Article(id=1325531522098553506, tenantId=1146029695717560320, journalId=1256213402557890569, issueId=1325530655635039055, articleNumber=null, orderNo=null, doi=10.16835/j.cnki.1000-9817.2025292, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746633600000, receivedDateStr=2025-05-08, revisedDate=1754064000000, revisedDateStr=2025-08-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1793960942052, onlineDateStr=2026-11-06, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793960942052, onlineIssueDateStr=2026-11-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793960942052, creator=13701087609, updateTime=1793960942052, updator=13701087609, issue=Issue{id=1325530655635039055, tenantId=1146029695717560320, journalId=1256213402557890569, year='2025', volume='46', issue='10', pageStart='1369', pageEnd='1520', issueExtLink='null', onlineDate='null', pubDate='1761321600000', pubDateStr='2025-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793960735472, creator='13701087609', updateTime=1793960735472, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1437, endPage=1442, ext={EN=ArticleExt(id=1325531523780469412, articleId=1325531522098553506, tenantId=1146029695717560320, journalId=1256213402557890569, language=EN, title=Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students, columnId=1259565855189418448, journalTitle=Chinese Journal of School Health, columnName=Mental Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the influencing factors of aggressive behaviors in high school students, rank the importance of relevant variables with visual presentation, so as to provide new references for developing intervention strategies.

Methods

In December 2024, a convenience sampling method was used to select 5 330 students from 5 high schools in Wuhan for an online questionnaire survey. The survey included the Buss-Perry Aggression Questionnaire (BPAQ), Interaction Anxiety Scale (IAS), Emotion Regulation Ability Scale, Inventory of Parent and Peer Attachment (IPPA), and a self-designed general information questionnaire. Variables were screened using LASSO regression, followed by multivariate Logistic regression analysis. The Random Forest model was then employed to rank and visualize variable importance.

Results

Univariate analysis and LASSO regression results indicated that gender, emotion regulation capability, paternal attachment quality, maternal attachment quality, maternal parenting style, paternal parenting style, parenting consistency, monthly household income per capita, school climate, relationship with classmates, interaction anxiety, and academic ranking were all significant variables (all P < 0.01). Multivariate Logistic regression analysis identified the following as influencing factors of aggressive behaviors in high school students: emotional management ability (OR=0.38, 95%CI=0.34-0.43), gender (OR=0.83, 95%CI=0.75-0.93), mother attachment quality (OR=0.89, 95%CI=0.74-1.07), father attachment quality (OR=0.76, 95%CI=0.66-0.87), interaction anxiousness (OR=1.58, 95%CI=1.40-1.78), consistency of parenting styles of parents (OR=1.40, 95%CI=1.21-1.62), school atmosphere (OR=1.50, 95%CI=1.14-1.98), academic performance ranking (upper-middle: OR=0.76, 95%CI=0.61-0.93; middle: OR=0.70, 95%CI=0.57-0.86) were the influencing factors of aggressive behavior in high school students (all P < 0.05). The results of Random Forest showed that emotional management ability was the most important factor affecting aggressive behavior in high school students.

Conclusion

Emotion regulation capability is identified as the core predictor of adolescent aggressive behaviors, which can significantly reduce the incidence of aggressive behaviors through enhanced emotion management.

, authors=Yaxin LI, Qibi HU, Xingyu WANG, Xin LIU, Hongjuan CHANG*, authorsList=Yaxin LI, Qibi HU, Xingyu WANG, 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=1325531534329143975, articleId=1325531522098553506, tenantId=1146029695717560320, journalId=1256213402557890569, language=CN, title=随机森林模型联合LASSO回归分析高中生攻击性行为影响因素, columnId=1259565858817491410, journalTitle=中国学校卫生, columnName=心理卫生, runingTitle=null, highlight=null, articleAbstract=
目的

分析高中生攻击性行为的影响因素,对相关变量进行重要性排序并可视化呈现,为该行为的干预措施制定提供新的参考依据。

方法

2024年12月采用方便抽样法在武汉市5所高中选取5 330名高中生进行线上问卷调查,包括Buss-Perry攻击性量表、交往焦虑量表、情绪管理量表、父母和同伴依恋量表及自编一般情况调查表。采用LASSO回归筛选变量后进行Logistic多因素回归分析,并利用随机森林模型进行变量重要性排序与可视化。

