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Professional Development and Training Courses
June 4–July 2
All Courses 1–5 pm ET
Fee Per Course $40 AERA Members / $55 Non-AERA Members
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Causal Moderated Mediation Analysis – A Causal Investigation of Heterogeneity in Mediation Mechanisms: Methods and Software
Tuesday, June 4, 2024
INSTRUCTOR
Xu Qin, University of Pittsburgh
Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and contextual characteristics.
The purpose of this course is to introduce the definition, identification, estimation, and sensitivity analysis for causal moderated mediation effects under the potential outcomes framework. Participants will also learn how to use a user-friendly R package to conduct the analysis and visualize results. The method introduction and the package implementation will be illustrated with a re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data.
The course will include the following activities and objectives:
1. A lecture introducing
- background of mediation analysis and moderated mediation analysis
- definition, identification, and estimation of causal mediation effects and causal moderated mediation effects under the potential outcomes framework
- sensitivity analysis for assessing how sensitive results are to potential unmeasured confounding
2. A hands-on training in using an R package to
- estimate the causal mediation effects and causal moderated mediation effects
- conduct sensitivity analysis
- visualize the original analysis results and sensitivity analysis results
The course is targeted at any researchers from PhD students, faculty, or research practitioners. Participants should have basic knowledge of statistical inference and multiple regression at least. Participants are expected to have the R software and the R package installed on their computers before the course.
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RL2024-2
Artificial Intelligence (AI) uses in Education Research
Tuesday, June 11, 2024
INSTRUCTORSINSTRUCTORS
Min Sun, University of Washington
Lavi Aulck, University of Washington
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This course will focus on preparing education researchers to use state-of-the-art tools in artificial intelligence (AI) and machine learning in educational contexts. The course will cover topics such as applications of AI in education for prediction and classification, model evaluation via performance metrics, human feedback in AI model development, randomized control trials, and cost-benefit analysis. There will also be a strong emphasis on data ethics and responsible AI throughout the session. The course will be intended for those who have at least some programming and statistics background. The goal for the course is to introduce core tools and concepts in artificial intelligence and machine learning and familiarize participants with potential use cases via examples in educational settings. Required material include an installation of Jupyter notebooks or a google colab account.
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RL2024-3
Understanding and Using Nationally Representative Survey Data from the RAND American Educator Panel
Thursday, June 27, 2024
INSTRUCTORS
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Julia Heath Kaufman, RAND Corporation
Sy Doan, RAND Corporation
Kate Brittain, Kitamba
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This course provides faculty, students, and other researchers with a primer on use and analyses of the many free teacher and principal survey datasets available through the RAND American Educator Panels (AEP). Participants will: (1) understand key features of probability-based sampling that ensures nationally representative survey data; (2) identify key AEP datasets for addressing their interests and research questions; (3) produce basic descriptive data regarding survey items of interest to them; (4) examine subgroup comparisons for survey items of interest; and (5) consider appropriate data visualizations for displaying results.
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RL2024-4
Putting Feelings Where They’re Useful: Using Emotions as Data in Qualitative Analysis
Tuesday, July 2, 2024
INSTRUCTORS
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Hilary Lustick, University of Massachusetts – Lowell
Abeer Hakouz, University of Massachusetts – Lowell
Xiaoye Yang, University of Massachusetts – Lowell
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| The Emotion Coding Technique (Lustick, 2021) is a systematic method of qualitative analysis that captures emotions as they arise and helps us process them as information about us, our participants, and our research objectives. In this course, we will review the emotion coding technique, which applies a set of reflexive questions to a chunk of data (Lustick, 2021). We will then talk about some of the complexities of naming and reflecting on emotions during data analysis. We will share our own best practices and hear some additional strategies from the instructor, including an emotion wheel to choose from. Lastly, we will shift into independent work time to practice and reflect on the technique. The course is open to all qualitative and mixed methods researchers, with graduate students and early career researchers in mind. Please have basic qualitative and mixed methods training, including an understanding of positionality and reflexivity. You are advised, though not required, to have available original qualitative data, such as an interview transcript, with which to practice the technique. |
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George L. Wimberly, Ph.D.
Virtual Research Learning Series, Director
For more information please contact
profdevel@aera.net
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