Chen, Ding-Geng (Din)Fraser, Mark W.2019-04-052019-04-052017Chen, D.-G. & Fraser, M.W. 2017, 'A Bayesian perspective on intervention research : using prior information in the development of social and health programs', Journal of the Society for Social Work and Research, vol. 8, no. 3, pp. 441-456.2334-2315 (print)1948-822X (online)10.1086/693432http://hdl.handle.net/2263/68927This paper was presented at the 2017 Annual Meeting of the Society for Social Work and Research in New Orleans, LA.OBJECTIVE : By presenting a simulation study that compares Bayesian and classical frequentist approaches to research design, this paper describes and demonstrates a Bayesian perspective on intervention research. METHOD : Using hypothetical pilot-study data where an effect size of 0.2 had been observed, we designed a 2-arm trial intended to compare an intervention with a control condition (e.g., usual services). We determined the trial sample size by a power analysis with a Type I error probability of 2.5% (1-sided) at 80% power. Following a Monte-Carlo computational algorithm, we simulated 1 million outcomes for this study and then compared the performance of the Bayesian perspective with the performance of the frequentist analytic perspective. Treatment effectiveness was assessed using a frequentist t-test and an empirical Bayesian t-test. Statistical power was calculated as the criterion for comparison of the 2 approaches to analysis. RESULTS : In the simulations, the classical frequentist t-test yielded 80% power as designed. However, the Bayesian approach yielded 92% power. CONCLUSION : Holding sample size constant, a Bayesian analytic approach can improve power in intervention research. A Bayesian approach may also permit smaller samples holding power constant. Using a Bayesian analytic perspective could reduce design demands in the developmental experimentation that typifies intervention research.en© 2018 by the Society for Social Work and Research. All rights reserved.Intervention researcht-testBayesianPrior distributionPosterior distributionStatistical powerMonte-Carlo simulationArticle