In 2018-2019, the Workshop in Methods series was directed by Dr. Patricia McManus and featured thematic workshops focused on experiments and causal inference, as well as additional workshops. Browse workshops from the 2018-2019 academic year below. You can also view a playlist of 2018-2019 thematic workshops, or a playlist of all workshop videos from the 2018-2019 academic year, in Media Collections Online. You may also browse videos from all years in the full Workshop in Methods collection on Media Collections Online, and access other materials, such as presentation slides, through the Workshop in Methods collection on IUScholarWorks.
Friday, August 24, 2018 toolkit
Dr. J. Scott Long, "Reproducible Results and the Workflow of Data Analysis"
Social Science Research Commons Grand Hall
2-3:30pm
Many disciplines are paying increasing attention to reproducible results. The fundamental idea is that other scientists should have access to your data and be able to obtain the same results—this is reproducibility. More generally, your results should be robust so that other scientists can confirm your findings using other data. Increasingly journals require authors to provide their data and analysis file before a paper is accepted to verify that that results. Producing reproducible results is highly dependent on your workflow for data analysis. This workflow encompasses the entire process of scientific research: Planning, documenting, and organizing your work; creating, labeling, naming, and verifying variables; performing and presenting statistical analyses; preserving your work; and ending with reproducible results. Most of the work in statistics classes focuses on estimating and interpreting models. In “real world” research projects, these activities may involve less than 10% of the total work. Professor Long’s talk is about the other 90% of the work. An efficient workflow saves time, introduces greater reliability into the steps of the analysis, and generates reproducible results.
Dr. Long is Distinguished Professor and Chancellor’s Professor Emeritus of Sociology and Statistics at Indiana University.
Materials on IU ScholarWorks (event flyer, presentation slides)
Friday, September 7, 2018 thematic
Dr. S. Michael Gaddis, "Correspondence Audits: Design Issues and Practical Examples"
Social Science Research Commons Grand Hall
2-4pm
During the past decade, field experiments in the social and behavioral sciences have gained in popularity as the internet has made implementing experiments easier, cheaper, and faster. However, although researchers may have a conceptual knowledge of how experiments work, the actual experience of implementing a field experiment for the first time is often frustrating and time consuming. Researchers without prior experience often struggle with a number of issues such as navigating IRB, obtaining true random sampling and assignment, understanding blocking, and interpreting different types of treatment effects. The initial learning curve may be steep but the rewards are plentiful as experiments produce highly valued original data, lend themselves to causal analysis in ways that traditional survey data cannot, and become easier to implement as a researcher’s experience level increases. This talk will introduce social scientists to the basics of a particular type of field experiment -- the correspondence audit -- and walk through a number of design issues that first time users often struggle with. Dr. Gaddis will provide practical examples from his own and others' work to illuminate some of the pitfalls of this method and help the audience gain confidence in embarking on their own field experiments.
Dr. S. Michael Gaddis is an Assistant Professor of Sociology at UCLA whose research focuses on racial discrimination, educational inequality, and mental health. He often uses experiments to examine levels of discrimination in employment and housing as well as the conditions under which racial discrimination occurs. He is editor and contributor to a recent book titled Audit Studies: Behind the Scenes with Theory, Method, and Nuance. His research has been published in top journals such as the American Journal of Sociology, Educational Evaluation and Policy Analysis, Social Forces, Social Science & Medicine, and Sociological Science and has been funded by the National Academy of Education, the Robert Wood Johnson Foundation, the Russell Sage Foundation, and the Spencer Foundation.
Materials on IUScholarWorks (event flyer, presentation slides
Video (Media Collections Online)
Friday, September 14, 2018 toolkit
Workshop in Methods + Russian Studies Workshop
Dr. Kate Graber, "Media and Discourse Analysis"
Social Science Research Commons Grand Hall
2-3:45pm
This workshop is part of the Russian Studies Workshop 2018 Graduate Methods Training Workshop, and is open to all IU graduate students in collaboration between the Russian Studies Workshop and WIM.
