Aggression differences among nonoffender, onset-offender, and recidivist migrant youth in China.
Since 2003, migrant youth have been responsible for 42.9% to 98% of juvenile delinquency in some areas of China (China Youth and Children Research Center, 2009; Liu & Li, 2015). Most crimes involving aggression were robbery, theft, intentional injury, criminal affray, and sexual assault (Xu, 2015).
We sought to further findings in existing studies in two ways. First, we explored the implicit aggression of youth offenders in an Asian context. Implicit attitudes are automatic affective reactions activated by a relevant stimulus, and are usually manifested in actions without individuals' awareness (Greenwald & Banaji, 1995). The Implicit Association Test (IAT), developed by Greenwald, McGhee, and Schwartz (1998), is a very well-established measure of implicit attitudes. The IAT is a computerized double-discrimination task in which participants are required to categorize a single stimulus as quickly as possible within a pair of target categories. The IAT effect relies on the assumption that subjects will be faster classifying a stimulus when the two concepts sharing the same response key are associated in their implicit attitudes than when the two concepts sharing the same response key are not implicitly associated. Explicit attitudes are attitudes that are deliberate (conscious) evaluative processes for stimuli in the environment compared with implicit attitudes that are associative (unconscious; Gawronski, & Bodenhausen, 2006). Whereas most existing evidence on the prediction of aggression is focused on explicit attitudes (e.g., Calvete, Orue, Gamez-Guadix, & de Arroyabe, 2016; Velotti et al., 2016), some scholars have provided empirical evidence about implicit attitudes, indicating that these may also affect behavior. For example, the discrepancy between explicit and implicit attitudes may play a key role in the occurrence of antisocial behavior (Suter et al., 2014). However, most studies of implicit aggression have been conducted in Western cultures, and this concept has not been rigorously tested in an Asian context with youth offenders.
Second, we focused on aggression differences among three different migrant youth groups of nonoffenders, onset-offenders, and recidivists. Several scholars have examined differences in aggression between offenders and nonoffenders. For example, Suter et al. (2014) assessed differences in aggression, as well as its relationship with psychopathic traits, with 36 offenders and 66 adolescents in the community, finding that there was no significant difference between community and offender adolescents in either explicit or implicit aggression. However, no researcher has yet empirically investigated aggression differences between nonoffenders, onset-offenders, and recidivists. Researchers have shown that changes in antisocial attitudes result in a corresponding increase or decrease in the risk of offending behavior (e.g., Shulman, Cauffman, Piquero, & Fagan, 2011). Therefore, revealing aggression differences among different youth groups may contribute to life-course theories and future correction research.
It has been found that the IAT has good psychometric properties, satisfactory reliability (Lemmer, Gollwitzer, & Banse, 2015), convergent and discriminate validity (Cunningham, Preacher, & Banaji, 2001; Gawronski, 2002), construct validity (Nosek, Greenwald, & Banaji, 2005), and predictive validity (Greenwald, Poehlman, Uhlmann, & Banaji, 2009). Therefore, we proposed the following hypotheses:
Hypothesis 1: The implicit measure of aggression will be reliable with young migrant offenders in an Asian context.
Hypothesis 2: Significant differences will exist in both explicit and implicit aggression among groups of young migrant nonoffenders, onset-offenders, and recidivist offenders.
Migrant youth were defined in this study as those aged between 15 and 25 years, who possessed a legal rural hukou ([TEXT NOT REPRODUCIBLE IN ASCII] formally registered permanent resident in a rural area in China), and who had migrated to urban and prosperous coastal regions for at least six months with their relatives or by themselves. From March 2011 to May 2015, we identified and recruited migrant youth in Wenzhou city, Zhejiang province, which was chosen as the study site because Zhejiang has the second largest migrant population in China and Wenzhou has the largest migrant population in Zhejiang (National Bureau of Statistics of the People's Republic of China, 2012).
The effective study sample comprised 227 male migrant youth (106 nonoffenders, 78 onset-offenders, and 43 recidivists; [M.sub.age] = 18.51 years, SD = 2.07). All data were individually collected between 2011 and 2015.
