Propiedades psicométricas del CERI y CERM en estudiantes universitarios de Lima
Bibliographic record
Abstract
The study focuses on the psychometric properties of the Internet Experiences Questionnaire (CERI) and the Cell pone Experiences Questionnaire (CERM) in 508 students of a private college in Lima. The instruments to determine convergent and discriminant validity of the scales were the Internet addiction scale of Lima (EAIL), The Zung Self-Rating Depression Scale (SDS), The State-Trait Anxiety Inventory (STAI), and the Toronto Alexithymia Scale (TAS-20). For evidence of criterion validity, comparisons were made with the daily hours of Internet and cell phone use. Results from the factorial analysis of CERI reveal a two dimensional structure with a 33.47% of explained variation. In addition, the reliability analysis shows a suitable internal consistency of .73 for the total score of the scale, and questionable for the areas with values of .69 and .62. The results from the factorial analysis of CERM reveal one dimensional structure with a 37.76% of explained variation. The reliability analysis shows a suitable internal consistency of .81. Significant, direct and large correlations of CERI and CERM with EAIL and significant, direct and medium correlations of CERI and CERM with SDS, STAI and TAS-20. The comparison of CERI, its two areas and CERM with the hours of Internet connection and cell phone use was significant and small for CERI, its first area and CERM. With this, CERI can’t show that has adequate psychometric properties the measure the Internet problematic use, but it is confirmed that CERM is an instrument with adequate psychometric properties to evaluate cell phone’s problematic use.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".