Spring 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)

Spring 2022 Workshops

All workshops are now in a Zoom-based online format, unless otherwise noted.

Jan.28th | 10:00am-noon

Introduction to R

Registration

Feb. 11th | 10:00am-noon

Introduction to Python for Social Sciences

 

Feb. 18th | 10:00am-noon

Creating Publication Worthy Visualizations without Code

Registration

Feb. 25th | 10:00am-noon

Introduction to Computational Text Analysis

 

Mar. 4th | 10:00am-noon

Reproducible research practices in Excel (yes, Excel)

 

Mar. 9th | 10:00am-noon

Data Management in transition: Strategies for when you graduate

 
Mar. 18th | 10:00am-noon [RESCHEDULED] Advanced Nvivo Registration

Mar. 25th | 10:00am-noon

Introduction to parallel computing

 

April 1st | 10:00am-noon

Introduction to SQL and Research Databases

 
June 3rd | 10:00am-noon
[RESCHEDULED]
Setting up a Dynamic Video Event in Zoom Using Professional Camcorders and Audio Registration
     

Available date

Asynchronous workshops

(Work at your own pace online in Canvas)

 

Available anytime

Introduction to Survey Sampling

 
 
Available anytime

Qualtrics - Tutorials

 
 
Available anytime

Working with data in R - Tutorials

 

 

Workshop Descriptions

AnchorIntroduction 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. 

AnchorAdvanced NVivo

Description

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. The software is provided for faculty and graduate students of the College of Liberal Arts and College of Education and Human Development. This workshop introduces the advanced functions of NVivo, with basic knowledge of NVivo recommended.

This workshop will cover

  • A brief review of adding and managing source materials and codes
  • Creating classifications & attributes (variables) with demographic data and importing them from Excel
  • Organizing materials into “cases” to facilitate comparison
  • Using “auto-coding” to segment transcripts and other structured text
  • Complex queries with codes and concepts subset by attributes, cases, or sources
  • Running the built-in interrater reliability metrics
  • Importing data from other software including Qualtrics, OneNote, and Zotero
  • Exporting frequencies and code counts to statistical packages

To be successful, you should

  • Have a basic understanding of qualitative research methods
  • Be familiar with NVivo’s interface and basic functions
  • Install NVivo from z.umn.edu/getNVivo prior to the session, or install a trial from QSR International’s website

 

AnchorIntroduction 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

AnchorCreating Publication Worthy Visualizations without Code

Advanced visualization and graphing are built into many popular statistical and data tools, such as R, Python, and Javascript. However, many of these powerful tools require knowledge of the programming language, which can be a barrier for those looking to create quick, accessible, visualizations for publication. Many non-coding data visualization tools are not ideal for research, as they require many manual steps, allow little customization, and offer little to no documentation of how plots were created. This workshop will introduce attendees to best practices for data visualization, considerations for accessibility, and tips for reproducible visualizations for research. We will conclude with  a discussion and demonstration of three graphing tools: ggPlot GUI, Plotly, and Microsoft Excel.

This workshop will cover:

  • Best practices for data visualization, including design theory, and case studies of great and not-so-great practices 
  • How to adjust plots for accessibility and publication requirements
  • Considerations of what to look for when selecting a visualization tool, including tips for reproducible visualization practices
  • How to implement data visualizations using ggplot GUI, Plotly, and Microsoft Excel.

To be successful, you should have:

  • A basic familiarity with data in the liberal arts
  • A basic familiarity with data visualization 
  • A working internet connection to use ggplot GUI and plotly in a web browser or R and Rstudio to use the ggplot GUI package in R (recommended for sensitive data that should not be uploaded to the ggplot GUI server - we’ll go over how to use ggplot GUI in R in the workshop!)
  • Access to Microsoft Excel (optional)
  • Some of your own data to visualize (optional)

AnchorIntroduction to Computational Text Analysis

Scholars in humanities and social science fields are using computational tools to explore large corpora of digital texts. This hands-on workshop will introduce some common 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 text for analysis (basic cleaning tasks such as normalizing case, stripping punctuation and whitespace, etc)
  • Count word frequencies
  • Create a document term matrix (a ‘bag of words’)
  • Build topic models and conduct sentiment analysis

This workshop will also briefly introduce concepts and tools related to other common computational text analysis tasks: regular expressions (regex) and text cleaning, string matching and fuzzy matching, NLTK tools such as named entity recognition and parts-of-speech tagging, word embeddings (word2vec), classification tasks (e.g., stylometry, genre identification…)

To be successful, you should have:

 

AnchorReproducible research practices in Excel (yes, Excel)

Excel is an easy to use, readily available tool for data entry, organization, and analysis. However, when used for research, pitfalls such as accidentally overwriting or disassociating data, unclear documentation, and potential for data corruption can lead to wasted time, non-reproducible results, and even retracted articles. In this workshop, we will present tips and strategies for using Excel efficiently and in ways that help ensure data remains intact, accessible, and understandable over the long-term. 

