Department of Community Health Sciences, University of Manitoba, S113-750 Bannatyne Ave, Winnipeg, R3E 0W3, Canada.
Healthy Child Manitoba Office, Winnipeg, MB, Canada.
Prev Sci. 2018 Aug;19(6):738-747. doi: 10.1007/s11121-018-0881-x.
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.
预防计划（如 PAX 良好行为游戏（PAX））的评估通常有多个结果变量（例如，情绪、行为和关系问题）。这些通常在多个时间点报告（例如，干预前和干预后），数据是多层次的（例如，学生嵌套在学校中）。在本文中，我们介绍了变量导向和个体导向的统计方法，以评估具有多层次、纵向多变量结果的干预计划。使用马尼托巴 PAX 研究的数据，我们展示了这两种方法如何为我们提供互补的不同信息。使用变量导向方法（多层次线性回归模型）进行数据分析为我们提供了每个结果变量的总体 PAX 计划效果；个体导向方法（潜在转变分析）使我们能够探索多个结果在多个时间点的转变，以及干预计划如何对具有不同风险特征的学生产生不同的影响。我们还使用这两种方法来研究性别和社会经济地位与计划效果的关系。讨论了这些结果的意义以及这两种方法在计划评估中的应用。
