Workshop Instructors

Kyung-Hae Bae

Kyung-Hee Bae
Director, Center for Academic & Professional Communication
Website

Effective Research Presentations

Communicating one’s research findings to various audiences is critical to any researcher’s academic and professional success, but many of us often find it quite challenging to create and deliver an effective presentation.

In this workshop, we will first look at how to create an effective research story and how to tailor it to your potential audience. We will then discuss basic guidelines for designing clear and visually appealing presentations, including setting up a slide template, using text effectively, and finding and/or creating visuals. Finally, we will consider various oral presentation delivery strategies for different settings and ways to mitigate presentation anxiety.


Arko Barman

Arko Barman
Assistant Teaching Professor at D2K Lab
Website

Automated Text Analysis I

Automated text analysis (a.k.a. Natural Language Processing) deals with the design, development, and application of AI algorithms for analyzing textual data. Natural Language Processing (NLP) has revolutionized today's world through countless applications including sentiment analysis, text classification and clustering, topic modeling, text summarization, and language translation. This workshop is designed to provide a bird's-eye view of NLP with the goal of introducing some of the most interesting applications relevant to the social sciences. We will discuss some challenges and fundamental methods in processing text data. Then, we will move on to a real-world example of an NLP application where we will see how NLP helps us in analyzing a large dataset of 1.9M occurrences of 30K popular media tropes that reinforce societal gender biases in the audience. Knowledge of Python will be helpful in understanding and running the example code in the workshop but is not essential in understanding the fundamental concepts.


Su Chen

Su Chen
Assistant Teaching Professor at D2K Lab
Website

Generalized Linear Models I

A generalized linear model (GLM) is useful when the response variable is not normally distributed and is linearly related to the factors and covariates via a specified link function. Some of the common examples of non-normal response variables include binary, categorical, ordinal and count data. This workshop provides a comprehensive practical introduction to GLMs using R. The specific models we cover include binary, multinomial and ordinal logistic regression, Poisson regression and negative binomial regression for count data. Besides implementation, the workshop emphasizes on the interpretation and communication of model results. Other practical issues about how to check model fit and how to perform model selection will be discussed briefly. The prerequisite for this workshop is basic knowledge of linear regression models, and some familiarity with R programming.


Christina Diaz

Christina Diaz
Assistant Professor of Sociology
Website

Linear Models I

The purpose of this seminar is to familiarize attendees with basic statistical operations so that they can read, understand, and evaluate quantitative research. We will begin with a review of basic statistics (estimates/uncertainty, hypothesis testing) and move on to focus on the linear regression model, which is the workhorse of quantitative social science. Specifically, we will use linear regression to examine how observed characteristics can predict important outcomes in the social world—such as wages. We will then focus on interpreting and showcasing our results.

Recommended prerequisites: undergraduate statistics; or review of supplemental material provided by the instructor.

Recommended materials: The use of a scientific calculator (e.g. TI-30) is encouraged for working through class material.


Shani Evans

Shani Evans
Assistant Professor of Sociology
Website

Data Collection via In-Depth Interviewing

This workshop will be beneficial for those who are interested in qualitative interviewing as a method of data collection. We will review examples of interview research and learn about the types of research questions that are well suited for qualitative interview studies. We will briefly examine the process of designing an interview study, including sampling, recruitment, writing the questionnaire, and developing an analytical strategy.

Participants will also learn strategies for conducting effective interviews.


Simon Fischer-Baum

Simon Fischer-Baum
Associate Professor of Psychological Sciences
Website

What Neuroimaging Is and What It Can (And Cannot) Do For Social Scientists

In this workshop, I will provide a brief overview of different tools and techniques that cognitive neuroscientists use for measuring neural activity and relating that neural activity to behavior in humans. We will then discuss the motivations for using neural measurement in the place of other more common, and less expensive or time-consuming techniques (behavioral experiments, survey responses) to address questions in the Social Sciences. We will go over potential pitfalls of neuroscientific research in the social sciences - the seductive allure problem, reverse inference, and the way that brain data can be misinterpreted as a measure of biological essentialism - and work through examples of recent neuroimaging studies in different social science disciplines with an eye towards how to critically assess this work. Finally, we will discuss how developing expertise in a neuroimaging modality could assist students in their academic goals, and practicalities of how that expertise could be obtained.


