Evaluation

Evaluation
Contribution analysis: How did the program make a difference – or did it?
March 6, 2018
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Contribution analysis is an evaluation method that was developed by John Mayne in the early 2000s to enable evaluators to produce rigorous impact analyses in the context of programs that cannot be evaluated using an experimental or quasi-experimental design. While the Science-Metrix Evaluation team has been conducting contribution analyses informally for a while now, the workshop conducted by Thomas Delahais (of Quadrant Conseil) for the SQEP annual conference in October 2017 inspired us to use this technique in a more rigorous, systematic and comprehensive way. This method appeared to us as perfectly suited to the kinds of evaluations we, and probably many of you, carry out—that is, evaluations of complex, multifaceted programs for which counterfactuals cannot easily be used. In this post, I’ll give a brief synopsis of the method and provide suggestions for further reading.
Evaluation
Maximizing the use of evaluation findings
February 21, 2018
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In a 2006 survey of 1,140 American Evaluation Association members, 68% reported that their evaluation results were not used. This suggests a serious need for evaluation results to make it off the bookshelf and into the hands of intended audiences. This week’s blog post looks at how we, as evaluators, can help maximize the use of the evaluations we produce.
Evaluation
Program evaluation basics
February 14, 2018
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The ScienceMetrics blog has so far focused on our scientometric, data mining and science policy activities, but we also have a long history of conducting evaluations of S&T-related programs and initiatives. In my opinion, the most fun to be had on the job is when we team up to combine our quantitative and qualitative analysis skills on a project. To kick off a series of posts from the evaluation side of Science-Metrix, in this post I’ll present an introductory Q&A on program evaluation, and next week I’ll discuss how to maximize the use of evaluation findings. Read on for the what, why, when and how of program evaluation.