Mixed functional data analysis for grade-varying longitudinal predictor effects¶
Research Method · All research methods
Evidence · 2 claims (2 for) · 1 study (1 associational),q3· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
The article applies longitudinal mixed functional data analysis (FDA) to estimate regression effects as functions of grade, using linear splines for fixed and random effect functions. It argues FDA "is not constrained by the parametric assumptions of cross-lagged and related models" and enables simultaneous estimation of overall domain-general and domain-specific effects, grade-by-grade contributions of individual variables, and linear and non-linear across-grade change in these effects on subsequent mathematics achievement.
Accounts¶
- Mixed functional data analysis for estimating grade-by-grade longitudinal predictor effects: The article applies longitudinal mixed functional data analysis (FDA) to estimate regression effects as functions of grade, using linear splines for fixed and random effect functions. It argues FDA "is not constrained by the parametric assumptions of cross-lagged and related models" and enables simultaneous estimation of overall domain-general and domain-specific effects, grade-by-grade contributions of individual variables, and linear and non-linear across-grade change in these effects on subsequent mathematics achievement. (Geary David C. et al. (2017))
Claims¶
- The overall magnitude of domain-general effects on subsequent mathematics achievement remains constant across grades 2 to 8 [+M]
- The overall importance of prior domain-specific mathematical knowledge for subsequent mathematics achievement increases across grades [+M]
Related Research Methods¶
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Key Sources¶
- Geary David C., Nicholas Alan, Li Yaoran, Sun Jianguo. (2017). Developmental Change in the Influence of Domain-General Abilities and Domain-Specific Knowledge on Mathematics Achievement: An Eight-Year Longitudinal Study. Journal of Educational Psychology. https://doi.org/10.1037/edu0000159
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
mixed-functional-data-analysis-for-grade-varying-longitudinal-predictor-effects |
title |
Mixed functional data analysis for grade-varying longitudinal predictor effects |
description |
The article applies longitudinal mixed functional data analysis (FDA) to estimate regression effects as functions of grade, using linear splines for fixed and random effect functions. |
status |
review |
generated |
by process:settle-candidates on 2026-10-24 |
trust |
unverified — no one has confirmed this page's content |
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