Adding cross-lagged effects significantly improves model fit over autoregressive models for every model tested¶
Claim · All claims
Evidence · 1 study · associationalr2·q3
Currency · newest study 1995 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q3 i? Achievement and self-concept influence each other over time beyond their initial correlation, as shown by significant fit improvements from adding cross-lagged paths. → Helmke 1995
Evidence¶
Helmke 1995¶
Helmke, A., & van Aken, M. A. G. (1995). The causal ordering of academic achievement and self-concept of ability during elementary school: A longitudinal study. Journal of Educational Psychology, 87(4), 624-637. https://doi.org/10.1037/0022-0663.87.4.624
q3 · i? · associational · r2
Across all tested LISREL models (Models 1, 2a, 2b, 3 variants) on the 697-student panel, autoregressive-only counterparts were tested against models with cross-lagged effects; the improvement was "significant (p < .001)" in every case. No effect size printed.
"In all cases, the addition of cross-lagged effects led to significant (p < .001) improvements in the fit of the models."
Discussion¶
Related Claims¶
- Across basic and extended RI-CLPMs and CLPMs of five-wave secondary-school data, math self-concept and achievement show consistent reciprocal (bidirectional) directional ordering — related
- The choice of longitudinal statistical model can determine whether reciprocal motivation-achievement relations are found — related
- The RI-CCLPM-C indicates a positive self-concept effect on achievement even when data are generated without any direct effects between self-concept and achievement — reports the opposite
- Adding lag-2 paths to CLPMs substantially improves fit and provides stronger controls for preexisting differences, positioning extended CLPMs as viable alternatives to RI-CLPMs — related
- RI-CLPMs control unmeasured time-invariant covariates better than CLPMs, but neither controls unmeasured time-varying covariates well — related
- Learning outcomes and skill transfer/retention show a bidirectional but developmentally asymmetrical longitudinal relationship, with prior learning exerting the stronger forward influence — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
cross-lagged-improve-fit-over-autoregressive |
title |
Adding cross-lagged effects significantly improves model fit over autoregressive models for every model tested |
description |
Adding cross-lagged effects significantly improves model fit over autoregressive models for every model tested |
status |
review |
evidence_strength |
moderate |
newest_evidence |
1995 |
generated |
by process:wiki-ingest on 2026-10-25 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — helmke-1995 |
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