结果

单因素分析与LASSO回归结果显示,性别、情绪管理能力、父母亲依恋质量、父母管教方式、父母教养方式是否一致、家庭人均月收入、学校风气、与同学关系、交往焦虑及成绩位置均为显著变量(P值均 < 0.01)。多因素Logistic回归结果显示,情绪管理能力(OR=0.38,95%CI=0.34~0.43)、性别(OR=0.83,95%CI=0.75~0.93)、母亲依恋质量(OR=0.89,95%CI=0.74~1.07)、父亲依恋质量(OR=0.76,95%CI=0.66~0.87)、交往焦虑(OR=1.58,95%CI=1.40~1.78)、父母教养方式是否一致(OR=1.40,95%CI=1.21~1.62)、学校风气(OR=1.50,95%CI=1.14~1.98)、成绩位置(中上游:OR=0.76,95%CI=0.61~0.93;中游:OR=0.70,95%CI=0.57~0.86)是高中生攻击性行为的影响因素(P值均 < 0.05)。随机森林结果显示情绪管理能力是影响高中生攻击性行为的最主要因素。

结论

情绪管理能力被确认为青少年攻击性行为的核心预测因子,可通过提升情绪管理能力来降低高中生攻击性行为发生率。

, authors=李亚昕, 胡旗碧, 汪星宇, 刘欣, 常红娟*, authorsList=李亚昕, 胡旗碧, 汪星宇, 刘欣, 常红娟, authorCompany=null, correspAuthors=常红娟, authorNote=

李亚昕(2000-),男,山西长治人,在读硕士,主要研究方向为儿童青少年卫生学

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常红娟,E-mail:
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Comparison of reporting rate of high aggressive behavior of different groups among high school students

, figureFileSmall=null, figureFileBig=null, tableContent=
组别选项人数高攻击行为人数χ2值P值组别选项人数高攻击行为人数χ2值P值
交往焦虑轻度2 8521 066(37.4)135.43< 0.01父母教养方式一致4 2121 754(41.6)79.18< 0.01
重度2 4781 320(53.3)是否一致不一致1 118632(56.5)
情绪管理能力较弱2 6161 538(58.8)408.79< 0.01家庭人均>5 000664330(49.7)7.79< 0.02
较强2 714848(31.2)月收入/元1 000~5 0003 3871 501(44.3)
父亲依恋质量较低2 9301 543(52.7)164.10< 0.01< 1 0001 279555(43.4)
较高2 400843(35.1)居住地社会风气较好2 5491 021(40.1)62.91< 0.01
母亲依恋质量较低2 6321 436(54.6)201.71< 0.01一般2 5591 225(47.9)
较高2 698950(35.2)较差222140(63.1)
同伴依恋质量较低2 6951 328(49.3)44.86< 0.01学校风气较好2 418957(39.6)69.84< 0.01
较高2 6351 058(40.2)一般2 5831 228(47.5)
性别男2 4831 149(46.3)4.28< 0.04较差329201(61.1)
女2 8471 237(43.4)与老师关系较好2 7031 075(39.8)70.48< 0.01
父亲管教方式严格4 0261 678(41.7)74.63< 0.01一般2 5201238(49.1)
溺爱392205(52.3)较差10773(68.2)
漠不关心219123(56.2)与同学关系较好3 7711 578(41.8)49.02< 0.01
粗暴3327(81.8)一般1 501770(51.3)
变化无常660353(53.5)较差5838(65.5)
母亲管教方式严格3 8001 582(41.6)64.56< 0.01成绩位置上游588276(46.9)33.28< 0.01
溺爱635324(51.0)中上游1 541644(41.8)
漠不关心11167(60.4)中游1 827769(42.1)
粗暴2923(79.3)中下游1 037516(49.8)
变化无常755390(51.7)下游337181(53.7)
父亲期望读研/留学1 286573(44.6)27.45< 0.01好朋友数量/个06339(61.9)32.98< 0.01
上大学3 5881 571(43.8)110866(61.1)
能有工作就行373183(49.1)2~31 062522(49.2)
无所谓8359(71.1)≥44 0971 759(42.9)
母亲期望读研/留学1 296563(43.4)19.38< 0.01自我外表满意度非常满意708276(39.0)49.82< 0.01
上大学3 5301 560(44.2)比较满意1 904801(42.1)
能有工作就行435217(50.1)一般2 4801 161(46.8)
无所谓6946(66.7)不满意178107(60.1)
非常不满意6041(68.3)
), ArticleFig(id=1325531569989116627, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531522098553506, language=CN, label=表1, caption=

