Interim achievement data exhibit distinctly seasonal patterns that few existing statistical models can approximate¶
Claim · All claims
Evidence · 1 study · designr?·q2
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2025: tool and model accuracy changes quickly
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.
q2 i? Longitudinal interim achievement data display distinctly seasonal patterns, and few statistical models exist that can approximate them. → James Soland 2022
Evidence¶
James Soland 2022¶
James Soland, Yeow Meng Thum. (2022). Estimating and comparing growth using longitudinal interim achievement data with seasonal trends. Journal of Research on Educational Effectiveness 15(3) 635-654. https://doi.org/10.1080/19345747.2021.2018744
q2 · i? · design · r?
The article's framing statement, offered as motivation for the study: interim assessments are expanding, yet the authors state that "few statistical models equipped to approximate the distinctly seasonal patterns in the data exist" and that guidance on choosing among models is scarce. No effect size is printed.
"Yet, few statistical models equipped to approximate the distinctly seasonal patterns in the data exist, nor is there much guidance on how to choose among models."
Discussion¶
Related Claims¶
- Seasonal patterns are discrepant across national data sets — related
- Few models exist for estimating growth in the presence of multiple raters despite growing multi-rater longitudinal data — related
- The study conducts analyses aimed at reducing barriers to generating empirical benchmarks for repeated measures achievement data — related
- The proposed seasonal growth model combines polynomial terms for year-to-year growth with piecewise terms for within-year gains and losses — related
- The CP model fits seasonal achievement data better than a traditional polynomial growth model on RMSE, AIC, and BIC while reducing residual autocorrelation — a narrower finding that bears on this claim
- Seasonality affects estimates of school effectiveness — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
interim-achievement-data-seasonal-patterns-few-models |
title |
Interim achievement data exhibit distinctly seasonal patterns that few existing statistical models can approximate |
description |
Interim achievement data exhibit distinctly seasonal patterns that few existing statistical models can approximate |
status |
review |
evidence_strength |
weak |
newest_evidence |
2022 |
recheck_by |
2025 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — james-soland-2022 |
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