022

收稿日期:2024-04-26 发布日期:2026-03-04 出版日期:2026-05-25
通讯作者: 蔡艳, E-mail: cy1979123@aliyun.com;
汪大勋, E-mail: wangda.xun@163.com;
涂冬波, E-mail: tudongbo@aliyun.com
作者简介:
郭治辰和王琴为本文共同第一作者。
基金资助:
国家自然科学基金项目(32300942);国家自然科学基金项目(62467002);国家自然科学基金项目(62167004);国家自然科学基金项目(32160203);江西省科技创新基地计划—智能信息处理与情感计算江西省重点实验室(20242BCC32021)

Development of main effect DIF and interactive DIF detection method in cognitive diagnosis assessments: A recursive partitioning-based perspective

LIU Kai¹^,², GUO Zhichen¹, WANG Qin¹, WANG Daxun¹(), CAI Yan¹(), TU Dongbo¹^,³^,⁴()

¹ School of Psychology, Jiangxi Normal University, Nanchang 330022, China
² College of Psychology, Liaoning Normal University, Dalian 116029, China
³ Jiangxi Laboratory of Philosophy and Social Sciences—Data Science and Intelligent Psychological Assessment and Service Laboratory of Jiangxi Normal University, Nanchang 330022, China
⁴ Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing, Nanchang 330022, China

Received:2024-04-26 Online:2026-03-04 Published:2026-05-25

摘要/Abstract

摘要：

在认知诊断评估中, 项目功能差异(DIF)检测是评估其测验公平性以及测量效度的重要技术手段。然而, 现有的认知诊断DIF检测方法局限于单一协变量视角下的主效应DIF检测, 对于由多个协变量交互作用引发的交互式DIF尚缺乏有效的检测手段。针对这一局限, 研究借鉴递归分割技术的核心思想, 提出了一种能够在认知诊断评估中同时检测主效应DIF和交互式DIF的新方法(记为ISRPM)。模拟研究结果表明, ISRPM不仅在主效应DIF检测中的整体表现与传统方法大体相当, 更重要的是, 其在交互式DIF检测方面的性能表现优于传统方法。实证研究则进一步支持了该方法的可用性, 结果显示, ISRPM与传统DIF检测方法在检测结果上具有较高一致性, 并在识别交互式DIF方面展现出潜在优势。总体而言, ISRPM的提出有望进一步提升认知诊断DIF检测精度, 并促进认知诊断评估在心理与教育测评实践中的推广与应用。

关键词: 认知诊断评估, 项目功能差异, 主效应DIF, 交互式DIF, 递归分割技术

Abstract:

With the growing recognition of the advantages of cognitive diagnosis (CD) in psychological and educational measurement, applying the CD framework to test development has become an important research direction in the field of psychology. In the development of cognitive diagnostic assessments, detecting differential item functioning (DIF) remains a crucial quality control procedure to ensure test fairness and validity. However, existing CD-based DIF detection methods typically focus on a single covariate at a time. While these approaches are effective for identifying main effect DIF induced by a single covariate, they are limited in detecting interactive DIF caused by the interaction among multiple covariates. Such limitations may compromise the fairness and interpretability of assessment outcomes. To address this issue, the present study integrates CD modeling with recursive partitioning techniques by proposing a novel DIF detection method, namely the Item-based Sequential Recursive Partitioning Method (ISRPM). Building on the core principles of recursive partitioning, the ISRPM allows the simultaneous consideration of multiple covariates within a single DIF detection procedure and facilitates the identification of both main effect DIF and interactive DIF in cognitive diagnostic assessments. To evaluate the performance of the proposed method, a series of Monte Carlo simulation studies were conducted focusing on two key objectives: (1) examining how factors such as sample size per group, DIF magnitude, DIF type, item quality, correlations among attributes, and the influence of demographic covariates on attribute mastery distribution affect the performance of ISRPM; and (2) comparing ISRPM with several existing DIF detection methods across varied experimental conditions. In addition, to illustrate its practical utility, ISRPM was applied to a cognitive diagnostic version of the Schizotypal Personality Questionnaire (DC-SPQ) and compared with five established DIF detection methods. The results showed that (1) sample size, DIF magnitude, and item quality substantially influenced the performance of all methods; and (2) when items exhibited interactive DIF, ISRPM achieved higher detection accuracy than the Wald, LR, FS-Wald, FS-LR, and Mantel−Haenszel (MH) approaches. When only the main effect DIF was present, the overall performance of ISRPM was comparable to that of the existing methods. These findings suggest that ISRPM provides a flexible and effective framework for identifying both main effect DIF and interactive DIF in cognitive diagnostic assessments, thereby contributing to methodological advancements in fairness evaluation and the broader application of CD-based measurement in psychological and educational measurement.

Key words: cognitive diagnosis assessments, differential item functioning, main effect DIF, interactive DIF, recursive partitioning

中图分类号:

B841

刘凯, 郭治辰, 王琴, 汪大勋, 蔡艳, 涂冬波. (2026). 认知诊断评估中主效应DIF与交互式DIF检测方法开发: 基于递归分割视角. 心理学报, 58(5), 995-1014.

LIU Kai, GUO Zhichen, WANG Qin, WANG Daxun, CAI Yan, TU Dongbo. (2026). Development of main effect DIF and interactive DIF detection method in cognitive diagnosis assessments: A recursive partitioning-based perspective. Acta Psychologica Sinica, 58(5), 995-1014.