We recruited offenders from the detention house (incarcerated) and the People's Procuratorate (waiting at home for their court appearance). We gave lectures to both types of offenders on behavioral self-control and mental health, and then asked if they wanted to take part in a psychological program that provided psychological assessments and interventions. This resulted in 121 migrant youth offenders agreeing to participate in the program. Inclusion criteria were being aged between 15 and 25 years, being confirmed as an offender by the People's Procuratorate, and having a relatively stable residence in Wenzhou. The migrant youth offenders were divided into two groups, onset-offender and recidivist. The onset-offender group had a mean age of 16.82 years (SD = 0.67), whereas the recidivist group had a mean age of 20.51 years (SD = 3.10).
We recruited the nonoffender migrant youth group on a voluntary basis (participation rate = 100%) from several communities in Wenzhou (Mage = 18.77 years, SD = 0.77). Researchers recruited them from the streets, shops, Internet cafes, and factories. Inclusion criteria were age (15 to 25 years old) and having a relatively stable residence in Wenzhou.
After completing the questionnaire, for various reasons, some participants refused to continue or could not take part in the IATs. Thus, after the exclusion of the high-error participants, the final effective numbers of participants for the IATs comprised 68 nonoffenders, 31 onset-offenders, and 26 recidivists.
All the participants had normal vision and were familiar with using a computer.
Before the study, we obtained informed consent. For the nonoffender migrant youth group, we obtained written informed consent from each of the youths or their parents. For the offender migrant youth group, we obtained written informed consent from the young person and the detention house. All participants completed the questionnaire individually and anonymously, within a single session lasting between 5 and 25 minutes. For the IAT procedure, each participant individually completed the two IATs on a laptop computer. The reaction time and accuracy for each participant were recorded on the computer. All procedures were approved by the Wenzhou Medical University Ethics Committee and complied with the ethical code of the China Society of Psychology.
We collected standard sociodemographic information, such as age and criminal history, as well as participants completing the aggression questionnaire and two IATs.
Explicit attitudes of aggression. We assessed explicit attitudes toward aggression using the Chinese version of the Buss and Perry Aggression Questionnaire (AQ-CV; Li et al., 2011), which is a 30-item self-report questionnaire. The scale consists of five subscales: physical aggression (PA), verbal aggression (VA), anger (ANG), hostility (HOS), and aggression toward oneself (ATO), and uses a 5-point Likert-type scale ranging from 1 = does not apply at all to 5 = applies very well. The internal
consistency of the total score and subscales in this study were Cronbach's [alpha] for total score = .87, Cronbach's [alpha] for PA = .75, Cronbach's a for VA = .79, Cronbach's a for ANG = .76, Cronbach's [alpha] for HOS = .77, and Cronbach's [alpha] or ATO = .78, respectively.
Implicit attitudes and self-concept of aggression. We assessed implicit attitudes and self-concept of aggression using the IAT. Each IAT comprises seven blocks (Greenwald et al., 1998), and each practice block (Blocks 1, 2, 3, 5, and 6) has 36 trials, whereas each combined test block (Blocks 4 and 7) has 48 trials. For example, the first IAT task is designed to assess implicit attitudes toward aggression, associated aggression, and positive words on one response key (the "Q" on the keyboard), and being attacked and negative words on the other response key (the "P" on the keyboard), and requires participants to classify words belonging to one of these four categories into the relevant category as quickly as possible (such as classifying "fight" as aggression and "honor" as a positive word). In contrast, the second task associates being attacked and positive words on one response key (the "Q" on the keyboard), and aggression and negative words on the other key (the "P" on the keyboard), and again requires participants to classify words into one of these four categories as quickly as possible. The difference between the average time in Blocks 4 and 7 is considered as reflecting the strength of the association between aggression and positive words, indicating the participants' implicit attitudes. That is, people who categorize items faster when aggression is combined with positive words (or being attacked and negative words) are considered to have an implicit preference for aggression compared to being attacked.
We used the following IAT stimuli. For the IAT evaluating implicit attitudes toward aggression, the associated stimuli of aggression with positive words (or being attacked with negative words) were considered as a compatible task, versus aggression with negative words (or being attacked with positive words) as an incompatible task. For the IAT evaluating implicit self-concept toward aggression, the associated stimuli for the compatible task were aggression with me (or nonaggression with others), and aggression with others (or nonaggression with me) for the incompatible task.
The reliability coefficients for the IAT assessing implicit attitudes toward aggression and implicit self-concept toward aggression had a Cronbach's a .66 (n = 125) and .78 (n = 125), respectively.
