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A Comparison of Variable- and Person-Oriented Approaches in Evaluating a Universal Preventive Intervention

机译:在普遍预防性干预评估可变和以人为本的方法的比较

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摘要

Evaluations of prevention programs, such as the PAX Good Behavior Game (PAX), often have multiple outcome variables (e.g., emotional, behavioral, and relationship problems). These are often reported for multiple time points (e.g., pre- and post-intervention) where data are multilevel (e.g., students nested in schools). In this paper, we present both variable-oriented and person-oriented statistical approaches, to evaluate an intervention program with multilevel, longitudinal multivariate outcomes. Using data from the Manitoba PAX Study, we show how these two approaches provide us with different information that can be complementary. Data analyses with the variable-oriented approach (multilevel linear regression model) provided us with overall PAX program effects for each outcome variable; the person-oriented approach (latent transition analysis) allowed us to explore the transition of multiple outcomes across multiple time points and how the intervention program affects this transition differently for students with different risk profiles. We also used both approaches to examine how gender and socio-economic status related to the program effects. The implications of these results and the use of both types of approaches for program evaluation are discussed.
机译:预防计划的评估,例如Pax良好行为游戏(PAX),通常具有多种结果变量(例如,情绪,行为和关系问题)。通常报告这些时间点(例如,介入和后期和后期),其中数据是多级(例如,学生嵌套在学校)。在本文中,我们介绍了以可变导向和以人为本的统计方法,以多级,纵向多元成果评估干预计划。使用来自Manitoba Pax研究的数据,我们展示了这两种方法如何为我们提供可以互补的不同信息。使用可变导向的方法(多级线性回归模型)的数据分析为每个结果变量提供了整个PAX程序效果;以人为本的方法(潜在过渡分析)允许我们探讨多个时间点的多种结果的转换以及干预程序如何对具有不同风险简档的学生不同地影响这种转变。我们还使用两种方法来审查与计划效应相关的性别和社会经济地位如何。讨论了这些结果的影响以及两种类型的程序评估方法的使用。

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