Retrospective Pretest and Counterfactual Self-Report: Different or Same?
Bibliographic record
Abstract
Purpose: To examine discriminant validity of treatment participants’ self-report of the state they would be in had they not received treatment (counterfactual); specifically, the distinction between self-report of counterfactual and self-report of preintervention state (retrospective pretest). Setting: An education department of a large University in North America. Intervention: Methods of self-reporting research self-efficacy with counterfactual items and with retrospective pretest items. Research design: A randomized comparison group design with two treatments that were defined by the version of the survey used in each. In the survey for the counterfactual condition, items about research self-efficacy without the influence of their program of studies were included. The survey in the retrospective pretest condition contained items regarding research self-efficacy before participating in their program of study. The same items about research self-efficacy at the current time (posttest) were included in both treatment conditions. Data collection & analysis: Participants were graduate students recruited via email who answered an online survey about research self-efficacy. These students were randomly assigned to one of the two aforementioned treatments. Responses were analyzed using a mixed 2 by 2 randomized factorial ANOVA design with self-report method (counterfactual or retrospective pretest) as the between-subjects factor and time (pre and post intervention) as the within-subjects factor. Findings: Our findings show that counterfactual and retrospective pretest scores and treatment effects computed based on these two sets of scores are virtually identical, casting doubt on participants’ ability to differentiate between a state of no treatment and a state at treatment commencement after they have received treatment.
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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 | 0.095 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".