Abstract
In this chapter, the authors seek to present a self-contained treatment of state-space modeling and attempt to make the exposition accessible to those who have relatively little prior knowledge of the subject. They focus on issues of modeling and show how statespace models offer a flexible and rich class of structures that accommodate both the dynamic and static nature of intensive longitudinal data. Longitudinal data obtained from a group or group of subjects followed over time often show within-subject serial correlations, involving random subject effects and the presence of observational errors. Researchers are usually interested in describing the trend over time, whether there are critical differences in the trend across groups of subjects, and what factors can be considered for this trend and the differences.
Original language | English (US) |
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Title of host publication | Models for Intensive Longitudinal Data |
Publisher | Oxford University Press |
ISBN (Electronic) | 9780199847051 |
ISBN (Print) | 9780195173444 |
DOIs | |
State | Published - Mar 22 2012 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© Oxford University Press, 2014.
Keywords
- Differences
- Dynamic processes
- Longitudinal data
- Modeling
- Serial correlations
- Structures
- Trend
ASJC Scopus subject areas
- General Psychology