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Article(id=1325501763582833406, tenantId=1146029695717560320, journalId=1256213402557890569, issueId=1325501647736156411, articleNumber=null, orderNo=null, doi=10.16835/j.cnki.1000-9817.2025083, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1729526400000, receivedDateStr=2024-10-22, revisedDate=1739203200000, revisedDateStr=2025-02-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1793953847070, onlineDateStr=2026-11-06, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793953847070, onlineIssueDateStr=2026-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793953847070, creator=13701087609, updateTime=1793953847070, updator=13701087609, issue=Issue{id=1325501647736156411, tenantId=1146029695717560320, journalId=1256213402557890569, year='2025', volume='46', issue='3', pageStart='305', pageEnd='456', issueExtLink='null', onlineDate='null', pubDate='1742832000000', pubDateStr='2025-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793953819450, creator='13701087609', updateTime=1793953819450, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=358, endPage=363, ext={EN=ArticleExt(id=1325501764836930303, articleId=1325501763582833406, tenantId=1146029695717560320, journalId=1256213402557890569, language=EN, title=Influencing factors and network analysis of aggressive behavior among Chinese college students based on propensity score matching, columnId=1259565855189418448, journalTitle=Chinese Journal of School Health, columnName=Mental Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the influencing factors and network structure of aggressive behaviors among college students based on propensity score matching (PSM), so as to provide precise targeted interventions for the prevention and improvement of aggressive behaviors among college students.

Methods

A total of 2 652 college students were selected by convenient sampling method from three colleges in Wuhan, Hubei Province in June 2023. Questionnaire surveys were carried out by using the Buss-Warren Aggression Questionnaire (BWAQ), Ruminative Responses Scale (RRS), Cognitive Emotion Regulation Questionnaire-Chinese Version (CERQ-C), Family APGAR Index (APGAR), Brief Fear of Negative Evaluation Scale (BFNES).By bias score matching (PSM) for 1∶1 matching, univariate and multivariate Logistic regression analysis, and network analysis were conducted on the college students.

Results

College students with higher levels of ruminant thinking, non-adaptive emotional regulation and fear of negative appraisal were more likely to have highly aggressive behaviors(OR=1.14, 1.18, 1.06), and those with higher adaptive emotional regulation and family care index were more likely to have highly aggressive behaviors (OR=0.88, 0.82)(P < 0.01). Network structure was significantly different between the two groups (M=0.27, P < 0.05). The core affective factors of college students with high levels of aggressive behavior were brooding reflective pondering and symptom rumination(EI=3.50, 3.49, 3.48), low aggressive behavior college students core affective factors were adaptive emotion regulation growth and non-adaptive emotion regulation(EI=4.37, 4.12, 4.08).

Conclusion

Factors affecting Chinese college students' aggressive behaviors are of different characteristics on different behaviour types, and targeted interventions should be adopted to reduce aggressive behaviors of college students.

, authors=Rui ZENG, Mengxiao ZHAO, Mengmeng HU, Xin LIU, Hongjuan CHANG*, authorsList=Rui ZENG, Mengxiao ZHAO, Mengmeng HU, Xin LIU, Hongjuan CHANG, authorCompany=null, correspAuthors=Hongjuan CHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2025 Chinese Journal of School Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1325501767831663364, articleId=1325501763582833406, tenantId=1146029695717560320, journalId=1256213402557890569, language=CN, title=基于倾向性评分匹配的中国大学生攻击性行为影响因素及网络分析, columnId=1259565858817491410, journalTitle=中国学校卫生, columnName=心理卫生, runingTitle=null, highlight=null, articleAbstract=
目的

基于倾向性评分匹配法探讨大学生攻击性行为影响因素及其网络结构，为预防和改善大学生攻击性行为提供精准靶向干预。

方法

于2023年6月，采用方便抽样方法选取湖北省武汉市3所高校2 652名大学生作为研究对象，采用攻击行为量表(BWAQ)、反刍性思维量表(RRS)、中文版认知情绪调节问卷(CERQ-C)、家庭关怀度指数(APGAR)、简明负面恐惧量表(BFNES)进行问卷调查，运用倾向性评分匹配(PSM)进行1∶1匹配，对纳入的大学生进行单因素、多因素Logistic回归分析以及网络分析。

