The Incremental Validity of Average States: A Replication and Extension of Finnigan and Vazire (2018)
Bibliographic record
Abstract
States refer to our momentary thoughts, feelings, and behaviors. Average states (aggregates across multiple time points) are discussed as a more accurate and objective measure of personality compared to global self-reports since they do not only rely on people’s general beliefs about themselves. Specifically, Finnigan and Vazire (2018) argued that, if average states better capture what a person is actually like, this should be reflected in their unique association with informant-reports of personality, and tested this idea based on two experience-sampling studies. Their results showed, however, that average self-reported states did not predict global informant-reported personality above and beyond global self-reports. In this research, we aimed at replicating and extending these results. We used data of five studies (total N = 806) that involved global self- and informant-reports and employed a variety of different experience-sampling methods (time-based with different sampling schedules, event-based). Across all studies, the original results (i.e., no incremental effects of average self-reported states) were replicated. Furthermore, as an extension to the original study, we found that average other-reported states (provided by peers, results based on one study) did indeed predict global informant-reports above and beyond global self-reports. These findings highlight the importance of differentiating between method effects (global reports vs. average states) from source of information effects (self vs. other). We discuss these results, focusing on the suitability of using informant-reports as a criterion variable and conceptual differences between assessment methods.
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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 | 0.040 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".