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AERA has announced a robust program of professional development courses for the 2024 AERA Annual Meeting in Philadelphia. The courses cover salient topics in education research design; quantitative, qualitative, and mixed research methods; meta-analysis; and other data analysis techniques. The courses begin on Wednesday, April 10, one day prior to the start of the Annual Meeting. Led by an expert faculty of researchers and scholars, these courses are designed at various levels to reach graduate students, early career scholars, faculty, and other education researchers working in public, non-profit, and private sector settings.
Professional development courses can be added at registration under "Optional Events." Early registration is encouraged as space is limited. Questions about the courses should be directed to profdevel@aera.net.
Courses can be added to existing Annual Meeting registrations in the My AERA section of the AERA website.
Click the link below to jump to each course. Courses are listed in chronological order. Full-Day Courses
Half-Day Courses
This one-day course will introduce the basics of systematic review and meta-analysis. Topics covered include developing a research question, searching the literature, evaluating and coding studies, conducting a meta-analysis, and interpreting results for various stakeholders. Participants are encouraged to bring an idea for a systematic review to the course, with time reserved for discussion about it with course instructors. Course activities will include lecture, hands-on exercises, small group discussion, and individual consultation. The target audience includes both those new to systematic review and meta-analysis as well as those currently conducting either type of project. Knowledge of basic descriptive statistics is assumed. Participants are required to bring a laptop computer.
Data from international large-scale assessments (ILSAs) reflect the nested structure of education systems and are, therefore, well suited to multilevel modeling (MLM). However, because these data come from complex cluster samples, there are methodological aspects that researchers need to understand when using MLM, including the need to use sampling weights and multiple achievement values for accurate parameter and standard error estimation. Using recently released ILSA data from TIMSS 2019, this course will teach participants how to conduct MLM, with an emphasis on the assessment design features of ILSAs (e.g., TIMSS, PIRLS, and PISA) and implications for MLM analysis.
Participants will learn how to specify two-level models using HLM software as well as about model comparison, centering decisions and their consequences, and the resources available for doing three-level models. Time will be allotted for hands-on exercises, with instructors available to mentor and answer participants' questions. Step-by-step demonstrations of each practice item will also be provided. Interactive activities will provide research networking opportunities throughout the course. Participants should have a solid understanding of Ordinary Least Squares (OLS) regression and a basic understanding of MLM. Prior experience using a statistical software program, such as Stata, R, or SPSS, is helpful. Prior knowledge of ILSAs—and prior experience using their databases or HLM—is not required. So that they can fully participate in the hands-on exercises, participants without an HLM8 license will be informed prior to the course how to temporarily access HLM8, which works in Windows and Parallels Desktop on Macs.
The National Center for Education Statistics (NCES) collects data on teachers, principals, and schools through various nationally representative surveys, such as the Teaching and Learning International Survey (TALIS), an international survey of lower secondary teachers and principals; and the National Teacher and Principal Survey (NTPS), a primary source of information about K–12 schools in the U.S. from the perspective of teachers and principals. While these large-scale surveys provide rich and reliable information, analyzing them may be overwhelming to potential users for various reasons, such as the large size of the files, which include thousands of variables; and the comprehensive technical documentation and analysis procedures that must be considered due to the surveys’ complex sample designs.
The purpose of this 4-hour mini-course is to familiarize participants with TALIS and NTPS and to help them conduct analyses with these large-scale datasets without having to use microdata files. It is designed for researchers, school administrators, teachers, and policymakers interested in international and national data on teachers, principals, or schools. It can be delivered virtually, if needed.
The course begins with an overview of TALIS and NTPS. Next, participants are instructed on how to use NCES’s two free online data tools—the International Data Explorer (IDE) for TALIS and DataLab for NTPS—to generate descriptive statistics, perform regression analysis, and interpret the results. Finally, using their own research questions, participants gain hands-on experience with the tools. No prerequisite skills or knowledge are needed for this course. Participants should bring their own laptops.
Labeling or classifying textual data is an expensive and consequential challenge for Mixed Methods and Qualitative researchers. The rigor and consistency behind the construction of these labels may ultimately shape research findings and conclusions. A methodological conundrum to address this challenge is the need for human reasoning for classification that leads to deeper and more nuanced understandings, but at the same time manual human classification comes with the well-documented increase in classification inconsistencies and errors, particularly when dealing with vast amounts of texts and teams of coders.
This course offers an analytic framework designed to leverage the power of machine learning to classify textual data while also leveraging the importance of human reasoning in this classification process. This framework was designed to mirror as close as possible the line-by-line coding employed in manual code identification, but relying instead on latent Dirichlet allocation, text mining, MCMC, Gibbs sampling and data retrieval and visualization. A set of output provides complete transparency of the classification process and aids to recreate the contextualized meanings embedded in the original texts.
In the pursuit of truly expanding access to data science, advance visualization tools, and machine learning to non-programmers, this analytic framework has been packaged in an open-access software application and is the second product of the analytic movement "Democratizing Data Science."
This course promotes a discussion about the basis of evidence for educational decisions of policy and practice. By imagining the conditions necessary to change an inference in the concrete terms of how different the data would have to be or the properties of an omitted variable, this course can inform scientific discourse from a broad set of stakeholders familiar with conventional statistical techniques. Furthermore, the techniques make explicit the role of the researcher in choosing how to estimate and interpret quantitative analysis, and thus careful examination of the positionality of researchers and consumers of quantitative research. Specifically, those challenging racial injustice can engage in discourse on equal footing with those who have historically used quantitative analysis to enforce racial inequities.