结果

Logistic回归分析结果显示，反刍性思维倾向越严重、倾向于使用非适应性情绪调节和负面评价恐惧水平越高的大学生更可能出现高攻击性行为(OR值分别为1.14，1.18，1.06)，倾向于使用适应性情绪调节和家庭关怀度指数越高的大学生越不易出现高攻击性行为(OR值分别为0.88，0.82)(P值均＜0.01)。网络结构在高攻击性行为和低攻击性行为组间差异有统计学意义(M=0.27, P < 0.05)。高攻击性行为大学生核心影响因素依次为“强迫思考”“反省深思”和“症状反刍”(EI值分别为3.50，3.49，3.48)；低攻击性行为大学生核心影响因素依次为“适应性情绪调节”“成长度”和“非适应性情绪调节”(EI值分别为4.37，4.12，4.08)。

结论

中国大学生攻击性行为的影响因素在不同行为类型上存在不同特点，应采取针对性的靶向干预以减少大学生攻击性行为的发生。

, authors=曾睿, 赵梦霄, 胡萌萌, 刘欣, 常红娟*, authorsList=曾睿, 赵梦霄, 胡萌萌, 刘欣, 常红娟, authorCompany=null, correspAuthors=常红娟, authorNote=

曾睿(2001-)，女，湖北武汉人，在读硕士，主要研究方向为儿童青少年卫生

, correspAuthorsNote=
常红娟，E-mail:
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注：实线代表正相关，虚线代表负相关，线越粗，表示连接越强。节点外圈填充程度越多表示可预测性指数越大。

, figureFileSmall=E2dPtHPqNlHW9LULjdM65Q==, figureFileBig=iQLsgkMhttj+kVzWYF2pVQ==, tableContent=null), ArticleFig(id=1325501793727296304, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325501763582833406, language=EN, label=Fig.2, caption=Centrality indicators of factors influencing college students with high and low aggressive behaviors, figureFileSmall=ocRD7iYJCB31YtWAzoDpng==, figureFileBig=zjWbe17aY/9utWhxlThYUA==, tableContent=null), ArticleFig(id=1325501794540991281, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325501763582833406, language=CN, label=图2, caption=高攻击性和低攻击性行为大学生影响因素的中心性指标, figureFileSmall=ocRD7iYJCB31YtWAzoDpng==, figureFileBig=zjWbe17aY/9utWhxlThYUA==, tableContent=null), ArticleFig(id=1325501794956227378, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325501763582833406, language=EN, label=Tab.1, caption=

Comparison of the distribution of baseline demographic characteristics between the two groups before and after PSM

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组别选项匹配前匹配后
低攻击性行为(n=1 675)高攻击性行为(n=1 543)χ2值P值低攻击性行为(n=1 326)高攻击性行为(n=1 326)χ2值P值
性别男530(31.6)437(28.3)4.210.04382(28.8)393(29.6)0.220.64
女1 145(68.4)1106(71.7)944(71.2)933(70.4)
民族汉族1 646(98.3)1523(98.7)1.010.311 311(98.9)1 307(98.6)0.480.49
少数民族29(1.7)20(1.3)15(1.1)19(1.4)
生源地城镇371(22.1)329(21.3)0.320.57287(21.6)304(22.9)0.630.43
农村1 304(77.9)1 214(78.7)1 039(78.4)1 022(77.1)
是否独生子女是186(11.1)186(12.1)0.710.40142(10.7)153(11.5)0.460.50
否1 489(88.9)1357(87.9)1 184(89.3)1 173(88.5)
父亲受教育程度小学及以下293(17.5)297(19.2)5.000.05245(18.5)237(17.9)3.370.50
初中827(49.4)788(51.1)696(52.5)663(50.0)
高中或中专388(23.2)319(20.7)275(20.7)297(22.4)
本科或大专159(9.5)131(8.5)104(7.8)123(9.3)
硕士及以上8(0.5)8(0.5)6(0.5)6(0.5)
母亲受教育程度小学及以下469(28.0)471(30.5)5.170.06375(28.3)398(30.0)5.820.21
初中749(44.7)693(44.9)624(47.1)592(44.6)
高中或中专337(20.1)274(17.8)244(18.4)243(18.3)
本科或大专112(6.7)101(6.5)76(5.7)91(6.9)
硕士及以上8(0.5)4(0.3)7(0.5)2(0.2)
家庭类型核心家庭635(37.9)537(34.8)13.750.02497(37.5)469(35.4)1.350.81
大家庭78(4.7)111(7.2)56(4.2)95(7.2)
单亲家庭13(0.8)13(0.8)11(0.8)10(0.8)
联合家庭26(1.6)30(1.9)20(1.5)27(2.0)
重组家庭21(1.3)29(1.9)15(1.1)17(1.3)
隔代家庭902(53.9)823(53.3)727(54.8)708(53.4)
留守经历无1 264(75.5)959(62.2)67.56＜0.01995(72.0)916(69.1)7.860.10
偶尔199(11.9)300(19.4)189(14.3)241(18.2)
一般100(6.0)130(8.4)92(6.9)89(6.7)
经常71(4.2)99(6.4)63(4.8)54(4.1)
一直41(2.4)55(3.6)27(2.0)26(2.0)
精神疾病遗传史有7(0.4)26(1.7)12.71＜0.012(0.2)3(0.2)0.200.65
无1 668(99.6)1517(98.3)1 324(99.8)1 323(99.8)
), ArticleFig(id=1325501795434378035, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325501763582833406, language=CN, label=表1, caption=

