​In 2017, the National Assessment of Educational Progress (NAEP) began its official transition to Digitally Based Assessment (DBA) format. The use of DBAs has enabled the recording of students’ interaction with assessment items (e.g., time on task, number of visits, response changes, interactions with graphics or interactive components), as well as with the test interface (use of support functions like drawing, etc.). Course participants will learn how to analyze NAEP Process Data using a process mining framework to understand students’ processes during the assessment. The course is designed for participants interested in using process data. The course is aimed at those with novice to advanced experience working with process data and a solid understanding of coding in R. Attendees will learn about NAEP assessment features, data manipulation and cleaning, sequence formation, which kinds of research questions can be addressed, and the analytic methods used in process mining approaches, specifically a) sequence clustering methods, b) business process mining algorithms, and c) natural language processing approaches.

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 workshop. 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 by the instructors in prior AERA Professional Development training sessions. The target audience is 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. Course Instructors Terri Pigott, Georgia State University Ryan Williams, American Institutes for Research Tasha Beretvas, The University of Texas at Austin Wim Van Den Noortgate, Katholieke Universiteit Leuven

This course will introduce the unique design features of the National Assessment of Educational Progress (NAEP) and TIMSS data to researchers and provide guidance in data analysis strategies that they require, including the selection and use of appropriate plausible values, sampling weights, and variance estimation procedures (i.e., jackknife approaches). The course will provide participants with hands-on practice training in analyzing public-use NAEP and TIMSS data files using the R package EdSurvey, which was developed for analyzing national and international large-scale assessment data with complex psychometric and sampling designs. Participants will learn how to perform: • data process and manipulation, • descriptive statistics • cross tabulation and plausible value means, and • linear and logistic regression The knowledge and analytic approach learned from this course can be applied to analyzing other large-scale national and international data with plausible values. This course is designed for individuals in government, universities, private sector, and nonprofit organizations who are interested in learning how to analyze large-scale assessment data with plausible values. Participants should have at least basic knowledge of R software (e.g., took an entry level training on R programming) as well as statistical techniques including statistical inference and multiple regression. Having working knowledge of Item Response Theory and sampling theory is preferred. Participants need to have a computer preloaded with the latest version of the R and RStudio software to participate in the hands-on portion. Course Instructors Emmanuel Sikali, U.S. Department of Education, National Center for Education Statistics Paul Bailey, American Institutes for Research Ting Zhang, American Institutes for Research Michael Lee, American Institutes for Research Eric Buehler, American Institutes for Research Martin Hooper, American Institutes for Research

The purpose of this four-hour -course is to survey how qualitative data can be analyzed inductively through three different methods from the canon of qualitative inquiry heuristics: 1) codes and categories; 2) thematic analysis; and 3) assertion development. Participants will explore these methods by analyzing authentic data sets. The first is in vivo coding and categorizing an interview excerpt of a teacher’s ways of working with her students. The second is thematic analysis of a teacher’s narrative about her relationships with students. The third is the development of interpretive assertions about an ethical dilemma in psychological research. Additional workshop topics include writing analytic memos, constructing diagrams and matrices, and poetic inquiry. Participants will explore these course activities and objectives: 1. differentiate the following terms: qualitative data analysis, pattern, code, category, theme, theoretical construct, assertion, inference-making, vignette 2. code and categorize an interview transcript except 3. analyze an interview transcript excerpt thematically 4. develop interpretive assertions about a dialogic encounter over research ethics 5. write short analytic memos 6. construct a process diagram 7. compose a found data poem The workshop is targeted to graduate students and novices to qualitative research. Qualitative research instructors may also find utility with the workshop to experience new pedagogical methods with their students. No pre-course assignments or special materials are needed for this course.

​Recent developments in qualitative research include increasing analysis of multimodality. This course introduces scholars to multimodal analysis via social semiotics using diverse perspectives from multimodality and narrative, frame analysis, and nexus analysis. Course objectives include introduction to social semiotics and multimodality, basic techniques in analysis, and considerations of the role of theory. The target audience is graduate students, early career scholars, and advanced researchers who may have limited knowledge of multimodality and social semiotics and seek to learn about theories and analysis related to multimodality.

The purpose of this workshop is to train researchers and evaluators how to plan efficient and effective cluster and multisite randomized studies that probe hypotheses concerning main effects, mediation, and moderation. We focus on the conceptual logic and mechanics of multilevel studies and train participants in how to plan cluster and multisite randomized studies with adequate power to detect multilevel mediation, moderation, and main effects. We introduce participants to the free PowerUp! software programs designed to estimate the statistical power to detect mediation, moderation, and main effects across a wide range of designs. The workshop will combine lecture with hands-on practice with the free software programs. The target audience includes researchers and evaluators interested in planning and conducting multilevel studies that investigate mediation, moderation, or main effects. Participants should bring a laptop to the session.

​This course will introduce the unique design features of large-scale assessment data and provide guidance in data analysis strategies, including the selection and use of appropriate plausible values, sampling weights, and variance estimation procedures (i.e., jackknife approaches). The course will provide participants with training virtually in analyzing public-use NAEP or TIMSS data files using the R package EdSurvey, which was developed for analyzing national and international large-scale assessment data with complex psychometric and sampling designs.

​This interactive training course will introduce the concepts of unidimensional IRT models and provide instruction, demonstration, and hands-on opportunities of using the free R software to estimate commonly used IRT models. Participants will receive a discount code for Using R for Item Response Theory Model Applications, written by the course instructors.

Appropriate for graduate students and seasoned academics, this hands-on course will be a straightforward guide to helping participants begin to understand and overcome the psychological, emotional, and logistical hurdles that can get in their way of being productive writers. Specifically, this course will intertwine a discussion of the research underlying the ways academic writers often sabotage their success with practical strategies designed to help session participants build a healthier relationship with writing to ultimately write more with less pain.

​The knowledge-based benefits resulting from the use of artificial intelligence, machine learning, and data science and visualization tools in education research remain conditioned on computer programing expertise. Democratizing Data Science (DDS), a new data analytics movement, frees these benefits by lifting computer programming restrictions and offering open software access to conduct qualitative and mixed method research. This course constitutes the first product released as part of the mission of DDS.