Fall 2023 Workshops

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)

Fall 2023 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 the workshop locations once rooms are confirmed. 

Register here for one or more workshops! 

Register today! 

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.

Workshop Descriptions

Introduction to R 

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. 

Infographics for Research Communication 

Infographics are a great way to visually describe abstract research concepts and to present data in a simplified format. Not only are they excellent for reminding us to wash our hands or explaining the protein content of milk, but you’ve probably used them in your lectures and created them for posters, presentations, and graphical abstracts. This workshop will take a look at how infographics can illustrate complex or abstract ideas so we can explain without using text heavy slides and make our research more accessible to wider audiences. Could this description have been an infographic? Find out at the workshop!

This workshop will cover how to:

• Develop concrete images from abstract or complex ideas.
• Convey messages/emotions/states through color, fonts, and layouts.
• Use various free online resources for making and editing infographics.

To be successful, you should have:

• A laptop you can bring to the workshop
• No previous knowledge of design software or artistic abilities are required.

Introduction to NVivo

NVivo is a qualitative data management, coding and markup tool, that facilitates powerful querying and exploration of source materials for both mixed methods and qualitative analysis. It integrates well with tools that assist in data collection and can handle a wide variety of source materials. This workshop introduces the basic functions of NVivo, with no prior experience necessary. Licensing 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
• 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 Git and GitHub

GitHub is a web application for hosting, sharing, and tracking digital assets like source code and datasets.  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 how to:

• Create a repository with the University-provided github.umn.edu website
• Use Git Desktop or the Git command line interface to track files on your own computer and push them up to GitHub. 
• Use Git to manage revisions and collaborate with team members 

To be successful, you should have:

• A computer with Git Desktop installed. There will also be an online environment available using the Git command line tools should you not wish, or are unable, to install Git Desktop.
• A University of Minnesota Internet ID

Introduction to SQL and Research Databases

Text files are fine when you have thousands of observations, but what do you do when you have millions (or billions)? In this workshop, participants will learn about choices in database technology that support data at scale; the building blocks of schema design; and how to write SQL queries to retrieve, delete, insert, and update data.

This workshop will cover:

• When and why to use a database over a flat file
• Basic database design: What are tables, relations, indices, etc.
• SQL and all that CRUD
  • Create (or import): Tables and data
  • Retrieve: Querying and exporting data in a way that makes sense
  • Update: Changing table structure and data they house
  • Delete: Deleting rows of data, truncating tables, deleting tables  

To be successful, you should have: 

• A laptop to bring to the workshop
• There will be an online environment available for learning SQL, no installation required

Introduction to Python for Social Science

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 Web APIs in Python

Web APIs (Application Programming Interfaces) provide a way for scholars to efficiently and legally access and download data from web platforms and publications such as the New York Times, or to access bulk data like citations with Scopus or OpenAlex. In this workshop we’ll use Python to query and download data from an API to get a handle on the full process, from gaining access credentials all the way to preparing data for analysis.

This workshop will cover how to:

• Use Python 3 in a JupyterLab computing environment
• Read API documentation to build successful API queries
• Use the Requests and JSON Python libraries to download data
• Use built-in Python functions such as type, len, and dir to explore API data
• Explore API data in Python using dictionaries

To be successful, you should have:

• A computer you can use during the workshop, with
  • Anaconda installed for using Python OR a computer that can run JupyterLab in an internet browser
  • Create a free NYTimes developer account 
• No prior experience with these tools is necessary, and participants do not need to have any coding skills. 
• The Introduction to Python workshop on October 13, 2023 is not required, but recommended.

Building 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 workshop is for you! 

Join LATIS and the University Survey and Assessment Services (USAS) for a workshop covering how to use some more complex functionality within Qualtrics. 

Topics will include:

• 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).

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: 

1. Differentiate between a census and a sample
2. Describe features and limitations of common sampling methods
3. Recognize different sources of survey error/bias
4. 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: 

1. 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]
    
2. 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]
    
3. 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

1. 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. 
2. Publication worthy graphs with ggplot2: Learn how to adjust colors, axises, legends, and themes, as well as how to reproducibility save graphs for publication. 
3. Create a table using dplyr: Learn how to aggregate data and create summaries for tables for publication. 
4. 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. 
5. R Markdown: Combine code, output, and text into readable documents with R Markdown. Learn how to create a basic R markdown document for research. 
6. 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:

1. Connecting to the CLA compute cluster
2. Navigating directory and file structure using the Linux command-line terminal
3. Creating, modifying, and moving files using the Linux command-line terminal
4. Submitting an interactive and a batch computing job and understanding when it is beneficial to use one or the other