Opportunities and Issues in Modeling Intensive Longitudinal Data

Learning from the COGITO Project

Research output: Contribution to journalArticle

Abstract

Technological developments increasingly permit the collection of longitudinal data sets in which the data structure contains a large number of participants N and a large number of measurement occasions T. Promising new dynamical systems approaches to the analysis of large N, large T data sets have been proposed that utilize both between-subjects and within-subjects information. The COGITO project, begun over a decade ago, is an early large N = 204, large T = 100 study that collected high quality cognitive and psychosocial data. In this introduction, I describe the COGITO project and conceptual and statistical issues that arise in the analysis of large N, large T data sets. I provide a brief overview of the five papers in the special section which include conceptual pieces, a didactic presentation of a dynamic structural equation approach, and papers reporting new statistical analyses of the COGITO data set to answer substantive questions. Although many challenges remain, these new approaches offer the promise of improving scientific inquiry in the behavioral sciences.

Original languageEnglish (US)
JournalMultivariate Behavioral Research
DOIs
StatePublished - Jan 1 2019

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Longitudinal Data
Learning
Modeling
Structural Equations
Behavioral Sciences
Dynamic Equation
Data Structures
Dynamical system
Datasets

Keywords

  • between-subjects
  • dynamic systems
  • intensive longitudinal data
  • within-subjects; time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

Cite this

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