Cited by 49Intimate Partner and Family ViolenceStalking, Cyberstalking, and HarassmentBullying, Victimization, and Aggression

Meta-Analysis of Cyber Intimate Partner Violence Perpetration and Victimization: Different Types and their Associations with Face-to-Face IPV among Men and Women

Open access
Gilbar, Ohad, Charak, Ruby, Trujillo, Oscar, Cantu, Jorge I., Cavazos, Valeria, Lavi, Iris·2023·Trauma, Violence, & Abuse

Abstract

Cyber intimate partner violence (C-IPV) is a technology-mediated form of violence. It has been examined only in the last 10 years as a form of violence that can cause psychological damage to its victims. How this phenomenon connects to and differs from face-to-face IPV (F2F-IPV) has been, as yet, little studied. Research has not made clear whether sex differences may impact its use, particularly in light of the fact that no physical coercion is used in C-IPV. Thus, the current research aimed to investigate through a meta-analysis: differences between the average levels of different types of C-IPV victimization and perpetration; the association between C-IPV and F2F-IPV victimization and perpetration; and whether the answers to these questions were dependent on sex. The current meta-analysis drew on 46 studies, within 44 papers, with a total sample of 27,491 participants. Findings from 22 of these studies showed no significant sex differences between the average levels of different types of C-IPV victimization and between different types of C-IPV perpetration. These 22 studies showed positive large effect sizes for the correlation between C-IPV and F2F-IPV perpetration and victimization. Moreover, in both perpetration and victimization, sex did not impact the level of association. The findings suggested that C-IPV and F2F-IPV are highly correlated, and though not the same, they may share similar characteristics. Additionally, the results suggested that sex differences do not impact non-physical aggression, such as C-IPV. The implications for preventive strategies include that IPV interventions should also focus on alleviating instances of C-IPV.

Integrity signals

Registry

Crossref · 10.1177/15248380221082087

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

The citation lists haven’t been fetched yet

References 49 · Cited by 49

Authors
Gilbar, O., Charak, R., Trujillo, O., Cantu, J. I., Cavazos, V., & Lavi, I.
Affiliations
  • Hebrew University of Jerusalem (IL)
  • The University of Texas at Arlington (US)
  • University of Bath (GB)
Corresponding author
Gilbar, Ohad
Venue
Trauma, Violence, & Abuse
Published
2022-05-21
Volume
24
Issue
3
Pages
1948-1965
ISSN
  • 1524-8380 (ISSN-L)
  • 1552-8324
OpenAlex
W4281258017
Cited by
49
References
49
Citations by year
2026: 11 · 2025: 18 · 2024: 8 · 2023: 9 · 2022: 3
FWCI
1.31
Type
journal article
Language
en
Locations
  • Trauma Violence & Abuse (publishedVersion)
  • PubMed (publishedVersion)
  • Pure (University of Bath) (publishedVersion, other-oa)
  • ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion)
Topics
  • Social Sciences › Health › Intimate Partner and Family Violence
  • Social Sciences › Sociology and Political Science › Stalking, Cyberstalking, and Harassment
  • Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
Keywords
cyberbullying, online harassment, sex differences, perpetration, victimization, IPV prevention
MeSH terms
Intimate Partner Violence, Cyberbullying, Coercion, Female, Humans, Male, Crime Victims, Bullying

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review