使用分类和回归树分析和障碍模型进行性骚扰预测:直接复制
Jan-Louw Kotzé1, Patricia A Frazier1, Kayla A Huber1
1Department of Psychology, University of Minnesota Twin Cities.
Journal of sex research
|July 24, 2023
概括
性骚扰在美国高等教育中很普遍. 这项研究证实了先前关于风险因素的发现,表明年龄较小,酒精使用和先前的逆境预测同行骚扰,而LGBQ+身份预测教师/工作人员骚扰.
科学领域:
- 社会学 社会学 社会学
- 高等教育研究 高等教育研究
- 公共卫生 公共卫生
背景情况:
- 性骚扰是影响美国高等教育机构众多学生的一个重大问题.
- 之前的研究使用统计模型确定了关键的风险因素.
研究的目的:
- 验证和复制以前识别的大学生性骚扰风险因素.
- 评估先前障碍模型和分类和回归树 (CART) 分析结果的稳定性.
主要方法:
- 对来自两年制和四年制大学的9552名学生的二级数据分析.
- 复制障碍模型和CART分析以评估统计学意义和效果大小的一致性.
- 对模型系数和变量预测的可复制性标准的评估.
主要成果:
- 关于性骚扰风险因素的原始发现得到了强有力的复制.
- 91%的障碍模型效应和88%的CART分析变量符合复制标准.
- 关键预测因素包括年龄较小,频繁饮酒,四年上大学,先前成为同行骚扰的受害者,以及教师/工作人员骚扰的LGBQ+身份.
结论:
- 对性骚扰的确定的风险因素在不同学生样本中是可靠的.
- 研究结果支持在高等教育中制定有针对性的预防和干预策略.
- 需要进一步的研究,以阐明人口统计和情境关联与骚扰风险背后的机制.
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