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Workshops & Training

Elevate your computing and data-related skills through workshop sessions on data processing, replication, and qualitative analysis.

  • Image of 291 seminar room in Clark Hall with white chairs facing presenter podium

Fall 2024 Workshops

Workshops are open to the Cornell community!

CCSS workshops are held in person at 291 Clark Hall and via Zoom. Get Directions.

Message socialsciences@cornell.edu for workshop-related questions. 

 

Data Processing

Discover data processing techniques like web scraping and data extraction.

Description:  

Web scraping is the process of extracting text and numeric data from a webpage using software. Join this workshop to discover how Python, the most popular software for web scraping, makes it easy to extract the data you need from a webpage and store it in a data frame. 

Register Here

Learning Objectives:

  • Identify and extract HTML data from a webpage with the inspect feature.
  • Use Python’s BeautifulSoup library to extract data and store it in a data frame.
  • Demonstrate how Python’s Selenium package allows for advanced data extraction using website interactions such as mouse clicks, filling out forms, etc.
  • Understand HTML format, including tags, attributes, etc.
  • Distinguish between extracting data through web scraping vs APIs. 

Helpful Information:

Instructor: Jacob Grippin

Description:   

The American Community Survey (ACS) is an annual survey conducted by the U.S. Census Bureau that gathers housing and population data for the United States. Researchers can access the ACS data through the IPUMS and Census Bureau websites. Workshop participants will gain hands-on experience using the tools on the IPUMS and Census Bureau websites to find and extract the ACS data needed for their research. 

Register Here

Learning Objectives:

  • Create a custom dataset on the IPUMS website by selecting housing and population variables.
  • Import custom dataset into a programming language (Stata, SAS, R) for analysis.

  • Find and create summary tables using ACS data on the data.census.gov website.

  • Filter ACS data by county, year, household size, household income, etc.

  • Review popular articles written by Cornell authors that include the ACS data. 

Instructor: Samantha De Leon Sautu

Description:   

The Cornell Federal Statistical Research Data Center (FSRDC) provides access to confidential federal data from several agencies, including the U.S. Census Bureau. The Cornell FSRDC administrator, Nichole Szembrot, will give an overview of the available data and proposal process. This workshop is recommended for faculty and Ph.D. students.

Register Here

Learning Objectives:

  • Understand the advantages of working with confidential data compared to public-use data
  • Become familiar with the data available through the Cornell FSRDC
  • Understand the process of applying for access to confidential data

Instructor: Nichole Szembrot

Description:  

This workshop will introduce participants to psychophysiological measures, the physiological correlates of psychological processes. It will describe common psychophysiological measures and their collection methods, analytical processes, and interpretation. 

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Learning Objectives:

  • Examine psychophysiology as a field of study and pupil diameter, skin conductance, and heart rate monitoring as psychophysiological measures. 

Instructor: Samantha De Leon Sautu

Description:  

Web scraping is the process of extracting text and numeric data from a webpage using software like R. Join this workshop to discover how R makes it easy to extract the data you need from a webpage and store it in a data frame. 

Register Here

Learning Objectives:

  • Identify and extract HTML data from a webpage with the inspect feature.

  • Use R’s Rvest package to extract data and store it in a data frame.

  • Demonstrate how R’s RSelenium package allows for advanced data extraction using website interactions such as mouse clicks, filling out forms, etc.

  • Understand HTML format, including tags, attributes, etc.

  • Distinguish between extracting data through web scraping vs APIs. 

Helpful Information:

Instructor: Jacob Grippin

Description:  

This workshop, geared toward social scientists, will explore the biomarker data hidden in our bodily fluids and the best methods for collecting and analyzing that data. After establishing a conceptual framework, participants will explore biomarker data in existing databases and the methods for collecting their own. 

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Learning Objectives:

  • Define biomarkers and classify them by biological system.
  • Explore biomarkers’ predictive, diagnostic and provisional values and their relevance to social science research.
  • Identify common methods for collecting biomarkers in bodily fluids.
  • Evaluate the usefulness of biomarker data based on research question and design. 

Instructor: Samantha De Leon Sautu

Replication

Make your research results reproducible before publication.

Description:  

In this workshop series we will use Stata to generate tables and figures directly from statistical output. You will never have to cut-and-paste results, making published works easily replicable. You will learn how to generate tables for word or LaTex output formats. Preparing your code and results so they can be comfortably uploaded to a repository. There are no pre-requisites, though some familiarity with Stata will be helpful.
 

Register Here

graph of CCSS data.

*Example of Multiple Regressions table

 

graph of CCSS data.

*Figure of Violin Plot to show distribution of data

 

Learning Objectives:

  • Increase awareness of the need for reproducible data visualizations.

  • Use Stata code to create and save reproducible tables and figures that require no additional formatting.
  • Apply formatting code to improve the appearance of tables and figures.
  • Learn how to maintain code for later reproducibility. 

Helpful Information:

Instructor: Jacob Grippin

Description:  

In this workshop series we will use R to generate tables and figures directly from statistical output. You will never have to cut-and-paste results, making published works easily replicable. You will learn how to generate tables for word or LaTex output formats. Preparing your code and results so they can be comfortably uploaded to a repository. There are no pre-requisites, though some familiarity with R will be helpful.

