Introduction to Meta-Analysis

Self-paced

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SHORT COURSE DESCRIPTION

Participants will gain all the tools necessary to conduct a high quality systematic review and meta-analysis. Although these procedures can be found in numerous textbooks, the latest techniques and technical tools often require nuanced and sophisticated applications of numerous online tools and applications. This course will focus on the hands-on, practical, and applied approach to conducting reviews by combining lectures with practice material designed to enable participants to conduct future meta-analyses. Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.

DATES AND TIMES

March 1-2, 2024 (Fri-Sat)

10am-5pm Eastern Standard Time (UTC-5)

The instructor will determine timing of lunch break, as well as morning and afternoon breaks.

COURSE FEES

Professional: $375

Full-time student*: $195

*Full-time students need to submit student status proof at https://go.umd.edu/CILVR-STUDENT-23 to request a discount code prior to registration.

*Course fee will be waived for HDQM Department faculty and degree-seeking students, although the UMD IT department will charge you a tech fee to register ($10). HDQM department registrants can request the discount code by submitting the following form: https://go.umd.edu/CILVR-HDQM-23.

COURSE TOPICS

Course topics include:

Understanding the purpose and goals of systematic review and meta-analysis
Conducting a systematic, comprehensive literature search and screening
Extracting information from primary studies efficiently and reliably
Calculating effect sizes and variances
Performing a synthesis and moderator analyses using R
Disseminating the results of a systematic review and meta-analysis.

SCOPE OF SHORT COURSE

Systematic review and meta-analysis are techniques used to synthesize and summarize large bodies of research literature. Compared to results from a single primary study, results from a meta-analysis provide greater generalizability, increased precision, and the ability to explore heterogeneity across studies (Borenstein, Hedges, Higgins, & Rothstein, 2010; Pigott, 2012). Meta-analysis has proven useful for policy makers and practitioners because the findings offer answers to ambiguous questions and synthesize large bodies of literature. As a result, published meta-analyses have increased exponentially over the past three decades (Williams, 2012).

TARGET AUDIENCE

The target audience for this course is any individual with an intermediate knowledge of statistical analyses who seeks to conduct or understand a systematic review and meta-analysis. This population includes all level of graduate students, assuming a basic knowledge of research design, statistical analysis, and experience conducting literature reviews. Researchers working within academic institutions or research firms are the ideal audience. Other individuals may benefit from the course as well, especially if their work focuses on summarizing large bodies of evidence.

REQUISITE KNOWLEDGE

Required:

Intermediate proficiency in online database searches (e.g., PsycInfo, ERIC, etc.)
Intermediate proficiency in a statistical programming language (e.g., SPSS, STATA, SAS, R)
Limited experience in R; this course will use R exclusively, but a course section will be provided on the basics
Intermediate proficiency in inferential statistics and multiple linear regression

Not required but advantageous:

Conducting a systematic search
Writing statistical scripts to perform statistical modeling
Understanding of the reporting of systematic reviews and meta-analyses

No level of proficiency beyond basic awareness is assumed for skills related to:

Meta-Analysis
Publication Bias
Moderator analyses

SOFTWARE

There are two software that will be used in this course. The first is MetaReviewer and the second is R/RStudio. MetaReviewer is a free, relatively new, meta-analysis software by AIR. You will need to request access and register for this software prior to the start of the workshop. R/RStudio will also be used in this course. The open-source R software can be downloaded here: https://www.r-project.org/. It is recommended that participants attend the course having downloaded the most recent version of R on their laptop.

LOCATION AND PLATFORM

The course materials and meeting links will be posted on the course page through UMD Open Learning (https://umd.catalog.instructure.com/)
This workshop will be delivered entirely online via the video conferencing software Zoom (https://zoom.us/).
Within a limited time, the video recordings of the short course will be accessible for both synchronous and asynchronous participants on the course page.

IMPORTANT COURSE DETAILS

Platform: Participants need to have a valid UMD associate ID in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details please click here.

Format: Participants will receive a personalized login code to use on their own computer to access a reliable live-stream of the short course over Zoom, showing the instructor as well as the handouts.

Materials: Participants will receive electronic copies of the short course materials, as well as any other relevant materials or information.

Timing/access: Participants may choose to watch the stream synchronously, or may elect to watch a recording of the short course asynchronously, or both. Recordings will be available to participants for six months following the end of the short course. This is especially useful for on-line participants in different time zones who may choose to watch at some later time than (but within six months of) the actual short course time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).

Technical support: Participants are responsible for installing the conferencing software Zoom on their own electronic devices and for obtaining a Zoom account that allows the participant to join Zoom meetings and webinars hosted by external organizations. Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection. Challenges at the user end must be resolved by the user. Fortunately, because the short course is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they have access.

Content support: During the lecture, real-time content support for on-line participants is mostly limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. Participants may have direct interactions with the instructor in some format during the practice sessions. On-line participants may e-mail the instructor for further content support that cannot be addressed in real-time.

THE INSTRUCTOR

Dr. Joshua R. Polanin is a Principal Researcher at the American Institutes for Research, a social science research firm that specializes in education program evaluation, systematic reviews, and technical assistance. Dr. Polanin has extensive expertise leading or co-leading meta-analyses, including (1) a meta-analysis of variation in science education effect sizes (Taylor et al., 2018), (2) an Institute of Education Sciences (IES) grant to meta-analyze the effects of college financial aid programs (LaSota, Polanin, & Perna, 2019), and (3) two National Institute of Justice grants to meta-analyze the effects of cyberbullying prevention programs (Polanin, Espelage, & Grotpeter, 2019a) as well as the relations between school violence on academic, social, and behavioral outcomes (Polanin, Espelage, & Grotpeter, 2019b). Dr. Polanin trains researchers and professors across the nation on meta-analysis best practices. He is co-PI and co-instructor of the IES-funded Meta-Analysis Training Institute workshop and of the NSF-funded Modern Meta-Analysis Research Institute. He has an extensive peer-reviewed publication track record in meta-analysis methods (Polanin, Pigott, Espelage, & Grotpeter, 2019), publication bias (Polanin, Tanner-Smith, & Hennessy, 2016), and transparency and reproducibility (Polanin & Terzian, 2019). Dr. Joshua Polanin can be reached at jpolanin@air.org.

REFUND POLICY

Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.

CONTACT

For any further questions, please contact the CILVR team at meta.cilvr@gmail.com.

To request a copy of the payment receipt, please contact the OES office at oes-finance@umd.edu.

CILVR Short Course Series

Center for Integrated Latent Variable Research (CILVR) at the University of Maryland (UMD)

CILVR is a center whose goal is to serve as a national and international focal point for innovative collaboration, state-of-the-art training, and scholarly dissemination as they relate to the full spectrum of latent variable statistical methods. CILVR is housed within the Quantitative Methodology: Measurement and Statistics (QMMS) program in the Department of Human Development and Quantitative Methodology at the University of Maryland. QMMS faculty are recognized scholars in various facets of latent variable statistical models, whether it be item response theory, latent class analysis, mixture models, latent growth models, or structural equation modeling.

Full course description