The Use of Mixed Methods in Randomized Control Trials
The Use of Mixed Methods in Randomized Control Trials
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混合方法在随机对照试验中的使用
DOI：
10.1002/ev.20058
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发表时间：
2013
影响因子：
--
通讯作者：
H. White
中科院分区：
文献类型：
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作者：
H. White
评价应是问题驱动，而不是方法驱动。起点应该是待评估的优先方案或待测试的政策。从这一点出发，确定了一系列评价问题。对于每个评价问题，任务是确定回答该问题的最佳方法。因此，任何一项研究都可能包含多种方法。影响评估的一个关键问题是归因问题：干预对世界状况有什么影响？（在任何具体评价中，界定为对有关指标采取的一项或一套明确规定的干预措施的差异）。对于具有大量分配单位的干预措施，这个问题最好通过定量实验或准实验设计来回答。对于前瞻性或事前评估设计，如果可行的话，随机对照试验（RCT）很可能是解决这一归因问题的最佳方法。但只有归属问题会得到回答。高质量的影响力评估将回答更广泛的评估问题，这些问题更具过程性，既可以为被评估项目的设计和实施提供信息，也可以为外部有效性提供信息。混合方法联合收割机将随机对照试验的反事实分析与事实分析相结合，利用定量和定性数据分析因果链，借鉴了一系列学科的方法。事实分析将涉及执行质量、目标选择、参与障碍或预期受益人采用等问题。©Wiley Periodicals，Inc.，和美国评估协会。
Evaluations should be issues driven, not methods driven. The starting point should be priority programs to be evaluated or policies to be tested. From this starting point, a list of evaluation questions is identified. For each evaluation question, the task is to identify the best available method for answering that question. Hence it is likely that any one study will contain a mix of methods. A crucial question for an impact evaluation is that of attribution: What difference did the intervention make to the state of the world? (framed in any specific evaluation as the difference a clearly specified intervention or set of interventions made to indicators of interest). For interventions with a large number of units of assignment, this question is best answered with a quantitative experimental or quasi‐experimental design. And for prospective, or ex ante, evaluation designs a randomized control trial (RCT) is very likely to be the best available method for addressing this attribution question if it is feasible. But just the attribution question will be answered. A high‐quality impact evaluation will answer a broader range of evaluation questions of a more process nature, both to inform design and implementation of the program being evaluated and for external validity. Mixed methods combine the counterfactual analysis from an RCT with factual analysis with the use of quantitative and qualitative data to analyze the causal chain, drawing on approaches from a range of disciplines. The factual analysis will address such issues as the quality of implementation, targeting, barriers to participation, or adoption by intended beneficiaries. ©Wiley Periodicals, Inc., and the American Evaluation Association.