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This one-day course will introduce the basics of  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 the most recently released ILSA data, from TIMSS 2019, this course will teach participants how to conduct MLM. Although the course will include a basic overview of MLM, it will emphasize the assessment design features of ILSAs (TIMSS, PIRLS, and PISA) and implications for MLM analysis. Participants will learn how to specify two-level models using HLM 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 questions for participants. Step-by-step demonstrations of each practice item will also be provided. 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 purpose of this course 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 course 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 highlights digital tools and qualitative analysis software integral to helping researchers organize and manage large qualitative data analysis (QDA) projects. This course, divided into two modules, is designed to facilitate participants’ fundamental knowledge of conducting a systemic literature review and initial QDA. While NVivo® is the primary analysis software for this course, participants will learn the usefulness of Transana, Citavi, and other digital tools. This course is designed as an interactive lecture and consultation—exploring how digital tools expedites literature reviews and truncates large qualitative datasets. Upon completion of this course, participants would have gained applicable knowledge on:

1. The basic features of NVivoâ and Transana.
2. How to expedite a literature review.
3. How to create their research design framework.
4. How to conduct and interpret initial QDA through coding techniques and visualizations.

Participants should have moderate to advanced experience in qualitative research. Participants will need access to a laptop with at least the NVivoâ and Transana free trial version software. Additionally, participants should be equipped with a literature database (i.e., research manuscripts downloaded on laptops and ready to be uploaded) and at least preliminary units of analysis (e.g., transcribed interviews, documents, etc.). No prior experience with qualitative software is necessary. Instructors will guide participants through the essential elements of multiple digital tools. This course is appropriate for graduate students, early-career scholars, and advanced researchers desiring to expand their knowledge of QDA.

Learning Analytics, as a computational research methodology, increases the capacity to understand and improve STEM learning and learning environments through the use of new sources of data and powerful analytical approaches. Learning Analytics is a relatively new, rapidly growing field with significant potential to improve digital learning environments. To address the need for trained researchers on a much broader scale, instructors will focus on Learning Analytics foundations and Text Mining with a STEM education focus. Topics broadly emphasize methodologies, literature, applications, and ethical issues as they relate to STEM education. Participants develop basic proficiency with R and RStudio, apply computational analysis techniques (e.g. data visualization, text mining) relevant and appropriate to their STEM education research interests. The target audience includes those who aim to leverage new data sources and apply computational methods in R Studio following the Learning Analytics workflow. The level of instruction will be appropriate for those with little or no experience using R, a popular free open source software program for data science, research, and technical communication. The first part of the course lays the foundations of Learning Analytics and R programming basics. The second part uses that base to dive into Text Mining techniques (e.g. word counts, sentiment analysis) in a STEM education context. Course activities include conceptual overviews, code-alongs, blended-learning labs, small group discussions, and individual consultations. Knowledge of basic descriptive and exploratory analysis is assumed. Participants are required to bring a laptop computer with R and Rstudio downloaded.

This introductory course introduces attendees to Bayesian estimation and missing data imputation. The course addresses a number of practical issues that researchers are likely to encounter in their research, including mixtures of categorical and continuous variables, interactive and nonlinear effects, and multilevel data structures. The primary goal of this course is to provide participants with the skills necessary to understand and apply cutting-edge missing data handling methods to their own data. Technical information will be presented in an accessible manner that is readily understandable by researchers who use, but do not specialize in, quantitative methods. Accordingly, the course will target practicing researchers (graduate students, professors, research professionals) who possess typical graduate-level statistics training, in particular, familiarity with multiple regression. The course will provide a mixture of lecture and computer applications. Presentation software will be used to deliver the course content, and attendees will be provided with extensive handouts. Estimation and imputation will be illustrated using the Blimp application, which is available as a free download at www.appliedmissingdata.com/blimp. Blimp was developed as part of an Institute of Education Sciences funded projects, and the program implements a number of cutting-edge approaches that are not available in other software. Course handouts will include computer code and scripts that attendees can use to analyze data.

This course provides qualitative researchers opportunity to develop analytic skills with tools specifically designed for using narratives or stories within their research. Qualitative Researchers often collect narratives or stories as data within a research project, yet may struggle at the analysis stage. Even Narrative Researchers may wish desired to have more tools when they are analyzing narrative data. This course begins with a moderated panel discussion with researchers who teach particular research tools in the course. Attendees will choose and then participate in four 30 min hands-on courses. Each course engages researchers in developing or honing skills with a particular analytic tool. The course ends with invited course participants sharing their insights and a final moderated question answer session with the narrative scholars from the courses. The available skills to be taught include: Autobiographical narrative Inquiry using indigenous knowing; Narrative beginnings as a tool for narrative analysis; Memory work for narrative research; Literary elements in narrative research; Narrative beginnings as the basis for data collection and analysis; Attending to multicultural issues in narrative analysis; Using digital tools and musical elements in narrative analysis and Narrative vignettes in Narrative Research.

This course aims to help beginning qualitative researchers (whether graduate students writing a qualitative dissertation or those learning qualitative methods so they can do mixed methods) learn some of the key expectations, practices, and conventions of writing traditional qualitative research. The session doesn’t teach about data collection and analysis—just writing, perhaps the least discussed topic in qualitative methods texts and courses. Using a range of formats, including short lecture, hands-on activities, and group discussion, the instructor and participants will accomplish several course objectives.

Participants will learn about, discuss, and practice the following key qualitative writing skills:

• Writing to Show You Were There
• Writing About and With Qualitative Data
• Writing Valid Qualitative Findings, Assertions, and Conclusions
• Writing About Qualitative Methods
• Basic Revision Strategies

Though attendees can be relative beginners, they should have basic familiarity with qualitative research methodology and practices. Participants are encouraged to bring a small writing sample (or small sample of data) for group critique and discussion.