儿童青少年同伴侵害与内化问题的双向关系: 纵向研究的元分析

doi:10.3724/SP.J.1041.2022.00828

摘要/Abstract

摘要：

本研究采用元分析技术探讨儿童青少年同伴侵害与内化问题的纵向关系, 检验人际风险模型、症状驱动模型与相互作用模型在两者关系中的适用性。共纳入89篇文献, 包含99个独立效应量, 涉及被试70598人。随机效应模型分析结果表明, 同伴侵害可以预测内化问题(β = 0.097, 95% CI [0.083, 0.110]), 内化问题对同伴侵害也存在预测作用(β = 0.119, 95% CI [0.104, 0.135]), 结果支持同伴侵害与内化问题的相互作用模型。同伴侵害对内化问题的预测作用中, 年龄与侵害类型存在调节效应, 测量时间间隔、侵害类型、评定方式与问题类型在内化问题对同伴侵害的预测中起调节作用, 中西文化类型在两者相互预测模型中均不存在调节作用, 各调节因素在同伴侵害及其亚类型外显侵害、关系侵害与内化问题双向关系中的作用基本一致。

关键词: 儿童青少年, 同伴侵害, 内化问题, 纵向研究, 元分析

Abstract:

There is a close relationship between peer victimization and internalizing problems in children and adolescents. According to the interpersonal risk model, negative interpersonal experiences, such as peer victimization, are important stressors, and they leave children with their basic need for belonging unsatisfied and so lead to a series of problem behaviors. The symptom-driven model emphasizes that internalizing problems leaves individuals vulnerable to attack, so internalizing problems are risk factors for predicting peer victimization. The interaction model shows that childhood and adolescence are in high-incidence periods for peer victimization and internalizing problems, and internalizing problems may lead to negative peer reaction (such as peer victimization and peer rejection), which in turn induces more internalizing problems, and vice versa.

Many studies have explored the longitudinal relationship between peer victimization and internalizing problems, but the results have been mixed. This meta-analysis was conducted to explore the strength and moderators of the longitudinal relationship between these two variables. Through literature retrieval, 99 independent effect sizes were selected, covering a total of 70, 598 participants, which met the inclusion criteria for meta-analysis. After coding the data, we analyzed the main effect and moderating effect using Comprehensive Meta-analysis Version 2.0. Heterogeneity testing indicated that the random effects model was suitable for the meta-analysis. Based on the funnel plot and Egger’s test of regression to the intercept, no significant publication bias was found in the included studies.

The main effect test indicated significant prospective pathways from peer victimization to internalizing problems (β = 0.097, 95% CI = [0.083, 0.110]) and from internalizing problems to peer victimization (β = 0.119, 95% CI = [0.104, 0.135]), which suggests that peer victimization and internalizing problems are related in a reciprocal manner. Moderation analysis revealed that the predictive effect of peer victimization on internalizing problems was moderated by age and also by the type of peer victimization. The predictive effect of internalizing problems on peer victimization was moderated by measurement interval, type of peer victimization, method of assessment, and type of internalizing problems. However, whether the participants lived in Chinese or Western cultures did not moderate the relationships observed.

The results supported the interaction model, which suggested that peer victimization and internalizing problems are mutually influencing factors. Future research and intervention program design should look for protective factors outside the individual system to help children and adolescents break the vicious circle between these two variables. Moreover, particular attention should be given to the effects of age, type of peer victimization, assessment method, measurement interval, and type of internalizing problems on the relationship between peer victimization and internalizing problems.

Key words: children and adolescents, peer victimization, internalizing problems, longitudinal study, meta-analysis

中图分类号:

B844
R395

廖友国, 陈建文, 张妍, 彭聪. (2022). 儿童青少年同伴侵害与内化问题的双向关系: 纵向研究的元分析. 心理学报, 54(7), 828-849.

LIAO Youguo, CHEN Jianwen, ZHANG Yan, PENG Cong. (2022). The reciprocal relationship between peer victimization and internalizing problems in children and adolescents: A meta-analysis of longitudinal studies. Acta Psychologica Sinica, 54(7), 828-849.

