| Session Title: How Practitioners and Evaluators Can Use Binary Logistic Regression and Other Methods in a Realist Evaluation of What Interventions Work and in What Circumstances |
| Demonstration Session 556 to be held in Suwannee 12 on Friday, Nov 13, 1:40 PM to 3:10 PM |
| Sponsored by the Social Work TIG |
| Presenter(s): |
| Mansoor Kazi, University at Buffalo - State University of New York, mkazi@buffalo.edu |
| Rachel Ludwig, Chautauqua County Department of Mental Health, mesmerr@co.chautauqua.ny.us |
| Melody Morris, Chautauqua County Department of Mental Health, morrisml@co.chautauqua.ny.us |
| Connie Maples, ICF Macro International, connie.j.maples@orcmacro.com |
| Patricia Brinkman, Chautauqua County Department of Mental Health, brinkmap@co.chautauqua.ny.us |
| Mary McIntosh, University at Buffalo - State University of New York, mary_mcintosh@msn.com |
| Doyle Pruitt, University at Buffalo - State University of New York, dpruitt14@hotmail.com |
| Ya-Ling Chen, University at Buffalo - State University of New York, yc96@buffalo.edu |
| Abstract: This demonstration will illustrate new data analysis tools drawn from both the efficacy and epidemiology traditions to investigate patterns in relation to outcomes, interventions and the contexts of practice. The demonstration will include the use of binary logistic regression models and regression discontinuity designs from real practice examples drawn from the SAMHSA funded System of Care community mental health services for children with serious emotional disturbance and their families in Chautauqua County, New York State. The presenters and facilitators include a combined team of evaluators and system of care project workers who will use datasets from their completed evaluations and discuss real-world applications of the analyses and their contribution to learning from evaluation. The didactic approach will be interactive, guiding the workshop participants through the requirements and limitations of each method, and demonstrating its use from practice examples, e.g. from Department of Mental Health. |