Factors Associated with Domestic Violence in Peru (2019-2021): an Approach from Data Science
DOI:
https://doi.org/10.71701/74dqkh19Keywords:
Domestic violence, risk factors, multinomial regression, random forestsAbstract
The main objective of this study is to identify the factors associated with domestic violence. In terms of methodology, it is an explanatory and applied non-experimental research of longitudinal method (2019, 2020 and 2021). The sample consisted of 295,000 reports of domestic violence published in Line 100 by the Minister for Women, which are available on the National Open Data Platform. The workflow followed the CRISP-DM methodology and two models of synergistic work, that is, multinomial regression and random forests. The first one helped to identify risk factors associated with domestic violence ; and the latter to weight their significance ( ). Results show a significant statistical correlation between domestic violence and variables like age (victim and offender), offender- victim relationship, city (victim), education level (victim and offender), victim’s risk level, number of children, educational gap, gender (offender), ethnicity (victim), employment status (offender) and frequency of aggressions. Likewise, the distribution of violence against women happened to be homogeneous at different ages, and against men it was more frequent at a young and old age.
Downloads
References
INEI. (2019). Perú: Indicadores de violencia familiar y sexual, 2012-2019. Instituto Nacional de Estadística e Informática.
Naved, R., y Persson, L. (2005). Factors Associated with Spousal Physical Violence against Women in Bangladesh. Studies in Family Planning, 36(4), 289-300.
Fulu, E., Jewkes, R., Roselli, T., & Garcia-Moreno, C. (2013). Prevalence of and factors associated with male perpetration of intimate partner violence: findings from the UN Multi-country Cross-sectional Study on Men and Violence in Asia and the Pacific. The lancet global health, 1(4), 187-207.
Saffari, M., Arslan, S. A., Yekaninejad, M. S., Pakpour, A. H., Zaben, F. A., y Koenig, H. G. (2017). Factors associated with domestic violence against women in Iran: An exploratory multicenter community-based study. Journal of interpersonal violence, 1-16.
Sen, S., y Bolsoy, N. (2017). Violence against women: prevalence and risk factors in Turkish sample. BMC women’s health, 17(1), 1-9.
Durán, R. L. (2019). ¿Más educadas, más empoderadas? Complementariedad entre escolaridad y empleo en la probabilidad de violencia doméstica contra las mujeres en el Perú. En W. Hernández Breña (Ed.), Violencias contra las Mujeres. La necesidad de un doble plural (pp. 117-146). Grupo de Análisis para el Desarrollo (GRADE).
Rahme, C., Haddad, C., