Internal consistency and structural validity of the nomophobia questionnaire (NMP-Q) and its translations: A systematic review with meta-analysis
Bibliographic record
Abstract
Background: A psychological condition known as NOMOPHOBIA causes individuals to fear losing ability to use or reach their mobile phones. The NMP-Q (nomophobia questionnaire) is a commonly used survey for assessing symptoms related to nomophobia. Materials and methods: We performed a meta-analysis using reliability generalization (RG) on the NMP-Q. Thirteen studies involving 15,929 participants have reported original reliability estimates of the NMP-Q determined through a comprehensive and methodical examination of the available literature. Results: For the total scores, the pooled internal consistency reliability was 0.93 [0.91; 0.95] and for the subscales it ranged from 0.83 to 0.91. Specifically, 0.91 [0.88; 0.93], 0.84 [0.80; 0.88], 0.83 [0.78; 0.88, and 0.83 [0.80; 0.85] for the subscales. Subscale 1 = not being able to communicate; subscale 2 = losing connectedness; subscale 3 = not being able to access information; and subscale 4 = giving up convenience", respectively. All reported effect sizes are Cronbach's alphas. Structural validity supported a solution of four-factors. Conclusions: NMP-Q has an excellent internal consistency. Structural validity of four factors appears to be vigorous in fitting NMP-Q items. Our recommendation is to require future studies using NMP-Q to provide a reliability estimate based on their own data.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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; 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".