This workshop will cover:

  • Common pitfalls for using Excel for research, and strategies to avoiding them
  • Excel commands and features for more efficient and documented research
  • Tips for preparing Excel data for long-term storage and publication

To be successful, you should:

  • Have a basic familiarity with Microsoft Excel
  • A computer with a current or recent version of Microsoft Excel

 

AnchorData Management in transition: Strategies for when you graduate

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 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 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
  • How to make a plan to ensure a smooth transition for your data and materials between graduate school and your next endeavor

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 Parallel Computing

Tired of having that awesome code you developed run on one core while the remaining cores of your CPU go unused? Or worse: is your script taking a long time to run? In cases such as this, having the knowledge to parallelize your code over multiple cores or nodes can significantly increase the speed at which you can solve your analyses. In this workshop, participants will learn how to think in parallel as well as learn techniques for implementing basic parallel schemes often found in social science data processing.

This workshop will cover:

  • Nomenclature of parallel computing
  • When it makes sense to parallelize code
  • Strategies and design patterns for implementing a parallel workflow
  • How to run embarrassingly parallel jobs on multiple cores/nodes with minimal code revision
  • Using the multiprocessor (Python) or parallel (R) libraries to achieve parallelism

To be successful, you should have:

Participants should have some familiarity of working with social science data and have an intermediate level of knowledge in their language of choice, though participants will be given exercises in Python or R for this workshop. Knowledge of the following would be considered prerequisite:

Python track

  • Data and control structures
  • Functions

R Track

  • The apply family

AnchorIntroduction 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 in a SQLite database; and how to connect their database with an R or Python script.

This workshop will cover:

  • Tools of the trade: Creating and accessing a database using software tools
  • 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 and SQLite 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

 

AnchorCreating Professional Video Recordings for Media Projects - Introduction to LATIS Equipment

Video is an instantly engaging medium, as long as the presentation is of high enough quality to effectively convey your message without distractions. But how do you get better quality? We will demystify the settings and answer questions like “could I just use my smart phone,” or “why wouldn’t I use everything in auto mode?” This hands-on workshop will guide you through the steps of conducting a stationary interview with one subject to produce a high quality media recording. We will focus on a repeatable camera and lighting setup that can be a versatile approach for many different scenarios. You will be introduced to, and have hands on practice setting up and using the professional equipment available to you in the LATIS checkout center. This workshop will be helpful for those planning on developing media projects as a component of their research output. We’ll practice setting up and using the equipment from 10:00-noon, take a quick, half hour lunch, then talk about planning and tips, and practice interviewing.

This workshop will cover:

  • The basics of setting up camera, tripod, lighting and microphones 
  • The essential items - whether, and how to use auto or manual - for camera and sound
  • Connecting the microphone and setting the audio levels on the camera
  • A quick setup for interview lighting 
  • General tips for successful interviewing on camera

To be successful, you should have:

  • Wear closed toe shoes

 

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.

 

AnchorQualtrics 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]

  1. 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: 35-35 minutes]

  1. 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]

AnchorWorking 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. 

 

Setting up a Dynamic Video Event in Zoom Using Professional Camcorders and Audio

With Zoom creating new opportunities for guest lectures and presentations, the ability to stream the in-person lectures and presentations has never been easier. Now that Zoom has added the function to also activate the stream on YouTube, the platform for reaching and archiving events has become very accessible for the user and the viewer. The only limitation is often the small cameras on a laptop or a web camera, making the image rather limited in quality and in its ability to zoom in or move freely. This workshop will introduce using professional video cameras and wireless lavalier microphones as a setup to assist any lecture or presentation scenario. We will look at two different cameras, available for checkout from the LATIS Video Equipment Center in Rarig 640, and the microphone options available as well. We will walk through the setup in Zoom and also send the stream to YouTube. 

This workshop will cover:

  • The basic setup for a camcorder using auto or manual features.
  • The basic setup for the wireless microphones.
  • Setting up Zoom with the camera video and audio.
  • Sending the Zoom stream to Youtube. 

What you will need for this workshop:

  • The basic understanding of Zoom.
  • A laptop to test out the process.