Extended Professional Development Courses—AERA 2019 Annual Meeting
 
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Extended Professional Development Course Descriptions

 PDC01:              Analyzing Data From International Large-Scale Assessments Using R
Instructors:       Ting Zhang, American Institutes for Research; Emmanuel Sikali, U.S. Department of Education; Paul Dean Bailey,                               American Institutes for Research; Ebru Erberber, American Institutes for Research
 
Date:                  Thursday, April 4
Time:                  9 a.m. – 5 p.m.
Room:                 711
Fee:                     $135
 
This course will introduce participants to the procedures of analyzing data from international large-scale assessments (ILSAs) using NCES’s EdSurvey R Package. Public-use TIMSS data files will be used as example data sets. Participants will begin installing the EdSurvey  R package and importing the data files into R. They will learn how to manipulate the data, including merging, subsetting, and recoding data. The participants will learn how to use the EdSurvey package to perform the statistical techniques used most often in ILSAs data analyses, including selecting an appropriate sample, estimating the mean scale scores for groups of students, benchmark analysis, gap analysis, linear regression, logistic regression, and correlations. This course will introduce unique design features of ILSAs 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. There will be designated time for participants using EdSurvey to practice the techniques with the variables of their own interest.
PDC02:            An Introduction to Multiple Imputation for Educational Research
Instructor:       Craig K. Enders, University of California – Los Angeles
 
Date:               Thursday, April 4
Time:               9 a.m. – 5 p.m.
Room:              713B
Fee:                  $135
 
This course introduces attendees to multiple imputation for missing data in educational research. It addresses practical issues that researchers are likely to encounter in their research, including mixtures of categorical and continuous variables, item-level missing data in questionnaires, interactive effects, and multilevel data. The primary goal of this course is to provide participants with the skills necessary to understand and apply multiple imputation 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. Imputation will be illustrated using the Blimp application, which is available as a free download at www.appliedmissingdata.com. Blimp produces multiply imputed data sets that interface with popular statistical software packages. Course handouts will include computer code and scripts that attendees can use to analyze multiply imputed data sets in Mplus, SAS, SPSS, Stata, and R.
PDC03:             Developmental Evaluation in Action: Multimodal Narratives to Democratize Evidence in Real-Time
Instructors:      Keiko Kuji-Shikatani, Ontario Ministry of Education; Megan Borner, Ontario Ministry of Education; Wendy Rowe, Royal Roads University
 
Date:                 Thursday, April 4
Time:                 9 a.m. – 5 p.m.
Room:                 714B
Fee:                    $135
 
This course provides participants with introductory knowledge in the fundamentals of Developmental Evaluation (DE) and its implementation, including the role and use of the Theory of Change/Action. The course is focused on how developmental evaluators track, document, and help interpret the nature and implications of innovations and adaptations as they unfold, and help extract lessons and insights from both processes and outcomes to inform the ongoing adaptive innovative process. Situational analysis and case studies will be used to authenticate the information being presented in order to equip evaluators with the practical application of DE within the complex, dynamic environments they may experience in their work. Throughout the course, participants will be invited to apply their learning to a program’s evaluation activities in which they are involved. This course is at the intermediate level and requires at minimum an intermediate understanding of program evaluation terms and methodology. It is for those in education who see themselves as social innovators who often find themselves dealing with problems, trying out strategies, and striving to achieve goals that emerge from their engagement with the change process. Participants are encouraged to bring their laptops so they can take advantage of the electronic tools shared in the course.
PDC04:             Evaluation Theories and Approaches: An Interactive and Case-Centered Primer
Instructors:      Bianca Montrosse-Moorhead, University of Connecticut; Daniela Schroeter, Western Michigan University; Lyssa Wilson Becho, Western Michigan University
 
Date:                 Thursday, April 4
Time:                 9 a.m. – 5 p.m.
Room:                714B
Fee:                    $135
 
