R Tutorial: Introduction to Longitudinal Data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/longitudinal-analysis-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome! I am Brandon LeBeau, an Assistant Professor at the University of Iowa. My research focuses on longitudinal data, research software engineering, and quantitative program evaluation. In this course, we will explore modern ways to model longitudinal data for both continuous and dichotomous outcome variables. Before getting into the modeling approaches, we will first learn what longitudinal data is, what it isn't, and how to explore the data descriptively first. In this course, we will define longitudinal data as data with at least three measurements on a single unit, with multiple units involved. Units are often individuals, but they can also be things like businesses or hospitals. A few examples of longitudinal data include measuring an individual's blood pressure every week for six weeks, math test scores of students in grades three through eight, or whether a student is enrolled in extracurricular activities measured every semester for grades seven through twelve. The repeated measurements could take many forms including data that are continuous or dichotomous. Dichotomous data take on two values whereas continuous data can take on a wide range of values, these will be explored in more detail later in the course. Before exploring data, let's discuss what longitudinal data is not. Situations with multiple measurements for a single unit require different methodology. Time-series analysis is often used with this type of data, which is common in business settings. If there are only two measurements of the same individual, for example, a pre and post-test, trajectories over time can't be explored. Other methods can be used including linear regression, like analysis of covariance or ANCOVA, or t-tests, depending on the purpose. These methods wil