For both theoretical and logistical reasons, many social scientists turn to media texts—archival newspapers, radio and television broadcasts, podcasts, Twitter feeds, etc.—to understand the society that produced them. It may seem easy, because as a regular consumer and producer of media, you are already constantly analyzing the mediated discourse around you: parsing sentences, assessing the veracity of a claim, and making judgments as to the authority, intelligence, and background of a writer or speaker. But how might you denaturalize your “native” media literacy and go about this in a more systematic and rigorous way? In this hands-on workshop, we will sample some of the key methods for analyzing mediated discourse: transcription, critical discourse analysis, building and working with a corpus, capturing digital circulation, and multimodal analysis. Some examples will come from Russian media, but this workshop is open to all IU graduate students.
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online)
Friday, September 28, 2018 (and Saturday, September 29, 2018) toolkit
Grant Application Workshop for Early-Career Social Scientists with Dr. Regina Werum
Social Science Research Commons Grand Hall
Currently at the University of Nebraska Lincoln, Dr. Regina E. Werum earned her M.A. and a combined Ph.D. in Sociology and American Studies from Indiana University, Bloomington. From 2010-2012, she served as a Program Director for Sociology at the National Science Foundation.
Friday, September 28, 2018
| 2:00-3:30pm | Introduction to Proposal Writing, Best Practices, and Q&A |
| 3:30-3:45pm | Break |
| 3:45-4:30pm | Q&A with recent awardees |
| 4:30-6:00pm | Funding opportunities for social science and social science methodology |
| 6:00pm | Reception |
Saturday, September 29, 2018
| 8:00-10:00am | Hands-on session with Dr. Werum for prospective applicants. Space is limited and registration required by September 14th with a one-page draft project summary. Register at https://go.iu.edu/24Rx. |
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online)
Friday, October 12, 2018 toolkit
Kristin Otto and Dr. Emily Meanwell, "Exploring Qualitative Data Analysis Software"
Social Science Research Commons Grand Hall
2-4pm
This workshop will provide an overview for participants of the three main qualitative data analysis (QDA) software packages: NVivo, MAXQDA, and ATLAS.ti. It will highlight the capabilities of each program, their specific strengths and weaknesses, and the types of research best suited to each by using a set of sample data that allows direct comparison across platforms. Rather than a tutorial of how to use specific QDA software, this workshop will provide participants with an understanding of how different QDA platforms may be used to facilitate analysis, and which program may be best suited to researchers’ needs.
The workshop is ideal for graduate students or faculty who are new to the idea of using QDA software and are curious about its utility for their research, or for those who have been exposed to QDA software before but would like to compare programs and learn more about their capabilities.
Kristin Otto is a PhD candidate in the anthropology department, Qualitative Data Analysis Lab assistant, and Mathers Museum of World Cultures Research Associate. Emily Meanwell is the director of the Social Science Research Commons and the study director for the Sociological Research Practicum.
Materials available by email (event flyer)
Friday, October 19, 2018 thematic
Dr. Coady Wing, "Using Instrumental Variables to Interpret Experiments, Choice Experiments, and Survey Nonresponse"
Social Science Research Commons Grand Hall
2-3:30pm
Classical applications of instrumental variables analysis are justified by structural models of behavior, and assumptions about the relationship between measured and unmeasured variables. Experimental and quasi-Experimental research designs present a partial alternative to structural modeling that is useful for answering certain types of research questions. It turns out that instrumental variables analysis can also help us make sense of several different research designs.
This workshop will introduce the key assumptions involved in instrumental variables analysis from the perspective of research design. It will examine the way instrumental variables can play a role in the analysis of data from (i) classical randomized experiments, (ii) experiments that mix randomization and participant choice, and (iii) surveys that suffer from nonresponse. In each case, research designs justify some instrumental variable assumptions and not others. Examples and best practices for applied research will be discussed throughout.
Coady Wing is an Assistant Professor in the School of Public and Environmental Affairs.
Materials available by email (event flyer, presentation slides)
Video (Media Collections Online)
Friday, October 26, 2018 toolkit
Workshop in Methods + CEWiT
Dr. Olga Scrivner, "Improving your Writing Project Workflow with a Collaborative Online Platform: Overleaf and ShareLaTeX"
Social Science Research Commons Grand Hall
2-4pm
Overleaf (recently merged with ShareLaTex) provides a collaborative interactive platform for writing, editing, and publishing articles. Overleaf also offers a variety of templates to create assignments, syllabi, reports, presentations, and newsletters.
