LATIS offers a series of workshops that are free and open to all faculty, graduate students, and staff. Join our LATIS Research Workshops Google Group to be the first to learn about workshops. You can view the videos, slides, and materials from past workshops at the LATIS Workshop Materials website.
Workshops are also offered on even more topics from partner departments:
- Research Data Services (UMN Libraries)
- MSI (MN Supercomputing Institute)
See workshops.umn.edu for a list of current Research and Computing Workshops across the University.
Fall 2024 LATIS Workshop Schedule
Workshops will be a mix of in-person and online formats. Click on the links below for a detailed description of each workshop. Note: Registrants will be notified of any changes to the workshop locations once rooms are confirmed.
Register here for one or more workshops!
Date & Time | Workshop Title | Location |
---|---|---|
Sept 13 | 9am -11am | Building Experiments & Complex Surveys in Qualtrics | Online (Zoom) |
Sept 20 | 9am -11am | Introduction to R for Social Scientists | Bruininks 512B |
Oct 4 | 9am -11am | Overview of Qualitative Analysis Tools (NVivo & ATLAS.ti) | Bruininks 512B |
Oct 11 | 9am -11am | Bruininks 131B | |
Oct 25 | 9am -11am | Introduction to Python for Social Scientists | Online (Zoom) |
Nov 1 | 9am -11am | Introduction to Text as Data | Online (Zoom) |
Nov 8 | 9am -11am | Vectors & Word Embeddings | Online (Zoom) |
Nov 13 | 9am -11am | Text Analysis with AI | Online (Zoom) |
Dec 6 | 9am -11am | On-campus and Online Participant Pool Recruitment | Bruininks 512B |
Asynchronous Workshops
We also offer asynchronous workshops in canvas that you can take at your own pace. Please contact us [email protected] with any questions or trouble enrolling. Click on the links below for a detailed description of each workshop.
Date & Time | Workshop Name | How to access |
---|---|---|
Available anytime | Introduction to Survey Sampling | Enroll Now |
Available anytime | Qualtrics - Tutorials | Enroll Now |
Available anytime | Working with data in R - Tutorials | Enroll Now |
Available anytime | Linux for Research Computing | Enroll Now |
Available anytime | Managing Data When You Graduate | Enroll Now |
Workshop Descriptions
Building Experiments & Complex Surveys in Qualtrics
Date(s): September 13, 2024; 9:00am - 11:00am
Venue: Online via Zoom
Instructor(s): Sasha Zarins, Alicia Hofelich Mohr
Format: Online
Level: Beginner
Audience: Graduate students
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Summary
Sometimes figuring out the right bells and whistles for more complex research designs in Qualtrics can be daunting. If you’re looking to build complex surveys or experimental tasks within Qualtrics, this workshop is for you!
Join LATIS for a workshop covering how to use some more complex functionality within Qualtrics.
This workshop will cover:
- using the survey flow to create complex survey designs
- randomization
- display & branching logic
- embedded data
- piped text
- automating tasks (such as sending custom follow-up emails) using ‘Workflows’
- using URL parameters to integrate Qualtrics with other websites or tools
This workshop is designed to be interactive so that you can follow along on your own instance of Qualtrics for all activities.
To be successful, you should have:
- Basic experience with Qualtrics-- either through your own research or by taking LATIS's Introduction to Qualtrics Canvas Module (~1 hour).
Introduction to R for Social Scientists
Date(s): September 20, 2024; 9:00am - 11:00am
Venue: Bruininks 512B
Instructor(s): Alicia Hofelich Mohr, Sasha Zarins, Phil Burton
Format: In person
Level: Beginner
Audience: Graduate students, faculty, staff
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Description
R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. R is free and designed for reproducible research. This workshop will teach you how to get started using R to explore and clean your data. We will focus on issues social scientists often encounter when using data in R. This workshop will be held in person, with asynchronous materials to review ahead of time.
This workshop will cover how to
- Create an R script (syntax/command file) to capture data cleaning steps in a reproducible way
- Load a comma-delimited spreadsheet (.csv) into R as a dataset
- View and examine data in R
- Check and correct missing values, rename variables, create new variables, and recode values in the data
- Save cleaned data file in formats for later use in R or other applications
To be successful, you should have
- A familiarity with data used in the social sciences
- A familiarity with another statistical or data processing tool, such as SPSS, Stata, SAS, or Excel
- A laptop you can bring to the workshop, with R (https://cran.r-project.org/) and RStudio (https://www.rstudio.com/products/rstudio/download/) installed.