Register Here

graph of ccss data

*Example of multiple regressions table

*Figure of Density Plot split by group

 

Learning Objectives:

  • Increase awareness of the need for reproducible data visualizations.
  • Use R code to create and save reproducible tables and figures that require no additional formatting.
  • Apply formatting code to improve the appearance of tables and figures.
  • Learn how to maintain code for later reproducibility. 

Helpful Information:

Instructor: Samantha De Leon Sautu

Description:  

This workshop is a continuation of the previous one 'Using Stata to Create Data Visualizations'. In this workshop series we will use Stata to generate tables and figures directly from statistical output. You will never have to cut-and-paste results, making published works easily replicable. You will learn how to generate tables for word or LaTex output formats. Preparing your code and results so they can be comfortably uploaded to a repository. There are no pre-requisites, though some familiarity with Stata will be helpful.

Register Here

Graph of CCSS data.

*Example of full and restricted Regression models

Graph of CCSS data.

*Figure of Bubble Chart split by type

 

Learning Objectives:

  • Use Stata code to create and save reproducible data visualizations.

  • Apply advanced formatting code so data visualizations display features like data markers, color mapping, significance, and legends.
  • Embed advanced data visualizations into a manuscript. 

Helpful Information:

Instructor: Jacob Grippin

Description:  

This workshop is a continuation of the previous one 'Using R to Create Data Visualizations'. In this workshop series we will use R to generate tables and figures directly from statistical output. You will never have to cut-and-paste results, making published works easily replicable. You will learn how to generate tables for word or LaTex output formats. Preparing your code and results so they can be comfortably uploaded to a repository. There are no pre-requisites, though some familiarity with R will be helpful.

Register Here

graph of ccss data.

*Example of Descriptive Statistics Table

graph of ccss data.

*Figure of Correlation Plot

 

Learning Objectives:

  • Use R code to create and save reproducible data visualizations.
  • Apply advanced formatting code so data visualizations display features like data markers, color mapping, significance, and legends.
  • Embed advanced data visualizations into a manuscript. 

Helpful Information:

Instructor: Samantha De Leon Sautu

Qualitative Analysis

Perform qualitative analysis using the software applications MaxQDA and Atlas.ti.

Description:  

Before you can analyze your qualitative data, you need to organize it! This workshop will share techniques to help attendees organize their qualitative data and format their transcripts so they’re ready for analysis using software like Atlas.ti and MaxQDA. Attendees will also leave the workshop with an understanding of what information to collect from their respondents and the ideal timeline for preparing and analyzing their qualitative data using qualitative software. 

Register Here

Learning Objectives:

  • Identify types of files most often used in qualitative analysis.
  • Format transcripts and structure folders for the best organization of qualitative data.
  • Create folders and sub-folders based on data type.

  • Set up folders for project teams.

  • Establish the importance of preparing and analyzing qualitative data after the first interview.

  • Discover computing resources at Cornell that are suited for qualitative analysis. 

Helpful Information:

Instructor: Florio Arguillas

Description:  

MaxQDA is a popular qualitative analysis software. This workshop covers importing qualitative data files into MaxQDA and using the software to identify themes and trends within text. It will also take participants through the steps of using MaxQDA’s AI Assist to summarize and explore their qualitative data. 

Register Here

Learning Objectives:

  • Import qualitative files including transcripts and recordings into MaxQDA.
  • Create and apply codes in MaxQDA to identified themes.
  • Generate reports for specific coded segments and query results.

  • Use MaxQDA’s AI Assist to summarize coded segments and documents, identify themes, and utilize suggested codes.

  • Properly save MaxQDA projects. 

Helpful Information:

Instructor: Florio Arguillas

Description:  

MaxQDA is a popular qualitative analysis software. This workshop will explore the various tools in MAXQDA that can analyze and visualize qualitative data. 

Register Here

Learning Objectives:

  • Discover mixed methods tools in MaxQDA for analyzing qualitative data, including cross tabulations and quantitizing (transforming codes into variables).
  • Visualize qualitative data using MaxMaps, Code Matrix Browser, Code Relation Browser, Word Cloud, and more.

  • Create worksheets in MaxQDA’s Questions-Theories-Themes (QTT) workspace to organize results by research question, theory, or theme. 

Helpful Information:

Instructor: Florio Arguillas

Description:  

Atlas.ti is a popular qualitative analysis software. This workshop covers importing qualitative data files into Atlas.ti and using the software to identify themes and trends within text. It will also take participants through the steps of using Atlas.ti’s AI features to summarize and explore their qualitative data. 

Register Here

Learning Objectives:

  • Import qualitative files including transcripts, interviews, audio and video recordings into Atlas.ti.
  • Create and apply codes in Atlas.ti to identify themes and trends within text.
  • Create folders and sub-folders to organize coded segments by theme.

  • Use Atlas.ti’s AI features to summarize coded segments and documents, identify themes, and utilize suggested codes.

  • Properly save Atlas.ti projects. 

Helpful Information:

Instructor: Jacob Grippin

Description:  

Atlas.ti is a popular qualitative analysis software. This workshop will explore the various tools in Atlas.ti that can analyze and visualize qualitative data. 

Register Here

Learning Objectives:

  • Use the Query Tool, Code Co-Occurrence Tool, and Code Document Table to analyze and visualize qualitative data.
  • Generate reports for specific coded segments and query results.

  • Perform sentiment analysis to categorize text segments by positive, negative, and neutral emotions.

  • Discuss how Atlas.ti can facilitate collaboration in a project team. 

Helpful Information:

Instructor: Jacob Grippin

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