Stability of Cybergrooming Victimization Among Adolescents
A One-Year Latent Transition Analysis
Bibliographic Data
| ID | 10921990 |
|---|---|
| Authors | Catherine Schittenhelm (0009-0002-0371-5178, University of Münster, corresponding author), Manuel Gámez-Guadix (Universidad Autónoma de Madrid), Manuel Gámez‐guadix (0000-0002-1575-1662), Sebastian Wach (0000-0003-2787-6646, University of Münster) |
| Year | 2026 |
| Publication date | 2026-03-19 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Youth and Adolescence (JOURNAL) |
| Journal identifiers | ISSN: 0047-2891 • E-ISSN: 1573-6601 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s10964-026-02333-w |
| PMID | 41854834 |
| OpenAlex | W7138882355 |
| Language | EN |
| References cited | 69 |
Given the high prevalence and potential harms of cybergrooming, understanding distinct patterns and the stability of victimization experiences among adolescents is imperative. Therefore, this one-year longitudinal study examined latent classes of cybergrooming and their stability. The sample consisted of 688 Spanish adolescents (56% girls, 88.5% heterosexual) with a mean age of 13.86 years (SD = 1.22). At both time points, latent class analysis with experienced grooming strategies as binary indicators supported a three-class solution: no-victimization, low-victimization, and high-victimization. The victimized classes were highly similar in their pattern of experienced strategies, except that, in contrast to the low-victimization class, sexualization was the most likely strategy to experience in the high-victimization class. Further, based on most likely class membership, experiencing multiple strategies was more prevalent in the high-victimization class than in the low-victimization class. Subsequent analyses accounting for classification uncertainty indicated that the three classes differed significantly on (1) selected socio-demographic variables, namely gender, age, and sexual orientation, and (2) psychosocial variables, namely depressive symptoms and emotional involvement, but not social support. Latent transition analysis revealed that, for example, higher age and higher depressive symptoms increased the risk of transitioning from the no- to the high-victimization class. Stability was particularly high in the no- and high-victimization classes. Limitations of the present study include the dichotomization of grooming items for the main analyses and a slight inconsistency in the classification of certain patterns of experienced strategies across time. Overall, these findings indicate that a subset of adolescents is particularly vulnerable to experiencing multiple strategies and chronic or recurring victimization
Health psychology · Injury prevention · Latent class model · Legal psychology · Longitudinal study · Poison control · Psychopathology · Psychosocial · Quality of Life Research · Bullying, Victimization, and Aggression · Intimate Partner and Family Violence · Psychopathy, Forensic Psychiatry, Sexual Offending
Test Theory
On the practice of dichotomization of quantitative variables.
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Prevalence of Multiple Forms of Sexting Behavior Among Youth
Social reactions to disclosure of interpersonal violence and psychopathology
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The bidirectional relationships between peer victimization and internalizing problems in school-aged children
Latent Class Analysis for Developmental Research
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Human Aggression
A Routine Activity Approach to Understand Cybergrooming Victimization Among Adolescents from Six Countries
Internet-Risk Classes of Adolescents, Dispositional Mindfulness and Health-Related Quality of Life
Stability of the online grooming victimization of minors
Improving School Environments for Preventing Sexual Violence Among LGBTQ + Youth
Typology of Cybercrime Victimization in Europe
Prevalence of Past-Year Sexual Assault Victimization Among Undergraduate Students
Understanding Cybergrooming
Longitudinal pathways of sexual victimization, sexual self-esteem, and depression in women and men
Alpha, Omega, and H Internal Consistency Reliability Estimates
Internet Risks
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Bullying in the digital age
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Why They Speak Up (or Don’t)
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Comparative fit indexes in structural models
Latent Class Analysis
Alternative Ways of Assessing Model Fit
A Comparison of Victim and Offender Perspectives of Grooming and Sexual Abuse
| Citation velocity | historical |
|---|---|
| Highly cited | No |