Research Workshop Series

LATIS offers a series of workshops created by our experts 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.

Introduction to Nvivo

Friday, January 26, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

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. This workshop introduces the basic functions of NVivo and will also explore more advanced features including: using classifications/attributes (variables) for sorting sources, importing survey data from Qualtrics, organizing analysis using “cases”, and exporting data from NVivo to a statistics package or for archiving purposes. (No prior experience with NVivo is necessary.)


Data Wrangling with Python (using pandas)

Friday, February 2, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

Pandas holds a special place in the pantheon of Python libraries as it provides researchers with high-performance, easy-to-use structures and tools for data analysis. In this workshop, participants will learn how to import, format, and clean data using pandas built-in methods as well as learn some common techniques to manipulate, filter, and analyze data using the pandas data frame. Some familiarity with social science data and Python is assumed.



Introduction to ATLAS.ti

Friday, February 9, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

ATLAS.ti is a long-standing qualitative analysis program, used to organize, tag, and analyze a variety of research materials including text, audio, and visual sources. This workshop will give an introduction to the major functions and uses of this research tool including: source material organization, codes and coding strategies, querying your materials, grouping and stored queries or super codes, backing up and sharing your project, and working collaboratively. (No prior experience with ATLAS.ti is necessary.)



Reproducible Experimental Research in Qualtrics

Friday, February 16, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

Qualtrics is a survey tool available to all University of Minnesota researchers, and can be used to support surveys as well as behavioral and experimental research. This workshop will teach participants how to develop online experiments using this tool, including how to randomize participants to conditions, customize participant paths based on responses, and embed multimedia stimuli. We will also cover best practices on using this tool reproducibly, including how to capture important metadata, prepare data for analysis, and integrate Qualtrics with other online tools, such as Amazon’s Mechanical Turk. This workshop is targeted toward UMN researchers who have some experience with Qualtrics and plan to conduct quasi-experimental or experimental research.



Introduction to Linux II

Friday, February 23, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

UNIX/Linux is a robust family of operating systems that has become the de factor standard in researching computing. This workshop builds on the materials presented in Intro to Linux I and will introduce I/O redirections, processes and job control, customizing the environment, data munging at the command line, and basic shell scripting. A basic level of comfort working in a Bash shell is assumed.


Qualitative Analysis - Coding Overview

Friday, March 2, 2018 I 9:30 a.m. – 12:00 p.m. Register (new tab)


The heart of most forms of qualitative analysis involves coding: the segmentation, bucketing, sorting, tagging, or otherwise categorizing of portions of source materials. This workshop will include hands-on exercises and cover a variety of approaches to coding qualitative materials, including various types of codes and their uses, while also addressing the mechanics of how to code with highlighter & paper or using qualitative analysis software. It will focus on best practices and techniques for doing the work of coding, rather than on epistemological and ontological concerns, which are best handled by disciplinary methodologists in participants' specific fields.


Introduction to Python

Friday, March 9, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

Python has seen wide adoption in academic research because it is a powerful but easy-to-learn programming language. In this workshop, participants will learn about the pros and cons of using Python for research computing, the fundamental building blocks and basic grammar of a Python program, and how the basic libraries of the scientific Python stack can be connected to create robust, reproducible analyses. This workshop is targeted towards grad students and other researchers who have had minimal exposure or are new to Python, though some previous scripting or programming experience (e.g., MATLAB or R) would be helpful.


Introduction to R

Friday, March 23, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

This workshop will provide an introduction to R, a popular tool for statistical computing. Participants will learn how to get started using R for social science data, including how to read data into R, what to consider when prepping your data for R, basic data cleaning (renaming, recoding, and converting variables), basic data exploration, and how to save files manipulated in R. No experience using R is assumed. Please bring a computer to the workshop with R (https://cran.r-project.org/) and RStudio (https://www.rstudio.com/products/rstudio/download/) installed.


Data Management in Transition: Strategies for When You Graduate

Friday, March 30, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

Research, scholarly, and creative work doesn't end with degree completion; however, access to many of the data storage tools and software that have supported their work changes when students become alumni. This workshop will help graduate students navigate questions of if and how they can take their data and materials with them when they leave. Participants will learn about University policies that guide data ownership, access changes that happen upon graduation, as well as resources, tips, and advice for planning to manage their own data and materials after they leave.



Introduction to Parallel Computing

Friday, April 6, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

This workshop is intended to provide participants with a quick overview of the broader parallel computing landscape, including: (a) the basic nomenclature and concepts underlying parallel computing, (b) some techniques for running "embarrassingly parallel" jobs, (c) libraries such as OpenMP and OpenMPI, and (d) when it makes sense to use  accelerators (e.g., GPUs) in the parallel pipeline. While the material presented in this workshop is able to be used over a number of languages, examples and exercises will focus on parallel computing in R or Python.



Mixed Methods Analysis in NVivo

Friday, April 13, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)


NVivo is a qualitative data management, coding and markup tool, that facilitates powerful querying and exploration of source materials for mixed methods analysis. This workshop covers the more advanced functions of NVivo, including queries and matrices that use classifications/attributes (variables) for sorting sources, importing and working with survey data from Qualtrics, organizing analysis using “cases” complete with demographic/bibliographic information, and exporting data from NVivo to a statistics package. (Basic knowledge of NVivo’s core functions is recommended.)


Introduction to SQL and Research Databases

Friday, April 27, 2018 I 9:30 a.m. – 12:00 p.m. I Register (new tab)

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 database; and how to connect their database to a R or Python script.