PEERS Research Methods Series
PEERS Research Methods Series

The AERA-ICPSR Partnership for Expanding Education Research in STEM (PEERS) Data Hub announces the PEERS Research Methods Series a webinar series of research capacity-building workshops that focus on research methods used in STEM education research. 

The PEERS Research Methods Series consists of workshops presented in collaboration with the 11 Institutes in Research Methods funded by the National Science Foundation  directed to NSF’s Building Capacity for STEM Education Research (BCSER) program. Aimed at early- and mid-career researchers and scholars, the capacity-building webinar series features projects that seek to promote research expertise in applications of computational, quantitative, qualitative, and evaluative research methods useful to STEM education researchers and broaden the STEM professional workforce.

Upcoming Workshop(s)

Title: ICQCM: Shaping Critical Data Science for a Diverse World


  • Odis Johnson Jr., Johns Hopkins University
  • Ezekiel Dixon-Roman, University of Pennsylvania
  • Ebony McGee, Vanderbilt University

Date/ Time:  Thursday, July 15, 2021; 1:00pm-4:00pm


The Institute in Critical Quantitative, Computational, & Mixed Methodologies (ICQCM) aims to advance the presence of scholars of color among those using data science methodologies, and challenge researchers to use those methods in ways that can dismantle the structural barriers to enable human flourishing for underrepresented communities, professionals, and young people. The workshop instructors will discuss and demonstrate how to employ quantitative and computational methods that are situated in context, history, material social relations, and as a product of material and discursive formations. They will explore and provide examples of non-instrumental approaches of quantification that do not essentialize, universalize, or treat data as self-evident, while emphasizing an analytical focus on multiplicity, diversity, and the marginal subject.

Past Workshop(s)

Introduction to Qualitative Meta-Synthesis Methods: Achieving STEM Equity and Inclusion through Syntheses
May 20, 2021; 1:00pm -4:00pm ET


  • Maria Ong, Institute for Meta-Synthesis, TERC
  • Nuria Jaumot-Pascual, Institute for Meta-Synthesis, TERC
  • Lisette Torres-Gerald, Institute for Meta-Synthesis, TERC
  • Christina B. Silva, Institute for Meta-Synthesis, TERC

This workshop is for early- and mid-career faculty, researchers, postdoctoral scholars, and graduate students in STEM education and related disciplines to gain basic skills in qualitative meta-synthesis research. Instructors from the Institute for Meta-Synthesis at TERC will present materials they have developed to build capacity in qualitative literature meta-synthesis methods, with a special focus on STEM equity and inclusion literature. They will introduce several aspects of qualitative meta-syntheses research, including: what differentiates literature meta-syntheses from literature reviews; steps for the pre-search process; literature search and selection processes; deductive, inductive, and hybrid coding; and thematic analysis. The instructors will provide demonstrations and hands-on activities for participants, which will be drawn from the instructors’ previous literature meta-synthesis projects focused on the experiences of women of color in STEM (e.g., see paper here: By the end of the workshop, participants will have the fundamental skills for conducting a literature search and an understanding of how raw qualitative literature can be coded as data, then transformed into the Findings and Discussion of a meta-synthesis paper.

Mentorship & Collaboration in Quantitative Research: The NSF Quantitative Research Methods Scholars Program

March 17, 2021; 1:00pm -4:00pm ET


  • Laura M. Stapleton, University of Maryland, College Park
  • Gregory R. Hancock, Professor, University of Maryland, College Park
  • Kimberly A. Griffin, University of Maryland, College Park

Early career education researchers often enter the academic and professional community without the training in quantitative research methods that allows them to design rigorous impact and evaluation studies and to be competitive to secure federal grant funding. Team science, a movement toward collaborative research among interdisciplinary research groups (Enhancing the Effectiveness of Team Science,, has promise for improving the research success of early career scholars. In this workshop, the instructors present the NSF Quantitative Research Methods Scholars Program, a year-long mentorship and training institute designed after the principles of effective team science that provides quantitative methods training, one-on-one quantitative research mentorship, and facilitated peer-to-peer mentorship for early career researchers.