图/表 11

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第一作者	年份	文化背景	样本量	均龄	时间间隔(年)	PV评定	IP评定	侵害类型	r_{PV, IP}	PV→IP^a	IP→PV^a
Adrian	2019	美国	287	—	0.33	自评	自评	3, 4, 5	0.34	0.13	—
Arora	2020	美国	232	12.96	1.00	自评	自评	1, 2, 3	0.53	0.28	—
Barchia	2010	澳大利亚	1167	13.30	0.67	自评	自评	1, 2, 3	0.41	0.07	0.12
Boivin	1995	加拿大	567	10.83	1.00	同学	自评	0	0.45	0.07	0.09
Brown	2016	美国	294	8.71	1.00	自评	自评	1, 2, 3, 4	0.37	—	0.20
Burke	2017	瑞士	837	13.20	0.50	自评	自评	0	0.24	0.06	0.09
Chen	2021	中国	1987	12.32	0.50	自评	自评	1, 2, 3	0.55	0.08	0.11
Cooley	2020	美国	464	8.64	0.50	自评	自评	0	0.43	0.16	—
Cooley	2017	美国	228	8.32	1.00	自评	自评	1, 3	0.36	—	0.16
Desjardins	2011	加拿大	580	15.50	2.00	自评	自评	3	0.30	0.03	0.36
Dill	2004	美国	296	—	1.00	自评	自评	2, 3, 5	0.39	0.13	0.13
Doyle	2016	美国	437	—	0.50	自评	自评	1, 2, 3	0.38	0.07	0.24
Drazdowski	2019	美国	317	12.10	1.00	自评	自评	3, 4	0.42	0.00	0.17
Ehrlich	2017	美国	692	15.62	0.25	自评	自评	3, 4, 5	0.29	—	0.11
Erath	2016	美国	123	12.03	0.83	自评	自评	0	0.57	0.28	—
Forbes	2019	澳大利亚	4169	10.30	2.00	综合	综合	1, 3, 5	0.53	0.13	0.09
Fox	2021	美国	178	9.58	1.00	自评	自评	1, 2, 3	0.43	0.11	—
Gibb	2012	美国	100	9.97	0.17	自评	自评	3, 4	0.45	0.07	0.20
Griese	2014	美国	511	10.62	1.00	同学	自评	3, 4	0.34	0.09	—
Hanish	2002	美国	1469	—	2.00	同学	教师	0	0.14	0.18	0.04
Healy	2018	澳大利亚	111	8.72	0.75	自评	父母	1, 2, 3	-0.09	-0.01	0.21
Helms	2015	美国	313	17.13	1.00	同学	自评	1, 3	0.01	0.07	—
Henrich	2014	以色列	1081	11.00	1.00	自评	父母	1, 2, 3	0.16	0.05	—
Herzig	2011	美国	286	13.51	1.00	自评	自评	3, 4	0.43	0.10	0.07
Hodges	1999	美国	173	11.30	1.00	同学	同学	0	0.69	0.12	0.17
Hodges	1999	加拿大	393	10.00	1.00	同学	教师	0	0.21	0.22	0.08
Irwin	2019	加拿大	396	11.80	0.50	自评	自评	1, 2, 3, 5	0.46	0.06	0.18
Isaacs	2008	芬兰	177	—	8.00	自评	自评	0	0.10	0.23	—
Kawabata	2017	日本	528	—	1年内	自评	自评	1, 3	0.40	0.03	0.10
Knoble	2015	美国	302	11.90	1.00	自评	自评	0	0.43	—	0.23
Knoble	2015	美国	291	11.90	1.00	自评	自评	0	0.29	—	0.06
Kochel	2017	美国	483	11.10	1.00	同学	父母	1, 2, 3, 5	0.41	0.12	0.18
Kochel	2012	美国	380	9.93	1.00	自评	父母	1, 2, 3, 5	0.19	0.15	0.13
Kochel	2020	美国	230	9.48	0.50	自评	自评	1, 2, 3, 5	0.50	0.03	0.14
Krygsman	2019	加拿大	464	12.00	1.00	同学	自评	0	0.07	0.09	0.03
Krygsman	2017	加拿大	703	11.91	1.00	自评	同学	1, 2, 3, 5	0.21	0.08	0.11
Landoll	2012	美国	941	15.80	0.13	自评	自评	0	0.33	0.12	0.08
Leadbeater	2014	加拿大	578	15.52	2.00	自评	自评	1, 3	0.29	0.01	0.11
Leeuwis	2015	荷兰	330	11.20	1.00	自评	自评	1, 3	0.44	0.11	0.05
Lepore	2019	美国	986	12.81	0.50	自评	自评	1, 3, 4	0.40	0.15	—
Liu	2018	中国	945	10.16	1.00	同学	自评	3, 4	0.20	0.11	0.04
Loukas	2013	美国	227	11.69	1.00	自评	自评	3, 4	0.52	0.22	—
Loukas	2013	美国	263	11.69	1.00	自评	自评	3, 4	0.47	0.19	—
Machmutow	2012	瑞士	765	13.18	0.50	自评	自评	5	0.27	0.08	0.11
McConnell	2018	美国	123	12.03	0.83	教师	自评	0	0.22	0.12	—
McLaughlin	2009	美国	1065	—	0.58	自评	自评	3, 4, 5	0.30	0.12	0.08
Mlawer	2019	美国	1264	10.10	0.58	自评	教师	1, 2, 3	0.10	0.08	0.04
Morabito	2021	美国	317	12.42	2.00	自评	自评	3, 4	0.37	0.03	0.15
Mulder	2017	荷兰	748	12.00	0.50	同学	自评	0	0.31	0.02	0.05
Mulder	2017	荷兰	755	12.00	0.50	同学	自评	0	0.25	0.03	0.01
Noret	2021	英国	533	12.56	1.00	自评	自评	1, 3, 5	0.41	0.33	0.12