不同组别高中生高攻击行为报告率比较

, figureFileSmall=null, figureFileBig=null, tableContent=
组别选项人数高攻击行为人数χ2值P值组别选项人数高攻击行为人数χ2值P值
交往焦虑轻度2 8521 066(37.4)135.43< 0.01父母教养方式一致4 2121 754(41.6)79.18< 0.01
重度2 4781 320(53.3)是否一致不一致1 118632(56.5)
情绪管理能力较弱2 6161 538(58.8)408.79< 0.01家庭人均>5 000664330(49.7)7.79< 0.02
较强2 714848(31.2)月收入/元1 000~5 0003 3871 501(44.3)
父亲依恋质量较低2 9301 543(52.7)164.10< 0.01< 1 0001 279555(43.4)
较高2 400843(35.1)居住地社会风气较好2 5491 021(40.1)62.91< 0.01
母亲依恋质量较低2 6321 436(54.6)201.71< 0.01一般2 5591 225(47.9)
较高2 698950(35.2)较差222140(63.1)
同伴依恋质量较低2 6951 328(49.3)44.86< 0.01学校风气较好2 418957(39.6)69.84< 0.01
较高2 6351 058(40.2)一般2 5831 228(47.5)
性别男2 4831 149(46.3)4.28< 0.04较差329201(61.1)
女2 8471 237(43.4)与老师关系较好2 7031 075(39.8)70.48< 0.01
父亲管教方式严格4 0261 678(41.7)74.63< 0.01一般2 5201238(49.1)
溺爱392205(52.3)较差10773(68.2)
漠不关心219123(56.2)与同学关系较好3 7711 578(41.8)49.02< 0.01
粗暴3327(81.8)一般1 501770(51.3)
变化无常660353(53.5)较差5838(65.5)
母亲管教方式严格3 8001 582(41.6)64.56< 0.01成绩位置上游588276(46.9)33.28< 0.01
溺爱635324(51.0)中上游1 541644(41.8)
漠不关心11167(60.4)中游1 827769(42.1)
粗暴2923(79.3)中下游1 037516(49.8)
变化无常755390(51.7)下游337181(53.7)
父亲期望读研/留学1 286573(44.6)27.45< 0.01好朋友数量/个06339(61.9)32.98< 0.01
上大学3 5881 571(43.8)110866(61.1)
能有工作就行373183(49.1)2~31 062522(49.2)
无所谓8359(71.1)≥44 0971 759(42.9)
母亲期望读研/留学1 296563(43.4)19.38< 0.01自我外表满意度非常满意708276(39.0)49.82< 0.01
上大学3 5301 560(44.2)比较满意1 904801(42.1)
能有工作就行435217(50.1)一般2 4801 161(46.8)
无所谓6946(66.7)不满意178107(60.1)
非常不满意6041(68.3)
), ArticleFig(id=1325531570395964116, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531522098553506, language=EN, label=Tab.2, caption=

Multivariable Logistic analysis of aggressive behaviors in high school students(n=5 330)

