Impact evaluation of job training programmes: Selection bias in multilevel models
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on the evaluation of a job training programme composed of several different courses. The aim is to evaluate the impact of the programme for the participants with respect to non-participants, paying attention to possible differences in the effectiveness between the courses. The analysis is based on discrete data with a hierarchical structure. Multilevel modelling is the natural choice in this setting, but the results may be severely affected by selection bias. We propose a two-step procedure, which suits both the hierarchical structure and the observational nature of data. The method selects the appropriate control group, using standard results of the propensity score methodology. A suitable multilevel model is formulated, and the dependence of the results on the amount of non-random sample selection is analysed within a likelihood-based framework. As a result, rankings for comparative performances are obtained, adjusted for the amount of plausible selection bias. The procedure is illustrated with reference to a data set about a job training programme organized in Italy in the late 1990s.
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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 | 0.003 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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 it