Linear-linear piecewise growth mixture models with unknown random knots: A primer for school psychology

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Studying change over time requires rigorous and sometimes novel statistical methods that can support increasingly complex applied research questions. In this article, we provide an overview of the potential of piecewise growth mixture models. This type of longitudinal model can be used to advance our understanding of group and individual growth that may follow a segmented, or disjointed, pattern of change, and where the data come from a mixture of two or more latent classes. We then demonstrate the practical utility of piecewise growth mixture models by applying it to a subsample of students from the Early Childhood Longitudinal Study – Kindergarten Cohort of 1998 (ECLS-K) to ascertain whether mathematics achievement is characterized by one or two latent classes akin to students with and without mathematics difficulties. We discuss the applicability for school psychological research and provide supplementary online files that include an instructional sample dataset and corresponding R routine with explanatory annotations to assist in understanding the R routine before applying this approach in novel applications (https://doi.org/10.1016/j.jsp.2019.03.004).

Original languageEnglish (US)
Pages (from-to)89-100
Number of pages12
JournalJournal of school psychology
Volume73
DOIs
StatePublished - Apr 2019

Keywords

  • Growth trajectories
  • Mathematics achievement
  • Piecewise function
  • Unobserved subgroups

PubMed: MeSH publication types

  • Journal Article

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