In this workshop you will learn about Overleaf and LaTeX, a markup language, which enables you to separate your context from formatting (e.g., font, size, margins), thus allowing you to concentrate solely on your ideas. Particularly, using LaTeX is beneficial if your writing incorporates formulae, equations, glosses or your journal requires a specific article format and bibliographic style.
Dr. Olga Scrivner is a research scientist with the Cyberinfrastructure for Network Science Center; corporate faculty in Data Analytics at Harrisburg University of Science and Technology; and CEWiT Faculty Fellow.
Materials on IUScholarWorks (event flyer)
Video (Media Collections Online)
Friday, November 2, 2018 timely
Dr. John Poe, "Fixed, Random, and Mixed Effects: Modern Approaches to Dealing with Nested, Clustered, Panel, and Longitudinal Data"
Social Science Research Commons Grand Hall
2-4pm
Dr. Poe will be also be offering a hands-on session, 4:30-5:30pm, sponsored by the Indiana Intensive Didactic Seminar. Participants should bring a laptop with R and RStudio installed to participate in the hands-on session.
Researchers often get contradictory advice from professors, colleagues, reviewers, and textbooks on how to deal with clustering across time and space. Economists argue strongly for “fixed effects” models. Psychologists and statisticians more typically push for “mixed effects” models. Most applied researchers in the social sciences are told to use a Hausman test to decide between fixed and random effects. This is complicated by the fact that different disciplines, articles, and books use very different terminology and notation to describe models. This lecture will walk participants through the basic problems of clustered data and translate the solutions from economics, psychology, and statistics into a common language. We will focus on how to make practical decisions on model choices for linear and nonlinear models, what problems can crop up, and how to describe/justify your methods to different audiences.
Dr. John Poe is currently a research methodologist working as a postdoctoral scholar for the Center for Public Health Services and Systems Research at the University of Kentucky. He received his PhD in the Department of Political Science at UK in 2017. He teaches the advanced course on multilevel modeling for the ICPSR summer program at the University of Michigan and the GSERM program in Europe. His methodological training comes mostly from econometrics, psychometrics, statistics, and biostatistics.
Dr. Poe's current substantive work is focused on understanding community health systems using network science. In particular, he's focused on understanding how health system structures and interactions affect health disparities in different segments of the population. His past (and future) work was split between research about the determinants of the policy process and understanding how different mechanisms in policy making operate and how people react to the their political and social environments. Methodologically, he is focused on problems of endogneity and model misspecification with clustered, multilevel, longitudinal, and network data structures.
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online)
Friday, November 9, 2018 toolkit
Helge-Johannes Marahrens, "Introduction to Python for Social Scientists"
Social Science Research Commons Grand Hall
2-4pm
Python has become the lead instrument for data scientists to collect, clean, and analyze data. As a general purpose programming language, Python is flexible and well-suited to handle large datasets. This workshop is designed for social scientists, who are interested in using Python, but have no idea where to start. Our goal is to "de-mystify" Python and to teach social scientists how to manipulate and examine data that deviate from the clean, rectangular survey format. Computers with Python pre-loaded are available in the SSRC on a first-come, first-served basis. This workshop is intended for social scientists who are new to programming. No experience required.
Helge-Johannes Marahrens is a third-year doctoral student in the department of Sociology at Indiana University, working toward a PhD in Sociology and an MS in Applied Statistics. His research interests include cultural consumption, stratification, and computational social science with a particular focus on Natural Language Processing (NLP).
Materials on IUScholarWorks (event flyer, presentation slides, hands-on exercise files)
Friday, January 18, 2019 thematic
Dr. Trenton Mize, "Survey Experiments: Testing Causality in Diverse Samples"
Social Science Research Commons (Woodburn Hall 200)
2-5pm
Experimental designs remain the gold standard for assessing causality; perhaps because of this, the use of experiments has grown rapidly in most social science fields such as economics, political science, sociology, and others. While laboratory studies remain popular in some fields, there is increasing interest in bringing the power of experimental designs to more diverse samples. Survey experiments offer the capability to assess causality in a broad range of samples, including targeted samples of specific populations or in large-scale nationally representative samples. The rise of online workplaces and the TESS program offer the ability to bring these samples to applied researchers at a minimal cost, greatly expanding the possibilities for research. This workshop will focus on how to design quality survey experiments, giving researchers the tools to implement best practices. I will also advocate for survey experiments as a tool for tests of intersectionality and other theoretical questions requiring diverse samples.
