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Postdoc in Chicago on statistical methods for evidence-based policy

Beth Tipton writes:

The Institute for Policy Research and the Department of Statistics is seeking applicants for a Postdoctoral Fellowship with Dr. Larry Hedges and Dr. Elizabeth Tipton. This fellowship will be a part of a new center which focuses on the development of statistical methods for evidence-based policy. This includes research on methods for meta-analysis, replication, causal generalization, and, more generally, the design and analysis of randomized trials in social, behavioral, and education settings.

The position will include a variety of tasks, including: Conducting simulation studies to understand properties of different estimators; performing reviews of available methods (in the statistics literature) and the use of these methods (in the education and social science literatures); the development of examples of the use of new methods; writing white papers summarizing methods developments for researchers conducting evidence-based policy; and the development of new methods in these areas.

Job Requirements

Required: Ph.D. (expected or obtained) in statistics, biostatistics, the quantitative social sciences, education research methods, or a related field; strong analytical and written communication skills; strong programming skills (R, desired) and familiarity with cluster-computing; and experience with education research, randomized trials, meta-analysis, and/or evidence-based policy.

This will be a one-year appointment beginning September 2019 (or a mutually agreed upon date), with the possibility of renewal for a second year based upon satisfactory performance.

Candidates should submit the following documents in PDF to Valerie Lyne ( with subject line “Post-Doc”:

· CV

· A 1-page statement regarding the candidate’s research interests, qualifications, and prior research experience relevant to this position

· Names and addresses of three references (no letters are required at this time)

We plan to begin reviewing applications on April 12th, 2019 and will continue to do so until the position is filled.

Looks fun, also this is important work.


  1. I’m always surprised when I hear things like “evidence-based policy” or “evidence-based medicne”, because it makes me realize what we’ve been doing is actually not very evidence-based at all! So getting decent evidence into policy is a huge and important problem.

    > Conducting simulation studies to understand properties of different estimators

    Are they only interested in point estimates or is there a more Bayesian component? I’ve heard Andrew describe max a posteriori (Andrew would write “Map”, not “MAP” say “posterior mode”) point estimates as approximate Bayes (but not ABC) by coupling them with Laplace approximations (multivariate normal centered at point estimate with covariance equal to the inverse Hessian at that point), perhaps with (Pareto smoothed?) importance sampling to compute expectations like parameter estimates or event probabilities.

    • Bob:

      Larry H started in meta-analysis even before me and his book with Irwin Olkin who did dabble in Bayesian methods – so I would encourage people to apply.

      From the history in my DPhil thesis – “Hedges and Olkin wrote a book[65] in 1985 directed (as the authors indicated) at providing different statistical methods from those of Fisher & Cochran that were designed to specifically deal with this new and different kind of meta-analysis – that of combining different outcomes using an index of magnitude. In 1990, Olkin[82], quoting Fisher, again highlighted this arguably different class of meta-analyses (which apparently are more common in psychology and education than clinical research) of determining the combined significance of independent tests on outcomes that may be of very different kinds (by combining their p_values.)”

      Now, today I think the real opportunities of meta-analysis are more clearly stated in my pre-print with Andrew

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