Improved Generalizability Through Improved Recruitment

Lessons Learned From a Large-Scale Randomized Trial

Elizabeth Tipton*, Bryan J. Matlen

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Randomized control trials (RCTs) have long been considered the “gold standard” for evaluating the impacts of interventions. However, in most education RCTs, the sample of schools included is recruited based on convenience, potentially compromising a study’s ability to generalize to an intended population. An alternative approach is to recruit schools using a stratified recruitment method developed by Tipton. Until now, however, there has been limited information available about how to implement this approach in the field. In this article, we concretely illustrate each step of the stratified recruitment method in an evaluation of a college-level developmental algebra intervention. We reflect on the implementation of this process and conclude with five on-the-ground lessons regarding how to best implement this recruitment method in future studies.

Original languageEnglish (US)
Pages (from-to)414-430
Number of pages17
JournalAmerican Journal of Evaluation
Volume40
Issue number3
DOIs
StatePublished - Sep 1 2019

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Keywords

  • cluster-randomized trials
  • community colleges
  • developmental algebra
  • generalization
  • web-based tutors

ASJC Scopus subject areas

  • Business and International Management
  • Social Psychology
  • Health(social science)
  • Education
  • Sociology and Political Science
  • Strategy and Management

Cite this

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Improved Generalizability Through Improved Recruitment : Lessons Learned From a Large-Scale Randomized Trial. / Tipton, Elizabeth; Matlen, Bryan J.

In: American Journal of Evaluation, Vol. 40, No. 3, 01.09.2019, p. 414-430.

Research output: Contribution to journalArticle

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