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 language | English (US) |
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Pages (from-to) | 11-33 |
Number of pages | 23 |
Journal | New directions for child and adolescent development |
Volume | 2021 |
Issue number | 175 |
DOIs | |
State | Published - Jan 11 2021 |
Externally published | Yes |
Keywords
- cross-lag panel model
- developmental
- longitudinal
- mathematics
- reading
ASJC Scopus subject areas
- Social Psychology
- Developmental and Educational Psychology