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量选项β值标准误Wald χ2值P值OR值(95%CI)
情绪管理能力较强-0.960.06250.87< 0.010.38(0.34~0.43)
性别女-0.190.069.54< 0.010.83(0.74~0.93)
母亲依恋质量较高-0.390.0731.11< 0.010.89(0.74~1.07)
父亲依恋质量较高-0.280.0716.26< 0.010.76(0.66~0.87)
交往焦虑较重0.460.0657.18< 0.011.58(1.40~1.78)
父母教养方式是否一致不一致0.330.0819.73< 0.011.40(1.21~1.62)
学校风气一般0.070.070.950.331.07(0.93~1.23)
较差0.410.148.37< 0.011.50(1.14~1.98)
成绩位置中上游-0.280.116.980.010.76(0.61~0.93)
中游-0.360.1111.90< 0.010.70(0.57~0.86)
中下游-0.210.123.270.070.81(0.65~1.02)
下游-0.300.153.810.050.74(0.55~1.00)
), ArticleFig(id=1325531570773451477, tenantId=1146029695717560320, journalId=1256213402557890569, articleId=1325531522098553506, language=CN, label=表2, caption=

高中生攻击性行为影响因素的多因素Logistic回归分析(n=5 330)

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量选项β值标准误Wald χ2值P值OR值(95%CI)
情绪管理能力较强-0.960.06250.87< 0.010.38(0.34~0.43)
性别女-0.190.069.54< 0.010.83(0.74~0.93)
母亲依恋质量较高-0.390.0731.11< 0.010.89(0.74~1.07)
父亲依恋质量较高-0.280.0716.26< 0.010.76(0.66~0.87)
交往焦虑较重0.460.0657.18< 0.011.58(1.40~1.78)
父母教养方式是否一致不一致0.330.0819.73< 0.011.40(1.21~1.62)
学校风气一般0.070.070.950.331.07(0.93~1.23)
较差0.410.148.37< 0.011.50(1.14~1.98)
成绩位置中上游-0.280.116.980.010.76(0.61~0.93)
中游-0.360.1111.90< 0.010.70(0.57~0.86)
中下游-0.210.123.270.070.81(0.65~1.02)
下游-0.300.153.810.050.74(0.55~1.00)
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随机森林模型联合LASSO回归分析高中生攻击性行为影响因素
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李亚昕 , 胡旗碧 , 汪星宇 , 刘欣 , 常红娟 *
中国学校卫生 | 心理卫生 2025,46(10): 1437-1442
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中国学校卫生 |心理卫生 2025 , 46 (10) : 1437 -1442
随机森林模型联合LASSO回归分析高中生攻击性行为影响因素
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李亚昕, 胡旗碧, 汪星宇, 刘欣, 常红娟*
作者信息
  • 武汉科技大学医学院,湖北 430000
通讯作者:
常红娟,E-mail:
作者简介:

李亚昕(2000-),男,山西长治人,在读硕士,主要研究方向为儿童青少年卫生学

Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students
Yaxin LI, Qibi HU, Xingyu WANG, Xin LIU, Hongjuan CHANG*
Affiliations
  • School of Medicine, Wuhan University of Science and Technology, Wuhan 430000, Hubei Province, China
出版时间: 2025-10-25 doi: 10.16835/j.cnki.1000-9817.2025292
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目的

分析高中生攻击性行为的影响因素,对相关变量进行重要性排序并可视化呈现,为该行为的干预措施制定提供新的参考依据。

方法

2024年12月采用方便抽样法在武汉市5所高中选取5 330名高中生进行线上问卷调查,包括Buss-Perry攻击性量表、交往焦虑量表、情绪管理量表、父母和同伴依恋量表及自编一般情况调查表。采用LASSO回归筛选变量后进行Logistic多因素回归分析,并利用随机森林模型进行变量重要性排序与可视化。

结果

单因素分析与LASSO回归结果显示,性别、情绪管理能力、父母亲依恋质量、父母管教方式、父母教养方式是否一致、家庭人均月收入、学校风气、与同学关系、交往焦虑及成绩位置均为显著变量(P值均 < 0.01)。多因素Logistic回归结果显示,情绪管理能力(OR=0.38,95%CI=0.34~0.43)、性别(OR=0.83,95%CI=0.75~0.93)、母亲依恋质量(OR=0.89,95%CI=0.74~1.07)、父亲依恋质量(OR=0.76,95%CI=0.66~0.87)、交往焦虑(OR=1.58,95%CI=1.40~1.78)、父母教养方式是否一致(OR=1.40,95%CI=1.21~1.62)、学校风气(OR=1.50,95%CI=1.14~1.98)、成绩位置(中上游:OR=0.76,95%CI=0.61~0.93;中游:OR=0.70,95%CI=0.57~0.86)是高中生攻击性行为的影响因素(P值均 < 0.05)。随机森林结果显示情绪管理能力是影响高中生攻击性行为的最主要因素。