In part I we will use Rubin’s causal model to interpret how much bias there must be to invalidate an inference in terms of replacing observed cases with counterfactual cases or cases from an unsampled population. This supports statements such as “XX% of the cases would have to be due to bias to invalidate the inference” In part II, we will quantify the robustness of causal inferences in terms of correlations associated with unobserved variables or in unsampled populations. This supports statements such as “an omitted variable would have to be correlated at qqq with both the predictor and outcome to change the inference.” Calculations for bivariate and multivariate analysis will be presented in the app: http://konfound-it.com as well as Stata and R.
This course will introduce the unique design features of the National Assessment of Educational Progress (NAEP) data to researchers and provide guidance in the required data analysis strategies, including the selection and use of appropriate plausible values, sampling weights, variance estimation procedures (i.e., jackknife approaches) and the generative AI chatbot specifically designed for NAEP data analysis. The course will provide participants with hands-on practice training in analyzing public-use NAEP data files using the R package EdSurvey, which is developed for analyzing national and international large-scale assessment data with complex psychometric and sampling designs, and generative AI chatbot, which is fine-tuned with NCES-approved materials, such as technical documentation on the web, published technical memos and reports. Participants will learn how to perform the following:
The knowledge and analytic approach from this course can be applied to analyzing other large-scale national and international data with plausible values, such as PISA, TIMSS, PIRLS.
This course is designed for individuals in government, universities, private sectors, and nonprofit organizations interested in analyzing large-scale assessment data. Participants should have at least basic knowledge/skills of R software, statistical inference, and multiple regression. Working knowledge of Item Response Theory and sampling theory is preferred. Participants must have a computer preloaded with the latest R and RStudio software version to participate in the hands-on portion.
This course will provide instruction on writing a successful grant application to the two primary research grant programs of the Institute of Education Sciences: the Education Research Grants Program (ALN 84.305A) and the Special Education Research Grants Program (ALN 84.324A). The course will discuss and provide examples on:
In addition, participants will be asked to bring initial outlines and drafts of key sections of their applications and will work in small groups to revise them based on instructor and participant feedback.
Participants should review the requirements and recommendations described in the Requests for Applications for the Education Research Grants Program (ALN 84.305A) or the Special Education Research Grants Program (ALN 84.324A). Participants should develop an initial research idea that will fall within a specific research topic and project type within one of these two grant programs and begin developing key sections of their application.
This course will introduce advanced methods in meta-analysis. Topics covered include models for handling multiple effect sizes per study (dependent effect sizes) and exploring heterogeneity, the use of meta-analysis structural equation modeling (MASEM), and an introduction to single-case experimental design meta-analysis. The statistical package R will be used to conduct the statistical techniques discussed. Participants are encouraged to bring their own research in progress to the course. The activities will include lecture, hands-on exercises, and individual consultation. This course is designed to follow the introduction to systematic review and meta-analysis course given in prior AERA Professional Development training sessions. The target audience are those researchers with systematic review and meta-analysis experience, but who are interested in learning advanced methods for meta-analysis. Knowledge of basic descriptive statistics, systematic review, and basic meta-analysis is assumed. Students are required to bring a laptop computer.
The conceptual framework is the investigative architecture for a qualitative research study that includes three components: the theoretical, methodological, and epistemological. This four-hour mini-course provides an overview of qualitative research design with the conceptual framework as its template. Participants will explore the development of a hypothetical study about identity (e.g., teacher identity, student identity, gender identity, intersectional identity, and so on), using a three-part template to compose their theoretical (literature review), methodological (genre of qualitative research and research questions), and epistemological (data generation and analysis) premises. The conceptual framework is then plotted as a visual diagram with an accompanying narrative to illustrate initial entrée into the study.
Participants will explore these course activities and objectives:
The course is targeted to graduate students and novices to qualitative research. Qualitative research instructors may also find utility with the course to experience new pedagogical methods with their students. No pre-course assignments or special materials are needed for this course. A hard copy workbook will be provided for handwritten analytic exercises. Participants may wish (but are not required) to bring a personal laptop for selected activities.
The purpose of this course is to support emerging and experienced qualitative researchers in developing skills for using analytic tools within narrative research projects. The tools taught include autobiographical narrative research, tools for multicultural studies, narrative beginnings, narrative music analysis, literary analysis, use of narrative vignettes with large data sets, memory work, and using visuals. The goal of the course is for researchers to develop versatility and strength in analytic skills that will enable them to produce nuanced and trustworthy findings. The course begins with a brief presentation about types of narrative research and each presenters giving a brief description of their session. The course contains four 45-minute rounds focused on tools for narrative analysis. Participants will learn the theoretical underpinnings of the analytic tools in narrative research, gain hands-on experiences using these tools, and receive guidance in developing skill through interaction with experienced, published scholars who engage in narrative research.
The purpose of this course is to introduce and train researchers and evaluators on the design and analysis of partially nested structures probing main, mediation, and moderation effects. Partially nested structures arise when the treatment and control conditions maintain different multilevel or hierarchical structures or forms of nesting. In many intervention studies, for instance, the treatment condition induces a form of nesting or clustering that does not naturally exist in the control condition (e.g., attending summer school introduces dependence or nesting among students in the same summer school classroom whereas students who do not participate in summer school remain unclustered). The course focuses on the conceptual logic and mechanics of partially nested studies and train participants in how to design, analyze and leverage partially nested studies to detect main, mediation, and moderation effects. Analyses and example code will be demonstrated in the statistical software R and in simple to use Shiny Apps. The course will combine lecture with hands-on practice with the free software programs. The target audience includes researchers and evaluators interested in learning about the flexibility of partially nested designs and those interested in planning and analyzing them to detect different types of effects. Participants should bring a laptop to the session.