A 2-Day Seminar Taught by David Wilson, Ph.D.
To see a sample of the course materials, click here.
How do you make sense of conflicting results across studies? It is common for a finding that is statistically significant in one study to be statistically nonsignificant in another. Does this reflect differences in the design of the studies? Do the effects actually agree in direction and magnitude even if they are different in statistical significance? What if we have 5 such studies, or 50, or 500? It would be surprising if all such studies were in perfect agreement with one another. Thus, we need a method to make sense of the variability in findings across studies.
Meta-analysis is a statistical solution to this problem and has become a widely-used method for synthesizing results across studies in the social and behavioral sciences and medicine, as well as several physical sciences, such as ecology. The key to meta-analysis is the effect size: it encodes the direction and magnitude of the finding on a common scale. Using specialized statistical methods, the average effect across studies can be estimated as well as an examination of the consistency of the effects. Analyses can also explore potential explanations for inconsistencies in findings, such as theoretically relevant design differences.
This course will provide hands-on instruction in conducting all aspects of a meta-analysis, including the systematic nature of the study search and retrieval process, systematic coding and data extraction, computation of various effect size types, and the estimation of both fixed and random-effects meta-analysis models. Students will also learn both categorical and regression based moderator analysis and methods for exploring publication selection bias. Finally, reporting and presentation of the results of a meta-analysis will be taught.
This seminar will use Stata for the worked exercises. However, no previous experience with Stata is assumed. Exercises can also be completed in SPSS and R (on request). To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer.
Versions 12 through 15 of Stata will work for the course examples and exercises. Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
Who should attend
This course will be helpful for researchers in any field who want to learn how to statistically synthesize results across independent studies using meta-analytic methods. Participants should have a basic working knowledge of introductory statistics through multiple regression analysis. No knowledge of matrix algebra is required or assumed but familiarity with standard statistical notation is useful.
LOCATION, Format, and MATERIALS
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and lodging
The fee of $995 includes all course materials.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $159 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STSH10 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, September 11, 2018.
If you make reservations after the cut-off date, ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.
- Logic of meta-analysis
- Scope and problem formulation
- Computing effect sizes
- Coding studies and database structure
- Fixed-effect meta-analysis
- Random-effects meta-analysis
- Categorical moderator analysis
- Regression based moderator analysis
- Creating forest plots
- Robust standard errors for dependent effect sizes
- Assessing publication bias
- Interpretation and reporting of results
“I started this course with some background in bioinformatics, some understanding of meta-analysis literature in the biomedical field, and what I thought was a goal to learn how to do metadata analysis to strengthen my dissertation. This course is so much more than I hoped! It broke down fundamental concepts and meta-analysis goals, addressed where to start and how to evaluate your questions, but it was also adaptable in context, and I could use almost everything that was taught. This course is invaluable. Highly recommend it for beginners and experts alike.”
Alysha Simmons, Brown University
“Dr. David Wilson is extremely knowledgeable about meta-analysis. The course is great for individuals completely unfamiliar with meta-analysis, as well as for those who are familiar with meta-analysis but who would like a refresher on the topic or to know what the latest recommended meta-analysis techniques are. I highly recommend this course.”
Ginette Blackhart, East Tennessee State University
“Dr. Wilson’s course on MA was very informative. He covered a lot of material clearly and in a detailed fashion despite the limited time available. The course allows someone with no previous knowledge to comfortably work on a meta-analysis project.”
“The course was just what I needed to write a meta-analysis proposal and actually begin to do it. Fun and knowledgeable instructor, just the right level of comprehensiveness and depth. Highly recommend anyone who is interested in meta-analysis to take the course.”
Yibing Li, American Institutes for Research
“Great hands-on experience. Short yet comprehensive.”
“I really enjoyed the hands-on exercises which helped to solidify my understanding of the material.”
Stephanie D’Souza, Johns Hopkins University
“I enjoyed how Dr. Wilson taught the rationale and still made time for in-class exercises. He also was very helpful addressing questions, whether during class or on a break.”
Ronald Hall, Texas Tech University