结论

情绪管理能力被确认为青少年攻击性行为的核心预测因子,可通过提升情绪管理能力来降低高中生攻击性行为发生率。

模型,统计学  /  回归分析  /  攻击  /  学生
Objective

To analyze the influencing factors of aggressive behaviors in high school students, rank the importance of relevant variables with visual presentation, so as to provide new references for developing intervention strategies.

Methods

In December 2024, a convenience sampling method was used to select 5 330 students from 5 high schools in Wuhan for an online questionnaire survey. The survey included the Buss-Perry Aggression Questionnaire (BPAQ), Interaction Anxiety Scale (IAS), Emotion Regulation Ability Scale, Inventory of Parent and Peer Attachment (IPPA), and a self-designed general information questionnaire. Variables were screened using LASSO regression, followed by multivariate Logistic regression analysis. The Random Forest model was then employed to rank and visualize variable importance.

Results

Univariate analysis and LASSO regression results indicated that gender, emotion regulation capability, paternal attachment quality, maternal attachment quality, maternal parenting style, paternal parenting style, parenting consistency, monthly household income per capita, school climate, relationship with classmates, interaction anxiety, and academic ranking were all significant variables (all P < 0.01). Multivariate Logistic regression analysis identified the following as influencing factors of aggressive behaviors in high school students: emotional management ability (OR=0.38, 95%CI=0.34-0.43), gender (OR=0.83, 95%CI=0.75-0.93), mother attachment quality (OR=0.89, 95%CI=0.74-1.07), father attachment quality (OR=0.76, 95%CI=0.66-0.87), interaction anxiousness (OR=1.58, 95%CI=1.40-1.78), consistency of parenting styles of parents (OR=1.40, 95%CI=1.21-1.62), school atmosphere (OR=1.50, 95%CI=1.14-1.98), academic performance ranking (upper-middle: OR=0.76, 95%CI=0.61-0.93; middle: OR=0.70, 95%CI=0.57-0.86) were the influencing factors of aggressive behavior in high school students (all P < 0.05). The results of Random Forest showed that emotional management ability was the most important factor affecting aggressive behavior in high school students.

Conclusion

Emotion regulation capability is identified as the core predictor of adolescent aggressive behaviors, which can significantly reduce the incidence of aggressive behaviors through enhanced emotion management.

