We need to talk about validity – A commentary on “Six solutions for more reliable infant research” from the viewpoint of an early executive functions researcher
Bibliographic record
Abstract
In their methodological article, "Six solutions for more reliable infant research", Byers-Heinlein, Bergmann and Savalei (2021) present compelling arguments for why developmental researchers should report and consider measures of reliability more frequently in their work. They also provide useful guidance on solutions to this "reliability crisis". In this commentary, I highlight a further methodological aspect that I think is key to successful and robust infancy research, that of construct validity. I also discuss recent reliability data from my own research on early executive function development, analyses which were directly inspired by the target article. Highlights: Considering measurement reliability and effect sizes is important for robust infant research and for optimising infant tasks to measure group-level effects or individual differences.Construct validity - making sure that we measure what we think we are measuring - is also important.A robust effect at the group-level may not always restrict reliability - it depends on the amount of true variation between infants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.168 | 0.422 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.013 | 0.010 |
| Research integrity | 0.057 | 0.119 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".