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Date: 22/10/2015
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Project Details
Project Status: Completed
This work has been completed by: Topwrite
Total payment made for this project was: $15.00
Project Summary: Techniques of Data Analysis You have now reached the point in your Final Project where you can begin to think about the methods you will employ to examine your data. A useful approach is often to visualize how your data might look in matrix form. Your choice of analysis techniques will depend on the type of data you have collected, as well as your evaluation design: whether you are using an experimental, a quasi experimental, or nonexperimental design. Evaluators also use tools such as significance tests to support or reject claims based on the sample data. Further, multivariate analysis techniques, like regression, are often utilized to take into account more than one statistical outcome variable at a time. To prepare for this Assignment, consider techniques of data analysis you have encountered in your course work and professional experience. QUESTION: Submit a 1- to 2-page paper that addresses the following: Explain whether you will be using significance tests or multivariate techniques such as regression in your Final Project. Explain your reasoning. Explain the considerations you have made relative to techniques of analysis. _________________________________________________________________________________ Analysis of Quantitative Data in Program Evaluation Introduction Assume that you have implemented your quantitative evaluation design, collected the relevant data, and you are now ready to answer questions about the effectiveness of your program. What techniques of analysis will you employ to answer the questions at the heart of your evaluation? You may or may not necessarily have a strong quantitative background or may be out of practice in statistical methods. Despite this, you realize you have to make the most out of the data you have collected. This week, you examine the logic employed in program evaluation to select appropriate methods of data analysis. Learning Objectives Students will: Analyze data to evaluate program effectiveness Analyze multivariate techniques of data analysis Analyze considerations relative to techniques of data analysis _____________________________________________________________________________________ Required Resources Readings Langbein, L. (2012). Public program evaluation: A statistical guide (2nd ed.). Armonk, NY: ME Sharpe. o Chapter 6, The Nonexperimental Design: Variations on the Multiple Regression Theme (pp. 143208) 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. o Chapter 7, Concepts and Issues in Economic Evaluation (pp. 271308) Spiers, N., Manktelow, B., & Hewitt, M. J. (2009). Practical statistics using SPSS. Retrieved fromhttp://www.rds-yh.nihr.ac.uk/wp-content/uploads/2013/05/13_Practical_Statistics_Using_SPSS_Revision_2009.pdf Walter, S. J. (2009). Using statistics in research. Retrieved from http://www.rds-yh.nihr.ac.uk/wp-content/uploads/2013/05/14_Using_Statistics_in_Research_Revision_2009.pdf Institute for Digital Research and Education (IDRE). (n.d.). What statistical analysis should I use?Retrieved January 5, 2015, from http://www.ats.ucla.edu/stat/spss/whatstat/ Optional Resources Blank, R. M. (2002). Evaluating welfare reform in the United States. Journal of Economic Literature, 40(4), 11051166. Lance, P., Guilkey, D., Hattori, A., & Angeles, G. (2014). How do we know if a program made a difference? A guide to statistical methods for program impact evaluation. Retrieved fromhttp://www.cpc.unc.edu/measure/publications/ms-14-87-en Evans, W. N., Farrelly, M. C., & Montgomery, E. (1999). Do workplace smoking bans reduce smoking?American Economic Review, 89(4), 728747.