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Workshops

Workshop schedule for the current semester, and workshop description pages.

  • Workshops

  • Workshops

    Register Now!

    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. 

    September

    • Using Conjoint Analysis in Analyzing Individuals’ Underlying Preferences – 9/29/2021, 12pm – 1pm
    • Open Refine – 9/30/2021, 1pm – 3pm

    October

    • Introduction to Stata – 10/5/2021, 1pm – 3pm
    • How to Make your Research Transparent and Reproducible – 10/6/2021, 1pm – 3pm
    • Introduction to SPSS – 10/13/2021, 1pm – 3pm
    • Introduction to Github – 10/14/2021, 2pm – 4pm
    • Introduction to SAS – 10/19/2021, 1pm – 3pm
    • Introduction to Atlas.ti – 10/20/2021, 2pm – 4pm

    November

    • Using NVivo as a Research Tool – 11/2/2021, 2pm – 4pm
    • Introduction to RMarkdown – 11/3/2021, 1pm – 3pm

  • Workshop Descriptions

    For the basics with your data, we provide a variety of training options. These range from scheduled workshops each semester with related online resources, to specialized training in classes and for research project teams.

    Qualitative:

    Quantitative\Qualitative:

    Quantitative:

    Versioning:

    CCSS-RS workshop downloads are available to the Cornell Community.  See additional workshops from CSCU.

  • Custom Training

    Custom Training for Classes

    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

    Custom Training for Project Teams

    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.

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