This book focuses on a span of statistical topics relevant to researchers who seek to conduct person-specific analysis of human data. The primary audience are students and researchers in psychometrics, quantitative psychology, psychophysiology and neurocognition. This book can be used for both teaching and research.

Peter C. M. Molenaar, Ph.D., is Distinguished Professor of Human Development and Family Studies at The Pennsylvania State University, University Park, USA.

Sy-Miin Chow, Ph.D., is Associate Professor of Human Development and Family Studies at The Pennsylvania State University, University Park, USA.

The analysis of intra-individual variation (IAV) concerns time series analysis applied to human processes such as psychological, psycho-physiological, and disease processes. There are many features unique to this methodology and a need for a book giving an overview of the methods, with examples and software. This book fills that gap, covering all the key topics, notably dynamic factor models together with detailed worked examples from the behavioral sciences and special software for the analyses.

Introduction. First encounter with IAV. Statistical analysis of IAV. Data sets. Overview of contents. Preliminaries. Ergodic theory: mathematical theorems about the relationship between analysis of IAV and IEV. Setting the stage. Some history about ergodic theory. Testing ergodicity of Gaussian processes. Conclusions. Dynamic factor models for stationary series to test for heterogeneity. Introduction. P-technique for N=1. General dynamic factor models and state space models for N=1. Block-Toeplitz approach. Kalman filtering and smoothing. Raw data likelihood approach. Hybrid dynamic factor models for EMA data. General dynamic factor models and state space models for N>1. How to test for and initially characterize heterogeneity. The dangers of pooling. The Idiographic Filter. GIMME. An innovative application: iFACE. Dynamic factor models for nonstationary time series. Introduction. SEKFIS. Heterogeneity and nonstationarity. Measurement invariance. An innovative application: Modeling the glucose dynamics of type 1 diabetic patients. Dynamic factor analysis in the frequency domain. Introduction. The discrete Fourier transform (DFT). Principal component analysis in the frequency domain. Identifiability and rotation. The dynamic factor model in the frequency domain. Dynamic structural equation modeling in the frequency domain. Optimal control of IAV. Setting the stage. LQG control. Extensions. Conclusions.