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Causal inferences on the effectiveness of complex social programs: Navigating assumptions, sources of complexity and evaluation design challenges

机译：关于复杂社会计划有效性的因果推论：导航假设，复杂性来源和评估设计挑战

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This paper explores avenues for navigating evaluation design challenges posed by complex social programs (CSPs) and their environments when conducting studies that call for generalizable, causal inferences on the intervention's effectiveness. A definition is provided of a CSP drawing on examples from different fields, and an evaluation case is analyzed in depth to derive seven (7) major sources of complexity that typify CSPs, threatening assumptions of textbook-recommended experimental designs for performing impact evaluations. Theoretically-supported, alternative methodological strategies are discussed to navigate assumptions and counter the design challenges posed by the complex configurations and ecology of CSPs. Specific recommendations include: sequential refinement of the evaluation design through systems thinking, systems-informed logic modeling; and use of extended term, mixed methods (ETMM) approaches with exploratory and confirmatory phases of the evaluation. In the proposed approach, logic models are refined through direct induction and interactions with stakeholders. To better guide assumption evaluation, question-framing, and selection of appropriate methodological strategies, a multiphase evaluation design is recommended. (C) 2016 Elsevier Ltd. All rights reserved.
机译：本文探讨了在开展研究时需要应对复杂的社会项目（CSP）及其环境的评估设计挑战的途径，这些研究要求对干预措施的有效性进行一般性的因果推断。根据不同领域的示例提供了CSP的定义，并对评估案例进行了深入分析，以得出代表CSP的七（7）个主要的复杂性来源，这威胁到教科书推荐的进行影响评估的实验设计的假设。从理论上讲，讨论了替代的方法论策略，以应对假设并应对CSP复杂配置和生态环境带来的设计挑战。具体建议包括：通过系统思考，系统信息逻辑建模对评估设计进行顺序优化；以及在评估的探索阶段和确认阶段使用扩展的混合方法（ETMM）方法。在提出的方法中，通过直接归纳和与利益相关者的交互来完善逻辑模型。为了更好地指导假设评估，问题框架和适当方法策略的选择，建议采用多阶段评估设计。 （C）2016 Elsevier Ltd.保留所有权利。

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