Explorations of Individual Change Processes and Their Determinants: A Novel Approach and Remaining Challenges

Kevin J. Grimm, Ross Jacobucci, Gabriela Stegmann, Sarfaraz Serang

Research output: Contribution to journalArticlepeer-review

Abstract

Over the past 40 years there have been great advances in the analysis of individual change and the analyses of between-person differences in change. While conditional growth models are the dominant approach, exploratory models, such as growth mixture models and structural equation modeling trees, allow for greater flexibility in the modeling of between-person differences in change. We continue to push for greater flexibility in the modeling of individual change and its determinants by combining growth mixture modeling with structural equation modeling trees to evaluate how measured covariates predict class membership using a recursive partitioning algorithm. This approach, referred to as growth mixture modeling with membership trees, is illustrated with longitudinal reading data from the Early Childhood Longitudinal Study with the MplusTrees package in R.

Original languageEnglish (US)
JournalMultivariate Behavioral Research
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • change
  • development
  • Longitudinal
  • machine learning
  • mixture

ASJC Scopus subject areas

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

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