Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches

Kevin J. Grimm, Jonathan Helm, Danielle Rodgers, Holly O'Rourke

Research output: Contribution to journalArticlepeer-review

Abstract

Developmental researchers often have research questions about cross-lag effects—the effect of one variable predicting a second variable at a subsequent time point. The cross-lag panel model (CLPM) is often fit to longitudinal panel data to examine cross-lag effects; however, its utility has recently been called into question because of its inability to distinguish between-person effects from within-person effects. This has led to alternative forms of the CLPM to be proposed to address these limitations, including the random-intercept CLPM and the latent curve model with structured residuals. We describe these models focusing on the interpretation of their model parameters, and apply them to examine cross-lag associations between reading and mathematics. The results from the various models suggest reading and mathematics are reciprocally related; however, the strength of these lagged associations was model dependent. We highlight the strengths and limitations of each approach and make recommendations regarding modeling choice.

Original languageEnglish (US)
Pages (from-to)11-33
Number of pages23
JournalNew directions for child and adolescent development
Volume2021
Issue number175
DOIs
StatePublished - Jan 11 2021

Keywords

  • cross-lag panel model
  • developmental
  • longitudinal
  • mathematics
  • reading

ASJC Scopus subject areas

  • Social Psychology
  • Developmental and Educational Psychology

Fingerprint Dive into the research topics of 'Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches'. Together they form a unique fingerprint.

Cite this