Trent Mize is an assistant professor of sociology at Purdue University and core faculty for the cluster in advanced methodologies for the social, behavioral, and health sciences at Purdue (AMAP). His research covers three core areas: (1) how gender and sexuality shape workplace interaction and labor market outcomes; (2) experimental methodology and statistical approaches for causal inference, cross-model comparisons, and for modeling categorical dependent variables; and (3) how social roles and relationships shape health behavior and health inequalities. Recent work has appeared in the American Sociological Review, Social Problems, Social Psychology Quarterly, and Social Science & Medicine.
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online)
Friday, January 25, 2019 thematic
Dr. Daniela Puzzello, "Why and How to Experiment in Economics"
Social Science Research Commons (Woodburn Hall 200)
2-4pm
Experimental economics uses human subjects to answer research and policy questions. This talk provides a brief discussion of the methodological guidelines adopted in economics experiments. It will also illustrate how experiments can be used to test the validity of economic theories or guide the design of market mechanisms and economic policies.
Professor Puzzello's research and teaching interests are in economic theory, monetary economics and experimental economics. Her work focuses on the efficiency of allocations in environments with decentralized trade. Some of her research integrates theory and experiments to study social norms of exchange and welfare improving trading institutions. Puzzello's research has been published in American Economic Review, Econometrica, Economic Theory, European Economic Review, Games and Economic Behavior, Journal of Economic Behavior and Organization, Journal of Economic Theory, Journal of Mathematical Economics, and Journal of Monetary Economics. She is an associate editor of Economic Theory, an advisory board member of the SAET Bulletin and an editor of The B. E. Journal of Theoretical Economics.
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online)
Friday, February 1, 2019 toolkit
Helge Marahrens, "Introduction to APIs for Social Scientists"
Social Science Research Commons (Woodburn Hall 200)
2-4pm
In recent years, social scientists have increased their efforts to access new datasets from the web or from large databases. An easy way to access such data are Application Programming Interfaces (APIs). This workshop introduces techniques for working with APIs in Python to retrieve data from sources such as Wikipedia or The New York Times. It is intended for researchers who are new to working with APIs, but are familiar with Python or have completed the Introduction to Python workshop.
In this workshop, we will retrieve data from the ProPublica Congress API. If you plan to follow along the code scripts, please take a few minutes to request a personal API key before the workshop: https://www.propublica.org/datastore/api/propublica-congress-api. Computers with Python 3 and libraries (requests, json, pandas, matplotlib, bs4, wikipedia) pre-loaded are available in the SSRC on a first-come, first-served basis.
This workshop is the second in a three-part series, followed by “Introduction to Text Mining for Social Scientists” (February 15, 2019). Materials from the first workshop, “Introduction to Python for Social Scientists” (November 9, 2018), are available through IUScholarWorks and Media Collections Online.
Helge-Johannes Marahrens is a third-year doctoral student in the department of Sociology at Indiana University, working toward a PhD in Sociology and an MS in Applied Statistics. His research interests include cultural consumption, stratification, and computational social science with a particular focus on Natural Language Processing (NLP).
Materials on IUScholarWorks (event flyer, presentation slides, hands-on exercise files)
Video (Media Collections Online)
Friday, February 8, 2019 timely
Dr. Alex Hollingsworth & Dr. Coady Wing, "Synthetic Control Groups: An introduction to key concepts, recent extensions, and a hands-on application"
Social Science Research Commons (Woodburn Hall 200)
2-4pm
Social scientists often look to policy change as a “natural experiment” that provides the opportunity to assess the causal effect of a policy treatment. For example, you might have data on an outcome both before and after an intervention for a “treated” unit and other "untreated" units. However, simply being untreated does not guarantee that those untreated units will serve as a valid control group for treated. Synthetic control methods use data on untreated units to produce a weighted control group that is more likely to serve as a valid control. These methods have become increasingly popular and can allow for causal inference in many settings where inference could not typically be done. This workshop introduces synthetic controls and will demonstrate a novel extension that exploits a machine-learning, data-driven approach that should be widely applicable to social scientists.