倾向性评分匹配前后两组大学生基线人口学特征分布比较

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组别选项匹配前匹配后
低攻击性行为(n=1 675)高攻击性行为(n=1 543)χ2值P值低攻击性行为(n=1 326)高攻击性行为(n=1 326)χ2值P值
性别男530(31.6)437(28.3)4.210.04382(28.8)393(29.6)0.220.64
女1 145(68.4)1106(71.7)944(71.2)933(70.4)
民族汉族1 646(98.3)1523(98.7)1.010.311 311(98.9)1 307(98.6)0.480.49
少数民族29(1.7)20(1.3)15(1.1)19(1.4)
生源地城镇371(22.1)329(21.3)0.320.57287(21.6)304(22.9)0.630.43
农村1 304(77.9)1 214(78.7)1 039(78.4)1 022(77.1)
是否独生子女是186(11.1)186(12.1)0.710.40142(10.7)153(11.5)0.460.50
否1 489(88.9)1357(87.9)1 184(89.3)1 173(88.5)
父亲受教育程度小学及以下293(17.5)297(19.2)5.000.05245(18.5)237(17.9)3.370.50
初中827(49.4)788(51.1)696(52.5)663(50.0)
高中或中专388(23.2)319(20.7)275(20.7)297(22.4)
本科或大专159(9.5)131(8.5)104(7.8)123(9.3)
硕士及以上8(0.5)8(0.5)6(0.5)6(0.5)
母亲受教育程度小学及以下469(28.0)471(30.5)5.170.06375(28.3)398(30.0)5.820.21
初中749(44.7)693(44.9)624(47.1)592(44.6)
高中或中专337(20.1)274(17.8)244(18.4)243(18.3)
本科或大专112(6.7)101(6.5)76(5.7)91(6.9)
硕士及以上8(0.5)4(0.3)7(0.5)2(0.2)
家庭类型核心家庭635(37.9)537(34.8)13.750.02497(37.5)469(35.4)1.350.81
大家庭78(4.7)111(7.2)56(4.2)95(7.2)
单亲家庭13(0.8)13(0.8)11(0.8)10(0.8)
联合家庭26(1.6)30(1.9)20(1.5)27(2.0)
重组家庭21(1.3)29(1.9)15(1.1)17(1.3)
隔代家庭902(53.9)823(53.3)727(54.8)708(53.4)
留守经历无1 264(75.5)959(62.2)67.56＜0.01995(72.0)916(69.1)7.860.10
偶尔199(11.9)300(19.4)189(14.3)241(18.2)
一般100(6.0)130(8.4)92(6.9)89(6.7)
经常71(4.2)99(6.4)63(4.8)54(4.1)
一直41(2.4)55(3.6)27(2.0)26(2.0)
精神疾病遗传史有7(0.4)26(1.7)12.71＜0.012(0.2)3(0.2)0.200.65
无1 668(99.6)1517(98.3)1 324(99.8)1 323(99.8)
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基于倾向性评分匹配的中国大学生攻击性行为影响因素及网络分析
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曾睿 , 赵梦霄 , 胡萌萌 , 刘欣 , 常红娟 *
中国学校卫生 | 心理卫生 2025,46(3): 358-363
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中国学校卫生 |心理卫生 2025 , 46 (3) : 358 -363
基于倾向性评分匹配的中国大学生攻击性行为影响因素及网络分析
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曾睿, 赵梦霄, 胡萌萌, 刘欣, 常红娟*
作者信息
  • 武汉科技大学医学院，湖北 430081
通讯作者:
常红娟，E-mail:
作者简介:

