Interactive Course

Longitudinal Analysis in R

Learn how to analyze and model longitudinal data.

  • 4 hours
  • 13 Videos
  • 49 Exercises
  • 1,260 Participants
  • 3,950 XP

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Course Description

What is longitudinal data and how can you analyze it? Here you will learn all about this kind of data and the descriptive analyses that can be used to explore it! You will also learn to model continuous and binary outcome variables. Linear mixed effects models will be used as a modern approach to modeling this kind of data, taking into account the correlated nature of it. For binary outcomes, generalized estimating equations will be introduced as an alternative to the generalized linear mixed models. Visualizations are used throughout the course to interpret model results and strategies for model selection are also explored. Along the way, you will use data from a number of longitudinal studies, including the Madras and Calcium datasets.

  1. Modeling Continuous Longitudinal Outcomes

    Chapter 2 will model continuous longitudinal outcomes with lme4. These observed score mixed models are common in the analysis of longitudinal data.

  2. Modeling Longitudinal Dichotomous Outcomes

    This chapter will shift from continuous to binary outcomes. Binary outcomes are ones in which the outcome are in two categories. Special considerations for this outcome are needed to appropriately model the data and receive valid statistical results.

  1. 1

    Introduction to Longitudinal Data

    Free

    This chapter introduces the user to longitudinal data. Exploration of what is and what isn't longitudinal data, exploration of the dependent data structure, and other numeric summaries of the data will be covered in this chapter.

  2. Modeling Continuous Longitudinal Outcomes

    Chapter 2 will model continuous longitudinal outcomes with lme4. These observed score mixed models are common in the analysis of longitudinal data.

  3. Add fixed predictor variables

    This chapter will further explore adding additional predictors to the longitudinal model. These predictors, referred to as fixed effects, allow different trajectories based on variable characteristics.

  4. Modeling Longitudinal Dichotomous Outcomes

    This chapter will shift from continuous to binary outcomes. Binary outcomes are ones in which the outcome are in two categories. Special considerations for this outcome are needed to appropriately model the data and receive valid statistical results.

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DataCamp Content Creator
DataCamp Content Creator

Course Instructor

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Collaborators
  • Sascha Mayr

    Sascha Mayr

  • Amy Peterson

    Amy Peterson

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