Measuring Stakeholder Participation in Evaluation
Bibliographic record
Abstract
BACKGROUND: Stakeholder participation is an important trend in the field of program evaluation. Although a few measurement instruments have been proposed, they either have not been empirically validated or do not cover the full content of the concept. OBJECTIVES: This study consists of a first empirical validation of a measurement instrument that fully covers the content of participation, namely the Participatory Evaluation Measurement Instrument (PEMI). It specifically examines (1) the intercoder reliability of scores derived by two research assistants on published evaluation cases; (2) the convergence between the scores of coders and those of key respondents (i.e., authors); and (3) the convergence between the authors' scores on the PEMI and the Evaluation Involvement Scale (EIS). SAMPLE: A purposive sample of 40 cases drawn from the evaluation literature was used to assess reliability. One author per case in this sample was then invited to participate in a survey; 25 fully usable questionnaires were received. MEASURES: Stakeholder participation was measured on nominal and ordinal scales. Cohen's κ, the intraclass correlation coefficient, and Spearman's ρ were used to assess reliability and convergence. RESULTS: Reliability results ranged from fair to excellent. Convergence between coders' and authors' scores ranged from poor to good. Scores derived from the PEMI and the EIS were moderately associated. CONCLUSIONS: Evidence from this study is strong in the case of intercoder reliability and ranges from weak to strong in the case of convergent validation. Globally, this suggests that the PEMI can produce scores that are both reliable and valid.
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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 | 0.200 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".