Giang Michael T, Graham Sandra
Department of Education, University of California, Los Angeles, California 90095-152, USA.
Aggress Behav. 2008 Mar-Apr;34(2):203-13. doi: 10.1002/ab.20233.
This study used latent class analysis (LCA) to identify and classify individuals into aggressor and victim latent classes. Participants were over 2,000 sixth grade students who completed peer nomination procedures that identified students who had reputations as perpetrators and/or victims of physical, verbal, or relational harassment. Results showed five latent classes. Consistent with previous research, LCA identified latent classes of victims, aggressors, and socially adjusted students. However, rather than a single aggressive-victim subgroup, LCA identified latent classes of highly-victimized aggressive-victims and highly-aggressive aggressive-victims. Comparisons showed differences in mean profiles and classification criteria between LCA and traditional dichotomization approaches. Adjustment outcomes showed that highly-victimized aggressive-victims generally experienced greater negative psychological and social adjustment outcomes than highly-aggressive aggressive-victims. Implications of these findings for better assessment of victim and aggressor subgroups were discussed.
本研究采用潜在类别分析（LCA）将个体识别并分类为攻击者和受害者潜在类别。参与者是2000多名六年级学生，他们完成了同伴提名程序，该程序识别出了在身体、言语或关系骚扰方面有施害者和/或受害者声誉的学生。结果显示有五个潜在类别。与先前的研究一致，LCA识别出了受害者、攻击者和社会适应良好学生的潜在类别。然而，LCA识别出的不是单一的攻击 - 受害者亚组，而是高度受侵害的攻击 - 受害者和高度攻击性的攻击 - 受害者潜在类别，而非单一的攻击 - 受害者亚组。比较结果显示，LCA与传统二分法在平均概况和分类标准上存在差异。调整结果表明，高度受侵害的攻击 - 受害者通常比高度攻击性的攻击 - 受害者经历更严重的负面心理和社会适应结果。讨论了这些发现对更好地评估受害者和攻击者亚组的意义。
