The realist evaluation approach (Kazi, 2003) has the central aim of investigating what interventions work and in what circumstances. This approach essentially involves the systematic collection of data on 1) the client circumstances (e.g. demographic characteristics, cultural differences and needs, environments in which people live and function, and the nature of baseline target problems); 2) the dosage, duration and frequency of each intervention in relation to each client; and 3) the changes in the outcomes as observed through the repeated use of reliable outcome measures with each client. This is a mixed methods approach, blending together efficacy research and epidemiology. The research designs fall into place naturally as the practice unfolds, and data analysis methods are applied to investigate the patterns between the client specific factors, the intervention variables, and the outcomes. These methods include the development of binary logistic regression models, hierarchical linear modeling, and regression discontinuity designs. These methods help to link the outcome to the potential causal factors with or without a control group. This evidence provides information about the effectiveness of the models of intervention in terms of what works, for whom and in what contexts, at regular intervals and in real-time to influence policy and practice. Examples will be used from practice in social services in the UK, USA and Finland.
|Keywords:||Realist Evaluation, Social Services, Social Work, Contexts, Interventions, Outcomes, Mixed Methods, Efficacy Research, Epidemiology, Binary Logistic Regression, Hierarchical Linear Modeling, Regression Discontinuity Designs, Effectiveness, UK, USA, Finland|
Director, Program Evaluation Center, School of Social Work, University at Buffalo (The State University of New York), Buffalo, New York, USA
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