The instructors will also review strategies for effective peer-collaboration for interdisciplinary research teams, with particular focus on the promises and challenges of collaborations between substantive and quantitative methodological researchers. Workshop instructors will discuss models of mentorship between education researchers and quantitative methodologists and outline ways to construct mentorship relationships to best support the professional development of the mentees. Workshop participants will evaluate their own mentorship and mentee styles and identify the mentorship models most appropriate to their needs. Finally, quantitative mentors and current members of the NSF QRM Scholars Program will share their experiences with the program.

Cutting-Edge Quantitative and Computational Methods for STEM Education Research
February 25, 2021; 1:00pm -3:30pm ET


  • Kenneth Frank, Michigan State University
  • Guanglei Hong, University of Chicago
  • Stephen Raudenbush, University of Chicago
  • Yanyan Sheng, University of Chicago
  • Kaitlin Torphy, Michigan State University


The overall goal of this webinar is to inform participants of a wide range of significant research questions, data structures, and advanced analytic techniques in the context of theory-driven and data-informed rigorous empirical investigations of STEM education, especially concerning under-represented groups. Cutting-edge methods are essential to study student and teacher experiences with STEM education programs developed, implemented, and evaluated in a complex environment that outstrips what can be rendered by conventional statistical techniques. To illustrate major methodological considerations, instructors will use a stylized case that evaluates the potentially differential impacts of curricular innovations representing the Next Generation Scientific Standards (NGSS) on instructional practices, student engagement, and science achievement. Key methodological issues will be discussed in six inter-related modules:

(1) Design: How to select the sample of schools and teachers and whether to adopt an experimental or a quasi-experimental design suitable for causal inference of the effects of the curricular innovation.
(2) Measurement: How to construct theoretically grounded instruments with strong psychometric properties to measure student engagement, student learning, teacher practices, etc.
(3) Social network analysis: How to represent and model teachers’ interactions with one another as they adapt and implement the new curriculum.
(4) Multilevel modeling: How to represent and model the student, teacher, and school level factors that affect the implementation and outcomes of the curriculum.
(5) Causal mediation analysis: How to examine instructional practices as a mediator of the effects of the curriculum on student outcomes.
(6) Computational methods: How to account for teachers’ and students’ engagement with one another and educational resources on-line.

Each module will consist of a 10-20 minute presentation followed by 5 minutes of Q&A. Some of the instructors will remain after the session for further conversation. Participants are encouraged to visit for information about a 3-year training initiative led by the team of instructors.

Modern Meta-Analysis Research
February 10, 2021; 1:00pm -3:30pm ET


  • Terri Pigott, Georgia State University
  • Joshua Polanin, American Institutes for Research
  • Ryan Williams, American Institutes for Research


In this workshop, instructors will introduce participants to basic principles of meta-analysis in the context of systematic reviews for STEM education. The instructors will provide a brief overview of the steps of a systematic review using examples from published STEM education meta-analyses. The workshop will focus on best practice methods for meta-analysis, the synthesis of results from quantitative studies. Instructors will introduce effect sizes typically used in STEM systematic reviews including the standardized mean difference and correlations. Participants will practice extracting information needed for effect sizes from STEM research studies. Instructors will use the R program, metafor, for computing effect sizes. Instructors will also introduce basic meta-analysis techniques for estimating the mean effect size from a set of studies and for exploring effect size heterogeneity across studies. Participants will use metafor to compute the mean effect size and its heterogeneity across a set of studies. The workshop will use small data sets derived from published STEM education meta-analyses to demonstrate the method.

The PEERS Data Hub is joint effort of AERA and the Inter-University Consortium for Political and Social Research (ICPSR) that is NSF funded (Award ECR 1937612). It provides collaborative space for STEM education research communities to build and advance knowledge by sharing innovative ideas, methods, and tools. Further information about the PEERS Data Hub is available here.

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