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Usage of RStudio for regression techniques and hierarchical linear modeling
September 8, 2021 @ 11:00 am - 3:00 pm$200
A 4-hour workshop taught by Mariola Moeyaert, Ph.D.Register
This is the second of four 4-hour workshops on the R statistical programming language. Sign up for the other workshops in the series using the links below:
This workshop focuses on the usage of RStudio to run regression models (including univariate and multivariate regression, and logistic regression) and hierarchical liner models in order to provide an answer to practical questions.
The workshop is hands-on, and annotated R code, together with data sets and detailed content slides, will be shared. Participants new to RStudio are recommended to take the workshop Introduction to the R statistical computing environment.
The R statistical programming language and computing environment is a free, open-source package for statistical analysis. R has become the de-facto standard in statistics (see Muenchen, 2018; Suda, 2017) and is widely used in the social, health, physical, and computational sciences. Researchers transition to R because it is powerful, flexible, makes routine data analysis easy and has a large and rapidly growing community of users.
Participants are strongly encouraged to have the most recent version of R installed. Participants are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. RStudio will be used during the workshop. This software is free and available for Windows, Mac, and Linux platforms.
After attending this workshop, participants will be able to use RStudio to run basic and advanced regression models and hierarchical linear models. The participants will understand the RStudio syntax needed to run these inferential statistical analysis techniques. The workshop focuses on real-life, practical questions, preparing participants to apply the techniques to their own substantive areas of interest.
Pricing and schedule
Time: Tuesday, September 8, 11AM to 3PM (EST)
We offer $50 discounts for graduate students and $25 discounts for multiple workshop enrollments. Find out about our discounts here.
The main workshop material is scheduled for four hours. Time permitting, Dr. Moeyaert will answer questions about participants’ specific research projects. Participants can ask questions via chat, microphone, or telephone. In order to allow sufficient time for questions, the number of workshop participants is limited to 30.
Who should attend?
The workshop is designed for those who want to become fluent in using RStudio to run regression models and hierarchical linear modes.
- Multivariate linear regression/logistic regression
- Hierarchical linear modeling (2 hours)
About the instructor
Dr. Mariola Moeyaert is an Associate Professor Statistics within the division of Educational Psychology and Methodology at UAlbany. She received her PhD in Educational Statistics from the Katholieke Universiteit Leuven (KU Leuven) in Belgium and worked as a Postdoctoral fellow at the Center of Advanced Study in Education at the City University of New York. Her major research interests and publications are in the field of multilevel analysis, meta-analysis and interrupted time series analysis. She is also interested in Bayesian statistics, bootstrapping, structural equation modeling, longitudinal data analysis, and international comparative research. She (co)authored about 47 international publications, in such journals as Psychological Methods, Multivariate Behavior Research, Journal of School Psychology, Behavior Research Methods, School Psychology Quarterly, etc., reporting about developments in research methodology (including several computing intensive Monte Carlo simulation studies) and about applications of statistical models on educational/medical data. Currently, she is involved as PI and Co-PI in research projects funded by the Institute of Education Sciences about methodological advancements in the field of multilevel meta-analysis. You can find more information about Dr. Mariola Moeyaert by following these two links (1) https://www.