Measuring executive function in early childhood: A focus on maximal reliability and the derivation of short forms.
Bibliographic record
Abstract
This study assesses the maximal reliability of a newly developed battery of executive function (EF) tasks for use in early childhood. It also demonstrates how changes in maximal reliability can inform the selection of different short forms of the battery, depending on child age. Participants included children from the Family Life Project (Vernon-Feagans, Cox, & Family Life Project Investigators, in press)--a prospective longitudinal study (N = 1,292) of families that were recruited at the time of the birth of a new child--who were assessed at ages 3, 4, and 5 years. Results indicate that the EF battery had reasonably good maximal reliability (ρ = .73, 95% confidence interval [CI] = .69, .76) in a mixed-age sample of children randomly selected from assessments at ages 3, 4, and 5. In contrast, maximal reliability of the battery ranged from poor to modest for within-age samples (ρs = .47 [95% CI = .37, .52], .62 [.57, .66], and .61 [.55, .66] at ages 3, 4, and 5, respectively). Although the derivation of a 3-task short form of the battery always resulted in statistically significant decrements in maximal reliability, in some cases the relative decrement was quite modest and may be tolerable given the time savings and potential reduction in participant burden. The benefits of using maximal reliability to both evaluate task batteries and derive short forms are discussed, as well as how a focus on maximal reliability informs ongoing questions about the measurement and conceptualization of EF in early childhood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".