In our work, we study the causal effects of Colorado’s recreational marijuana law on the sales of other legal psychoactive substances that might serve as complements or substitutes for marijuana. To do this we employ a novel extension of the synthetic control estimator. Synthetic control estimators are weighted combinations of untreated groups that are designed to serve as a control group. We extend the estimator most typical synthetic control estimator by incorporating a LASSO into the way the weights for the untreated groups are constructed. The data underlying our analysis come from a retail grocery store scanner database and DEA prescription drug monitoring data. We use detailed product codes to classify the sales of alcohol and tobacco products into a set of homogenous product categories. Then we construct a weekly state level time series for each alcohol and product category. In addition, we construct a weekly state-level time series for the sales of a large set of other product categories that are unlikely to be affected by the availability of legal recreational marijuana in any state. The alcohol and tobacco products in Colorado are potentially treated goods observed before and after Colorado legalized marijuana. The goal of our project is to estimate the counterfactual time series of psychoactive substance sales that would have prevailed in Colorado in the post-periods if the state had not legalized marijuana.
The time series of the sales of alcohol, tobacco, and other products in other states represent a very large set of candidate comparison groups. The Synthetic Control Using Lasso (SCUL) approach is a machine-learning, data-driven way to comb through a very large set of candidate comparison time series, exclude a large number of candidates that are very different from the treated time series, and construct a weighted combination of a small number of candidates that closely resembles a target series. We use cross validation to choose the LASSO penalty parameter and to guard against overfitting the pre-treatment data. Constructing our synthetic control group using lasso has a few advantages over the traditional synthetic control estimator. The first is that the synthetic control can be constructed in a setting where there is a larger candidate set of control states and products than there are observations. This is a common occurrence in many "big data" settings. A second is that our estimator reduces researcher degrees of freedom by automating the model selection process. In general, the estimator allows for a comparison interrupted time series research design and should be broadly applicable to any research design where there are either a small number of treated units or where there are a larger number of candidate controls than observations.
The results of our analysis suggest that Colorado’s recreational marijuana law did affect the sales of other legal psychoactive substances. Some products appear to be substitutes for legal marijuana and others seem to be complements. In particular, we find that the law reduced sales of hard liquor and malt liquor and increased sales of cases of light beer. The recreational marijuana law did not appear to affect sales of a many other alcohol and tobacco products. And it also did not appear to affect the volume of prescription opioid use in Colorado.
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online, with CAS login)
Friday, February 15, 2019 toolkit
Helge Marahrens, "Introduction to Text Mining for Social Scientists"
Social Science Research Commons (Woodburn Hall 200)
2-4pm
Textual data are central to the social sciences. However, they often require several pre-processing steps before they can be utilized for statistical analyses. This workshop introduces a range of Python tools to clean, organize, and analyze textual data. It is intended for researchers who are new to working with textual data, but are familiar with Python or have completed the Introduction to Python workshop. Computers with Python pre-loaded are available in the SSRC on a first-come, first-served basis.
This workshop is the third in a three-part series. Materials from the first workshop, “Introduction to Python for Social Scientists” (November 9, 2018), are available through IUScholarWorks and Media Collections Online. Materials from the second workshop, "Introduction to APIs for Social Scientists" (February 15, 2019) is also available through IUScholarWorks and Media Collections Online.
Helge-Johannes Marahrens is a third-year doctoral student in the department of Sociology at Indiana University, working toward a PhD in Sociology and an MS in Applied Statistics. His research interests include cultural consumption, stratification, and computational social science with a particular focus on Natural Language Processing (NLP).
Materials on IUScholarWorks (event flyer, presentation slides, hands-on exercise files)
Video (Media Collections Online)
Friday, February 22, 2019 timely
Dr. Beth Gazley & Dr. Cassidy R. Sugimoto, "Systematic Literature Reviews"
Social Science Research Commons (Woodburn Hall 200)
2-4pm
Narrative literature reviews, systematic literature reviews, meta reviews, meta analyses, research in context: what should you do when you are asked to provide a review of the literature? What may have served as a fairly routine task in your early years as a student or scholar has been complicated by the growing volume of published research and the interdisciplinarity of many domains. It is becoming common practice to not only meticulously document the methods of your research design, but also to demonstrate the ways in which you searched the literature. Furthermore, there is increased value in the use of reviews to summarize the literature and find evidence across published results. Review articles have high value to the field—as demonstrated through citations—but can also lose their value when authors use ad hoc approaches or fail to acknowledge bias in how the review was assembled or analyzed.
