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Learning to Master R: From Basics to Discontinuous Growth Modelling
Mon, 17 Apr 2017, 9:00 AM – Thu, 20 Apr 2017, 5:00 PM MDT
This workshop is divided into three modules:
1) Introduction to R,
2) Multilevel Analysis in R, and
3) Random Coefficient & Discontinuous Growth Modeling in R.
Each module will include a lecture component in which we explain the theoretical background of an analysis and show you how this type of analysis is done in R by means of several examples. Next, it is up to you to work on exercises while we walk around to answer any question you might have about R and the analyses.
Part 1: Introduction to R (Monday 17th and Tuesday 18th of April).
Are you eager to use R for your statistical analyses, but don’t know where and how to start? Then this first module is ideal for you! The aim of the first module is to introduce R to new users and to highlight the benefits and possibilities of the R program in statistical analyses. During an 1.5 day course we will provide you with an introduction into the R-language, the application of R in data-analysis, the organization of your data in an R-compatible way, and the procedures to graphically represent your results. After this module you will be able to master the basics of R, request descriptive statistics, run (multiple) regression analysis, and ANOVA analysis.
Part 2: Multilevel Analysis in R (Wednesday 19th of April).
Ready to kick it up a notch and explore nested data? Then this second module is ideal for you! In this second module we deal with multilevel data, meaning data that are nested within multiple levels (e.g., children nested within classes). During a 1 day course we will provide an introduction to multilevel analysis in R. After a brief introduction of multilevel modelling in general, we will show you how to prepare data for multilevel analysis in R, how to perform multilevel regression analysis and how to test for (cross-level) interaction effects. After this module you will be able to understand the background of multilevel modelling, prepare data for multilevel analysis (reshaping data, creating time-lagged variables), use the "lme4" and "multilevel" packages, calculate ICC values, perform multilevel regression analysis, and test for (cross-level) interactions. This module requires adequate knowledge of basic statistical analysis and R. We recommend that people who are not familiar with R first follow the module titled “Introduction to R”.
Part 3: Random Coefficient & Discontinuous Growth Modelling in R (Thursday 20th of April).
Want to understand how people or variables change over time and how change in one variable might be related to change in another variable? Then this third module is ideal for you! In this third module we deal with growth over time; either continuous (relating a series of events to each other) or discontinuous (allowing for a breaking point in between two series of events). After this module you will be able to understand the background of basic growth modelling and discontinuous growth modelling, prepare data for (discontinuous) growth modeling, when to use which type of growth modelling, use the "ggplot2" and "nlme" packages, determine the optimal shape of the growth model, and perform basic and discontinuous growth modelling. This module requires adequate knowledge of basic statistical analysis and multilevel modelling in R. We recommend that people who are not familiar with R and/or multilevel modelling in R first follow the modules titled “Introduction to R” and "Multilevel analysis in R".
Date and Time
University of Calgary
2500 University Drive Northwest
Social Sciences Building - Room SS018
Calgary, AB T2N 1N4