Scoring procedure for the IAT. We used Greenwald, Nosek, and Banaji's (2003) improved scoring procedure, so that data for participants with more than 10% of responses made in a time less than 300 ms were eliminated from the analyses, as well as for participants with more than 20% error responses. For each participant, response latencies greater than 10,000 ms or error latencies were replaced by the mean latency of the block, to which 600 ms were added. We used data from Blocks 3, 4, 6, and 7. We computed D as the difference in average response latency between the two combined blocks of the IAT, divided by an average standard deviation of participants' response latencies in the two combined blocks. In this study, D = (d1+ d2) / 2, d1 = ([M.sub.B6] - [M.sub.B3]) / [SD.sub.B6B3], d2 = ([M.sub.B7] - [MB.sub.4]) / [SD.sub.B7B4].
First, we conducted reliability analyses and partial correlations to test Hypothesis 1. Greenwald et al. (2003) suggested computing the reliability of IAT effect scores by randomly generating block halves (by trials within blocks) and computing split-half coefficients. Because of the size of each group, data from the entire sample were used. Second, we compared group differences for aggression between nonoffenders, onset-offenders, and recidivists using a one-way analysis of variance (ANOVA) to test Hypothesis 2. Moreover, we computed absolute values of effect sizes to document the magnitude of the difference. With our sample size, the power to detect large effects ([eta.sup.2] = .14) was .96, meaning that between-group differences of practical significance had a very high probability of being detected. We performed all statistical analyses using SPSS version 17.0.
Reliability of the IAT
For the complete sample, the IAT reliability coefficients for assessing implicit attitudes toward aggression and implicit self-concept toward aggression were .66 (n = 125) and .78 (n = 125), respectively. The results suggest that each IAT could capture a substantial amount of systematic variance.
Comparison of Aggression Differences Among Nonoffender, Onset-Offender, and Recidivist Groups
Explicit measure. As we predicted in Hypothesis 2, the three groups of migrant youth showed significant differences in their explicit aggression total scores, F(2, 224) = 8.26, p < .001, [[eta].sup.2] = .07, and in all subscale scores (see Table 1). The nonoffender group had significantly lower total explicit aggression scores than did the onset-offender and recidivist groups (mean difference [MD] = -11.49, SE = 2.88, p < .001; MD = -7.61, SE = 3.50, p < .05), indicating their disapproval of aggression.
When compared with the nonoffender group, the onset-offender group had significantly higher scores in all subscales (see Table 1) and the recidivist group had significantly higher scores in the verbal aggression and anger subscales (MD = -1.63, SE = 0.66,p < .05 and MD = -2.67, SE = 0.89, p < .05, respectively).
There were no significant differences between the onset-offender and recidivist groups in the total score or aggression subscale scores, except in the aggression toward oneself subscale. The onset-offender group scored much higher on this subscale than did the recidivist group (MD = 2.11, SE = 0.71, p < .05).
Implicit measures. Contrary to our expectations in Hypothesis 2, the three groups showed no significant differences in implicit attitudes regarding aggression (see Table 1). Specifically, the nonoffender and onset-offender groups' mean D values were positive, indicating that the response time for aggression and negative word was longer than that for aggression and positive word, showing that the nonoffender and onset-offender groups preferred aggression. Moreover, the recidivist group's mean D value was negative, demonstrating their disapproval of aggression.
Concerning implicit self-concept of aggression, the three groups showed significant differences in their scores on this test, F(2, 122) = 4.00, p < .05, [[eta].sup.2] = .07). Specifically, the onset-offender group demonstrated significantly stronger implicit self-concept than did the nonoffender group, meaning that their implicit self-concept toward aggression was favorable. The recidivist group showed no significant differences in implicit self-concept compared to the onset-offender and nonoffender groups. Moreover, the recidivist and nonoffender groups' mean D values for implicit self-concept were both negative, showing that their response times for aggression and me were much longer than those for aggression and others, indicating that their implicit self-concept was unfavorable.
Reliability of the IAT Measures
In line with previous studies, the reliability analysis for the two IAT measures used with young Asian offenders showed acceptable results. The reliability coefficient for the IAT assessing implicit self-concept toward aggression was .78 (n = 125), which was satisfactory, whereas the reliability coefficient for the IAT assessing implicit attitude toward aggression was not as high at .66 (n = 125). Schmitt (1996) noted that reliability is related to test length and satisfactory levels of Cronbach [alpha] depending on test use and interpretation, and that relatively low (e.g., .50) levels of criterion reliability do not seriously attenuate validity coefficients. In the IAT test, there were usually more than 20 responses to a certain task, and this may have decreased the reliability of the test. However, this result does not mean that the IAT is not a reliable test in an Asian context with young offenders. Although the minimum acceptable Cronbach's alpha is considered to be .70, the results show that the IAT assessing implicit attitude toward aggression was .66, which was quite close to .70, meaning that the IAT assessing implicit attitude toward aggression was still able to capture a substantial amount of systematic variance, suggesting that the IAT may still be a reliable procedure to assess young offenders' implicit attitude and implicit self-concept toward aggression in an Asian context.
