TRANSLATING EVIDENCE

What is the best approach to convey evidence to a diverse audience of consumers? This is the fundamental question for the translation of evidence. Evidence should be useful for researchers, but also for policymakers and practitioners, and methods and tools appropriate for one audience may not be optimal for another. The STEPP Center endeavors to facilitate research designed to determine best practices for reporting effects and conveying uncertainty visually, in documents, webtools, and software.

The center is beginning to engage in research on how statistical summaries in research reviews are understood by users. Research has involved how users interpret effect sizes representing program impacts, understand different ways of representing uncertainty, interpret heterogeneity of findings, and standards for reporting research.

To make these methods more accessible to researchers the Center provides tutorial papers, online tools and resources including working papers, seminars and short courses that train students and practitioners, and professional development institutes for established researchers.

Translating Evidence Research

Interpretation of Effect Sizes

 

Hedges, L. V. & Olkin, I. (2016). Overlap between treatment and control group distributions of an experiment as an effect size measure. Psychological Methods, 21, 61-68. DOI:10.1037/met0000042.

Hedges. L. V. (2008). What are effect sizes and why do we need them? Developmental Psychology Perspectives, 2, 167-171. DOI:10.1111/j.1750-8606.2008.00060.x.

Konstantopoulos, S. & Hedges, L. V. (2008). How large an effect can we expect from school reforms? Teachers College Record, 110, 1613-1640. TCID:15151.

Representing Uncertainty

 

Kim, Y.S., Wallis, L. A., Krafft, P, & Hullman, J. (2019). A Bayesian cognition approach to improve data visualization. ACM Human Factors in Computing Systems (CHI) 2019. DOI:10.1145/3290605.3300912.

Phelan, C., Hullman, J., Kay, M., & Resnick, P. (2019). Some prior(s) experience necessary. ACM Human Factors in Computing Systems (CHI) 2019. DOI:10.1145/3290605.3300709.

Hullman, J., Resnick, P., & Adar, E. (2015). Hypothetical outcome plots outperform error bars and violin plots for inferences about reliability of variable ordering. PLOS ONE, e0142444. DOI:10.1371/journal.pone.0142444.

Representing Heterogeneity of Findings

 

Hedges, L. V. & Schauer, J. M. (2019). Statistical analyses for studying replication: Meta-analytic perspectives. To appear in Psychological Methods. DOI:10.1037/met0000189.

Borenstein, M., Higgins. J. P T., Rothstein, H. R., & Hedges, L. V. (2017). I2 is not an absolute measure of heterogeneity in a meta-analysis. Journal of Research Synthesis Methods, 8, 5-18. DOI:10.1002/jrsm.1230.

Standards for Reporting Research

 

Grant, S., Mayo-Wilson, E., Montgomery, P., MacDonald, G., Michie, S., Hopewell, S., & Moher, D. (2018). CONSORT-SPI 2018 explanation and elaboration: Guidance for reporting social and psychological intervention trials. Trials, 19(1). 406. DOI:10.1186/s13063-018-2735-z.