Systematic literature reviews (SLRs) offer a way of producing less biased and more generalizable findings. SLRs use explicit selection criteria and a rigorous, rules-driven approach to the analysis of prior scholarship. The presenters will walk participants through the process of designing and conducting a systematic literature review using Cochrane-Campbell protocols, discussing bibliometric sources for systematically identifying literature, and providing tips and suggestions based on their own research experience.
Beth Gazley is Professor in the School of Public and Environmental Affairs. Her scholarship has addressed nonprofit governance, inter-organizational collaboration, the management of membership associations, and volunteerism. Her current research focus is on two areas: civil society and philanthropic behaviors related to climate change adaptation, and governmental reliance on charities to fund public services. A member of the IU faculty since 2004, Gazley has received the Indiana University 2018 W. George Pinnell Award for Service and the 2012 Indiana University Board of Trustees Thomas Ehrlich Award. Gazley is a Co-Principal Investigator on the Grand Challenge Grant “Prepared for Environmental Change”, and a member of the Indiana University Environmental Resilience Institute Steering Committee.
Cassidy R. Sugimoto is Associate Professor in the School of Informatics and Computing. She researches within the domain of scholarly communication and scientometrics, examining the formal and informal ways in which knowledge producers consume and disseminate scholarship. She has written extensively on this topic—with more than four edited monographs and over 100 papers to her name. Her latest book, “Measuring Research: What everyone needs to know” provides an introduction to the topic. Her work has been presented at numerous conferences and has received research funding from the National Science Foundation, Institute for Museum and Library Services, and the Sloan Foundation, among other agencies. She is currently on rotation at the National Science Foundation as the Program Director for the Science of Science and Innovation Policy program.
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online)
Friday, March 1, 2019 thematic
Dr. Long Doan, "Lab Experiments in Social Science Research"
Social Science Research Commons (Woodburn Hall 200)
2-4pm
Despite the growing popularity of experimental designs in sociological research, lab experiments remain relatively rare. Nevertheless, lab experiments are the gold standard for testing theory and assessing causal arguments, especially those that difficult to test using questionnaire measures. This workshop focuses on the logic of experiments, types of questions that are ideal for answering with lab experiments, issues of internal and external validity, and contrasting lab experiments to other experimental and observational methods. Using exemplars from sociology, I will walk through the design of lab experiments, common pitfalls that may surprise unaccustomed researchers, and ways to deal with these issues. The workshop is a mixture of lecture and hands-on exercises and is designed for those interested in designing their first few experiments or those on the fence about using lab experiments in their own research.
Long Doan is an Assistant Professor of Sociology at the University of Maryland. He is broadly interested in how various social psychological processes motivate behavior and explain patterns of inequality. In particular, Doan is interested in the intersections of sexuality, gender, and race. His work examines how seemingly subtle differences in evaluations of individuals based on their social characteristics lead to larger, more concrete implications, such as the acceptance or denial of legal rights or decisions related to hiring.
Materials on IUScholarWorks (event flyer, presentation slides)
Video (Media Collections Online)
Friday, March 22, 2019
Karl F. Schuessler Lecture in Social Science Methodology
Dr. Bruce Western, "Life After Prison: Studying Freedom with Three Methods"
Social Science Research Commons Grand Hall (Woodburn Hall 200)
2p
Bruce Western is Professor of Sociology and co-director of the Justice Lab at Columbia University. He received his BA from the University of Queensland, Australia, and his PhD in Sociology from the University of California, Los Angeles. Western's research examines trends in American economic inequality and the growth of the US penal population. These topics are joined by an interest in the shifting landscape of American poverty over the last 40 years. He is the author of Punishment and Inequality in America (2007) and served as Vice-Chair of a consensus panel of the National Academy of Sciences on the causes and consequences of high rates of incarceration in the United States. His new book is called Homeward: Life in the Year After Prison (2018). Western is a Guggenheim Fellow, a Radcliffe Fellow, and an elected member of the American Academy of Arts and Science and the National Academies of Science.
