Coordinated Data Analysis: Knowledge Accumulation in Lifespan Developmental Psychology
Bibliographic record
Abstract
Coordinated analysis is a powerful form of integrative analysis, and is well suited in its capacity to promote cumulative scientific knowledge, particularly in subfields of psychology that focus on the processes of lifespan development and aging. Coordinated analysis uses raw data from individual studies to create similar hypothesis tests for a given research question across multiple datasets, thereby making it less vulnerable to common criticisms of meta-analysis such as file drawer effects or publication bias. Coordinated analysis can sometimes use random effects meta-analysis to summarize results, which does not assume a single true effect size for a given statistical test. By fitting parallel models in separate datasets, coordinated analysis preserves the heterogeneity among studies, and provides a window into the generalizability and external validity of a set of results. The current paper achieves three goals: First, it describes the phases of a coordinated analysis so that interested researchers can more easily adopt these methods in their labs. Second, it discusses the importance of coordinated analysis within the context of the credibility revolution in psychology. Third, it encourages the use of existing data networks and repositories for conducting coordinated analysis, in order to enhance accessibility and inclusivity. Subfields of research that require time- or resource- intensive data collection, such as longitudinal aging research, would benefit by adopting these methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; both teacher heads 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".