Hua Gong

Hua Gong
Assistant Professor of Sport Management
Website

Introduction to Python

The workshop, Introduction to Python, aims to teach students how to use Python to perform data analysis. Three main topics of Python will be covered during the workshop. First, two widely used packages, Pandas and Numpy, will be introduced and detailed information on how to use them in data manipulation will be provided. Second, we will draw attention to data visualization in Python. The package Matplotlib will be used to create professional graphs, such as line and box plots, in Python. Lastly, we will discuss how to perform linear and logistic regression models by using the Scikit-learn package in Python. At the end of the workshop, students are expected to become familiar with Python and develop essential skills of using Python to perform data analysis.

There are no prerequisites for this workshop.


Brian Holzman

Brian Holzman
Research Scientist at Houston Education Research Consortium
Website

Quasi-Experimental Methods

This seminar will provide attendees with an introduction to two quasi-experimental research methods: instrumental variables (IV) and regression discontinuity (RD). These methods can be used to estimate causal effects in the absence of random assignment. We will begin by providing a brief overview of causal inference and the randomized controlled trial (RCT). After that, we will discuss why experiments are difficult to design and implement in the real-world and how social scientists can leverage IV and RD in observational data analyses. The instructor will discuss the assumptions behind the methods and, using data, will walk through how the methods can be used in practice by demonstrating statistical code and interpreting results.

Recommended prerequisites: graduate statistics (e.g., familiarity with linear models)

Recommended materials: none


Jonathan Homola

Jonathan Homola
Assistant Professor of Political Science
Website

Navigating the Academic Job Market

In this workshop, you will learn about the academic job market. How does it work? What do I need to prepare? How do I maximize my chances of landing an academic job? And what does it look like from the hiring university's/faculty's perspective? We will talk about what happens before, during, and after a job interview. What do you need to prepare, how does an interview usually look like, and what happens afterwards. This workshop will not guarantee that you get an academic job, but hopefully it will help demystify this oftentimes opaque process and give you a good idea of how to best prepare yourself.


Cymene Howe

Cymene Howe
Associate Professor of Anthropology
Website

Ethnographic Techniques I

Ethnographic Techniques I is an introduction to the art and science of observing, documenting and analyzing cultural forms. Among qualitative research methods, ethnographic approaches are exceptional in their detail, illustrative capacities and ability to highlight the voices of everyday folk and the complexities of their lives. In this workshop we will overview participant observation, interviewing techniques, field notation and ethnographic writing and we will engage with the complexities of decolonizing representation.

Prerequisites: Prior to the workshop, please find (and bring) a piece of descriptive writing--a paragraph or two--that you find especially compelling. It can be any genre: literary, social scientific, journalistic, etc. Please also bring to class a small notebook or pad of paper and pen or pencil.


Derek Keller

Derek Keller
Research Computing Infrastructure Specialist, Center for Research Computing
Website

Designing and Deploying an Interactive Data Visualization App

This workshop will introduce you to A) two popular technologies (“containerization” and “deployment pipelines”) and B) two very popular quasi-methods (“interactive data visualization”/“exploratory data analysis”). Finally, C) we will discuss what this dataset can teach us about our methods and technologies. All of the technology we will use is free of cost.

A) We will use Docker to show you how to build a lightweight application on your laptop with very simple code, and Git and Heroku to deploy that application to the cloud, where it can be used and shared with others.

B) We will use R Studio Cloud to try out a specialized epidemiological tool on a public COVID dataset, and then we will plug those results into an interactive data visualization platform named Plotly. This application can then be published online using the steps we learned in part A.

C) We will then discuss how the public dataset we’re using from the CDC pushes at the limits of interactive data visualization (how interactive can we make this? what does interactivity show us about this dataset? and can something be too interactive?) and containerized deployment (what are the infrastructural underpinnings of containerization?).

Our goal is to help you know what the uses and abuses of these widespread technologies and approaches are, and to be able to make an educated guess at (or even look under the hood of!) how some of the slick data dashboards work, that are proliferating in the age of big data and cloud computing.