Models, statistical  /  Regression analysis  /  Aggression  /  Students
李亚昕, 胡旗碧, 汪星宇, 刘欣, 常红娟. 随机森林模型联合LASSO回归分析高中生攻击性行为影响因素. 中国学校卫生, 2025 , 46 (10) : 1437 -1442 . DOI: 10.16835/j.cnki.1000-9817.2025292
Yaxin LI, Qibi HU, Xingyu WANG, Xin LIU, Hongjuan CHANG. Random forest model combined with LASSO regression to analyze the influencing factors of aggressive behavior among high school students[J]. Chinese Journal of School Health, 2025 , 46 (10) : 1437 -1442 . DOI: 10.16835/j.cnki.1000-9817.2025292
攻击性行为是指有意对他人造成身体或心理伤害的行为[1]。学生的攻击性行为会在学生群体中传递不安全感,并对受害者、施害者以及目击者的教育、健康和心理发展产生负面影响[2]。研究表明,攻击性行为具有双重危害性,不仅会对受害者造成身心伤害,还会导致施害者出现学业困难、社交适应不良及情绪调节障碍等发展性后果,甚至可能诱发抑郁、焦虑等心理症状,同时破坏校园和谐与教学秩序[3]。此前相关研究大多围绕攻击性行为的相关因素展开,发现了情绪调节能力、家庭教养方式及同伴依恋质量等核心要素是攻击性行为的影响因素[4-5],然而目前对这些影响因素重要性排序仍不清楚,因此尚未出现有效的干预措施。随机森林作为集成学习方法[6],能通过模型的节点纯度增益,客观评估变量重要性;LASSO回归通过正则化对变量进行筛选和系数压缩,可有效解决变量多重共线性问题并筛选影响因素[7]。二者结合既简化模型复杂度,又精准定位关键因素。本研究联合LASSO回归分析与随机森林模型,为快速识别高中生攻击性行为的原因及降低发生率提供有力支持与参考。
2024年12月采用方便抽样法,选取武汉市5所中学的高中生为研究对象。纳入标准:(1)年龄为14~18岁;(2)在当前学校连续就读至少一个学期;(3)具有基本的沟通与理解能力,可自行完成问卷填写。排除标准:(1)已经在接受针对攻击性行为或其他相关心理问题咨询或治疗的学生;(2)近期经历了重大生活事件(如亲人离世、父母离异、遭受重大创伤等)的学生;(3)在本研究期间中途转学或休学的学生。征得学生知情同意后,共发放问卷5 500份,收回有效问卷5 330份,有效回收率96.91%。其中男生2 483名,女生2 847名。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)。
由研究者自行设计,内容包括:性别、年龄、学习成绩位置、父母期望(读研或留学/上大学/能有工作就行/无所谓)、学校风气(较好/一般/较差)、父母管教方式(严格/溺爱/漠不关心/粗暴/变化无常)、抚养人相关信息(文化程度、职业、对子女期望)、家庭人均月收入、人际关系(与老师/同学关系、好朋友数量)、对自身外表满意度等。其中学习成绩位置参考2024年上学期期末考试成绩: 排名前20%为上游、>20%~40%为中上游、>40%~60%为中游、>60%~ < 80%为中下游、后20%为下游。
量表共34个条目,采用Likert 5点评分,从“一点也不像我”到“几乎和我一样”依次计1~5分,条目19为反向计分。总分为34~170分,得分越高表示被试越具有攻击倾向。参照张羿等[9]研究,本研究将得分高于均数的被试定义为高攻击性行为,反之为低攻击行为。本研究中该量表的Cronbach α系数为0.93。
用于评定独立于行为之外的主观社交焦虑体验的倾向。该量表含有15条自述条目,采用Likert 5点评分,从“一点也不相符”到“极其相符”依次计1~5分,其中第3,6,10,15题采用反向记分,将15个条目的分数相加即为总评分,范围在15~75分,得分越高表示社交焦虑程度越高。将45~75分视为焦虑较重,16~44分视为焦虑较轻,15分视为无焦虑,本研究中未出现无焦虑学生。本量表的Cronbach α系数为0.87。
用于评估青少年对父母和亲密同性朋友的依恋关系的觉知,包括父亲、母亲、同伴3个分量表[11-13],3个分量表可以结合使用,也可以独立使用。本研究将IPPA的3个分量表分开使用,每个分量表25个条目。各条目采用Likert 5点计分,“1=从不这样”~“5=几乎总是这样”,得分越高表示依恋安全性越高。3个子量表总分均为25~125分,将子量表总分≥75分视为父亲/母亲/同伴依恋质量较高, < 75分视为父亲/母亲/同伴依恋质量较低。母亲依恋量表、父亲依恋量表、同伴依恋量表的Cronbach α系数分别为0.87,0.89,0.88。