曾睿(2001-)，女，湖北武汉人，在读硕士，主要研究方向为儿童青少年卫生

Influencing factors and network analysis of aggressive behavior among Chinese college students based on propensity score matching
Rui ZENG, Mengxiao ZHAO, Mengmeng HU, Xin LIU, Hongjuan CHANG*
Affiliations
  • School of Medicine, Wuhan University of Science and Technology, Wuhan(430081), Hubei Province, China
出版时间: 2025-03-25 doi: 10.16835/j.cnki.1000-9817.2025083
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摘要
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目的

基于倾向性评分匹配法探讨大学生攻击性行为影响因素及其网络结构，为预防和改善大学生攻击性行为提供精准靶向干预。

方法

于2023年6月，采用方便抽样方法选取湖北省武汉市3所高校2 652名大学生作为研究对象，采用攻击行为量表(BWAQ)、反刍性思维量表(RRS)、中文版认知情绪调节问卷(CERQ-C)、家庭关怀度指数(APGAR)、简明负面恐惧量表(BFNES)进行问卷调查，运用倾向性评分匹配(PSM)进行1∶1匹配，对纳入的大学生进行单因素、多因素Logistic回归分析以及网络分析。

结果

Logistic回归分析结果显示，反刍性思维倾向越严重、倾向于使用非适应性情绪调节和负面评价恐惧水平越高的大学生更可能出现高攻击性行为(OR值分别为1.14，1.18，1.06)，倾向于使用适应性情绪调节和家庭关怀度指数越高的大学生越不易出现高攻击性行为(OR值分别为0.88，0.82)(P值均＜0.01)。网络结构在高攻击性行为和低攻击性行为组间差异有统计学意义(M=0.27, P < 0.05)。高攻击性行为大学生核心影响因素依次为“强迫思考”“反省深思”和“症状反刍”(EI值分别为3.50，3.49，3.48)；低攻击性行为大学生核心影响因素依次为“适应性情绪调节”“成长度”和“非适应性情绪调节”(EI值分别为4.37，4.12，4.08)。

结论

中国大学生攻击性行为的影响因素在不同行为类型上存在不同特点，应采取针对性的靶向干预以减少大学生攻击性行为的发生。

攻击  /  行为  /  回归分析  /  倾向评分匹配  /  学生
Objective

To explore the influencing factors and network structure of aggressive behaviors among college students based on propensity score matching (PSM), so as to provide precise targeted interventions for the prevention and improvement of aggressive behaviors among college students.

Methods

A total of 2 652 college students were selected by convenient sampling method from three colleges in Wuhan, Hubei Province in June 2023. Questionnaire surveys were carried out by using the Buss-Warren Aggression Questionnaire (BWAQ), Ruminative Responses Scale (RRS), Cognitive Emotion Regulation Questionnaire-Chinese Version (CERQ-C), Family APGAR Index (APGAR), Brief Fear of Negative Evaluation Scale (BFNES).By bias score matching (PSM) for 1∶1 matching, univariate and multivariate Logistic regression analysis, and network analysis were conducted on the college students.

