Workshop Instructors

This workshop introduces fundamental principles and practical applications of text analysis and visualization. It's designed for both qualitative studies and large-scale document analysis. You'll learn how to transform qualitative text into a machine-readable database and explore text characteristics using compelling visuals.

We will also cover advanced visualization techniques, including creating heatmaps or "ethnoarrays" to map patterns in text or codes, and semantic networks to illustrate connections between concepts. You'll gain skills to apply these techniques for comparisons across sets or subpopulations, such as different field sites, interview demographics, document genres, or projects.

The workshop utilizes free, open-source tools and Python code. Exercises will be run in Google collab, but code for offline use will be provided. No prior experience with coding or computational text analysis is necessary.

Prerequisites

There are no prerequisites to take this course, but students are asked to review some short readings and ensure they can access files in the week prior to the start of the course. You're welcome to bring your own data, whether it's structured, unstructured, or from qualitative data analysis software. You will need a laptop to run analyses.

This workshop will introduce students to the idea of representing the economy as a mathematical model. We will work through a “cake-eating” problem to introduce the idea of dynamic programming and then discuss how this approach scales to other more complex economic models. We will also spend some time discussing how to write performant code and using technologies like just-in-time compilation (JIT) can allow Python code to be as fast as languages like C.

Prerequisites

• Please bring a laptop with Python installed (instructions forthcoming)
• You should understand basic Python code and be able to use numpy and other scientific libraries

Workshop information coming soon.

Machine learning, according to the IBM website's definition, is a branch of Artificial Intelligence (AI) which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Most sources consider three different types of machine learning: Supervised, Unsupervised and Reinforcement learning. The goal of this workshop is to provide some of the fundamentals for some of the applications of machine learning: regression, classification, dimensionality reduction, and clustering.

This workshop explores techniques for writing clearly as an academic, offering practical strategies for editing your own and other people's sentences. Have you ever read an academic sentence and been completely baffled by what the author was trying to say? Sometimes the ideas are very complex, but more often than not, this is a problem of bad writing. After this workshop, you will have a ready checklist that focuses on the mechanics of sentences and paragraphs on the page, a checklist that will allow you to identify opaque sentences quickly, and even more importantly, know how to fix them.

The workshop aims to teach participants 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.

This workshop 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 as well as 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 how representational forms can achieve decolonizing effects.

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.

Workshop information coming soon.

Large language models can help researchers classify, extract, summarize, and interpret text, but how reliable are their outputs? In this hands-on workshop, participants will use an LLM to analyze a small collection of texts, develop prompts and coding instructions, and compare model-generated annotations with human judgments. We will explore practical strategies for producing structured and reproducible results while examining prompt sensitivity, inconsistent outputs, linguistic bias, privacy, and research validity. Participants will leave with a working notebook and a framework for deciding when, and how, to incorporate LLMs into social science research.

Linguistics is the scientific study of language, and this short course provides an overview of the discipline from a sociolinguistic perspective — seeing language as inextricably bound to society. It will center around language variation (the notion that not all speakers of a language talk the same, not even in every social situation), language change (the notion that language does not stay the same across time), and language attitudes (the attitudes, ideas, and beliefs people have about speaking with certain styles and dialects). We will address what it means to “sound like” a category, how some varieties aren’t treated the same as others, and the potential social motivations for these phenomena.

Linguistics is the scientific study of language, and this short course provides an overview of the discipline from a sociolinguistic perspective — seeing language as inextricably bound to society. It will center around language variation (the notion that not all speakers of a language talk the same, not even in every social situation), language change (the notion that language does not stay the same across time), and language attitudes (the attitudes, ideas, and beliefs people have about speaking with certain styles and dialects). We will address what it means to “sound like” a category, how some varieties aren’t treated the same as others, and the potential social motivations for these phenomena.

Regularized regression is a foundational class of models in statistical machine learning. Following Sections 6.1 and 6.2 from An Introduction to Statistical Learning with R (free PDF online), we will discuss how to use regularization to do variable selection and improve predictions in linear models. We will focus on the lasso and ridge regression (the two simplest forms of regularization), and we will practice implementing these models using the R package glmnet and real-world data.

Prerequisites

Please bring a laptop. In the second half of the workshop, we will use a Google Colab notebook (using your web browser) to complete R exercises.

This workshop provides an introduction to scraping data off HTML websites using R. We will discuss the basic workflow of web scraping, the types of scrapers, the structure of HTML code, and legal/ethical considerations with web scraping. We will cover some practical examples on how web scraping can be used in research. We will go through a guided example using R to extract information from a static web page.

Prerequisites

• Please bring a laptop with R and RStudio installed.
• Understanding of basic R commands and objects, including how to set working directories, create vectors, and load data

This workshop introduces strategies for analyzing qualitative data, with a specific focus on coding techniques and approaches to memoing. We will discuss when to utilize these tools in a research study as well as a number of different ways to use coding and memoing to categorize, analyze, and summarize your qualitative data effectively. We will cover how to establish an iterative analytic strategy that integrates both coding and memoing. Finally, we will discuss the tools available to you in QDA (qualitative data analysis) software programs, and review examples in one of these programs.

Prerequisites

If you have collected your own qualitative data in any form, you may bring a sample of several pages to work with for practice. If not, example material will be provided for you.

An introduction to the core concepts and tools for analyzing spatial data. Students will gain hands-on experience using R software to create spatial data, import and merge data sources, create and interpret thematic maps, and analyze spatial patterns and relationships.

Prerequisites

A laptop with R and the following R packages installed: sf, mapsf, and RColorBrewer.
Familiarity with R, including how to set working directories, load data, and create vectors, data frames, and lists. No prior experience with spatial data or spatial analysis is required. Suggested readings will be available on Canvas prior to the workshop.

This workshop will guide participants through the essential process of developing R packages, the fundamental units for creating shareable, reusable, and reproducible R code. Participants will learn practical steps, including setting up the package files and structure, adding and documenting functions with roxygen2, managing data, writing tests to prevent bugs, and creating vignettes for comprehensive understanding. The workshop will also touch upon local installation and distribution.

Prerequisites

• Experience with R, including using packages and writing functions.
• You will need a laptop with R and RStudio installed to follow along.
• Install the devtools and roxygen2 packages with install.packages().