Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning
For adolescents, high levels of aggression are often associated with suicide, physical injury, worsened academic performance, and crime. Therefore, there is a need for the early identification of and intervention for highly aggressive adolescents. The Buss–Warren Aggression Questionnaire (BWAQ) is o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Behavioral Sciences |
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| Online Access: | https://www.mdpi.com/2076-328X/13/10/799 |
| _version_ | 1797574745417318400 |
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| author | Xiuyu Jiang Yitian Yang Junyi Li |
| author_facet | Xiuyu Jiang Yitian Yang Junyi Li |
| author_sort | Xiuyu Jiang |
| collection | DOAJ |
| description | For adolescents, high levels of aggression are often associated with suicide, physical injury, worsened academic performance, and crime. Therefore, there is a need for the early identification of and intervention for highly aggressive adolescents. The Buss–Warren Aggression Questionnaire (BWAQ) is one of the most widely used offensive measurement tools. It consists of 34 items, and the longer the scale, the more likely participants are to make an insufficient effort response (IER), which reduces the credibility of the results and increases the cost of implementation. This study aimed to develop a shorter BWAQ using machine learning (ML) techniques to reduce the frequency of IER and simultaneously decrease implementation costs. First, an initial version of the short-form questionnaire was created using stepwise regression and an ANOVA F-test. Then, a machine learning algorithm was used to create the optimal short-form questionnaire (BWAQ-ML). Finally, the reliability and validity of the optimal short-form questionnaire were tested using independent samples. The BWAQ-ML contains only four items, thirty items less than the BWAQ, and its AUC, accuracy, recall, precision, and F1 score are 0.85, 0.85, 0.89, 0.83, and 0.86, respectively. BWAQ-ML has a Cronbach’s alpha of 0.84, a correlation with RPQ of 0.514, and a correlation with PTM of −0.042, suggesting good measurement performance. The BWAQ-ML can effectively measure individual aggression, and its smaller number of items improves the measurement efficiency for large samples and reduces the frequency of IER occurrence. It can be used as a convenient tool for early adolescent aggression identification and intervention. |
| first_indexed | 2024-03-10T21:26:40Z |
| format | Article |
| id | doaj.art-aadcc95bb0d8482da6c7934fc46b43c8 |
| institution | Directory Open Access Journal |
| issn | 2076-328X |
| language | English |
| last_indexed | 2024-03-10T21:26:40Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj.art-aadcc95bb0d8482da6c7934fc46b43c82023-11-19T15:39:53ZengMDPI AGBehavioral Sciences2076-328X2023-09-01131079910.3390/bs13100799Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine LearningXiuyu Jiang0Yitian Yang1Junyi Li2College of Psychology, Sichuan Normal University, Chengdu 610066, ChinaCollege of Psychology, Sichuan Normal University, Chengdu 610066, ChinaCollege of Psychology, Sichuan Normal University, Chengdu 610066, ChinaFor adolescents, high levels of aggression are often associated with suicide, physical injury, worsened academic performance, and crime. Therefore, there is a need for the early identification of and intervention for highly aggressive adolescents. The Buss–Warren Aggression Questionnaire (BWAQ) is one of the most widely used offensive measurement tools. It consists of 34 items, and the longer the scale, the more likely participants are to make an insufficient effort response (IER), which reduces the credibility of the results and increases the cost of implementation. This study aimed to develop a shorter BWAQ using machine learning (ML) techniques to reduce the frequency of IER and simultaneously decrease implementation costs. First, an initial version of the short-form questionnaire was created using stepwise regression and an ANOVA F-test. Then, a machine learning algorithm was used to create the optimal short-form questionnaire (BWAQ-ML). Finally, the reliability and validity of the optimal short-form questionnaire were tested using independent samples. The BWAQ-ML contains only four items, thirty items less than the BWAQ, and its AUC, accuracy, recall, precision, and F1 score are 0.85, 0.85, 0.89, 0.83, and 0.86, respectively. BWAQ-ML has a Cronbach’s alpha of 0.84, a correlation with RPQ of 0.514, and a correlation with PTM of −0.042, suggesting good measurement performance. The BWAQ-ML can effectively measure individual aggression, and its smaller number of items improves the measurement efficiency for large samples and reduces the frequency of IER occurrence. It can be used as a convenient tool for early adolescent aggression identification and intervention.https://www.mdpi.com/2076-328X/13/10/799aggression questionnairemachine learningshort-form questionnaireadolescents |
| spellingShingle | Xiuyu Jiang Yitian Yang Junyi Li Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning Behavioral Sciences aggression questionnaire machine learning short-form questionnaire adolescents |
| title | Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning |
| title_full | Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning |
| title_fullStr | Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning |
| title_full_unstemmed | Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning |
| title_short | Developing a Short-Form Buss–Warren Aggression Questionnaire Based on Machine Learning |
| title_sort | developing a short form buss warren aggression questionnaire based on machine learning |
| topic | aggression questionnaire machine learning short-form questionnaire adolescents |
| url | https://www.mdpi.com/2076-328X/13/10/799 |
| work_keys_str_mv | AT xiuyujiang developingashortformbusswarrenaggressionquestionnairebasedonmachinelearning AT yitianyang developingashortformbusswarrenaggressionquestionnairebasedonmachinelearning AT junyili developingashortformbusswarrenaggressionquestionnairebasedonmachinelearning |