Workshop Instructors

Kyung-Hee Bae
Director, Center for Academic & Professional Communication; Senior Lecturer
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Effective Research Presentations

Learn how to effectively present your research to diverse audiences in this comprehensive workshop. We will explore essential strategies for organizing your research presentation, capturing your audience's attention, and delivering your research findings with confidence. You will also discover how to visually represent your data to enhance the audience's understanding of the significance of your research findings. Whether you're presenting at a conference or sharing your work with colleagues, this workshop will equip you with the tools to elevate your research.

Roberto Bertolusso
Senior Pfeiffer Lecturer, Department of Statistics
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Intermediate R

Workshop information coming soon.

Chase Coleman
Lecturer, Economics
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Computational Economics

Workshop information coming soon.

Bryan Denny
Assistant Professor, Psychological Sciences
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fMRI Methods in Cognitive Neuroscience

Workshop information coming soon.

Christina Diaz
Assistant Professor of Sociology
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Introduction to Linear Models

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.

Prerequisites

undergraduate statistics; or review of supplemental material provided by the instructor. The use of a scientific calculator (e.g. TI-30) is encouraged for working through workshop material.

Todd Ferguson
Assistant Teaching Professor of Sociology; Director of Social Sciences Quantitative Methods Program
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Introduction to R

This workshop introduces R and RStudio, with an emphasis on analyzing and visualizing quantitative data. Participants will learn to examine and modify datasets, index and transform dataframes, calculate descriptive statistics, and create basic plots in R. No programming experience is needed!

Prerequisites

Familiarity with descriptive statistics and the essentials of statistical hypothesis testing is recommended. Participants will need to bring their own device with R and RStudio pre-installed. Installation instructions and suggested readings will be available on Canvas prior to the workshop.

Luz Maria Garcini
Assistant Professor, Department of Psychological Sciences
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Mixed Methods for Diversity Health Research

This workshop will provide an introductory overview on the use of mixed methods research for working with historically underrepresented and marginalized communities.

At the conclusion of the workshop, attendees will be able to:

• Understand the need for and importance of mixed methods for diversity research
• Learn to identify a topic and formulate scientific questions for mixed methods studies
• Identify different types of mixed methods core study designs
• Learn basic strategies for collecting, analyzing and interpreting mixed methods data
• Understand the basics of writing mixed methods papers

Prerequisites

To get the most out of this workshop, it will be helpful for attendees to come prepared with a research idea that they could explore using mixed methods. Templates will be provided to outline your ideas using mixed-methods.

Edgar Avalos Gauna
Lecturer, Department of Statistics
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Machine Learning

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.

Ilana Gershon
Professor of Anthropology
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The Science and Art of Academic Writing

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.

Hua Gong
Assistant Professor of Sport Analytics and Sport Management
Website

Introduction to Python

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.

Cymene Howe
Professor of Anthropology
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Ethnographic Techniques I

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 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.

Ashley Leeds
Professor of Political Science
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Success in Grad School and Beyond--What Does This Mean and Tips for Achieving It

This workshop is aimed at helping participants navigate a Ph.D. program and prepare for a career after earning the Ph.D. We will discuss the following: 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? How can you work effectively with a mentor? How might one manage the common phenomenon known as “imposter-syndrome”? What kinds of post-Ph.D. paths match your interests and goals, and how do you prepare for them? The goal of the workshop is to help students negotiate the norms and practices of the graduate school environment in ways that allow them to achieve their version of success and do the work they find most meaningful.

Jing Li
Adjunct Associate Professor of Sociology
Website

Introduction to Multilevel Modeling

This applied course introduces the basic concepts and applications of multilevel models, also known as hierarchical linear models or mixed 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) with a focus on conceptual understanding and interpretation of results. Both Stata and R codes will be discussed.

Prerequisites:

A good understanding of descriptive statistics, significance testing, bivariate regression, multivariate regression and generalized linear models. Basic knowledge of Stata or R is preferred but nor required.

Melissa Marschall
Professor of Political Science
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Experiments in the Social Sciences

Workshop information coming soon.

Bryce McCleary
Lecturer in Linguistics
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Sociolinguistics and Language Discrimination

Workshop information coming soon.

Nancy Niedzielski
Department Chair and Associate Professor of Linguistics
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Sociolinguistics and Language Discrimination

Workshop information coming soon.

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

Conducting Meta-Analysis

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 with me.

Scott Powers
Assistant Professor of Sport Analytics and of Statistics
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Lasso and Ridge Regression in R

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 basketball data from the WNBA.

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. The following STaRT@Rice workshops could be helpful but are not required:

• Introduction to Linear Models, Introductory Track
• Introduction to R, Introductory Track
• Intermediate R, Intermediate Track
• Machine Learning, Intermediate Track

Alex Pugh
Lecturer, Social Policy Analysis
Website

Web-Scraping with R

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.

Anna Rhodes
Assistant Professor of Sociology
Website

Coding and Memoing in Qualitative Analysis

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.

Elizabeth Roberto
Assistant Professor of Sociology
Website

Agent-Based Modeling

Workshop information coming soon.

Kaitlyn Robinson
Assistant Professor of Political Science
Website

Data Cleaning, Wrangling, and Visualization in R Using tidyverse

This workshop provides an introduction to using the R tidyverse to load, clean, shape, and visualize data. We will begin by discussing how to use tidyverse commands to clean and shape raw datasets (e.g., data downloaded from third party sources online) into forms that can be used for analysis and visualization. Participants will learn how tidyverse pipe operators are used and how they can help streamline multiple steps into a single chunk of code. We will also discuss the basics of ggplot2 – a component of the tidyverse – and how it can be used to create and customize plots and figures.

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.
• Understanding of basic data structure, formats, and variable types (e.g., factor variables, character variables, logical variables)

Sean Smith
Data Services Specialist, Fondren Library
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Analyzing and Visualizing Census Data with R

Workshop information coming soon.

Angela Thompson
Assistant Director of Survey Research, Office of Institutional Effectiveness
Website

Survey Design and Implementation via Qualtrics

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.

Matt Tyler
Assistant Professor of Political Science
Website

Approaches to Recovering Missing Data

The goal of this seminar is to equip attendees with the skills to address the all-too-familiar problem of missing data. We will begin by discussing what missing data problems can and cannot be "fixed" with standard methods. We will then introduce the theory and practical implementation of multiple imputation as a general missing data solution. (Note: our focus will be on large-N cross-sectional or large-N/small-T panel datasets.)

Prerequisites

• Comfort with linear regression models. Familiarity with generalized linear models or machine learning methods would help attendees but is not strictly necessary.
• Comfort with R and R packages. Package installation instructions and example datasets will be available on Canvas prior to the workshop.
• Comfort with statistical ideas like conditional dependence and independence. Comfort with conditional expectation.

Marina Vannucci
Noah Harding Professor, Department of Statistics
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Bayesian Thinking and Analysis

Workshop information coming soon.

Emily Wager
Postdoctoral Researcher, Political Science
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Experiments in the Social Sciences

Workshop information coming soon.

Miranda Waggoner
Associate Professor of Sociology
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Archival Methods for Health Research in the Social Sciences

This workshop introduces key approaches to archival methods in the social sciences, focusing particularly on research related to health and medicine. We will discuss the various kinds of archived sources and data that researchers may seek out and use in their work, and we will discuss multiple strategies for undertaking archival research. Using examples from both digital archives and physical archives, we will cover how to access, collect, and analyze archival sources/data, and we will consider how such work can add to, expand, and enrich conversations about contemporary issues and debates in health research. We will also examine the meaning of “the archive” and review ethical and practical considerations surrounding archival research.