Expanding the Toolbox: 25 Years of Methodological Change in Infant Research
Bibliographic record
Abstract
The landscape of infant behavior research has undergone a remarkable transformation over the past quarter-century. In this article, we synthesize these methodological changes and their implications for developmental science. Drawing on a systematic comparative assessment of empirical articles published in Infant Behavior and Development in 2000 and 2024, we critically evaluate five key methodological dimensions: research contexts, sample and cell sizes, coding practices, data analysis techniques and statistical software, and open science practices. Our synthesis reveals how the field has expanded beyond traditional laboratory settings toward more diverse research environments, including remote and archival approaches that enhance ecological validity and sample diversity. We trace how sample sizes have nearly doubled and experimental cell sizes have increased 2.5-fold, strengthening statistical power and replicability. We examine the selective adoption of automated methodologies in domains like eye tracking and speech analysis, alongside the persistent value of manual coding for complex behaviors. The transition from classical statistical methods to more nuanced analytical approaches increasingly implemented in open source software reflects both technological capabilities and theoretical shifts toward capturing developmental complexity. Finally, we document the emergence of open science practices, which now appear in over a third of published studies. Throughout, we highlight how these methodological transformations reflect broader drivers: the replication crisis, technological innovations, and evolving theoretical perspectives. Looking forward, we offer a roadmap for methodological development that builds on these advances while addressing persistent challenges in the field.
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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 | 0.767 | 0.728 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".