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Using Interviews to Understand the Assignment Mechanism in a Nonexperimental Study

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  • Jordan H. Rickles

Abstract

Many inquiries regarding the causal effects of policies or programs are based on research designs where the treatment assignment process is unknown, and thus valid inferences depend on tenuous assumptions about the assignment mechanism. This article draws attention to the importance of understanding the assignment mechanism in policy and program evaluation studies, and illustrates how information collected through interviews can develop a richer understanding of the assignment mechanism. Focusing on the issue of student assignment to algebra in 8th grade, I show how a preliminary data collection effort aimed at understanding the assignment mechanism is particularly beneficial in multisite observational studies in education. The findings, based on ten interviews and administrative data from a large school district, draw attention to the often ignored heterogeneity in the assignment mechanism across schools. These findings likely extend beyond the current research project in question to related educational policy issues such as ability grouping, tracking, differential course taking, and curricular intensity, as well as other social programs in which the assignment mechanism can differ across sites.

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  • Jordan H. Rickles, 2011. "Using Interviews to Understand the Assignment Mechanism in a Nonexperimental Study," Evaluation Review, , vol. 35(5), pages 490-522, October.
    • Handle: RePEc:sae:evarev:v:35:y:2011:i:5:p:490-522
    DOI: 10.1177/0193841X11428644
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    1. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    2. Shadish, William R. & Clark, M. H. & Steiner, Peter M., 2008. "Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1334-1344.
    3. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
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