Translating and validating the Ghosting Questionnaire into Arabic: results from classical test theory and item response theory analyses
Bibliographic record
Abstract
BACKGROUND: Ghosting refers to the sudden cessation of communication in interpersonal relationships. Ghosting has gained attention as a phenomenon commonly encountered in the context of digital communication. Earlier studies on ghosting mostly focused on Western societies while, in Arab societies, research into this practice has yet to be initiated. The current study aimed to address this gap by translating and validating the commonly used Ghosting Questionnaire (GHOST) into Arabic. METHODS: The translation process involved forward and back translation, expert review, and pilot testing to ensure linguistic and cultural equivalence. A convenience sample of 607 participants from Bahrain, Egypt, Jordan, Oman, and Tunisia completed the Arabic version of the GHOST. Statistical analyses, including reliability testing and confirmatory factor analysis, were conducted to assess the psychometric properties of the instrument. RESULTS: The Arabic version of the GHOST demonstrated high reliability. The Cronbach's alpha (α = 0.87) and McDonald's omega (ω = 0.87) coefficients indicated strong internal consistency. Test-retest reliability coefficients confirmed the stability of the responses over time (ICC 0.89, p < 0.001). CFA supported a single-factor structure in alignment with the conceptual framework of the original English version. CONCLUSIONS: The successful translation and validation of the GHOST into Arabic provide researchers with a reliable tool for investigating ghosting behavior within Arab societies. Future research endeavors can build upon these findings to explore the psychological implications of ghosting. Researchers can now also develop culturally sensitive understanding of online dating and related practices in Arab communities.
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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 | 0.039 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".