Results

College students with higher levels of ruminant thinking, non-adaptive emotional regulation and fear of negative appraisal were more likely to have highly aggressive behaviors(OR=1.14, 1.18, 1.06), and those with higher adaptive emotional regulation and family care index were more likely to have highly aggressive behaviors (OR=0.88, 0.82)(P < 0.01). Network structure was significantly different between the two groups (M=0.27, P < 0.05). The core affective factors of college students with high levels of aggressive behavior were brooding reflective pondering and symptom rumination(EI=3.50, 3.49, 3.48), low aggressive behavior college students core affective factors were adaptive emotion regulation growth and non-adaptive emotion regulation(EI=4.37, 4.12, 4.08).

Conclusion

Factors affecting Chinese college students' aggressive behaviors are of different characteristics on different behaviour types, and targeted interventions should be adopted to reduce aggressive behaviors of college students.

Aggression  /  Behavior  /  Regression analysis  /  Propensity score matching  /  Students
曾睿, 赵梦霄, 胡萌萌, 刘欣, 常红娟. 基于倾向性评分匹配的中国大学生攻击性行为影响因素及网络分析. 中国学校卫生, 2025 , 46 (3) : 358 -363 . DOI: 10.16835/j.cnki.1000-9817.2025083
Rui ZENG, Mengxiao ZHAO, Mengmeng HU, Xin LIU, Hongjuan CHANG. Influencing factors and network analysis of aggressive behavior among Chinese college students based on propensity score matching[J]. Chinese Journal of School Health, 2025 , 46 (3) : 358 -363 . DOI: 10.16835/j.cnki.1000-9817.2025083
正文
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攻击性行为是指任何有意伤害他人(包括躯体和心理)的行为倾向[1]。统计数据结果显示，全球范围内36%青少年曾与同龄人发生过身体攻击，32%青少年曾在学校受到其他学生的欺凌[2]。中国青少年欺凌受害的发生率为22.7%，欺凌实施的发生率为15.7%[3]，可见攻击性行为和暴力事件已成为全球青少年中一个严重的公共卫生问题。攻击事件对个体的身心健康有负面影响，可引起焦虑抑郁等多种负面情绪，严重时可导致自杀念头和行为，对社会稳定性和经济也具有一定危害性[4-5]。
既往关于校园欺凌和攻击的研究主要集中在中小学生[6]，然而有证据表明，大学生同样容易受到这类行为的影响[7]。且既往研究在资料分析阶段往往纳入混杂因素，当混杂因素较多或处理组与对照组的某些变量差异较大时，组间均衡性欠佳，进而引发偏倚[8]。倾向性评分匹配法(propensity score matching, PSM)作为观察性研究中常用的一种分析策略，可均衡组间混杂因素和减少偏倚[9]。虽然已有研究确定了大学生攻击性行为受个人、家庭、社会等多方面内外部因素的影响，但这些因素之间存在复杂的相关性[10]。网络分析是一种新颖的统计方法，能以更直观的方式呈现变量之间的内在关联和相互作用；并且可以识别网络模型中最核心或最具影响力的因素[11]。因此，本研究采用PSM均衡基线水平和组间混杂因素后，进一步通过网络分析方法针对大学生人群构建攻击性行为影响因素的网络模型，并考察2个模型中最具影响的中心变量，为大学生攻击性行为的心理社会干预提供更精确、更有效的切入点。
