GESIS Training Courses
user_jsdisabled
Search

Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Modern Longitudinal Analysis using R

About
Location:
Cologne / Unter Sachsenhausen 6-8
 
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 550 €
Academics: 825 €
Commercial: 1650 €
 
Keywords
Additional links
Lecturer(s): Alexandru Cernat, Nick Shryane

About the lecturer - Alexandru Cernat

About the lecturer - Nick Shryane

Course description

Longitudinal data are an essential tool for researchers as they can help answer questions about change in time, causal relations, and the timing of events. They come in many shapes, from traditional panel surveys to social media and sensor data. Because of their additional complexity, specialized statistical models are needed to analyze them.
 
In this course, you will learn how to analyze longitudinal data using R. The course is developed to include statistical models from a number of different fields, giving you a comprehensive knowledge of models such as: multilevel models for change, latent growth models, cross-lagged models, and survival models. The course is also hands-on, each topic being accompanied by real world applications using R and practical exercises. In addition to learning about statistical models, you will also learn how to prepare and visualize longitudinal data. You will also have the opportunity to discuss your own research projects and get guidance on how you can use the methods covered in the course in your own work.
 
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.


Target group

Participants will find the course useful if:
  • they are PhD students working with quantitative data
  • they are Quantitative researchers


  • Learning objectives

    By the end of the course participants will:
  • Learn how to clean longitudinal data
  • Learn how to visualize longitudinal data
  • Learn how to use cross-lagged models
  • Learn how to use the multilevel model for change
  • Learn how to use the latent growth model
  • Learn how to use survival models
  •  
    Organizational structure of the course
    Each day will be divided in two sections (approximately morning and afternoon). In each one we will follow a structure of lecturing followed by hands-on practicals with real data and then going through the solution together. The lecturers will be available to help you if you have questions regarding the practicals. During this time you will also be able to get feed-back and guidance from the lecturer on your own research.


    Prerequisites

  • Working knowledge of R
  • Good knowledge of regression modelling
  •  
    Software and hardware requirements
    Course participants will need to bring a laptop with the latest versions of R (https://cran.r-project.org/) and RStudio installed (https://www.rstudio.com/). Both programs are free and open source. Participants will need to be able to download files from the internet (free Wifi is provided by GESIS) and have the rights to install packages on their laptops during the course. The following packages will be used and should be installed before the course: tidyverse, foreign, lme4, lavaan, survival, plm.


    Recommended readings