Training and Events
methods@manchester summer school 2019 - Introduction to longitudinal data analysis using R
Dr. Alexandru Cernat
08/07/2019 - 12/07/2019
The University of Manchester
View in Google Maps (M13 9PL)
Longitudinal data (data collected multiple times from the same cases) is becoming increasingly popular due to the important insights it can bring us. For example, it can be used to track how individuals change in time and what are the causes of change, it can also be used to understand causal relationships or used as part of impact evaluation. Unfortunately, traditional models such as ordinary least squares regression are not appropriate as multiple individuals are nested in different time points. For this reason specialised statistical models need to be learned.
In this course you will learn the most important skills needed in order to prepare and analyse longitudinal data. We will cover statistical methods used in multiple research fields such as: economics, sociology, psychology, developmental studies, marketing and biology. At the end of the course you will be able to answer a number of different types of questions using longitudinal data: questions about causality and causal order, about changes in time and what explains it, as well as about the occurrence of events and their timing.
Throughout the week we will be using a combination of lecturing and applied sessions. For the applied sessions we will be using the statistical package R. R is becoming one of the leading statistical software due to its free and open source nature. In this course you will learn how to effectively use it to answer longitudinal questions. We will cover both data management and cleaning as well as different statistical methodologies such as: regression analysis, multilevel analysis, structural equation modelling and survival analysis.
• To gain competence in the concepts, designs and terms of longitudinal research;
• To be able to apply a range of different methods for longitudinal data analysis;
• To have a general understanding of how each method represents different kinds of longitudinal processes;
• To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.
No prior knowledge of R is required.
Entry (no or almost no prior knowledge)
Students - £675
Website and registration
Quantitative Data Handling and Data Analysis, Longitudinal Data Analysis, Quantitative Software, R
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