第一作者	年份	文化背景	样本量	均龄	时间间隔(年)	PV评定	IP评定	侵害类型	r_{PV, IP}	PV→IP^a	IP→PV^a
Adrian	2019	美国	287	—	0.33	自评	自评	3, 4, 5	0.34	0.13	—
Arora	2020	美国	232	12.96	1.00	自评	自评	1, 2, 3	0.53	0.28	—
Barchia	2010	澳大利亚	1167	13.30	0.67	自评	自评	1, 2, 3	0.41	0.07	0.12
Boivin	1995	加拿大	567	10.83	1.00	同学	自评	0	0.45	0.07	0.09
Brown	2016	美国	294	8.71	1.00	自评	自评	1, 2, 3, 4	0.37	—	0.20
Burke	2017	瑞士	837	13.20	0.50	自评	自评	0	0.24	0.06	0.09
Chen	2021	中国	1987	12.32	0.50	自评	自评	1, 2, 3	0.55	0.08	0.11
Cooley	2020	美国	464	8.64	0.50	自评	自评	0	0.43	0.16	—
Cooley	2017	美国	228	8.32	1.00	自评	自评	1, 3	0.36	—	0.16
Desjardins	2011	加拿大	580	15.50	2.00	自评	自评	3	0.30	0.03	0.36
Dill	2004	美国	296	—	1.00	自评	自评	2, 3, 5	0.39	0.13	0.13
Doyle	2016	美国	437	—	0.50	自评	自评	1, 2, 3	0.38	0.07	0.24
Drazdowski	2019	美国	317	12.10	1.00	自评	自评	3, 4	0.42	0.00	0.17
Ehrlich	2017	美国	692	15.62	0.25	自评	自评	3, 4, 5	0.29	—	0.11
Erath	2016	美国	123	12.03	0.83	自评	自评	0	0.57	0.28	—
Forbes	2019	澳大利亚	4169	10.30	2.00	综合	综合	1, 3, 5	0.53	0.13	0.09
Fox	2021	美国	178	9.58	1.00	自评	自评	1, 2, 3	0.43	0.11	—
Gibb	2012	美国	100	9.97	0.17	自评	自评	3, 4	0.45	0.07	0.20
Griese	2014	美国	511	10.62	1.00	同学	自评	3, 4	0.34	0.09	—
Hanish	2002	美国	1469	—	2.00	同学	教师	0	0.14	0.18	0.04
Healy	2018	澳大利亚	111	8.72	0.75	自评	父母	1, 2, 3	-0.09	-0.01	0.21
Helms	2015	美国	313	17.13	1.00	同学	自评	1, 3	0.01	0.07	—
Henrich	2014	以色列	1081	11.00	1.00	自评	父母	1, 2, 3	0.16	0.05	—
Herzig	2011	美国	286	13.51	1.00	自评	自评	3, 4	0.43	0.10	0.07
Hodges	1999	美国	173	11.30	1.00	同学	同学	0	0.69	0.12	0.17
Hodges	1999	加拿大	393	10.00	1.00	同学	教师	0	0.21	0.22	0.08
Irwin	2019	加拿大	396	11.80	0.50	自评	自评	1, 2, 3, 5	0.46	0.06	0.18
Isaacs	2008	芬兰	177	—	8.00	自评	自评	0	0.10	0.23	—
Kawabata	2017	日本	528	—	1年内	自评	自评	1, 3	0.40	0.03	0.10
Knoble	2015	美国	302	11.90	1.00	自评	自评	0	0.43	—	0.23
Knoble	2015	美国	291	11.90	1.00	自评	自评	0	0.29	—	0.06