Aggression Differences Among Different Migrant Youth Groups
Our second major finding is that offender groups showed significantly higher explicit aggression than did the nonoffender group, and the onset-offender group showed the highest scores for explicit aggression. Moreover, although the three groups showed no significant difference in implicit attitudes toward aggression, the onset-offender group showed significantly stronger implicit self-concept toward aggression than did the nonoffender group. This finding contradicts those in a previous study showing that adolescent offenders and community adolescents shared similar unfavorable attitudes toward aggression, explicitly and implicitly (Suter et al., 2014), but is congruent with some other studies that showed offenders had higher explicit aggression levels than did the general population (Garofalo, Holden, Zeigler-Hill, & Velotti, 2016; Moeller, Novaco, Heinola-Nielsen, & Hougaard, 2016).
According to the cognitive scripts model, advanced by Henry et al. (2000), it is suggested that social behavior, especially aggression, is largely controlled by people's cognitive scripts. Researchers have shown that aggressive adolescents lack social problem-solving skills and have increased aggression-supporting beliefs (Serin & Preston, 2003). The preference differences toward aggression found between the two offender groups and the nonoffender group in our study corroborate findings in previous studies.
The onset-offender group obtained the highest scores for the explicit measure of aggression and showed a significant difference in the aggression toward oneself subscale compared with the recidivist group, and the recidivist group's mean D values for implicit attitudes and self-concept for aggression were both negative, showing their implicit attitudes and self-concept toward aggression were unfavorable. There are several explanations for these results. First, aggression levels may change with age. A review showed that the prevalence of physical aggression tends to increase from approximately 6 to 10 years old, yet, from approximately 10 to 13 years old, the prevalence of physical aggression tends to decrease, as some individuals mature. However, in the subset of individuals who continue to perpetrate destructive behaviors, these acts become most severe at approximately 15 to 16 years of age (Loeber et al., 2012). The approximate mean age for the onset-offenders was 16 years, for the recidivists, 20 years, and for the nonoffenders, 18 years. Second, because of social desirability, the nonoffender and recidivist groups, who were more mature and sophisticated, may have hidden their true attitude toward aggression. The result may also suggest that the onset-offender group was characterized by more accurate self-awareness than was the recidivist group. Results in a previous study comparing community adolescents with adolescent offenders showed a discrepancy between their attitude toward aggression and their recurrent antisocial behaviors (Suter et al., 2014). In addition, the discrepancy between explicit and implicit self-concepts seems to result in some negative, unpleasant, or dysfunctional outcomes (Sandstrom & Jordan, 2008). Moreover, the results may indicate that when people commit a crime for the first time against their prior social and moral standards, their aggression levels are even higher than those of recidivists and that their angry energy was directed not only toward the victims, but toward themselves as well.
Furthermore, although the three groups showed no difference in their implicit attitudes regarding aggression, the nonoffender group preferred aggression more than the other two groups did, with the recidivist group disliking aggression the most. A similar finding has been previously reported, in that, because of the effects of incarceration, adult offenders and adolescent offenders demonstrated stronger disapproval for violence than did nonmurderers and community offenders (Suter et al., 2014). However, criminal behaviors may be influenced by many factors besides aggression differences in cognition. Sampson and Laub (1995) claimed that individuals with weak or broken social bonds had an increased chance of committing crimes, regardless of prior offending history. Further studies are needed to shed light on this issue in migrant youth.
Limitations of the Study
This study has four limitations. First, we used a convenience sampling method, which may limit the generalizability of the findings to the target population. Second, the data for the explicit measure regarding aggression were self-reported and may be subject to reporting bias, although most explicit measures in aggression studies are self-reported. Third, all the participants were male, meaning gender differences were not addressed in this study. Fourth, this study was not longitudinal and we did not address the criminals' sentence terms; it is possible that incarceration length would change offenders' views of aggression. Further longitudinal study is suggested to clarify this issue.