Ashley Leeds

Ashley Leeds
Radoslav Tsanoff Professor of Political Science
Website

How to Apply to Grad School in the Social Sciences and Tips for Success Once You Get There

This session will be broken into two halves. Students are welcome to attend one or both halves.

In the first half, we will discuss the application process for Ph.D. programs and address the following questions: How do you choose where to apply? What makes an application compelling to an admissions committee? What questions should you ask about financial support? How should you decide which admissions offer to accept?

In the second half, we will discuss tips for success in graduate school. How is a Ph.D. program different from undergraduate education? What are some things that successful graduate students tend to do? How might you handle different kinds of obstacles that may arise while in graduate school? This second half session is appropriate both for those planning to attend graduate school and those early in their time in graduate school.


Jing Li

Jing Li
Adjunct Associate Professor of Sociology
Website

Introduction to Multilevel Modeling

Prerequisite Knowledge: A good understanding of descriptive statistics, significance testing, bivariate regression, multivariate regression and generalized linear models.

Description: This applied course introduces the basic concepts and applications of multilevel models, also known as hierarchical linear models. The course will start with an introduction of nested data and common methods of analyzing such data, followed by advantages of using multilevel modeling methods. It will cover contextual analysis (e.g. neighborhood effects research, school effect research), as well as growth curve modeling (e.g. repeated measures on individuals) with a focus on conceptual understanding and interpretation of results.


John Mulligan

John Mulligan
Digital Scholarship Postdoctoral Fellow, Center for Research Computing
Website

Designing and Deploying an Interactive Data Visualization App

This workshop will introduce you to A) two popular technologies (“containerization” and “deployment pipelines”) and B) two very popular quasi-methods (“interactive data visualization”/“exploratory data analysis”). Finally, C) we will discuss what this dataset can teach us about our methods and technologies. All of the technology we will use is free of cost.

A) We will use Docker to show you how to build a lightweight application on your laptop with very simple code, and Git and Heroku to deploy that application to the cloud, where it can be used and shared with others.

B) We will use R Studio Cloud to try out a specialized epidemiological tool on a public COVID dataset, and then we will plug those results into an interactive data visualization platform named Plotly. This application can then be published online using the steps we learned in part A.

C) We will then discuss how the public dataset we’re using from the CDC pushes at the limits of interactive data visualization (how interactive can we make this? what does interactivity show us about this dataset? and can something be too interactive?) and containerized deployment (what are the infrastructural underpinnings of containerization?).

Our goal is to help you know what the uses and abuses of these widespread technologies and approaches are, and to be able to make an educated guess at (or even look under the hood of!) how some of the slick data dashboards work, that are proliferating in the age of big data and cloud computing.


Fred Oswald

Fred Oswald
Herbert S. Autrey Chair in Social Sciences and Professor of Psychology
Website

Psychometrics and Scale Development I

The objective of this course is for you to develop broad conceptual knowledge, as well as specific concrete skills when developing, analyzing and interpreting psychological measures and the data that come from them. We will learn by example: taking some tests, analyzing some data, and deciding from those analyses how we know that a measure/test is measuring what we think it should.


Elizabeth Roberto

Elizabeth Roberto
Assistant Professor of Sociology
Website

Agent-Based Modeling

This workshop offers an introduction to Agent Based Models — computer simulations in which agents interact with each other and generate social outcomes such as norms, culture, and inequality. Agent based models allow us to study dynamic social processes and ask questions such as: Why do social norms emerge and spread? Why does cooperation occur in some contexts and not in others? Why do individuals’ opinions change or polarize?

In this workshop, we will discuss how agent based models are constructed, examine their application in the social sciences, and gain hands-on experience running models and analyzing their results, including becoming the agents in our own simulation.

No prior programming experience is required. The workshop will briefly introduce a variety of options for developing agent based models, including R, Python, and NetLogo.


Michelle Torres

Michelle Torres
Assistant Professor of Political Science
Website

Methods for Visual Analysis in the Social Sciences

This course provides an introduction to image analysis including core concepts of image structure, feature definition and measurement, and classification. In particular, we will review the intuition, statistical basis and implementation of two methods for the unsupervised and supervised analysis of images: a visual structural topic model based on the Bag of Visual Words, and Convolutional Neural Networks (CNNs).