The purpose of this interactive, case-centered course is to learn about historical and contemporary theories and approaches to designing and implementing evaluation through real-life case studies. Brief lectures, group activities, and audience engagement will encourage participants to (a) recognize different methods-, use-, values-, and social justice-oriented evaluation theories and approaches; (b) identify strengths, limits, and opportunities associated with the various evaluation theories and approaches in differing educational contexts; and (c) apply different theories and approaches in evaluation practice. Participants completing the course will gain insight into how their own backgrounds, training, and contexts may influence their choice of or preference for particular approaches. Target groups for this course include graduate students, early career researchers, and evaluators with little or no prior knowledge of evaluation theories and approaches. Senior researchers and evaluators who wish to expand their knowledge and use of contemporary theories and approaches may also benefit. Participants will receive course materials via email approximately one week in advance of the course, will be asked to read one case in advance, and will need to bring a computer to access materials during the course.
PDC05:             Exploring a Data-Informed Approach to the Development of Students’ Social-Emotional Learning Competencies
Instructors:      Rolf K. Blank, STEM K-12 Research; Katie H. Buckley, Transforming Education; Sara Krachman, Transforming Education
 
Date:                 Thursday, April 4
Time:                 9 a.m. – 5 p.m.
Room:                715A
Fee:                    $135
 
The purpose of this course is to provide researchers, educators, and administrators with best practices in assessing and supporting student social-emotional learning (SEL) competencies. Participants will have the opportunity to explore different measures designed for accessing data on students’ SEL competences; best practices for integrating SEL development into districts, schools and classrooms; and SEL-related strategies to support improvement of student achievement. The course will begin with a discussion of the research and rationale behind measuring SEL and an examination of the intersection between SEL, school environment, and student agency. Instructors will provide an overview of the different use-cases for measuring SEL and applicable assessments for the differing purposes. Participants will learn how to examine and use SEL data to make data-informed decisions at the district, school, and classroom levels, and will model concrete, hands-on strategies and tools to strengthen student skills. Participants will discuss ways to integrate SEL practice measures and resources in classrooms through a continuous improvement approach.
PDC06:             Fitting Multilevel Models to ECLS Data Using R
Instructors:      Ting Zhang, American Institutes for Research; Emmanuel Sikali, U.S. Department of Education; Paul Dean Bailey, American Institutes for Research; Shannon Russell, American Institutes for Research; Claire Kelley, American                                    Institutes for Research; Trang Nguyen, American Institutes for Research
 
Date:                Friday, April 5
Time:                8 a.m. – 5 p.m.
Room:               713B
Fee:                   $135
 
This course is designed to provide an applied understanding and hands-on practice in multilevel mixed models using the R package EdSurvey, which was developed for analyzing NCES cross-sectional and longitudinal assessment data. Using the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011) public-use datafile provided in the course, participants will be introduced to the EdSurvey package and learn how to use the package to obtain results from various types of mixed models, including the null model and random intercept and slope models. Multilevel models for binary outcomes and longitudinal data will also be covered. This course will introduce the unique design features of ECLS-K:2011 data to researchers and provide guidance on specific analytic strategies related to these features, including centering and the selection and use of appropriate sampling weights and variance estimation procedures. The knowledge and analytic approach covered in this course can be applied to working with other ECLS data sets. There will be time designated for participants to use EdSurvey to practice the techniques discussed with variables of their own interest. Participants should have basic knowledge of statistical techniques, including statistical inference and multiple regression analysis. It is helpful, though not required, to have some experience in binary logistic regression. Having working knowledge of R and multilevel modeling is preferred. Participants need to bring a laptop preloaded with the latest version of the R and RStudio software to participate in the hands-on portion.
PDC07:             Fully Integrated Mixed Method Approaches
Instructor:       Elizabeth G. Creamer, Virginia Polytechnic Institute and State University
 
Date:                Thursday, April 4
Time:                9 a.m. – 5 p.m.
Room:               715B
Fee:                   $135
 
