The psychometric properties of the Bangla Nomophobia Questionnaire
Bibliographic record
Abstract
Nomophobia (the fear of being without a mobile phone or being unable to use it) is an area of growing research interest. The 20-item Nomophobia Questionnaire (NMP-Q) is the most popular instrument to assess nomophobia. However, there is no validated scale to assess nomophobia in Bangladesh. The present study translated and validated the NMP-Q into Bangla. A cross-sectional study using convenience sampling was conducted among 585 university students. The study collected data on socio-demographics, behavioural health, academic attainment, nomophobia, smartphone addiction, and Facebook addiction. Confirmatory factor analysis was performed to validate the scale’s factor structure. The four-factor structure was supported by confirmatory factor analysis (CFA). Cronbach alphas were 0.915 for the total scale, 0.88 for Factor 1 (not being able to communicate), 0.83 for Factor 2 (losing connectedness), 0.71 for Factor 3 (not being able to access information), and 0.78 for Factor 4 (giving up convenience). Convergent validity was supported by significant associations between nomophobia and both smartphone addiction and Facebook addiction. Measurement invariance across gender was supported in multigroup CFA. The findings indicated that the NMP-Q Bangla appears to be a psychometrically valid and reliable instrument and can be used for further studies to assess nomophobia among Bangladeshi students.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".