图/表 8

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DIF形式	$P{({\alpha }_{v})}_{F}$的计算方法
只有主效应DIF	$P{({\alpha }_{v})}_{R}+z\cdot I({x}_{2}=1)$
只有交互式DIF	$P{({\alpha }_{v})}_{R}+\left\{\begin{array}{l}z\cdot I(\{{x}_{1}=1\}\cap \{{x}_{2}=1\})\\ z\cdot I(\{{x}_{1}=2\}\cap \{{x}_{2}=2\})\end{array}\right.$
同时存在主效应DIF和交互式DIF	$P{({\alpha }_{v})}_{R}+z\cdot I({x}_{1}=2)+z\cdot I(\{{x}_{1}=1\}\cap \{{x}_{2}=2\})$

DIF形式	$P{({\alpha }_{v})}_{F}$的计算方法
只有主效应DIF	$P{({\alpha }_{v})}_{R}+z\cdot I({x}_{2}=1)$
只有交互式DIF	$P{({\alpha }_{v})}_{R}+\left\{\begin{array}{l}z\cdot I(\{{x}_{1}=1\}\cap \{{x}_{2}=1\})\\ z\cdot I(\{{x}_{1}=2\}\cap \{{x}_{2}=2\})\end{array}\right.$
同时存在主效应DIF和交互式DIF	$P{({\alpha }_{v})}_{R}+z\cdot I({x}_{1}=2)+z\cdot I(\{{x}_{1}=1\}\cap \{{x}_{2}=2\})$

题目	ISRPM				Wald		MH		LR		FS-Wald		FS-LR
题目	首层变量	1^st	2^nd-L	2^nd-R	性别	地区	性别	地区	性别	地区	性别	地区	性别	地区
1	性别	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)
2	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)
3	性别	√(√)	×(×)	×(×)	√(√)	√(×)	√(×)	×(×)	×(×)	√(√)	×(×)	√(×)	√(×)	√(×)
5	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)
7	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)
9	户籍	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(×)	×(×)	√(√)
12	性别	√(√)	×(×)	×(×)	√(√)	×(×)	×(×)	√(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
14	性别	√(×)	×(×)	×(×)	√(×)	√(×)	√(√)	√(√)	√(√)	√(√)	×(×)	√(√)	√(×)	√(√)
15	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(×)
16	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	√(√)	√(×)	√(√)	√(√)	√(×)	√(√)	√(√)	√(√)
18	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)
20	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
22	性别	√(√)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
24	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(×)	√(×)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
25	性别	√(√)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
26	性别	√(√)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
27	性别	√(√)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
28	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)
29	性别	×(×)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)	×(×)
30	性别	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)
31	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(×)
34	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)
35	性别	×(×)	×(×)	√(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)	×(×)
38	性别	√(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)	×(×)
40	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	√(×)	√(√)	×(×)	√(√)	√(×)
43	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)
44	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)
46	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
48	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)
49	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
50	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(×)
52	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	×(×)	×(×)	√(×)
54	性别	√(√)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	√(×)
55	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)
59	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)
61	户籍	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)
62	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
64	性别	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(×)	×(×)	√(×)	×(×)
65	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)
69	性别	√(×)	×(×)	×(×)	√(×)	×(×)	×(×)	√(√)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
71	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	√(×)	×(×)	×(×)	√(×)	×(×)
72	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)
74	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)

题目	ISRPM				Wald		MH		LR		FS-Wald		FS-LR
题目	首层变量	1^st	2^nd-L	2^nd-R	性别	地区	性别	地区	性别	地区	性别	地区	性别	地区
1	性别	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)
2	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)
3	性别	√(√)	×(×)	×(×)	√(√)	√(×)	√(×)	×(×)	×(×)	√(√)	×(×)	√(×)	√(×)	√(×)
5	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)
7	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)
9	户籍	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(×)	×(×)	√(√)
12	性别	√(√)	×(×)	×(×)	√(√)	×(×)	×(×)	√(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
14	性别	√(×)	×(×)	×(×)	√(×)	√(×)	√(√)	√(√)	√(√)	√(√)	×(×)	√(√)	√(×)	√(√)
15	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(×)
16	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	√(√)	√(×)	√(√)	√(√)	√(×)	√(√)	√(√)	√(√)
18	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)
20	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
22	性别	√(√)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
24	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(×)	√(×)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
25	性别	√(√)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
26	性别	√(√)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
27	性别	√(√)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
28	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)
29	性别	×(×)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)	×(×)
30	性别	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)
31	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(×)
34	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)
35	性别	×(×)	×(×)	√(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)	×(×)
38	性别	√(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)	×(×)
40	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	√(×)	√(√)	×(×)	√(√)	√(×)
43	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)
44	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)
46	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)
48	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)
49	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
50	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(×)
52	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	×(×)	×(×)	√(×)
54	性别	√(√)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	√(×)
55	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	×(×)
59	户籍	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(×)	×(×)	×(×)	×(×)	√(×)
61	户籍	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)
62	性别	√(×)	×(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
64	性别	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	×(×)	√(×)	×(×)	√(×)	×(×)	√(×)	×(×)
65	户籍	√(×)	×(×)	×(×)	×(×)	√(×)	×(×)	√(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)
69	性别	√(×)	×(×)	×(×)	√(×)	×(×)	×(×)	√(√)	√(√)	×(×)	√(×)	×(×)	√(√)	×(×)
71	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	√(×)	×(×)	×(×)	√(×)	×(×)
72	性别	×(×)	×(×)	×(×)	×(×)	×(×)	×(×)	√(√)	√(×)	×(×)	√(√)	×(×)	√(×)	×(×)
74	性别	√(√)	×(×)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)	√(√)	×(×)

认知诊断评估中主效应DIF与交互式DIF检测方法开发: 基于递归分割视角

Development of main effect DIF and interactive DIF detection method in cognitive diagnosis assessments: A recursive partitioning-based perspective

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