Resources

APA-ATI Analysis of Intensive Longitudinal Data: Experience Sampling and Ecological Momentary Assessment

Course materials and code associated with the APA Advanced Training Institute: Analysis of Intensive Longitudinal Data: Experience Sampling and Ecological Momentary Assessment. Additional background about the workshop can be found at https://www.apa.org/science/resources/ati/analysis-longitudinal-data. For some of the examples, code for other…

APA-ATI Structural Equation Modeling in Longitudinal Research

Course materials for the APA Advanced Training Institute: Structural Equation Modeling in Longitudinal Research. Background information about the yearly workshop can be found at https://www.apa.org/science/resources/ati/equation-model.

Intensive Longitudinal Data: Analysis of Experience Sampling and EMA Data

Intensive longitudinal data are often collected using ecological momentary assessment (EMA), experience sampling (ESM), daily diary, ambulatory assessment, and related designs. Chronicling our experience working with data from such studies, we are building a repository of scripts and tutorials that researchers may find useful during analysis of…

Growth Modeling: Structural Equation and Multilevel Modeling Approaches

Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and…

Multivariate Analysis in Developmental Science

These are materials that accompany the 4th semester methods course in the HDFS Department.

State space techniques in structural equation modeling: Transformation of latent variables in and out of latent variable models

This book contains 3 chapters. The first chapter re-derives the equivalence between the regression estimator of factor scores and the Kalman filter, adding a proof of the equivalence of second-order moments. Chapter 2 is quite lengthy and derives the so-called Houdini transformation with which latent variables can be transformed out of latent…

What’s for dynr: A package for linear and nonlinear dynamic modeling in R

The past several decades have seen the rise of intensive longitudinal data (e.g. via ecological momentary assessments) and the resulting dynamic modeling methods in social and behavioral sciences. To make the estimation of these models more accessible to researchers, we have created an R package that is based on a novel and efficient state-space…

Data Mining

More and more we are integrating data mining approaches and analyses into our work - both to work with big data streams and for knowledge discovery. Along the way, we are building a repository of scripts and tutorials that researchers may find useful as they make use of the data mining tools. Our notes follow courses and workshops we teach on the…

Handling missing data in the modeling of intensive longitudinal data

This code accompanies “Handling missing data in the modeling of intensive longitudinal data” (Ji, Chow, Schermerhorn, Jacobson, & Cummings, in press).

Survival Analysis

Survival analysis is a statistical approach for estimating the timing of events. This series of tutorials demonstrates how to conduct survival analysis specifically on observational data (i.e., video recordings of participant behavior in situ), although these resources will also be applicable to other types of time-to-event data.

Bayesian factor analysis using variable selection approaches, with comparisons to model comparison criteria

This code accompanies Lu, Chow, & Loken (2016) and Lu, Chow, & Loken (in press).

Bayesian multi-resolution techniques for fitting nonlinear stochastic differential equation models

This code accompanies the paper "Bayesian analysis of ambulatory cardiovascular dynamics with application to irregularly spaced sparse data" (Lu, Chow, Sherwood, & Zhu, 2015)

Cusp Catastrophe Model as a Mixture SEM

This code accompanies "The Cusp Catastrophe Model as Cross-Sectional an Longitudinal Mixture Structural Equation Models" (Chow, Witkiewitz, Grasman, & Maisto, 2015)

Regime-Switching Bivariate Dual Change Score Model

This code accompanies "Regime-switching bivariate dual change score model" (Chow, Grimm, Filteau, Dolan, & McArdle, 2013)

Stochastic approximation expectation-maximization algorithm for fitting nonlinear differential equation models

This code accompanies the paper "Fitting nonlinear differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm" (Chow, Lu, Sherwood, & Zhu, 2016)

BERD Workshop: An Introduction to Text Mining for Scientific Inquiry

The language that humans use to interact, communicate, and record their thoughts and experiences carries a great deal of information of interest to the health and behavioral sciences. For example, individuals may talk about their symptoms, discuss their emotions, record notes from clinical meetings, or post on social media about their experiences…

Fitting a Bayesian Growth Curve Model in JAGS and R

Step-by-step guidelines, implemented in JAGS and R, on how to fit a growth curve model with categorical predictors in the hierarchical Bayesian framework, using real data from a longitudinal study of marital relationship quality. See also https://git.psu.edu/zzo1/FittingGCMBayesian.