This interactive course introduces fully integrated mixed method research as a methodology for integrating qualitative and quantitative approaches throughout the research process, from its initial inception through its execution. This course is targeted toward graduate students and early career researchers with some familiarity with mixed methods research. Learning goals for the course include to explore ways that multiple sources of data can be engaged dialectically in (a) the construction of research questions, (b) the development of a sampling plan, (c) data collection and analysis, and (d) the drawing of multi-dimensional conclusions. The course offers an opportunity to experiment with innovative ways to integrate qualitative and quantitative data during analysis through the use of an activity that involves the critical incident technique. Participants completing the course will come away with a list of ideas about creative ways the qualitative and quantitative data can be integrated. A pre-course assignment is to read “A Primer About Mixed Methods Approaches for Research in an Educational Context,” available at https://www.researchgate.net/publication/303541823_A_Primer_About_Mixed_Methods_for_Research_in_an_Educational_Context.
PDC08:              How to Get Published: Guidance From Emerging and Established Scholars
Instructors:       Patricia A. Alexander, University of Maryland - College Park; Yuting Sun, University of Maryland - College Park; Sophie Jablansky, University of Maryland - College Park; Anisha Singh, University of Maryland - College Park; DeLeon Gray, North Carolina State University; Matthew T. McCrudden, The Pennsylvania State University; Panayiota Kendeou, University of Minnesota; Laura M. Stapleton, University of Maryland; Diane L. Schallert, The University of Texas at Austin; Sofie Loyens, University College Roosevelt; Jeff A. Greene, University of North Carolina - Chapel Hill
 
Date:                Thursday, April 4
Time:                9 a.m. – 5 p.m.
Room:               713A
Fee:                   $135
 
This course will provide graduate students and early career faculty with critical information about how to publish. The course will begin with an overview of the nuts and bolts of academic publishing. Following this, scholars will present detailed sessions that cover the entire publishing process—from conceptualizing studies to preparing well-crafted manuscripts targeted to relevant journals. More specifically, sessions led by top scholars will cover institutional and career fit, how to be a productive writer, finding equilibrium in academia, contemporary publishing topics and how to select appropriate journals, quality quantitative research, quality qualitative research, and ethical issues in publishing. Question-and-answer sessions will follow each presentation to allow course participants to interact with the scholars about the topics presented. Further, at the end of the day there will be a final presentation that addresses any lingering questions and concerns. Each participant will be provided with materials, including handouts and work samples that elaborate the important points shared during the session.
PDC09:             Introduction to Systematic Review and Meta-Analysis
Instructors:      Amy L. Dent, University of California – Irvine; Terri D. Pigott, Loyola University Chicago; Joshua R. Polanin, American Institutes for Research; Joseph Taylor, BSCS
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Date:                Thursday, April 4
Time:                9 a.m. – 5 p.m.
Room:               716A
Fee:                   $135
 
This 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 and discuss 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 and those currently conducting either type of project. Knowledge of basic descriptive statistics is assumed. Participants are required to bring a laptop computer.
PDC10:             Multilevel Modeling With Large-Scale International Databases Using the HLM Software Program
Instructors:      David C. Miller, American Institutes for Research; Martin Hooper, American Institutes for Research; Francis Howard Lim Huang, University of Missouri; Sakiko Ikoma, American Institutes for Research; Mengyi (Elaine) Li, American Institutes for Research; Sabine Meinck, IEA Hamburg; Yuan Zhang, American Institutes for Research
 
Date:                Thursday, April 4
Time:                9 a.m. – 5 p.m.
Room:               716B
Fee:                   $135
 
Data from international large-scale assessments (ILSAs) reflect the nested structure of education systems and is very well suited for multilevel modeling (MLM). However, because these data come from complex cluster samples, there are methodological aspects that a researcher needs to understand when doing MLM, such as the need for using sampling weights and multiple achievement values for parameter estimation. This course will teach participants how to do MLM with data from ILSAs, such as PIRLS, TIMSS, and PISA. The content of the course will include an overview of the ILSAs and a presentation on the design of these studies and databases and implications for MLM analysis. Participants will learn how to specify two-level models using the HLM software program and also learn about model comparison, centering decisions and their consequences, and available resources for doing three-level models. Time will be allotted for participants to work on practice exercises, with several instructors available to mentor and answer questions. Participants should have a solid understanding of OLS regression and a basic understanding of MLM. Prior experience using a statistical software program, such as Stata or SPSS, is helpful. Prior knowledge about ILSAs or prior experience using the respective databases or HLM software is not required. To fully participate in the hands-on demonstrations and example analyses, participants should bring their own laptops with HLM software (a free student version is available).
PDC11:              Network Analysis of Qualitative Data: Toward Multimodal Narratives to Democratize Evidence
Instructor:        Manuel S. Gonzalez Canche, University of Pennsylvania
 
Date:                 Thursday, April 4
Time:                 9 a.m. – 5 p.m.
Room:                717A
Fee:                    $135
 
