Towards an evidence base of theory-driven evaluations: Some questions for proponents of theory-driven evaluation

Evaluation

Volumn 18, Issue 3, 2012, Pages 378-395

  Sridharan, Sanjeev ^a   Nakaima, April ^b  

^a ST MICHAEL'S HOSPITAL   (Canada)

^b YORK UNIVERSITY   (Canada)

Author keywords

complexity; contribution analysis; realist evaluation; theory driven evaluation; timeline of impact

Indexed keywords

EID: 84863651976     PISSN: 13563890     EISSN: 14617153     Source Type: Journal    
DOI: 10.1177/1356389012453289     Document Type: Article

References (32)

1. AusposPKubischAC (2004) Building Knowledge about Community Change: Moving Beyond Evaluations. The Aspen Institute. URL: http://www.aspeninstitute.org/atf/cf/%7BDEB6F227-659B-4EC8-8F84-8DF23CA704F5%7D/BUILDINGKNOWELDGE.pdf.
2. Blamey A,Mackenzie M.Theories of change and realistic evaluation: peas in a pod or apples and oranges?.Evaluation. 2007;13 (4): 439-55.
3. Cochrane WG.The planning of observational studies of human populations (with Discussion).Journal of the Royal Statistical Society. 1965;A 128:134-55.
4. Cook TD.The false choice between theory-based evaluation and experimentation.New Directions in Evaluation. 2000;87:27-34.
5. Coryn CLS,Noakes LA,Westine CD,Schroter DC.A systematic review of theory-driven evaluation practice from 1990 to 2009.American Journal of Evaluation. 2011;32 (3): 199-226.
6. Davidoff F.Heterogeneity is not always noise: lessons from improvement.JAMA. 2009;302 (23): 2580-6.
7. Deaton A.Instruments of development: randomization in the tropics, and the search for the elusive keys to economic development.Proceedings of the British Academy, 2008 Lectures. 2009;162:123-60.
8. Greenhalgh T,Kristjansson E,Robinson V.Realist review to understand the efficacy of school feeding programmes.BMJ. 2007;335:858-61.
9. Kubisch AC,Auspos P,Brown P,Dewar TVoices from the Field III: Lessons and Challenges from Two Decades of Community Change Efforts. Washington, DC: Aspen Institute; 2010:.
10. Mark MM,Henry GT,Julnes GEvaluation: An Integrated Framework for Understanding, Guiding, and Improving Policies and Programs. San Francisco, CA: Jossey-Bass; 2000:.
11. Mayne JEvaluating the Complex. Schwartz RForss KMarra M, ed. New Brunswick, NJ: Transaction Publishers; 2011:53-96.
12. Morell JAEvaluation in the Face of Uncertainty: Anticipating Surprise and Responding to the Inevitable. New York: Guilford Press; 2010:.
13. Nagin DS,Tremblay RE.Developmental trajectory groups: fact or a useful statistical fiction?.Criminology. 2005;43:873-904.
14. Patton MDevelopmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use. New York: Guilford Press; 2010:.
15. Pawson R.Nothing as practical as a good theory.Evaluation. 2003;9 (4): 471-490.
16. Pawson REvidence-Based Policy: A Realist Perspective. London: SAGE; 2006:.
17. Pawson R,Sridharan SEvidence-Based Public Health: Effectiveness and Efficiency. Killoran AKelly M, ed. Oxford: Oxford University Press; 2009:43-61.
18. Pawson R,Tilley NEvaluation for the 21st Century: A Handbook. Chelimsky EShadish WR, ed. Thousand Oaks, CA: SAGE; 1997:405-18.
19. Pawson R,Greenhalgh T,Harvey G,Walshe KUniversity of Manchester; 2004:.
20. Pickett STA,Cadenasso ML.Landscape ecology: spatial heterogeneity in ecological systems.Science. 1995;269:331-4.
21. RavallionM (2009) Should the randomistas rule?Economists Voice, Feb. URL: http:www.bepress.com/ev.
22. Rodrik DWhat Works in Development? Thinking Big and Thinking Small. Cohen JEasterly W, ed. Washington, DC: Brookings Institution Press; 2009:.
23. Rogers PJ.Using programme theory for complicated and complex programmes.Evaluation. 2008;14 (1): 29-48.
24. RubiM (2009) The causality principle: complexity is the limit. In: ØstrengW (ed.) Confluence. Interdisciplinary Communications 2007/2008. Oslo: Centre for Advanced Study, 119-22. URL: http://www.cas.uio.no/Publications/Seminar/Confluence_Rubi.pdf.
25. Sampson RJ,Laub J.Seductions of method: rejoinder to Nagin and Tremblay's 'Developmental Trajectory Groups: Fact or Fiction?'.Criminology. 2005;43 (4): 905-14.
26. Schlattman PMedical Applications of Finite Mixture Models. New York: Springer; 2009:.
27. Scott JSeeing Like a State: How Well-Intentioned Efforts to Improve the Human Condition have Failed. New Haven, NJ: Yale University Press; 1998:.
28. Shadish WR,Cook TD,Campbell DTExperimental and Quasi-Experimental Designs for Generalized Causal Inference. New York: Houghton Mifflin; 2002:.
29. Sridharan S,Nakaima A.Ten steps to making evaluations matter.Evaluation and Program Planning. 2011;34 (2): 135-46.
30. Sridharan S,Campbell B,Zinzow H.Developing a stakeholder-driven timeline of change for evaluations of social programs.American Journal of Evaluation. 2006;27 (2): 148-62.
31. Sridharan S,Mayne J,McKellar K,Gauvreau CCanada: International Development Research Centre; 2011:.
32. Woolcock M.Toward a plurality of methods in project evaluation: a contextualized approach to understanding impact trajectories and efficacy.Journal of Development Effectiveness. 2009;1:1-14.