Workshop schedule for the current semester including descriptions for each session.
Check back here for our spring workshops!
All CCSS Research Support workshops will be held online via Zoom this semester. Students registered for workshops will be emailed the Zoom link prior to the start of the workshop.
Introduction to Atlas.ti
Overview: Atlas.ti is a powerful workbench for qualitative data analysis. You may bring your own qualitative data for processing during the workshop.
- Learn how to create a project in Atlas-ti
- Assign documents
- Quote and code segments of text
- Write comments and memos
- Create and assign relations between codes or quote
- View the relationships using the Network view
- Create code and groups
- Produce output
- Search for text
- Auto code
- Export project bundle as well as export to an SPSS job
Using Conjoint Analysis in Analyzing Individuals' Underlying Preferences
Overview: Conjoint Analysis helps researchers across different fields to identify individuals’ preferences and evaluate their choice trade-offs in the context of a survey.
- How to successfully configure conjoint experiments in Qualtrics using the Conjoint Choice-based Application provided by Qualtrics
- Explain how to analyze and utilize Conjoint-based data
- Successfully design and implement their own questionnaire in the form of a Conjoint experiment and will learn to access, analyze, and present the findings of their Conjoint survey using R
Software/tool/method: Qualtrics Choice-Based Conjoint and R Studio
Area of expertise connects to: surveys, survey experiments, social sciences, business analytics, marketing
Prerequisites: Basic understanding of Qualtrics, survey analysis, and R is helpful.
Introduction to Github
Overview: Git is a tool that helps keep track of changes made in project documents such as program files or source codes, effectively versioning them, and allows teams to collaborate via a central repository hub such as Github, Bitbucket, or Gitlab.
- Creation and configuration of a git repository:
- Editing, staging and committing files
- Retrieving previous versions of files
- Setting up of a repository on Github for individual use and/or project team collaboration
- Creation and configuration of a git repository:
How to Make your Research Transparent and Reproducible
Overview: Replication of results is a core requirement of the scientific method. Satisfying this requirement becomes increasingly complex when data from disparate sources is integrated and reused. While code used for analysis of data must be verified, it is also imperative that code and processes used to clean, integrate and harmonize data be documented and verified. This can be time-intensive and intimidating, especially for individual researchers seeking to openly share their work.
- Introduction to the Cornell Center for Social Sciences’ Results Reproduction Service
- Walk you through the process of reviewing your manuscript, data, code and output to ensure that the reproduction materials you will share to the public or submit to a journal for publication will reproduce your results exactly
- Discuss common mistakes to avoid in manuscripts and code
- How to package reproduction materials for easy re-use and independent understandability
- CCSS Data and Reproduction Archive as the institutional repository for your reproducibility package
Using NVivo as a Research Tool
Overview: NVivo is software that supports qualitative and mixed methods research. It is designed to help users organize, analyze and find insights in unstructured, or qualitative data like: interviews, open-ended survey responses, articles, social media and web content.
- Create an NVivo project
- Add documents
- Create references and codes
- Generate outputs and visuals
- Use various tools for autocoding and querying
Instructor: Florio Arguillas, CCSS Research Associate
Overview: OpenRefine is a user-friendly tool for cleaning, transforming, and preparing your data for analysis. This workshop will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them months of work.
To do the exercises along with the workshop, you can first download and install OpenRefine along with the data file used in the workshop:
Preparation for the Workshop:
Introduction to RMarkdown
Overview: RMarkdown is a tool that allows you to keep all in the same document your code, your outputs (summaries, plots, etc), your text and, most importantly, your thoughts and comments. You can produce nicely formatted output in html, pdf or even Microsoft Word. It is extremely easy to use and will force you to document well what you are doing. RMarkdown is a file format for making dynamic documents with R. An RMarkdown document is written in markdown (an easy-to-write plain text format) and contains chunks of embedded R code to create output.
Instructor: Jacob Grippin, CCSS IT Help Desk, Lead Statistical Consultant
Preparation for the Workshop:
Introduction to SAS
Overview: SAS is a program that teaches to access, explore, prepare, and analyze data used in data science, machine learning, and artificial intelligence.
This is a virtual hands-on workshop in SAS programming.
- Learning the structure of a SAS program
- Debug a SAS Program
- Creating SAS data sets from the three most common data formats: Excel, Column-delimited ASCII, Existing SAS data sets
- Exploring the structure and contents of a SAS data set
- Selecting observations and variables in a data set
- Creating and modifying variables
- IF-THEN-ELSE and iterative DO loops
- Working with Dates
- Create multiple output data sets
- SAS Procedures for performing statistical analysis such as MEANS, UNIVARIATE, FREQ, CORRELATION, and REGRESSION procedures
- SAS Output Delivery System (ODS)
Post Workshop files:
Advanced SAS Macro Language
Overview: The SAS Macro Facility allows SAS users to extend and customize their SAS code and reduce the amount of text that must be entered by the user.
- Learn and use some basic features of the SAS Macro Language
- Write code which we will then submit to the SAS Macro Processor
- Generate examples of both static and dynamic SAS program code for processing
Prerequisites: Introduction to SAS, or substantive experience with SAS (must understand SAS Data and Proc Steps, SAS libraries, etc).
Introduction to SPSS
Overview: SPSS is short for Statistical Package for the Social Sciences, and it’s used by various kinds of researchers for complex statistical data analysis.
- Creating an SPSS data file
- Exporting an SPSS data to another format
- Running simple statistical procedures (frequencies, crosstabs, means, correlations, regression)
- Using the output viewer
- Creating a syntax file
- Importing an Excel data file to SPSS
- Merging data sets
- Creating new variables using the COMPUTE, RECODE and IF commands
- Selecting a subset of cases
- Data cleaning
- Creating graphs
Post Workshop Files:
The Introduction to SPSS workshop session 1 may also be viewed in the following 1-hour video:
Introduction to Stata
Overview: Stata is a general-purpose integrated statistical software package used for data manipulation, analysis, management, graphics, and automated reporting.
- Entering, accessing, Importing, modifying, organizing and analyzing data
- Generating basic summary statistics, combining datasets, arranging and analyzing data
- Creating new variables
- Subsetting of data
- Basic regression and correlation
- Creating and saving batch .do files
The Introduction to Stata workshop may also be viewed in the following 2-hour video:
Overview: Advanced Stata is a program that introduces new commands in Stata, enhancing more complex estimation commands and building off of the foundations of Stata.
- Accessing automatically saved results
- Working with matrices
- Using global and local macros
- Incorporating loops(foreach, forvalues)
- Installing ado-files
Prerequisites: Introduction to Stata, or substantive experience with SAS (must understand SAS Data and Proc Steps, SAS libraries, etc).
We provide class training tailored to your group’s specific needs on topics related:
- Data processing and management
- Use of qualitative software packages such as Atlas.ti and NVivo
- Use of statistical software packages such as SAS, SPSS, STATA, and R
- Use of CCSS-RS research and class computing servers
We provide just-in-time training for project teams in both qualitative and quantitative data management and processing. Research data management and processing is a long and complex process. We provide training that is needed at a particular phase of the data lifecycle so as to eliminate loss of knowledge and skills caused by a large gap between training and actual use. Using this approach we also eliminate the need for refresher training due to subject knowledge loss or loss of people who leave the team before the training they received is used on the job.