1　对象与方法
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1.1　对象
2023年6月，采用方便抽样方法在湖北省武汉市选取3所高校大学生，共发放问卷5 527份，收回有效问卷5 019份，有效回收率为90.81%。基于PSM，本研究以大学生攻击行为量表(Buss-Warren Aggression Questionnaire, BWAQ)[12]的T分数为依据，选取有效问卷中攻击行为水平高者(1 543名)和低者(1 675名)的数据，进行1∶1匹配后最终纳入2 652名学生(高、低攻击性行为各1 326名)。其中男生721名(27.19%)，女生1 931名(72.81%)；年龄18~26岁，年龄中位数为22(21, 23)岁。本研究通过武汉科技大学医学院医学伦理委员会审查(批号：2023177)，且调查获得所有被试的知情同意。
1.2　调查方法
1.2.1　一般情况调查
由研究者自行设计，主要包含性别、年龄、民族、生源地、是否为独生子女、父亲及母亲受教育程度和家庭类型等信息。
1.2.2　BWAQ
采用Buss-Warren攻击问卷中文修订版[12]，该问卷包含身体攻击、言语攻击、间接攻击、愤怒、敌意5个分量表共34个条目。采用Likert 5级评分(1~5分)，其中第19题采用反向计分，得分越高表明攻击性越强。将BWAQ总分按T分数“＜-2 s、-2 s~-1 s、>-1 s~-0.5 s、>-0.5 s~0.5 s、>0.5 s~1 s、>1 s~2 s、＞2 s”的评分标准分为7个等级：非常低、很低、较低、平均水平、较高、很高、非常高，其中“平均水平”以上的定义为表现出相应的攻击性行为，其他组为无相应的攻击性行为[13]，基于此根据研究目的和T分数标准，将总分≥85分定义为高攻击性水平组，＞65~＜85分定义为平均水平组，≤65分定义为低攻击性行为组。该问卷的Cronbach α系数为0.94。
1.2.3　反刍性思维量表(Ruminative Responses Scale, RRS)
采用由韩秀等[14]修订的量表，由22个项目构成，分为症状反刍(12个条目)，强迫思考(5个条目)，反省深思(5个条目)3个因子。采用Likert 4级评分法(“1=从不”~“4=总是”)，总分为22~88分，得分越高的个体表明反刍思维倾向越严重。该问卷的Cronbach α系数为0.93。
1.2.4　中文版认知情绪调节问卷(Cognitive Emotion Regulation Questionnaire-Chinese Version, CERQ-C)
采用董光恒等[15]汉化和应用的中文版问卷，共9个分量表，分为适应性情绪调节策略(5个)和非适应性调节策略(4个)2个维度。问卷以Likert 5级评分法(“1=从不”~“5=总是”)，总分为18~90分，各维度得分高表明个体在面对负性事件时更倾向于使用该特定策略。该问卷的Cronbach α系数为0.93。
1.2.5　家庭关怀度指数(Family APGAR Index, APGAR)
由Smikestein[16]设计，包含适应度、合作度、成长度、情感度、亲密度5个维度。采用Likert 3级评分法(“0=几乎很少”~“2=经常这样”)，总分为0~10分，总分越高代表调查对象家庭功能越好。该问卷的Cronbach α系数为0.91。
1.2.6　简明负面恐惧量表(Brief Fear of Negative Evaluation Scale, BFNES)[17]
共12道题目，包括正向描述分量表(8道)和反向描述分量表(4道)。采用Likert 5点计分法(“1=完全不符合”~“5=极其符合”)，总分为12~60分，分数越高代表负面评价恐惧水平越高。该问卷的Cronbach α系数为0.90。
1.3　质量控制
征得被试者知情同意后，本研究通过问卷星收集，发放问卷前由主试采用统一指导语向被试说明研究的内容、目的及意义，问卷匿名填写，所有题目均为必答题，相同IP地址仅限填写1次。问卷进行了禁止重复做答、必答项、逻辑检查项等设置，以确保提交数据的准确性。
1.4　统计学分析
