Intensive Longitudinal Analysis of Human Processes
R code for analyses and recorded lectures for each chapter available via a link available at: https://www.personspecific.com/
This book focuses on a span of statistical topics relevant to researchers who seek to conduct person-specific analysis of human data. Our purpose is to provide one consolidated resource that includes techniques from disciplines such as engineering, physics, statistics, and quantitative psychology and outlines their application to data often seen in human research. The book balances mathematical concepts with information needed for using these statistical approaches in applied settings, such as interpretative caveats and issues to consider when selecting an approach.
The statistical topics covered here include foundational material as well as state-of-the-art methods. These analytic approaches can be applied to a range of data types such as psychophysiological, self-report, and passively collected measures such as those obtained from smartphones. We provide examples using varied data sources including functional MRI (fMRI), daily diary, and ecological momentary assessment data.
- Description of time series, measurement, model building, and network methods for person-specific analysis
- Discussion of the statistical methods in the context of human research
- Empirical and simulated data examples used throughout the book
- R code for analyses and recorded lectures for each chapter available at the book website: https://www.personspecific.com/
Across various disciplines of human study, researchers are increasingly seeking to conduct person-specific analysis. This book provides comprehensive information, so no prior knowledge of these methods is required. We aim to reach active researchers who already have some understanding of basic statistical testing. Our book provides a comprehensive resource for those who are just beginning to learn about person-specific analysis as well as those who already conduct such analysis but seek to further deepen their knowledge and learn new tools.
1.Theory: Mathematical theorems about the relation between IAV and IEV.
3. Vector Autoregression (VAR).
4. Dynamic Factor Analysis.
5. Model Specification and Selection Procedures.
6. Models of Intraindividual Variability with Time-Varying Parameters (TVPs).
7. Control Theory Optimization of Dynamic Processes.
8. The Intersection of Network Science and IAV.