Quasi-Experimental Design and Methods

This guide, written by Howard White and Shagun Sabarwal for UNICEF looks at the use of Quasi-Experimental design and methods in Impact Evaluation. The paper provides a brief overview and then provides an outline of when it is appropriate to use and some of the ethical and practical limitations of its use.

Excerpt

"Quasi-experimental research designs, like experimental designs, test causal hypotheses. In both experimental (i.e., randomized controlled trials or RCTs) and quasi-experimental designs, the programme or policy is viewed as an ‘intervention’ in which a treatment – comprising the elements of the programme/policy being evaluated – is tested for how well it achieves its objectives, as measured by a pre specified set of indicators (see Brief No. 7, Randomized Controlled Trials). A quasi-experimental design by definition lacks random assignment, however. Assignment to conditions (treatment versus no treatment or comparison) is by means of self-selection (by which participants choose treatment for themselves) or administrator selection (e.g., by officials, teachers, policymakers and so on) or both of these routes."

Contents

  • Quasi-experimental design and methods: a brief description
  • When is it appropriate to use quasi- experimental methods?
  • Quasi-experimental methods for constructing comparison groups
  • Quasi-experimental methods for data analysis
  • Ethical issues and practical limitations
  • Which other methods work well with this one?
  • Presentation of results and analysis
  • Example of good practices
  • Examples of challenges 

See more in the Impact Evaluation Series here.

Source

Howard White and Shagun Sabarwal (2014), Quasi-Experimental Design and Methods, UNICEF. Retrieved from: http://devinfolive.info/impact_evaluation/img/downloads/Quasi-Experimental_Design_and_Methods_ENG.pdf

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Comments

Anonymous's picture
Jane Casey
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Can you use qausi-experimental methods on smaller groups. I was wanting to use quasi experiential using NRCT but the small sample size numbers  appears to be an issue. What would be the minimum number required before it loses credibility?

SimonDavies's picture
Simon Davies
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Dear Jane

This is a great question - I would encourage you to join our discussion group and pose this to our members, many of whom have expertise in this area. Here's the link for you to join: Peregrine Discussion Group - Home

Kind regards

Simon

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