主要用于测量青少年的情绪管理能力[14]。本研究选取了该量表中情绪管理因素分量表,共4道题目,用于评估青少年能否有效适度地控制、了解、宣泄或改变自己的负面情绪感。采用4点评分法,“1=总是这样”~“4=不曾这样”,分数越高说明被试的情绪管理能力越强。本研究中按照研究对象得分的平均数进行高低组划分。该量表Cronbach α系数为0.75。
展开调查前提前与校方负责人取得联系,得到校方知情同意,以班级为单位发放问卷星链接,规定时间内作答,并在问卷说明页说明注意事项及知情同意授权。本次调查经由学校问卷收集平台收集,确保数据的保密性,同时问卷设置了禁止重复做答、必答项、逻辑检查项等,以确保提交数据的准确性。
计数资料用频数或百分比描述,使用SPSS 27.0软件进行单因素分析,将计数资料纳入χ2检验筛选出具有统计学意义的自变量。使用R Studio软件的glmnet函数对χ2检验筛选出的变量进行LASSO回归分析,通过最小化交叉验证误差选择最优λ,在变量精简与预测误差间取得平衡,避免过拟合的同时确定稳定性最强的显著变量子集[15]。基于该子集,采用Logistic回归识别最终显著影响因素。为评估变量相对重要性,采用R Studio软件的随机森林程序包,基于上述Logistic回归筛选出的显著变量构建随机森林模型(设定决策树数量为1 000棵)。该模型通过计算各变量的Mean Decrease Gini(基尼不纯度减少,代表的是在构建随机森林的过程中,每个变量对节点纯度提升的平均贡献[16],该值越大说明该变量对提升节点纯度的作用越大,即该变量越重要),得出各变量的重要性并进行可视化排序[17],直观呈现对攻击性行为的关键预测变量。检验水准α=0.05。
被试高中生攻击性行为高分组2 386人,低分组2 944人。其中不同交往焦虑、情绪管理能力、父母依恋质量、同伴依恋质量、性别、父母管教方式、父母期望、父母教养方式是否一致、家庭人均月收入、居住地社会风气、学校风气、与老师关系、与同学关系、成绩位置、好朋友数量和自我外表满意度高中生高攻击行为报告率差异均有统计学意义(P值均 < 0.01),见表1。
LASSO回归通过最小化交叉验证误差选择最优λ,λ=1se(标准误最小的λ值),筛选出稳定性最强的12个影响因素子集。筛选出的变量包括性别、情绪管理能力、父亲依恋质量、母亲依恋质量、母亲管教方式、父亲管教方式、父母教养方式是否一致、家庭人均月收入、学校风气、与同学关系、交往焦虑及成绩位置。
将LASSO回归分析筛选出的影响因素纳入Logistic回归分析。以攻击性行为(低=0,高=1)作为因变量;以性别(男=0,女=1),情绪管理能力(较弱=0,较强=1),父亲/母亲依恋质量(较低=0,较高=1),交往焦虑(轻度=0,重度=1),父亲/母亲管教方式(严格=0,溺爱=1,漠不关心=2,粗暴=3,变化无常=4),父母教养方式是否一致(一致=0,不一致=1),家庭人均月收入(>5 000=0,1 000~5 000=1, < 1 000元=2),学校风气(较好=0,一般=1,较差=2),与同学关系(较差=0,一般=1,较好=2),成绩位置(上游=0,中上游=1,中游=2,中下游=3,下游=4)为自变量进行Logistic回归分析,结果表明,情绪管理能力、性别、父母依恋质量、交往焦虑、父母教养方式是否一致、学校风气、成绩位置为攻击性行为的影响因素(P值均 < 0.05),见表2。
基于多因素Logistic回归分析结果,采用R Studio软件,将随机森林模型中的随机种子设置为123,同时把5 330个样本拆分成训练集(n=3 731)和验证集(n=1 599),基于1 000棵分类与回归树构建随机森林模型。在该模型中通过计算各变量的Mean Decrease Gini得出每个变量对节点纯度提升的平均贡献并基于此进行重要性排序,结果显示情绪管理能力是攻击性行为最重要的影响因素,见图1。
随机森林结果显示,情绪管理能力是高中生攻击性行为的最重要影响因素,表明了情绪管理能力在个体攻击性行为中起关键保护作用[18]。情绪管理能力较弱的高中生在面对挫折、冲突或压力源时,往往难以有效识别、理解、表达和调控负面情绪,极易将内部压力外化为攻击性行为,与金秋等[19]研究结果一致。建议学校将情绪管理训练作为预防和干预攻击性行为的基石,调整教育体系内的课程设置(可增设情绪管理等相关辅修课程)和健全心理健康方面评价体系[20]。