Calvete, E., Orue, I., Gamez-Guadix, M., & de Arroyabe, E. L. (2016). Social information processing in dating conflicts: Reciprocal relationships with dating aggression in a one-year prospective study. Journal of Interpersonal Violence, 31, 1159-1183. https://doi.org/bxpr
China Youth and Children Research Center. (2009). Investigations of crime and delinquency of migrant adolescents in eight cities in China [In Chinese]. Issues on Juvenile Crimes and Delinquency, 1, 26-32.
Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Implicit attitude measures: Consistency, stability and convergent validity. Psychological Science, 12, 163-170.
Garofalo, C., Holden, C. J., Zeigler-Hill, V., & Velotti, P. (2016). Understanding the connection between self-esteem and aggression: The mediating role of emotion dysregulation. Aggressive Behavior, 42, 3-15. https://doi.org/bxps
Gawronski, B. (2002). What does the Implicit Association Test measure? A test of the convergent and discriminant validity of prejudice-related IATs. Experimental Psychology, 49, 171-180. https:// doi.org/fkk2p5
Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102, 4-27. https://doi.org/gmt
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464-1480. https://doi.org/gmv
Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: 1. An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197-216. https://doi.org/g8s
Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji, M. R. (2009). Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology, 97, 17-41. https://doi.org/c8c9rj
Grumm, M., Hein, S. D., & Fingerle, M. (2011). Predicting aggressive behavior in children with the help of measures of implicit and explicit aggression. International Journal of Behavioral Development, 35, 352-357. https://doi.org/fc2p3w
Henry, D., Guerra, N., Huesmann, R., Tolan, P., VanAcker, R., & Eron, L. (2000). Normative influences on aggression in urban elementary school classrooms. American Journal of Community Psychology, 28, 59-81. https://doi.org/cs3qwr
Lemmer, G., Gollwitzer, M., & Banse, R. (2015). On the psychometric properties of the aggressiveness-IAT for children and adolescents. Aggressive Behavior, 41, 84-95. https://doi.org/bxpt
Li, X. Y., Phillips, M. R., Zhang, Y. L., Niu, Y. J., Tong, Y. S., & Yang, S. J. (2011). Development, reliability and validity of the Chinese version of Buss & Perry aggression questionnaire. Chinese Journal of Nervous and Mental Diseases, 37, 607-613.
Liu, Y. H., & Li, C. (2015). The empirical analysis of local legislation of prevention of juvenile crimes in Jiangsu Province [In Chinese]. Legal Forum, 30, 145-152.
Loeber, R., Menting, B., Lynam, D. R., Moffitt, T. E., Stouthamer-Loeber, M., Stallings, R., Farrington, D. P., & Pardini, D. (2012). Findings from the Pittsburgh Youth Study: Cognitive impulsivity and intelligence as predictors of the age-crime curve. Journal of the American Academy of Child & Adolescent Psychiatry, 51, 1136-1149. https://doi.org/bxpv
Moeller, S. B., Novaco, R. W., Heinola-Nielsen, V., & Hougaard, H. (2016). Validation of the Novaco Anger Scale-Provocation Inventory (Danish) with nonclinical, clinical, and offender samples. Assessment, 23, 624-636. https://doi.org/bxpw
National Bureau of Statistics of the People's Republic of China. (2012). Tabulation on the 2010 population census of the People's Republic of China [In Chinese]. Beijing: China Statistics Press.
Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Method variables and construct validity. Personality and Social Psychology Bulletin, 31, 166-180. https://doi.org/cgpgdf
Nunes, K. L., Hermann, C. A., Ratcliffe, K. (2013). Implicit and explicit attitudes toward rape are associated with sexual aggression. Journal of Interpersonal Violence, 28, 2657-2675. https:// doi.org/bxpx
Sampson, R. J., & Laub, J. H. (1995). Crime in the making: Pathways and turning points through life. Cambridge, MA: Harvard University Press.
Sandstrom, M. J., & Jordan, R. (2008). Defensive self-esteem and aggression in childhood. Journal of Research in Personality, 42, 506-514. https://doi.org/ccv4hv
Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8, 350-353. https://doi.org/cjqw3x
Serin, R. C., & Preston, D. L. (2003). Designing, implementing and managing treatment programs for violent offenders. In G. A. Bernfeld, D. P. Farrington, & A. W. Leschied (Eds.), Offender rehabilitation in practice: Implementing and evaluating effective programs (pp. 205-221). Chichester, UK: Wiley.
