{"id":5183,"date":"2023-04-11T18:37:24","date_gmt":"2023-04-11T18:37:24","guid":{"rendered":"https:\/\/connect.apsanet.org\/apsa2023\/?page_id=5183"},"modified":"2024-10-31T15:17:34","modified_gmt":"2024-10-31T15:17:34","slug":"pre-conference-short-courses","status":"publish","type":"page","link":"https:\/\/connect.apsanet.org\/apsa2023\/pre-conference-short-courses\/","title":{"rendered":"Pre-Conference Short Courses"},"content":{"rendered":"

Pre-conference short courses\u00a0provide diverse opportunities, either half day or full day, for professional development and offer attendees the chance to connect with scholars from a range of backgrounds. They are sponsored by\u00a0APSA Organized Sections<\/a>\u00a0and other affiliated organizations.\u00a0 APSA will offer pre-conference short courses as part of the in-person event format. All short course participants must be registered for the Annual Meeting and have a badge before attending.<\/p>\n

These courses will run on Wednesday, August 30, in Los Angeles.\u00a0<\/b>There is an additional $25 fee for pre-conference short courses. If you have already registered for the Annual Meeting and would like to add a short course registration, please contact\u00a0meeting@apsanet.org<\/a>.\u00a0<\/p>\n\n\n

SC02: Advocacy and the Academy<\/h4><\/div>
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Jennifer Dresden
Full Day, 9:00 AM \u2013 5:00 PM
Los Angeles Convention Center, 507<\/p>\n\n\n\n

Political scientists, including those that make their primary careers within traditional university roles, have increasingly expressed interest in doing work that influences public debate and that uses their expertise for purposes outside of traditional academic functions. There is widespread recognition that many areas of public policy would benefit from being better informed by the kind of expertise rooted in rigorous research and scholarly training.<\/p>\n\n\n\n

The proposed short course is designed as a one-day professional development session for advanced graduate students and early- and mid-career scholars. Its goal is to offer a preliminary introduction to the knowledge and skills that enable academics to be effective partners in advocacy.<\/p>\n\n\n\n

The short course is designed to focus on advocacy within the context of the United States, and many of the examples used will involve cases related to policies and reforms focused on strengthening American democracy. However, the training itself is focused on skills rather than substance, and scholars with all areas of expertise are welcome.<\/p>\n\n\n\n

The sessions within the short course will be led by Protect Democracy staff and scholars with experience bridging the divide between academia and public policy debates.<\/p>\n\n\n\n

Topics include designing and adapting research programs for value to policymakers, writing for general audiences, communicating outside the ivory tower, and understanding differences between engagement at the local, state, and national levels.<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n

SC03: Africa Research Development Group<\/h4><\/div>
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Andrew Stinson
Full Day, 9:00 AM \u2013 5:00 PM 
Los Angeles Convention Center, 405<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n

SC04: Asia Research Development Group<\/h4><\/div>
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Andrew Stinson
Full Day, 9:00 AM \u2013 5:00 PM 
Los Angeles Convention Center, 404B<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n

SC05: Bayesian Reasoning for Case Studies and Comparative Research (QMMR B)<\/h4><\/div>
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Tasha Fairfield
1:30 PM \u2013 5:30 PM
Los Angeles Convention Center, 406A<\/p>\n\n\n\n

This short course introduces participants to the Bayesian logic of qualitative case studies, with practical advice, examples, and small-group exercises to enable them to use this method in their work. It builds on Social Inquiry and Bayesian Inference: Rethinking Qualitative Research, by Tasha Fairfield and Andrew Charman (Cambridge University Press, 2022). The material presented here complements the morning short course on process tracing led by Andrew Bennett, Jeffrey T. Checkel, and Tasha Fairfield, but each course can also be usefully taken independently from the other.<\/p>\n\n\n\n

The core idea motivating this course is that the way we intuitively approach qualitative case research is similar to how we read detective novels. We consider various different hypotheses to explain what occurred\u2014whether the emergence of democracy in South Africa, or the death of Samuel Ratchett on the Orient Express\u2014drawing on the literature we have read (e.g. theories of regime change, or other Agatha Christie mysteries) and any salient previous experiences we have had. As we gather evidence and discover new clues, we update our beliefs about which hypothesis provides the best explanation\u2014or we may introduce a new alternative that occurs to us along the way.<\/p>\n\n\n\n

Bayesianism provides a natural framework that is both logically rigorous and grounded in common sense, that governs how we should revise our degree of belief in the truth of a hypothesis\u2014e.g., \u201cmobilisation from below drove democratization in South Africa by altering economic elites\u2019 regime preferences,\u201d (Wood 2001), or \u201ca lone gangster sneaked onboard the train and killed Ratchett as revenge for being swindled\u201d\u2014given our relevant prior knowledge and new information that we obtain during our investigation. Bayesianism is enjoying a revival across many fields, and it offers a powerful tool for improving inference and analytic transparency in qualitative research.<\/p>\n\n\n\n

The first part of this course introduces basic principles of Bayesian reasoning with the goal of helping us leverage common-sense understandings of inference and improve intuition when conducting causal analysis with qualitative evidence. We begin with the general logic of Bayesian inference, that is, how we update our prior view about which explanation is more plausible when we learn new evidence. We explain the importance of working with rival hypotheses and discusses how to formulate well-constructed explanations to compare. We then elaborate practical procedures for evaluating the inferential import of the evidence by \u201cmentally inhabiting\u201d the world of each hypothesis and asking which one makes the evidence more expected, and then updating our prior views about which hypothesis provides the best explanation. We include examples and exercises to illustrate how this process works with real-world qualitative evidence.<\/p>\n\n\n\n

The second part of the course turns to comparative case studies. Methodological literature often treats cross-case (e.g., comparative) analysis and within-case analysis (e.g., process tracing) as distinct analytical endeavors that draw on different logics of inference. Within a Bayesian framework, however, there are no fundamental distinctions; all evidence contributes to inference in the same manner, whether we are studying a single case or multiple cases. In essence, each piece of evidence we obtain weighs in favor of one explanation over a rival to some degree, which we assess by asking which explanation makes that evidence more expected. Evidentiary weight then aggregates both within any given case, and across different cases that fall within the scope of the theories we are testing. In addition to showing how this process works with examples drawn from published comparative case studies, we will introduce a Bayesian approach to case selection and discuss how to articulate scope conditions and tentatively generalize our hypotheses.<\/p>\n\n\n\n

Note: This course does not require any prior training in process training, Bayesianism, probability theory, or logic. The only technical skills that will be assumed are basic arithmetic.<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n

SC06: Causal Inference and Treatment Effect Estimation Using Stata<\/h4><\/div>
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Joerg Luedicke
Full Day, 9:00 AM \u2013 5:00 PM
Los Angeles Convention Center, 510<\/p>\n\n\n\n

In this workshop, we discuss methods for drawing causal inferences when analyzing observational rather than experimental data. We present a variety of estimators for average treatment effects (ATEs) and average treatment effects on the treated (ATETs) and discuss when each estimator is useful. Throughout the workshop, we cover the conceptual and theoretical underpinnings of treatment effects and demonstrate the methods with many practical examples worked using Stata software.<\/p>\n\n\n\n

After a discussion of the potential-outcome framework and an overview of the parameters estimated, the workshop introduces the following treatment-effect estimators<\/p>\n\n\n\n