Overview of Qualitative Analysis Tools (NVivo & ATLAS.ti)
Date(s): October 4, 2024; 9:00am - 11:00am
Venue: Bruininks 512B
Instructor(s): Michael Beckstrand, Tessa Cicak
Format: In person
Level: Beginner
Audience: Graduate students, faculty, staff
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Description
Qualitative data analysis software is a family of tools, including NVivo and ATLAS.ti that provide a workbench for conducting data management, coding and markup of both text and non-text qualitative data. These software also facilitate powerful querying and exploration of source materials for both mixed methods and qualitative analysis. They integrate well with tools that assist in data collection and can handle a wide variety of source material and have recently added functions utilizing OpenAI’s GPT large language models. This workshop introduces the basic functions of these tools with no prior experience necessary and discusses their attempts to integrate AI. Licensing for NVivo is provided for faculty and graduate students of the College of Liberal Arts and the College of Education and Human Development; others can run the software in trial-mode for two weeks or can be given temporary access to the software for this workshop.
This workshop will cover
- Adding your source materials (text, images, audio/video, survey/spreadsheets)
- Working with concepts (or codes/tags) and their definitions
- Making annotations and analytical memos
- Exploring generative AI-based features
- Using text queries to speed up coding
- Finding patterns in the concepts identified in the source materials
- Importing data from other tools including Qualtrics, OneNote, and Zotero
- Exporting excerpts and making backups
- Working in teams
To be successful, you should
- Be familiar with source materials used in qualitative research (interviews, focus groups, field notes, archival documents, etc.)
- Be familiar with the types of questions asked in qualitative research
- Download and install NVivo from z.umn.edu/getNVivo prior to the workshop
Introduction to GitHub for Non-Coders
Date(s): October 11, 2024; 9:00am - 11:00am
Venue: Bruininks 131B
Instructor(s): David Hahn, Alicia Hofelich Mohr, David Olsen
Format: In person
Level: Beginner
Audience: Graduate students, faculty, staff
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Summary
Are you interested in learning about GitHub for version control but don’t know how to get started? Join us for a beginner introduction to GitHub - we'll keep the code to a minimum and unpack the jargon to help you learn all about what GitHub can do.
Description
GitHub is a web application for hosting, sharing, and tracking digital files like code, datasets, and text. GitHub, and the git family of tools, keep track of changes to your files as you work and provide easy ways to integrate changes from multiple people. If you’ve ever found yourself making files named “copy_copy_final” and “copy_copy_real_final”, git is for you.
This workshop will cover
- An overview of git and GitHub, including the differences between the two, and what all the actions (pull, push, fork, clone) mean for your workflow.
- How to use GitHub Desktop to track changes to files on your own computer and how to share them with collaborators.
- How to use GitHub to work with collaborators.
To be successful, you should
- Have a laptop you can bring to the workshop
- Install GitHub Desktop and VSCode (a free text editor) on your laptop prior to the workshop
- Have some basic familiarity with plain text and markdown files
Introduction to Python for Social Scientists
Date(s): October 25, 2024; 9:00am - 11:00am
Venue: Zoom
Instructor(s): Michael Beckstrand, Cody Hennessy, David Olsen
Format: Online
Level: Beginner
Audience: Graduate students, faculty, staff
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Description
Python has seen wide adoption in academic research because it is a powerful but easy-to-learn programming language. It can be used in a manner similar to R or Stata for statistical processing, but also provides wider application in data processing, collection, and file management. Python is free and can be used in many phases of a project to enhance the reproducibility of research. This workshop will teach you how to get started using Python and some of its basic syntax, grammar and structures. It will also introduce the popular package Pandas which provides a familiar dataframe structure to import, format, and clean data as well as functions to manipulate, filter, and analyze data.
This workshop will cover how to:
- Use Python 3 in a JupyterLab computing environment
- Create an script (syntax/command file) to capture steps in a reproducible way
- Use Python to grab data from a large number of files quickly
- Load a comma-delimited spreadsheet (.csv) into Pandas as a dataframe
- View and clean that data
- Save cleaned data file in formats for later use
To be successful, you should have
- A familiarity with data used in the social sciences
- A familiarity with another statistical or data processing tool, such as R, SPSS, Stata, SAS, or Excel
- A computer that can run JupyterLab in an internet browser
Introduction to Text as Data
Date(s): November 1, 2024; 9:00am - 11:00am
Venue: Zoom
Instructor(s): Michael Beckstrand, Cody Hennesy, David Olsen
Format: Online
Level: Beginner
Audience: Graduate students, faculty, staff
Tags:
Summary
An introduction to processing and analyzing text documents using Python.
