ReviewBehavioral sciences (Basel, Switzerland)2026
Categorical Data in the Evaluation of School-Based Cyberbullying Prevention Programs: A Review of the Literature.
Review in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
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Abstract
Categorical data analysis offers valuable tools for evaluating school-based prevention programs, yet these methods remain rarely applied in cyberbullying research. This literature review examined how categorical approaches, including contingency tables and related techniques, have been used in studies evaluating school-based cyberbullying prevention. A comprehensive search was conducted in Web of Science covering publications from 2020 to 2025, yielding 100 articles. After applying predefined inclusion and exclusion criteria, 24 studies were reviewed in full, of which 8 met all requirements for final analysis. The results revealed a predominant reliance on linear statistical techniques, such as
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