Transitioning to R for Statistics

A hands-on guide to transitioning from other software (e.g., SPSS, SAS, or Stata) to
the open-source R platform for conducting statistical analyses: correlations, group comparisons, multiple regression, moderation, and generalized linear modeling.

Instructor:
Jeffrey Girard, PhD (University of Kansas)

Workshop Dates and Times:
Monday, June 17, 2024, 9:30am to 4:30pm ET
Tuesday, June 18, 2024, 9:30am to 12:30pm ET

Workshop Format:
1½ Day Synchronous Online Workshop

R is a popular software option for analyzing, wrangling, and visualizing data in the social, behavioral, and medical sciences. It is completely free, runs on all major platforms (i.e., Windows, Mac, and Linux), and offers a rich catalog of tools and techniques that are updated frequently. It is an excellent choice for researchers who conduct statistical analyses and is often the first software to support exciting new modeling approaches.

This one-and-a-half-day workshop is aimed at helping researchers transition to R from other software packages (e.g., SPSS, SAS, or Stata) for conducting statistical analyses such as correlations, group comparisons, multiple regression, moderation, and generalized linear modeling. It assumes that learners already know the concepts and theory underlying these techniques (e.g., from a graduate statistics course or textbook, perhaps using a different software package) and is not intended as a substitute for such a course or textbook. It also assumes that learners have a basic understanding of how R works (e.g., from the Introduction to R for Researchers workshop).

The knowledge gained in this workshop is designed to be modernized and tailored to the needs of researchers in the social, behavioral, and medical sciences. It will be invaluable for other workshop offerings that focus on more advanced techniques in R.

What you’ll learn

  • Basic Statistics

    Estimate correlations, Flexibly compare many groups, and Apply non-parametric approaches

  • Linear Models

    Fit multiple regression, Test for moderation, Fit polynomial effects, and Check model assumptions

  • Extensions

    Adjust standard errors for heteroskedasticity and clustering, Predict non-normal outcomes

Registration Options

Transitioning to R for Statistics

  • Professional
  • $449
  • Baseline Price for Faculty,
    Staff, and Other Professionals
  • Click Register Below
  • Trainee
  • $449 $299
  • 33% Discount for
    Students and Postdocs
  • Use code "TRAINEE" at Checkout

Combo 1: Transitioning to R for Statistics + Transitioning to R for Multilevel Modeling

  • Professional
  • $899 $719
  • Baseline Price for Faculty,
    Staff, and Other Professionals
  • 20% Combination Discount
  • Click Register Below
  • Trainee
  • $719 $479
  • 33% Discount for
    Students and Postdocs
  • 20% Combination Discount
  • Use code "TRAINEE" at Checkout

Combo 2: Transitioning to R for Statistics + Introduction to R for Researchers

  • Professional
  • $1048 $839
  • Baseline Price for Faculty,
    Staff, and Other Professionals
  • 20% Combination Discount
  • Click Register Below
  • Trainee
  • $839 $559
  • 33% Discount for
    Students and Postdocs
  • 20% Combination Discount
  • Use code "TRAINEE" at Checkout

 FAQs

  • This workshop was custom built for researchers in the social, behavioral, and medical sciences who have completed an introductory statistics course in graduate school and are looking to transition to R from other software packages (e.g., to modernize their statistical practices or take advantage of the many powerful features that R offers). This workshop is intermediate and appropriate for faculty, staff, and students at both the graduate and advanced undergraduate levels.

  • This workshop assumes that learners already have a good grasp on graduate-level applied statistics. That is, learners should be confident in their knowledge of statistical inference, correlations, group comparisons, and multiple regression. It also assumes that learners have a basic grasp of how R works (e.g., have completed the Introduction to R for Researchers workshop).

  • Because generalized linear modeling (e.g., logistic, poisson, and ordinal regression) is only a small part of the workshop, learners would likely still get a lot out of attending this workshop. However, there won’t be time to teach the theory behind these methods, so learners are encouraged to try our Deep Dive into GLM workshop to gain this specific knowledge.

  • Registered learners will gain access to the Zoom link and passcode to attend the synchronous workshop, as well as video recordings of the lectures, downloadable slideshows, and example data files.