家庭因素在重要性排序中占据显著位置,尤其以母亲依恋质量和父亲依恋质量最为突出,同时父母教养方式一致性也至关重要。母亲通常为高中生主要照料者,其依恋质量在高中生情绪管理与行为调控方面具有奠基性影响[21]。高质量的母亲依恋为孩子提供稳定的“安全基地”,使其在压力下能有效寻求支持[22]。父亲依恋质量同样重要,提供不同的支持维度(如规则引导、探索鼓励)。而父母教养方式不一致是显著风险因子,父母教养方式不一致易导致高中生认知混乱与行为矛盾[23-24],削弱其规则意识和安全感,增加其采用攻击性行为应对冲突的风险[25]。基于此,本研究认为家长应着力提升自身情绪管理能力与沟通技巧,共同构建温暖、支持且一致的教养环境,确保教育方式的一致性以及规则与回应的连贯性,从而巩固父母亲依恋质量[26],从根本上降低高中生出现攻击性行为的风险。
学校相关影响因素中,成绩位置与学校风气是高中生攻击性行为的重要影响因素。成绩中下游及下游学生出现高攻击行为的风险更高,说明在高度竞争的环境下,学业挫败可能引发羞耻、愤怒等负面情绪[27],并可能因家庭压力或自我否定而外化为攻击性行为(如对“优等生”的嫉妒性攻击或对“挑衅者”的过度反应)。与此同时,不良学校风气可能直接诱发冲突和攻击性行为,弱化学生对规则和同理心的认同,营造出利于攻击性行为滋生的环境氛围[28]。反之,积极健康的学校风气能有效抑制攻击行为并为学生提供清晰的行为边界、正向的榜样示范和安全的情感支持网络,显著提升其亲社会行为倾向并降低攻击性行为的可能性[29]。综上,建议建立多元成绩评价体系(关注努力、进步、非认知技能),并为学业困难学生提供针对性支持(学业辅导+心理疏导)。校方应明确并公正执行反欺凌政策,培育积极的师生/同学关系,建立有效的冲突调解机制,提升教师处理学生情绪与行为问题的能力。
首先,个人层面需要加强自身及时觉察情绪的能力,选择合理的情绪调节策略并用科学的方法应对情绪;家庭干预层面,需着力提升原生家庭的养育效能。其次,重点优化父母依恋质量,通过安全依恋关系建模推动家庭教育逐步从学业导向转换至心理健康本位[30];学校层面,应建立多维度、发展性的心理健康监测体系,具体包括:构建动态化情绪危机预警系统[31]、形成精准化心理干预响应机制、完善分级分类的危机处置流程。最终实现从被动应对到主动预防的范式转变,确保对高危学生群体实施及时有效的心理援助;最后,需要政府的政策层面支持,将学生心理健康的培养纳入学校的考核机制,从而推动学生心理健康工作的真正落实。针对青少年攻击性行为防治,应当建立“认知重塑-家庭赋能-学校支持-社会包容”四维联动的干预体系,重点突破家校社协同中的责任弥散困境,形成具有文化适切性的非认知能力培育范式。
本研究也存在一定局限性:虽然旨在探讨高中生攻击性行为影响因素的重要性排序,但样本仅来自于武汉市的5所中学,不同地区的教育水平、文化氛围、经济发展状况等差异,可能存在地域局限性,无法代表全国范围内高中生的普遍情况。其次,学生可能出于社会期望偏差,不愿意如实报告自己的攻击性行为经历或内心真实想法,尤其涉及一些敏感问题,如校园霸凌参与情况、家庭冲突细节等,从而导致数据失真。未来研究应考虑不同地区不同年级的影响,并结合其他方法尽可能消除潜在误差,进一步验证和扩展本研究的结论。
  • 国家自然科学基金项目(81803252);湖北省教育厅重点项目(24D083)
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2025年第46卷第10期
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doi: 10.16835/j.cnki.1000-9817.2025292
  • 接收时间:2025-05-08
  • 首发时间:2026-11-06
  • 出版时间:2025-10-25
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  • 收稿日期:2025-05-08
  • 修回日期:2025-08-02
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国家自然科学基金项目(81803252);湖北省教育厅重点项目(24D083)
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    武汉科技大学医学院,湖北 430000

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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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