Description
Scholars in humanities and social science fields are using computational tools to explore large collections of digital texts. This hands-on workshop will introduce common machine learning methods such as topic modeling and sentiment analysis, as well as fundamental cleaning and processing tasks for a text analysis workflow in Python.
This workshop will cover how to:
- Read and write text files in Python
- Manipulate ‘strings’ of text
- Pre-process and clean text for analysis
- Count word frequencies
- Build topic models and conduct sentiment analysis
To be successful, you should
- Be familiar with Python. Have previous experience with an introductory Python workshop, for example.
- Have a computer that can run JupyterLab in an internet browser
Vectors and Word Embeddings
Date(s): November 11, 2024; 9:00am - 11:00am
Venue: Zoom
Instructor(s): Michael Beckstrand, Cody Hennesy, David Olsen
Format: Online
Level: Intermediate
Audience: Graduate students, faculty, staff
Tags:
Summary
Word embeddings and vectorized text are the building blocks of large language models and foundational to modern forms of computational text analysis. Learn how to generate word vectors on a corpus of texts in Python.
Description
This workshop will introduce different ways to represent words in numerical forms to allow for computational analysis using Python. Specifically we will introduce transforming and analyzing documents using Term Frequency - Inverse Document Frequency matrices, and working with word vector models that were generated by neural networks. We’ll introduce methods to generate word similarity scores, plot word embedding in 2D graphs, and perform semantic searching using word vectors across a larger corpus of documents.
This workshop will cover
- Representing words as numbers and in matrices
- Term frequency - Inverse document frequency (TF-IDF)
- Word vectors - what they are and how to use them
- Word similarity scores
- Word embeddings in 2D plots
- Calculating distance between pairs
- Semantic searching across documents
To be successful, you should
- Be familiar with Python as well as basic text as data methods.
- We recommend attending the Introduction to Text as Data workshop before attending this one.
- Have a computer that can run JupyterLab in an internet browser
Text analysis with AI
Date(s): November 15, 2024; 9:00am - 11:00am
Venue: Zoom
Instructor(s): Michael Beckstrand, Cody Hennesy, David Olsen
Format: Online
Level: Intermediate
Audience: Graduate students, faculty, staff
Tags:
Summary
Use the ChatGPT API to begin to explore how large language models can assist with tasks to classify documents and texts into various categories.
Description
Learn how to use a large language model to classify texts. We will use the ChatGPT application programming interface (API) to explore how LLMs can assist with text classification tasks such as binary classification, labeling, applying confidence intervals to judgments, and more. We will get to know the API, engineer model prompts, and automate API calls for large data sets.
This workshop will cover how to
- Understand text classification with LLMs, and how it can be useful for text-based research.
- Interact with the ChatGPT API.
- Structure API calls using different models and prompts
- Set up classification tasks with ChatGPT.
- Understand and parse API JSON responses.
- Understand risks in using generative AI for classification.
To be successful, you should
- Be familiar with Python as well as basic text as data methods and word embeddings.
- We recommend attending the Vectors and Word Embeddings workshop before attending this one.
- Have a computer that can run JupyterLab in an internet browser.
On-campus and Online Participant Pool Recruitment
Date(s): December 6, 2024; 9:00am - 11:00am
Venue: Bruininks 512B
Instructor(s): Sasha Zarins, Thomas Lindsay
Format: In person
Level: Beginner
Audience: Graduate students, faculty, staff
Tags:
Description
Finding people to participate in your research study can be tricky. Whether you are looking to bring people into the lab or recruit them online, it can be hard to find and keep “good” research participants. This workshop will begin by providing an overview of on-campus and online participant pools. We will work through good practices in recruitment, including timelines, contacts, and modes of recruitment. Participants will have the opportunity to workshop their own recruitment materials.