Video (Media Collections Online)
Friday, March 29, 2019 timely
Workshop in Methods + Graduate Mentoring Center
Dr. Kakali Bhattacharya, "Culturally Congruent Critical and Contemplative Qualitative Research"
Social Science Research Commons (Woodburn Hall 200)
2-4pm
Often when we engage in qualitative research, we are focused more on the technocratic approach to “doing” qualitative research. In this workshop we will move away from the technocratic ways of designing and executing qualitative research to an engagement in criticality and contemplative orientations. How might your values, beliefs, intentions, histories, experiences, cultural background, career aspirations, stuck places, and places of stillness come into being when you engage in qualitative research? What are the implications for relationship building with participants, research design, data collection, analysis, and representation? What rules do you observe and what could you break? What might be your ethical orientation and positionality as a researcher? How much of yourself should be revealed in your study? What self excavation is necessary to conduct in one’s particular study? We will explore these questions in the workshop through deep and interactive engagement with this ideas and identify productive tensions and their limits and possibilities.
Dr. Kakali Bhattacharya is the IU Graduate Mentoring Center's 2019 Trailblazers and Innovators Scholar-in-Residence. Dr. Bhattacharya is a Professor at Kansas State University, winner of the 2018 AERA Scholars of Color Mid-Career Contribution Award and the 2017 Outstanding Book Award from the International Congress of Qualitative Inquiry (ICQI). She holds a Ph.D. in Educational Psychology from the University of Georgia, Athens, as well as a graduate certificate in qualitative inquiry and women's studies. Dr. Bhattacharya's research interests include de/colonizing epistemologies and methodologies in transnational contexts of higher education. She also explores technology-integration in social and learning spaces. She is also deeply immersed in arts-based approaches in qualitative inquiry.
Friday, April 5, 2019 thematic
Dr. Christopher Parker (with Dr. Jorge Mejia)
Social Science Research Commons (Woodburn Hall 200)
2-4pm
Show or Tell? Improving Agent Decision Making in a Tanzanian Mobile Money Field Experiment
When workers make operational decisions, the firm's global knowledge and the worker's domain-specific knowledge complement each other. Oftentimes workers have the final decision-making power. Two key decisions a firm makes when designing systems to support these workers are: 1) what guidance to deliver, and 2) what kind of training (if any) to provide. We examine these choices in the context of mobile money platforms?systems that allow users in developing economies to deposit, transfer, and withdraw money using their mobile phones. Mobile money has grown quickly, but high stockout rates of currency persist due to sub-optimal inventory decisions made by contracted employees (called agents). In partnership with a Tanzanian mobile money operator, we perform a randomized controlled trial with 4,771 agents over eight weeks to examine how differing types of guidance and training impact the agents' inventory management. We find agents who are trained in person and receive an explicit, personalized, daily text message recommendation of how much electronic currency to stock are less likely to stock out. These agents are more likely to alter their electronic currency balance on a day (rebalance). In contrast, agents trained in person but who receive summary statistics of transaction volumes or agents who are notified about the program and not offered in-person training do not experience changes in stockouts or rebalances. We observe no evidence of learning or fatigue. Agent-level heterogeneity in the treatment effects shows that the agents who handle substantially more customer deposits than withdrawals benefit most from the intervention.
When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms
Passenger discrimination in transportation systems is a well-documented phenomenon. With the advent and success of ridesharing platforms, such as Lyft, Uber and Via, there has been hope that discrimination against under-represented minorities may be reduced. However, early evidence has suggested the existence of bias in ridesharing platforms. Several platforms responded by reducing operational transparency through the removal of information about the rider's gender and race from the ride request presented to drivers. However, following this change, bias may still manifest after a request is accepted, at which point the rider's picture is displayed, through driver cancellation. Our primary research question is to what extent a rider's gender, race, and perception of support for lesbian, gay, bisexual, and transgender (LGBT) rights impact cancellation rates on ridesharing platforms. We investigate this through a large field experiment using a major ridesharing platform in North America. By manipulating rider names and profile pictures, we observe drivers' patterns of behavior in accepting and canceling rides. Our results confirm that bias at the ride request stage has been eliminated. However, at the cancellation stage, racial and LGBT biases are persistent, while biases related to gender appear to have been eliminated. We also explore whether dynamic pricing moderates (through increased pay to drivers) or exacerbates (by signaling that there are many riders, allowing drivers to be more selective) these biases. We find a moderating effect of peak pricing, with consistently lower biased behavior.