Applied Regression Analysis for Evaluators

Description: Evaluators often face the situation where program outcomes vary across different participants and they want to explain those differences.  To understand the contribution of the program to the outcomes, it is often necessary to control for the influence of other factors.  In these situations, regression analysis is the most widely used statistical tool for evaluators to apply. The objective of this course is to describe and provide hands-on experience in conducting regression analysis, and to aid participants in interpreting regression results in an evaluation context.  The course begins with a review of hypothesis testing (t-tests) and a non-mathematical explanation of how the regression line is computed for bivariate regression.  A major focus is on accurately interpreting regression coefficients and tests of significance, including the slope of the line, the t-statistic, and the statistics that measure how well the regression line fits the data.  Participants will also learn how to find outliers that may be unduly influencing the results.  Participants will have opportunity to estimate multivariate regression models on cross-sectional data; diagnose the results to determine if they may be misleading; and test the effects of program participation with pretest-posttest and posttest-only data.  Regression-based procedures for testing mediated and moderated effects will be covered.  On the third day, students will be given the opportunity to conduct an independent analysis and write-up the findings.  Both peer feedback and instructor feedback will be provided to build skills in interpreting findings and explaining them to interested audiences. Participants will use SPSS software to compute regression analyses and given opportunity to apply it on data from an actual evaluation. Students and instructor will work on interpreting the results and determining how to present them to evaluation audiences. The class will be in a lab where each person has a computer for application of content.  

Instructor: Dr. Gary T. Henry is a professor in the Andrew Young School of Policy Studies at Georgia State University.  He previously served as the Director of Evaluation and Learning Services for the David and Lucile Packard Foundation.  Henry has evaluated a variety of policies and programs, including Georgia's Universal Pre-K, public information campaigns, and the HOPE Scholarship, as well as school reforms and accountability systems.  He served as Director of the Applied Research Center at GSU from 1991 until 2001 and the Director of the Georgia Council for School Performance from 1993-2000.  He is jointly appointed in the Department of Public Administration and Policy Studies and Department of Political Science at Georgia State University.  He is author of Practical Sampling (Sage 1990), Graphing Data (Sage 1995) and co-author of Evaluation: An Integrated Framework for Understanding, Guiding, and Improving Policies and Programs (Jossey-Bass 2000); and has published extensively in the field of evaluation and policy analysis.  In addition, he served as deputy secretary of education for the Commonwealth of Virginia and chief methodologist with the Joint Legislative Audit and Review Commission, Virginia General Assembly.  He received the Evaluation of the Year Award from the American Evaluation Association in 1998 for his work with the Georgia's Council for School Performance and the Joseph S. Wholey Distinguished Scholarship Award in 2001 from the American Society for Public Administration and the Center for Accountability and Performance.

Pre-requisites: Familiarity with the basics of descriptive and inferential statistics, SPSS, and outcome evaluation.  Class materials and a text (to be announced soon) will be provided as a part of the registration fee.

Certificates: CEP IB.f or CAEP IIB.f; and CQEM III.h

Fee:  $1150