Kochel	2017	美国	483	11.10	1.00	同学	父母	1, 2, 3, 5	0.41	0.12	0.18
Kochel	2012	美国	380	9.93	1.00	自评	父母	1, 2, 3, 5	0.19	0.15	0.13
Kochel	2020	美国	230	9.48	0.50	自评	自评	1, 2, 3, 5	0.50	0.03	0.14
Krygsman	2019	加拿大	464	12.00	1.00	同学	自评	0	0.07	0.09	0.03
Krygsman	2017	加拿大	703	11.91	1.00	自评	同学	1, 2, 3, 5	0.21	0.08	0.11
Landoll	2012	美国	941	15.80	0.13	自评	自评	0	0.33	0.12	0.08
Leadbeater	2014	加拿大	578	15.52	2.00	自评	自评	1, 3	0.29	0.01	0.11
Leeuwis	2015	荷兰	330	11.20	1.00	自评	自评	1, 3	0.44	0.11	0.05
Lepore	2019	美国	986	12.81	0.50	自评	自评	1, 3, 4	0.40	0.15	—
Liu	2018	中国	945	10.16	1.00	同学	自评	3, 4	0.20	0.11	0.04
Loukas	2013	美国	227	11.69	1.00	自评	自评	3, 4	0.52	0.22	—
Loukas	2013	美国	263	11.69	1.00	自评	自评	3, 4	0.47	0.19	—
Machmutow	2012	瑞士	765	13.18	0.50	自评	自评	5	0.27	0.08	0.11
McConnell	2018	美国	123	12.03	0.83	教师	自评	0	0.22	0.12	—
McLaughlin	2009	美国	1065	—	0.58	自评	自评	3, 4, 5	0.30	0.12	0.08
Mlawer	2019	美国	1264	10.10	0.58	自评	教师	1, 2, 3	0.10	0.08	0.04
Morabito	2021	美国	317	12.42	2.00	自评	自评	3, 4	0.37	0.03	0.15
Mulder	2017	荷兰	748	12.00	0.50	同学	自评	0	0.31	0.02	0.05
Mulder	2017	荷兰	755	12.00	0.50	同学	自评	0	0.25	0.03	0.01
Noret	2021	英国	533	12.56	1.00	自评	自评	1, 3, 5	0.41	0.33	0.12

指标	模型	研究数	异质性				Tau-squared
指标	模型	研究数	Q值	df (Q)	p-value	I-squared	Tau Squared	SE	Variance	Tau
PV→IP	随机模型	89	221.673	88	<0.001	60.302	0.002	0.001	0.000	0.047
IP→PV	随机模型	79	260.104	78	<0.001	70.012	0.003	0.001	0.000	0.055

指标	模型	研究数	异质性				Tau-squared
指标	模型	研究数	Q值	df (Q)	p-value	I-squared	Tau Squared	SE	Variance	Tau
PV→IP	随机模型	89	221.673	88	<0.001	60.302	0.002	0.001	0.000	0.047
IP→PV	随机模型	79	260.104	78	<0.001	70.012	0.003	0.001	0.000	0.055

指标	研究数	效应值及95%置信区间			双尾检验
指标	研究数	点估计	下限	上限	Z值	p
PV→IP	89	0.097	0.083	0.110	14.012	<0.001
IP→PV	79	0.119	0.104	0.135	15.068	<0.001