The goal of this course is to teach participants how to harness the mathematical power of network analysis in order to find structure in written content. The Network Analysis of Qualitative Data analytic method blends quantitative, mathematical, and qualitative principles to analyze text data. The course relies on both lecture and hands-on exercises. All data and software are freely available. Throughout the course, the analysis and interpretation of data will be prioritized. Once participants have mastered the network concepts covered—including measures of centrality and network visualization—the course will move toward the application of these principles and techniques to highlight the most important actors, codes, and words in a data set. This course is appropriate for graduate students, early career scholars, and advanced researchers. The course will be based on the use of R, but requires no previous familiarity with the software. Participants are required to bring their computers and must be able to install software. Assignments entail replicating and interpreting the in-class analyses, with an emphasis on the relevance of discovered network structures.
PDC12:             The Success Case Method Revisited: Opportunities for Educational Evaluation and Research Practice
Instructor:       Daniela Schroeter, Western Michigan University
 
Date:                 Friday, April 5
Time:                8 a.m. – 4 p.m.
Room:               713A
Fee:                   $135
 
The Success Case Method (SCM) is a theory-driven, utilization-focused, participatory approach to impact evaluation that uses mixed methods questionnaires and interviews to identify high- and low-impact examples with the ultimate goal of informing improvement of processes and results of an intervention. The purpose of this hands-on course is to introduce the SCM and present opportunities for applying the method in educational evaluation and research practice. By the end of the course, participants will understand the steps involved in success case research and evaluation, create a theory-driven impact model based on a case scenario, draft question sets suitable for identifying high- and low-success cases via web-based questionnaires, and develop possible interview questions for documenting stories of success and opportunities for improvements. The course concludes with a discussion of strengths, limitations, and opportunities associated with using the SCM in education research and evaluation. Target groups for this course include graduate students, researchers, and evaluators with little or no prior knowledge of the method. Research and evaluation administrators and sponsors who wish to expand their knowledge and use of SCM may also benefit. Participants will receive course materials via email in advance and are encouraged to bring a computer to access materials during the course.
PDC13:             Using ATLAS.ti Windows 8 Qualitative Data Analysis Software Across the Research Process
Instructors:      Jessica Nina Lester, Indiana University; Trena M. Paulus, University of Georgia – Athens
 
Date:                 Thursday, April 4
Time:                 9 a.m. – 5 p.m.
Room:               717B
Fee:                    $135
 
This course introduces how ATLAS.ti 8 qualitative data analysis software (QDAS) can be used to support the entire research process, including managing and collaborating on projects, conducting paperless literature reviews, collecting data through mobile apps and social media, synchronizing audio and video files with transcripts, analyzing text/Geo-docs/multimedia data, and visualizing and representing findings. Although most researchers understand the benefits of QDAS, fewer have thought about how such software can support every aspect of their work. Through a variety of instructional strategies including demonstrations and hands-on work with data, participants will learn how to integrate ATLAS.ti 8 into a research study. The target audience includes graduate students, methods course instructors, practitioners, and qualitative or mixed methods researchers. Participants should have a working knowledge of qualitative research and, ideally, a research study in the design phase. While no prior experience with ATLAS.ti 8 is required, those who have never used it will be asked to watch a short webinar to become familiar with the interface. Participants should bring a laptop computer with the software installed (trial versions will be made available prior to the course). While sample data will be provided, participants may bring the following in the context of their own studies: text, audio, and/or video data; PDFs of scholarly literature; an iPad with the free app installed; a Twitter and/or Evernote account; and an XML file from a citation management system.
PDC14:              Advanced Meta-Analysis
Instructors:      Terri D. Pigott, Loyola University Chicago; Ryan Williams, American Institutes for Research; Ariel M. Aloe, University of Iowa; Susan Natasha Beretvas, The University of Texas – Austin; Wim Van den Noortgate, KU Leuven
 
Date:                Friday, April 5
Time:               8 a.m. – 4 p.m.
Room:              715B
Fee:                  $135
 
This course will introduce advanced methods in meta-analysis. Topics covered include the computation of effect sizes from complex research designs, models for handling multiple effect sizes per study (dependent effect sizes) and exploring heterogeneity, power analysis in meta-analysis, 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 course 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 includes researchers who have 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.