Shulman, E. P., Cauffman, E., Piquero, A. R., & Fagan, J. (2011). Moral disengagement among serious juvenile offenders: A longitudinal study of the relationship between morally disengaged attitudes and offending. Developmental Psychology, 47, 1619-1632. https://doi.org/fw2fvc
Suter, M., Pihet, S., de Ridder, J., Zimmermann, G., & Stephan, P. (2014). Implicit attitudes and self-concepts towards transgression and aggression: Difference between male community and offender adolescents, and associations with psychopathic traits. Journal of Adolescence, 37, 669-680. https://doi.org/bxtx
Velotti, P., Garofalo, C., Petrocchi, C., Cavallo, F., Popolo, R., & Dumaggio, G. (2016). Alexithymia, emotion dysregulation, impulsivity and aggression: A multiple mediation model. Psychiatry Research, 237, 296-303. https://doi.org/bxtz
Xu, W. (2015). Statistical analyses and preventive measures of adolescent crimes in China based on data between 1997 and 2013 [In Chinese]. Youth Exploration, 198, 89-94. https://doi.org/bx62
XIAODAN XIE, QIANG ZHOU, AND LI CHEN
Wenzhou Medical University
Shanghai University of Traditional Chinese Medicine
The People's Procuratorate of Longwan District
East China Normal University
The First Affiliated Hospital of Wenzhou Medical University
Xiaodan Xie, Qiang Zhou, and Li Chen, Department of Psychology, Wenzhou Medical University; Bing Feng, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine; Changwei Ji, The People's Procuratorate of Longwan District; Wenxiu Geng, School of Psychology and Cognitive Science, East China Normal University; Xinchao Zhou, Department of Information, The First Affiliated Hospital of Wenzhou Medical University.
The authors thank the migrant youth who volunteered to participate in the study. The study was funded by the Visiting Scholar Development Project (FX2014051) of the Department of Education of Zhejiang Province, the Zhejiang Education Planning Project (2016SCG175), and Zhejiang Wise Medical Engineering Technology Research Center (2016E10011).
Correspondence concerning this article should be addressed to Xinchao Zhou, Department of Information, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang 325000, People's Republic of China. Email: email@example.com
Table 1. Comparison of Aggression Differences in Migrant Youth Groups M (SD) Non- Onset- Recidivist offender offender (Group 1) (Group 2) (Group 3) (n = 106) (n = 78) (n = 43) Total AQ-CV score 63.930 (18.54) 75.420 (19.77) 71.550 (20.60) Physical aggression 15.440 (5.60) 18.600 (6.62) 17.620 (6.62) Verbal aggression 11.320 (3.14) 12.580 (4.07) 12.950 (4.09) Anger 12.850 (4.62) 15.110 (5.28) 15.510 (5.08) Hostility 14.530 (5.00) 16.510 (4.61) 14.960 (5.15) Aggression toward 9.780 (3.45) 12.620 (4.16) 10.500 (3.74) oneself Implicit attitudes .042 (.292) .005 (.414) -.069 (.413) Implicit -.075 (.367) .124 (.279) -.029 (.251) self-concept F [[eta]. sub.2] Total AQ-CV score 8.26 *** .07 Physical aggression 6.22 ** .05 Verbal aggression 4.19 * .04 Anger 6.80 ** .06 Hostility 3.78 * .03 Aggression toward 13.01 *** .10 oneself Implicit attitudes .86 Implicit 4.00 * .07 self-concept Mean differences Groups 1 Groups 1 Groups 2 and 2 and 3 and 3 Total AQ-CV score -11.49 *** -7.61 * 3.88 Physical aggression -3.16 *** -2.18 .98 Verbal aggression -1.27 * -1.63 * -.37 Anger -2.27 *** -2.67 ** -.40 Hostility -1.98 *** -.42 1.55 Aggression toward -2.83 *** -.72 2.11 ** oneself Implicit attitudes .04 .11 .08 Implicit -.20 *** -.05 .15 self-concept Note. AQ-CV = the Chinese version of the Buss and Perry Aggression Questionnaire. * p < .05, ** p < .01, *** p < .001.
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|Author:||Xie, Xiaodan; Zhou, Qiang; Chen, Li; Feng, Bing; Ji, Changwei; Geng, Wenxiu; Zhou, Xinchao|
|Publication:||Social Behavior and Personality: An International Journal|
|Date:||May 1, 2017|
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