采用PSM平衡两组调查对象的基线数据差异，因变量为分组变量(0=低攻击性行为，1=高攻击性行为)，匹配协变量为性别、年龄、民族、生源地等一般人口学资料。采用1∶1最邻近匹配法进行匹配，卡钳值设定为0.02。采用SPSS 25.0和R 4.3.2软件进行统计分析。计数资料以频数和百分率描述，组间比较采用χ2检验。对计量资料采用Kolmogorov-Smirnov检验进行正态性检验，结果表明年龄不服从正态分布，采用M(P25，P75)描述，组间比较采用Mann-Whitney U检验。运用线性回归模型，评估容忍度和方差膨胀因子，进行共线性诊断。用PSM匹配后的数据集，以大学生攻击性行为水平为因变量(0＝低攻击性行为，1＝高攻击性行为)，其他各量表得分作为自变量纳入Logistic回归模型，探讨影响大学生攻击性行为的因素。双侧检验水准α=0.05
使用R语言“Bootnet”程序包分别构建高和低攻击性行为大学生影响因素的网络模型，该网络包括6个节点，需估计16个参数，每个参数需要3~5个观测值。因此，本研究样本量可以进行统计分析[18]。在构建的网络模型中计算节点与节点之间的相关性以及评估高斯图模型(gaussian graphical model, GGM)，以“EBICglasso”作为默认方法。可预测性被定义为网络模型中由其相邻节点解释的节点的方差，使用包“mgm”估计[19]。其次，使用网络比较测试(Network Comparison Test, NCT)程序包进行网络比较测试，使用网络最大差异值(max difference，M)和全局强度差异值(strength difference，S)检验2种网络之间的网络全局差异[20]。用“qgraph”包计算症状中心性来比较节点与其他节点之间紧密程度。计算预期影响(expected influence, EI)指数以确定网络中最核心的变量[11]，EI越高节点被认为影响越大，意义也越大；最后采用“Bootnet”程序包计算相关稳定性(correlation stability, CS)系数，评估节点的稳定性，规定CS值不应低于0.25，最好要高于0.50[18]。
2　结果
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2.1　两组大学生基线人口学特征分布比较
匹配后，低攻击性行为组和高攻击性行为组大学生年龄中位数均为22(21, 23)岁，差异无统计学意义(Z=-0.16)；两组其余基线人口学特征分布比较差异均无统计学意义(P值均＞0.05)。匹配后，所有变量标准化差异(standardized mean difference, SMD)均＜0.1，表明各变量在组间分布均衡[21]，匹配效果良好，具有可比性。见表1。
2.2　匹配后大学生攻击性行为影响因素的多因素Logistic回归分析
利用PSM匹配后的数据进行分析，共线性诊断结果显示自变量的容差均>0.1，方差膨胀因子均 < 10，表明无共线性。多因素Logistic回归结果显示，反刍性思维倾向越严重(OR=1.14，95%CI=1.12~1.17)、倾向于使用非适应性情绪调节(OR=1.18，95%CI=1.12~1.24)和负面评价恐惧水平越高(OR=1.06，95%CI=1.04~1.09)的大学生更可能出现高攻击性行为；倾向于使用适应性情绪调节(OR=0.88，95%CI=0.85~0.91)和家庭关怀度指数越高(OR=0.82，95%CI=0.77~0.87)的大学生越不易出现高攻击性行为(P值均＜0.01)。
2.3　匹配后大学生攻击性行为影响因素的网络结构及可预测性
匹配后大学生攻击性行为影响因素的网络结构如图1所示，量表内部的节点连边相对更强。对于高攻击性行为大学生，整个网络的平均可预测性为0.48，表明每个变量的48%的方差可以被其他关联变量解释。其中，“症状反刍”具有最高的可预测性；而对于低攻击性行为大学生，网络的平均可预测性为0.49，“合作度”具有最高的可预测性。
2.4　匹配后大学生攻击性行为影响因素的网络结构组间比较测试
NCT结果显示，网络结构在高攻击性行为组和低攻击性行为组之间差异有统计学意义(M=0.27, P=0.01)，全局强度在高攻击性行为组和低攻击性行为组之间差异无统计学意义(S=0.86, P=0.07)，说明节点间的相互关系存在差异，但整体连通性无差异。
2.5　匹配后大学生攻击性行为影响因素的网络中心性和稳定性
结果显示，高攻击性行为大学生网络模型中“强迫思考”的预期影响指数最高(EI=3.50)，其次是“反省深思”(EI=3.49)和“症状反刍”(EI=3.48)。见图2a。在低攻击性大学生网络模型中，“适应性情绪调节”的预期影响指数最高(EI=4.37)，其次是“成长度”(EI=4.12)和“非适应性情绪调节”(EI=4.08)。见图2b。
Bootstrapping结果显示，高攻击性和低攻击性行为大学生预期影响的CS系数均为0.75，表示75%的样本可以被丢弃，而网络的结构不会发生明显变化。
3　讨论
收起
