Fall 2022 Workshops

LATIS offers a series of workshops that are free and open to all faculty and graduate students. 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:

  • DASH (Digital Arts, Sciences, and Humanities)
  • MSI (MN Supercomputing Institute)

Fall 2022 LATIS Workshop Series

This fall, we are offering an Online Data Collection Workshop Series (ODCS) as well as our regular LATIS workshops. See below for more information about each of our workshops. 

Fall 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.

Register here for one or more workshops!

Sept 16 | 10:00am-noon

Introduction to R

In-Person

Sept 23 | 10:00am-noon

Introduction to NVivo

In-Person

Sept 30 | 10:00am-noon

Introduction to ATLAS.ti

In-Person

Oct 7 | 10:00am-noon

Introduction to Databases and SQL

In-Person

Oct 14 | 10:00am-noon

Introduction to Python for Social Science

Online
Oct 21 | 10:00am-noon Introduction to Web APIs with Python - Twitter Online
Asynchronous (see below)  Online Data Collection Series: Introduction to Qualtrics Online
Oct 28 | 10am-noon Online Data Collection Series: Reproducible Research in Qualtrics  Online
Nov 4 | 10am-12:30pm Online Data Collection Series: Building Experiments and Complex Surveys in Qualtrics Online
Nov 11 | 10am-noon Online Data Collection Series: Data Quality in Online Data Collection Online
Dec 2 | 10:00am-noon Introduction to Git/GitHub In-Person

 

Register today!

Asynchronous Workshops

We also offer asynchronous workshops in canvas that you can take at your own pace. Please contact us [email protected] if you do not receive an invitation to the canvas course within a week. Click on the links below for a detailed description of each workshop.

Available anytime Introduction to Survey Sampling Register
Available anytime Qualtrics - Tutorials Register
Available anytime Working with data in R - Tutorials Register

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.  

Workshop format:
  • BEFORE WORKSHOP: Asynchronous materials to review on Canvas (2 sections: 30-40 minutes each)
  • DURING WORKSHOP: The workshop will be held in person (as local guidance allows). We will offer a link to an asynchronous all-online version for those who would prefer to not attend in person. 
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.

 

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; 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 ATLAS.ti

ATLAS.ti is a qualitative analysis program, used to organize, tag, and analyze a variety of research materials including text, audio, and visual sources. Its lineage is linguistic and discursive and provides a flexible workbench in which to conduct interpretive research, as well as other types of qualitative inquiry. This workshop introduces the basic functions of ATLAS.ti, with no prior experience necessary. It is held in a computer lab with the trial version of the software installed. (The full version of the software is to faculty and graduate students of the College of Liberal Arts, Carlson School of Management and the Humphrey School of Public Affairs.)

This workshop will cover
  • Adding your source materials (text, images, audio/video)
  • Working with codes (i.e. tags/themes/concepts)
  • Making annotations (comments) and analytical memos while linking them to sources
  • Using groups (of sources, codes, etc) to organize and segment materials
  • Performing searches using your codes
  • 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

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; how to write SQL queries to retrieve, delete, insert, and update data; and how to connect their database with an R or Python script.

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  
  • Scripting and SQL: Writing scripts to access, view and manipulate data
To be successful, you should have: 
  • A laptop to bring to the workshop
  • Optional: Install Python on your laptop (we recommend Anaconda).
  • There will be an online environment available for using Python or R, so local installation is not required.
  • Some familiarity with the concept of a relational database 
  • An intro-level familiarity with the Python programming language and/or R

Introduction to Python for Social Sciences

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 Twitter and the New York Times. In this workshop we’ll use Python to query and download data using the new Twitter API, eligible for Academic access to historical data.

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 from the Twitter v2 API
  • 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
  • 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 14, 2021 is not required, but recommended.

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 laptop you can bring to the workshop
    • Install Git Desktop on your laptop prior to the workshop
    • 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

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ODCS: Conducting Reproducible Research in Qualtrics 

Well documented experiments and data are the foundations for reproducible research. This workshop will focus on methods reproducibility within Qualtrics - specifically, what things to document and capture to ensure that others (and future you!) could repeat the same study. We will also focus on best practices in Qualtrics to help you ensure data integrity and to make your data management and analysis processes more efficient. No matter if you are new to Qualtrics or a long-time user, this workshop will benefit any Qualtrics user who is interested in: 

  1. best practices for conducting reproducible research within Qualtrics
  2. tips to improve their Qualtrics data management process
  3. how to make Qualtrics data exports more understandable/human-readable
  4. best practices for sharing and archiving their Qualtrics survey information 

This session is designed to be 1h 30min, with 30 additional minutes for general questions at the end. It will be half webinar, half interactive workshop. 

To be successful, you should have:
  • Basic experience with Qualtrics-- either through your own research or by taking our “Introduction to Qualtrics” Canvas module

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ODCS: 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! 

This workshop will cover how to use some more complex functionality within Qualtrics, such as:
  • using the survey flow to create complex survey designs
  • branching logic
  • embedded data
  • embedded media
  • piped text
  • “loop & merge”
  • integration with MTurk/Prolific, and more! 

This workshop is designed to be interactive so that you can follow along on your own instance of Qualtrics for all activities. Taking our “Conducting Reproducible Research in Qualtrics” workshop beforehand is recommended, but not required.

To be successful, you should have:
  • Basic experience with Qualtrics-- either through your own research or by taking our “Introduction to Qualtrics” Canvas module

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ODCS: Data Quality in Online Data Collection 

Even when study data is collected in-person, outlining the steps to ensure data quality for human subjects data can feel a little overwhelming. Now, with the large shift to online data collection, there is even MORE to think about– How do we ensure that only the people who should be responding to our studies  are responding? What are the best ways for making sure our participants are actually paying attention? What’s the deal with bots gaining access to studies? How do I set a data quality plan in place that is reproducible?

We’ll go over these questions (and more!) in our interactive workshop. In particular, we will discuss: 

  • Strategies to prevent bad actors and bots from taking your online study
  • Strategies to assess the quality of data after it has been collected online
  • The upsides and downsides of implementing these strategies 
  • How your participant pool (e.g. social media vs. MTurk vs. Prolific vs. market research panel vs. your own participant pool) can influence data quality and the data quality measures you want to put in place 
  • How to build a data-quality game plan for your online study, with an emphasis on research reproducibility
To be successful, you should have:
  • A general familiarity with survey and/or experimental study data collection in the social sciences

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Introduction to Survey Sampling (Canvas Module)

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]

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Working with Data in R - Tutorials (Canvas Module)

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.