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.

Workshop information coming soon.

Workshop information coming soon.

The goal of this workshop is to provide students with an introduction to the research methods of social, cognitive, and affective neuroscience. Students will learn about the theoretical underpinnings of methodologies common to these allied fields, with a particular focus on functional magnetic resonance imaging (fMRI). We will discuss different types of analysis, and demonstrations of techniques for fMRI analysis of cognitive neuroscience experiments will be provided.

Prerequisites

No specific prerequisites other than an interest in social, cognitive, and/or affective neuroscience. Please bring a laptop.

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 paragraph on the page, a checklist that will allow you to identify opaque sentences quickly, and even more importantly, know how to fix them.

Experiments are one of the most powerful tools researchers can use to test causal relationships. This workshop will begin by reviewing the fundamental concepts of experimental design, including its principles, objectives, and significance in the social sciences. We will discuss how experiments help uncover causal relationships and refine their research questions to achieve precise and meaningful results. The course will explore various types of experiments, with special emphasis on survey and field experiments. Participants will learn when and how to employ these techniques, considering the context, resources, and research objectives. Throughout the workshop, participants will examine real-world case studies and practical examples of successful experimental designs in the social sciences. This hands-on approach will help participants apply their knowledge to diverse research scenarios.

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.

Meta-analysis is one of the most popular statistical methods in the sciences for aggregating statistical effects in a wide variety of research disciplines, including (but certainly not limited to) the social sciences, medicine, education, and business. Although meta-analysis is motivated by a conceptual or theoretical framework, actually conducting the meta-analysis requires identifying, sifting, combining, analyzing, comparing, and interpreting effect sizes across a set of studies investigating the same (or similar) psychological phenomena. In short, meta-analysis is a process that requires a solid conceptual foundation and judicious decision making as much as (and perhaps more than) statistical skill. This course will walk you through this process: e.g., developing and using a coding sheet; understanding statistical artifacts (e.g., sampling error variance, measurement error variance); choosing between fixed-, random, and mixed-effects models; the visualization of heterogeneity and potential publication bias; and some extensions of basic meta-analysis models.

Prerequisites

Within your research area(s) of interest, please locate the PDF of one published meta-analysis that looks interesting to you, and please email me that
(foswald@rice.edu). Also, it is helpful (though not necessary) to bring a laptop with R and RStudio installed, to run analyses together with me.

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.

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 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().

This interactive workshop will introduce participants to the core concepts behind generative artificial intelligence, large language models, and effective academic use case workflows. Participants will explore how these tools work, what they can (and cannot) do, and how to leverage them responsibly to support scientific tasks. Emphasis will be placed on hands-on exercises and critical reflection to foster confidence, creativity, and ethical awareness when integrating AI into social science research. No technical background is required.

Prerequisites

No strict prerequisites. Preferably, participants should bring a laptop (for the hands-on activities) and have some previous exposure to basic research workflows (e.g., literature review, data collection/analysis) to better contextualize workshop examples.

This workshop is an introduction to the Qualtrics Survey Software (DesignXM product). The workshop will include:

• Step-by-step instructions for creating a survey, disseminating the survey using various methods, exporting data, and viewing results
• Overview of XM Directory
• Qualtrics tips and tricks including custom coding to add a print button and how to hide the submit button to prevent survey submission (and why that may be necessary)
• Useful but less commonly used survey features
• Qualtrics review of survey methodology & compliance best practices
• Examples of surveys and applications built using Qualtrics
• An opportunity to create and administer your own survey

Prerequisites

• Please bring a laptop with Microsoft Excel installed. To get the most out of this workshop, it will be helpful for attendees to come prepared with 5 sample survey questions and 5-7 test email addresses to use for developing and administering a survey during the workshop.
• If you are a current Rice University SoSS graduate student, then you must sign up for a Rice Qualtrics account before the workshop. You will be doing the hands-on portion using your current campus-wide Rice Qualtrics account.
• If you are a prospective SoSS graduate student and do not have a Qualtrics account of any kind, then send the Workshop Instructor an email to create a temporary Rice University Qualtrics account within the OIE Qualtrics platform.
• If you are a prospective SoSS graduate student and have a Qualtrics account from another institution or personal account, then you can use that account for the demo (but may be limited if permissions are different than what we have at Rice University) OR you can provide the Workshop Instructor with a separate email address that is NOT affiliated with any Qualtrics account and the Workshop Instructor can create a temporary Rice University Qualtrics account within the OIE Qualtrics platform.
• Finally, please complete this short intake form.

Many statistical methods implicitly assume that all variables are fully observed. Yet, in practice, data sets often have missing observations. This workshop will cover techniques for analyzing incomplete data sets as if they were complete. Particular emphasis will be placed on introducing the theory and practical implementation (in the R programming language) of multiple imputation as a default—but not always ideal—missing data solution.

Prerequisites

• Familiarity with regression.
• Familiarity with the concept of conditional independence.
• Familiarity with the R programming language (for coding examples).