This workshop will cover
- On-campus participant pools and university governance (U-SAT) of campus survey recruitment
- Pros and cons of various online participant pools
- How to manage and interact with your participant pools
- How to write and develop recruitment materials
To be successful, you should
- Have a basic understanding of survey and/or experimental research
Managing Data When you Graduate (Canvas Modules)
Research and creative work doesn't end with degree completion; however, access to many of the data storage tools and software that have supported that work changes when students become alumni. This asynchronous workshop will help graduate students navigate questions about whether they can take their data and materials with them when they leave the university, and if so, how to do it. This workshop is co-organized by the University Libraries.
The workshop will cover:
- The University policies that guide ownership of data
- Access changes to storage, software, and services that happen upon graduation
- Strategies and tips for ensuring data are accessible and understandable long after graduation
Schedule a consultation to discuss:
- How to make a plan to ensure a smooth transition for your data and materials between graduate school and your next endeavor
- Specific advice and troubleshooting for your own research and situation.
To be successful, you should:
- Be a graduate student at the University of Minnesota at least a year into your program (it never hurts to plan early!), or who is nearing the end of your program.
- Have a research project (part of a dissertation or thesis) that has generated data or materials that you want to keep track of after you leave. This can include collaborative projects that will continue at UMN after graduation.
Introduction to Survey Sampling (Canvas Modules)
This is an interactive, self-paced Canvas course, designed for those who are either 1) completely new to surveying or 2) have never had formal instruction in survey/sampling design. By the end of course, you should be able to:
- Differentiate between a census and a sample
- Describe features and limitations of common sampling methods
- Recognize different sources of survey error/bias
- Describe how different sources of survey error/bias affects the conclusions you can draw with your survey
This brief, introductory course to sampling is designed to take around 1-3 hours to complete, depending on the material you choose to engage with.
Qualtrics Tutorials (Canvas Modules)
We have three asynchronous Canvas courses available for you to take:
- Introduction to Qualtrics: Are you brand new to using Qualtrics? Or has it been a really long time since you used Qualtrics? Start here to learn the ropes. [Expected time: 1 hour]
- Qualtrics Data Integrity & Management: No matter if you are new to Qualtrics or a long-time user, this module is a must for any Qualtrics user who is interested in 1) how to make Qualtrics data more readable and suitable to their needs, 2) best practices for conducting reproducible research within Qualtrics (e.g., sharing and archiving survey information, how to export data reproducibly, etc.). [Expected time: 35-45 minutes]
- Designing Experiments & Complex Surveys in Qualtrics: Sometimes figuring out the right bells and whistles for more complex research designs in Qualtrics can be daunting. If you’re looking to build complex surveys or experimental tasks within Qualtrics, this tutorial is for you! We cover how to use some more complex functionality within Qualtrics, such as the using the survey flow, branching logic, embedded data, embedded media, piped text, “loop & merge”, integration with MTurk/Prolific, and more! In this module, you will watch a video walkthrough from our Fall 2021 workshop. [Expected time: 10-20 minutes for Canvas content; 2 hours of video content]
Working with Data in R - Tutorials (Canvas Modules)
R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. R is designed for reproducible research and can be used for many parts of the research process besides statistical analysis. This asynchronous course includes introductory readings, videos, and activities to build on and advance your data skills in R.
Topics include
- Foundations in R: Just starting in R? Welcome! This module will walk you through the basics of R and set the foundation for the more advanced modules below.
- Publication worthy graphs with ggplot2: Learn how to adjust colors, axises, legends, and themes, as well as how to reproducibility save graphs for publication.
- Create a table using dplyr: Learn how to aggregate data and create summaries for tables for publication.
- Reshaping data: Data are not always in the right format for analysis or visualization. Learn how to transform data from wide to long format and back again.
- R Markdown: Combine code, output, and text into readable documents with R Markdown. Learn how to create a basic R markdown document for research.
- Working with Qualtrics data in R: Qualtrics is a popular tool for survey research, but the resulting data often require cleaning before analyzing in R. Learn how to efficiently clean Qualtrics data for use in R, including how to reproducibly remove the multiple headers, save labels, and combine multi-response columns.
Linux for Research Computing (Canvas Modules)
This asynchronous course is a gentle introduction to command line programming using Linux. It is designed for CLA researchers and students who need to use high performance computing resources for their work (for example, to run fMRI analyses, parallel computing, or large scale analyses), but have little to no experience with Linux.
This course guides participants through:
- Connecting to the CLA compute cluster
- Navigating directory and file structure using the Linux command-line terminal
- Creating, modifying, and moving files using the Linux command-line terminal
- Submitting an interactive and a batch computing job and understanding when it is beneficial to use one or the other