Literature DB >> 29954223

Designs of Empirical Evaluations of Nonexperimental Methods in Field Settings.

Vivian C Wong1, Peter M Steiner2.   

Abstract

Over the last three decades, a research design has emerged to evaluate the performance of nonexperimental (NE) designs and design features in field settings. It is called the within-study comparison (WSC) approach or the design replication study. In the traditional WSC design, treatment effects from a randomized experiment are compared to those produced by an NE approach that shares the same target population. The nonexperiment may be a quasi-experimental design, such as a regression-discontinuity or an interrupted time-series design, or an observational study approach that includes matching methods, standard regression adjustments, and difference-in-differences methods. The goals of the WSC are to determine whether the nonexperiment can replicate results from a randomized experiment (which provides the causal benchmark estimate), and the contexts and conditions under which these methods work in practice. This article presents a coherent theory of the design and implementation of WSCs for evaluating NE methods. It introduces and identifies the multiple purposes of WSCs, required design components, common threats to validity, design variants, and causal estimands of interest in WSCs. It highlights two general approaches for empirical evaluations of methods in field settings, WSC designs with independent and dependent benchmark and NE arms. This article highlights advantages and disadvantages for each approach, and conditions and contexts under which each approach is optimal for addressing methodological questions.

Keywords:  design and evaluation of programs and policies; design replication; methodology (if appropriate); quasi-experimental design; replication; within-study comparison

Mesh:

Year:  2018        PMID: 29954223     DOI: 10.1177/0193841X18778918

Source DB:  PubMed          Journal:  Eval Rev        ISSN: 0193-841X


  3 in total

1.  Effect Estimates in Randomized Trials and Observational Studies: Comparing Apples With Apples.

Authors:  Sara Lodi; Andrew Phillips; Jens Lundgren; Roger Logan; Shweta Sharma; Stephen R Cole; Abdel Babiker; Matthew Law; Haitao Chu; Dana Byrne; Andrzej Horban; Jonathan A C Sterne; Kholoud Porter; Caroline Sabin; Dominique Costagliola; Sophie Abgrall; John Gill; Giota Touloumi; Antonio G Pacheco; Ard van Sighem; Peter Reiss; Heiner C Bucher; Alexandra Montoliu Giménez; Inmaculada Jarrin; Linda Wittkop; Laurence Meyer; Santiago Perez-Hoyos; Amy Justice; James D Neaton; Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2019-08-01       Impact factor: 5.363

2.  How Within-Study Comparisons can Inform Research and Evaluation Design for the Third Sector.

Authors:  Kaitlin P Anderson; Patrick J Wolf
Journal:  Voluntas       Date:  2022-02-25

3.  Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Authors:  Ellicott C Matthay; Erin Hagan; Laura M Gottlieb; May Lynn Tan; David Vlahov; Nancy E Adler; M Maria Glymour
Journal:  SSM Popul Health       Date:  2019-12-09
  3 in total

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