Exploring Causal and Noncausal Hypotheses in Nonexperimental Data
Bibliographic record
Abstract
This chapter provides an overview of behavioral observation, including the contexts researchers use when observing, the forms in which they record behaviors for analysis (e.g., coding), the methods available to document that different observers coded behaviors similarly (i.e., interrater agreement, an element of reliability), the necessity of establishing other forms of reliability as well as validity, and methods of analyzing behavioral observation data. Observational settings exist along a continuum of researcher influence ranging from unfettered natural environments to tightly controlled experimental situations. Behavioral observation coding systems are of two types: topographical coding systems and dimensional coding systems. The chapter discusses the most common interrater agreement statistics, as well as some useful alternatives. When analyzing behavioral data, one must consider both how the behavior is measured and how often it is measured. The chapter describes recent analytic developments for observational data that will likely be of interest to many social-psychological researchers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".