SSI 2017 New Course Spotlight: Introduction to Meta-Analysis with Tasha Beretvas

SSI 2017 New Course Spotlight: Introduction to Meta-Analysis with Tasha Beretvas

Instructor Biography

Tasha Beretvas is a Professor in the Quantitative Methods program in the Department of Educational Psychology at UT. One facet of her research focuses on methodological challenges in meta-analysis. Tasha was elected to join the Society for Research Synthesis Methodology and in 2017 will being serving as the co-Editor in Chief of the Research Synthesis Methods journal. In addition to her research on meta-analysis, Tasha also teaches a graduate course on Meta-Analysis. Tasha has received multiple teaching awards including UT's Outstanding Graduate Teaching and the UT Regents' Outstanding Undergraduate Teaching Awards.

What is the main goal of this course?

This workshop is designed to help students master the statistical techniques used to conduct quantitative meta-analyses.

What makes you excited about teaching this course?

Methodologists continue to update and innovate the field of quantitative meta-analysis. Given the usefulness of meta-analysis for informing evidence-based practice, I’m eager to help applied researchers learn about how to conduct their own meta-analyses.

How much background knowledge or experience in this subject is required to be able to follow the course material?

Participants are required to have a firm foundation in both correlation and regression techniques and in analysis of variance (ANOVA). While the workshop will not provide and does not require fluency in mathematical derivations, an understanding of core multiple regression content (including dummy and effects coding, interpretation of regression slopes, etc.) and of ANOVA (including interpretation of main and interaction effects) is essential for participants to gain an easy understanding of the meta-regression analyses that we will be conducting. We will be using statistical software for some of the analyses including SPSS and R. Therefore some basic ability with R and SPSS syntax will be helpful although it is not required.

What skills and knowledge can participants expect to acquire by the end of the course?

By the end of the workshop, attendees should hopefully be able to 1) calculate the three most frequently used kinds of effect sizes (standardized mean difference, correlation and log-odds ratio), 2) synthesize (combine) effect size estimates across studies while weighting more heavily estimates that are more precise, 3) explore variability in effect sizes as a function of sample and study characteristics (moderator analyses) using meta-regression analysis techniques, 4) handle methodological dilemmas commonly encountered in real-world meta-analyses (such as selecting and understanding differences between fixed-, random- and mixed-effects models; handling within-study dependence; and assessing and correcting for publication bias). Students will come away knowing how to use the various macros in R and SPSS for conducting meta-regression analyses.

How do you feel about returning to SSI after a break?

I’m looking forward to having the opportunity to return to SSI after so long.

What are you most looking forward to with teaching Introduction to Meta-Analysis?

I always enjoy watching students learn and seeing them develop a deeper understanding of statistical techniques while building their analysis skills.

The Department of Statistics and Data Sciences at The University of Texas at Austin is proud to host its 10th annual UT Summer Statistics Institute (SSI) May 22-25, 2017.

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