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Data collection requires evaluators to consider a wide and
diverse variety of data sources. These include both primary and
secondary data sources. Data collection can significantly impact
your evaluation project. Evaluators sometimes overlook
problems where secondary data is involved and overlook the
fact that data for comparison purposes can affect validity. The
types of data in the Learning Resources are not meant to be
exhaustive but are the most common types in program
evaluation.
To prepare for this Discussion, choose one of the cases
provided, review the specified Learning Resources, and
complete the corresponding prompts.
Case 1 Steven Levitt, the author of Freakonomics, is exemplary
in his ability to make data tell “stories.” Students of public
administration and organizational behavior are familiar with the
famous Hawthorne effect. Levitt and List’s (2009) reanalysis of
the original Hawthorne study (which uses incomplete, archived,
and secondary data) shows how management programs that were
developed to increase worker productivity can be evaluated or
reevaluated, even though the experiments were conducted
almost a century ago. Post by Day 3 a description of at least
two data collection problems that the reanalysis of Levitt and
List might bring to the Hawthorne effect. Respond by Day 5 to
two colleagues and explain whether the problems have to do
with design or data collection. What other data collection
problems could, in your view, also be important? Explain.
Case 2 Barbara Geddes (1990) points out the pitfalls of
selecting cases, units, or observations purely on the basis of the
dependent variable.
Post a brief explanation of the relevance of Barbara Geddes’
argument for program evaluation. Then, explain when a scenario
of choosing cases solely on the dependent variable might be
permissible. Provide a rationale for your explanations.
•Langbein, L. (2012). Public program evaluation: A statistical
guide (2nd ed.). Armonk, NY: ME Sharpe. ◦Chapter 7,
“Designing Useful Surveys for Evaluation” (pp. 209–238)
•McDavid, J. C., Huse, I., & Hawthorn, L. R. L. (2013).
Program evaluation and performance measurement: An
introduction to practice (2nd ed.). Thousand Oaks, CA: Sage.
◦Chapter 4, “Measurement for Program Evaluation and
Performance Monitoring” (pp. 145–185)
•Geddes, B. (1990). How the cases you choose affect the
answers you get: Selection bias in comparative politics.
Political Analysis, 2(1), 131–150. Retrieved from
http://www.uky.edu/~clthyn2/PS671/Geddes_1990PA.pdf
•Levitt, S., & List, J. (2009). Was there really a Hawthorne
effect at the Hawthorne plant? An analysis of the original
illumination experiments. Retrieved from
http://www.nber.org/papers/w15016.pdf
•Urban Institute. (2014). Outcome indicators project. Retrieved
from
http://www.urban.org/center/cnp/projects/outcomeindicators.cf
m
•Bamberger, M. (2010). Reconstructuring baseline data for
impact evaluation and results measurement. Retrieved from
http://siteresources.worldbank.org/INTPOVERTY/Resources/33
5642-1276521901256/premnoteME4.pdf
•Parnaby, P. (2006). Evaluation through surveys [Blog post].
Retrieved from
http://www.idea.org/blog/2006/04/01/evaluation-through-
surveys/
•Rutgers, New Jersey Agricultural Experiment Station. (2014).
Developing a survey instrument. Retrieved from
http://njaes.rutgers.edu/evaluation/resources/survey-
instrument.asp
•MEASURE Evaluation. (n.d.). Secondary analysis of data.
Retrieved February 24, 2015, from
http://www.cpc.unc.edu/measure/our-work/secondary-
analysis/secondary-analysis-of-data
•Zeitlin, A. (2014). Sampling and sample size [PowerPoint
slides]. Retrieved from
http://www.povertyactionlab.org/sites/default/files/2.%20Sampl
ing%20and%20Sample%20Size_AFZ3.pdf

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Data collection requires evaluators to consider a wide and diverse v.docx

  • 1. Data collection requires evaluators to consider a wide and diverse variety of data sources. These include both primary and secondary data sources. Data collection can significantly impact your evaluation project. Evaluators sometimes overlook problems where secondary data is involved and overlook the fact that data for comparison purposes can affect validity. The types of data in the Learning Resources are not meant to be exhaustive but are the most common types in program evaluation. To prepare for this Discussion, choose one of the cases provided, review the specified Learning Resources, and complete the corresponding prompts. Case 1 Steven Levitt, the author of Freakonomics, is exemplary in his ability to make data tell “stories.” Students of public administration and organizational behavior are familiar with the famous Hawthorne effect. Levitt and List’s (2009) reanalysis of the original Hawthorne study (which uses incomplete, archived, and secondary data) shows how management programs that were developed to increase worker productivity can be evaluated or reevaluated, even though the experiments were conducted almost a century ago. Post by Day 3 a description of at least two data collection problems that the reanalysis of Levitt and List might bring to the Hawthorne effect. Respond by Day 5 to two colleagues and explain whether the problems have to do with design or data collection. What other data collection problems could, in your view, also be important? Explain. Case 2 Barbara Geddes (1990) points out the pitfalls of selecting cases, units, or observations purely on the basis of the dependent variable.
  • 2. Post a brief explanation of the relevance of Barbara Geddes’ argument for program evaluation. Then, explain when a scenario of choosing cases solely on the dependent variable might be permissible. Provide a rationale for your explanations. •Langbein, L. (2012). Public program evaluation: A statistical guide (2nd ed.). Armonk, NY: ME Sharpe. ◦Chapter 7, “Designing Useful Surveys for Evaluation” (pp. 209–238) •McDavid, J. C., Huse, I., & Hawthorn, L. R. L. (2013). Program evaluation and performance measurement: An introduction to practice (2nd ed.). Thousand Oaks, CA: Sage. ◦Chapter 4, “Measurement for Program Evaluation and Performance Monitoring” (pp. 145–185) •Geddes, B. (1990). How the cases you choose affect the answers you get: Selection bias in comparative politics. Political Analysis, 2(1), 131–150. Retrieved from http://www.uky.edu/~clthyn2/PS671/Geddes_1990PA.pdf •Levitt, S., & List, J. (2009). Was there really a Hawthorne effect at the Hawthorne plant? An analysis of the original illumination experiments. Retrieved from http://www.nber.org/papers/w15016.pdf •Urban Institute. (2014). Outcome indicators project. Retrieved from http://www.urban.org/center/cnp/projects/outcomeindicators.cf m
  • 3. •Bamberger, M. (2010). Reconstructuring baseline data for impact evaluation and results measurement. Retrieved from http://siteresources.worldbank.org/INTPOVERTY/Resources/33 5642-1276521901256/premnoteME4.pdf •Parnaby, P. (2006). Evaluation through surveys [Blog post]. Retrieved from http://www.idea.org/blog/2006/04/01/evaluation-through- surveys/ •Rutgers, New Jersey Agricultural Experiment Station. (2014). Developing a survey instrument. Retrieved from http://njaes.rutgers.edu/evaluation/resources/survey- instrument.asp •MEASURE Evaluation. (n.d.). Secondary analysis of data. Retrieved February 24, 2015, from http://www.cpc.unc.edu/measure/our-work/secondary- analysis/secondary-analysis-of-data •Zeitlin, A. (2014). Sampling and sample size [PowerPoint slides]. Retrieved from http://www.povertyactionlab.org/sites/default/files/2.%20Sampl ing%20and%20Sample%20Size_AFZ3.pdf