Effectiveness of psychometric tests for the selection of personnel in jobs in the retail sector
Bibliographic record
Abstract
The aim of this study was to determine the effectiveness of psychometric tests in the selection of personnel in retail sector jobs. The study used the scientific deductive method of explanatory level, with non-experimental design on companies in the retail sector in the region of Junín, Peru. The most relevant psychometric tests in the study was the interview whose intention was to go into the life of the interviewee ensuring, suggestions, opinions and behavioral attitudes, knowledge tests to assess the capabilities and skills of the candidate and finally personality tests that allow to know the working relationship, performance, satisfaction and staff turnover. The results generated through structural equations, show that the interview, positively influences the selection of personnel (p≤0.05). In relation to knowledge tests based on IQ, the results reveal that it had a positive impact on personnel selection (p≤0.05). Finally, personality tests based on psychological traits, significantly influence in personnel selection (p≤0.05). The conclusion of the study indicates that the interview, knowledge tests and personality tests in the selection of personnel contribute to the efficiency of the human resources area, thus optimizing the resources of the organization.
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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 | 0.045 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".