本研究通过PSM使两组大学生在人口学特征上趋于平衡后，采用Logistic回归分析发现，反刍性思维、适应性和非适应性情绪调节、家庭关怀度指数及负面评价恐惧在一定程度上可预测大学生的攻击性行为。其中，4个因素与个体的内在情绪状态紧密相关，说明大学生的攻击性行为受个人情绪因素的影响，与既往研究结果一致[22]。此外，本研究通过网络分析的方法揭示了高攻击性与低攻击性行为大学生核心影响因素的差异，发现两类大学生在影响因素的网络结构及节点关系上存在不同。
对于高攻击性行为大学生，本研究发现，最具影响力的风险因素是“强迫思考”。强迫思考是指个体持续、不由自主出现的强迫思维，相比“反省”多了强迫性、消极性和完美主义倾向[23]。适度的思考可能在一定程度上具有积极意义，但有研究表明，陷入过度沉思不仅是抑郁和自杀的重要风险因素[24]，还与攻击性行为密切相关[25]。在反刍思维的强迫思考阶段中，拥有此特质的大学生在经历负性事件后容易陷入情绪漩涡、过度关注负性事件的细节与原因，导致情绪循环叠加并加剧消极体验。根据情绪级联模型，情绪失调和行为失调之间的联系通过“情绪级联”的过程实现，而反刍过程是行为失调的关键要素[26]，过度沉思会促使个体采取失调行为以分散注意力[27]。此外，这种思维模式会削弱个体对他人行为的洞察力，进而导致误解并引发愤怒或敌对情绪。当正常的分心方法无效时，个体可能会采取非适应性行为(如冲动攻击或自伤行为)以打破这一级联过程[28]。理解这一机制可为心理干预提供方向，如认知行为疗法或表达性写作等干预手段，可通过聚焦积极情绪，阻断反刍思维引发的负性“情绪级联”，帮助大学生更有效地调节情绪与应对冲突。
研究结果表明，情绪调节方式也与大学生的攻击性行为相关，并在一定程度上具有预测作用。对于低攻击性行为大学生，“适应性情绪调节”是最重要的影响因素，与既往研究相似[29]。此外，相比非适应性情绪调节策略，大学生更倾向于采用积极的适应性认知策略[30]，进一步支持了本研究结果。从儿童期到青春期，个体的情绪调节能力不断发展，逐渐倾向于采用认知再评价和心理疏导等积极策略以应对负面情绪[31]。因此，大学生在面对压力或冲突时，若能通过重新审视情境并从积极角度进行思考，可能有助于减少负面情绪体验，并降低攻击性行为的风险。
家庭关怀度指数的“成长度”是指大学生在身心发展过程中获得家庭成员积极支持与引导的程度，在低攻击性行为的形成与发展中起到重要的保护作用，与以往研究结果一致[32]。一项针对中国学生的Meta分析同样表明父母教养方式与攻击性之间的关联在大学生群体中最为显著，积极的养育方式与青少年的攻击性呈负相关[33]，获得积极家庭支持的个体通常具有更高的正向情绪水平，并倾向于以积极视角看待事物发展；相反，如果父母采用拒绝、冷漠或忽视的教育方式，孩子在成长过程中更容易发展出反社会行为倾向[34]。此外，个体感知到的来自家庭的接纳与支持可以缓解人际关系失败对青少年自我评价和社会认知的负面影响[35]。良好的家庭支持还能够帮助大学生建立更广泛的社会支持网络[36]。这一网络在个体面临压力或挑战时能够提供额外支持，从而有效缓解孤独感与挫败感，减少攻击性行为的发生[37]。因此，家庭教育应注重理解与支持。
本研究也存在一定局限性：首先，作为一项横断面研究，无法揭示网络中节点间的动态变化轨迹，未来研究可采用时间序列数据进一步探索节点间的时间因果关系；其次，研究的受试者仅选取了湖北省武汉市3所高校，未来可以扩展到全国不同省份；最后，研究的网络结构受到所研究节点范围的限制，可能存在本研究未包括的因素。
基金
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  • 国家自然科学基金项目(81803252)；武汉科技大学教学研究项目(2023X046)
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2025年第46卷第3期
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doi: 10.16835/j.cnki.1000-9817.2025083
  • 接收时间：2024-10-22
  • 首发时间：2026-11-06
  • 出版时间：2025-03-25
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  • 收稿日期：2024-10-22
  • 修回日期：2025-02-11
基金
国家自然科学基金项目(81803252)；武汉科技大学教学研究项目(2023X046)
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    武汉科技大学医学院，湖北 430081

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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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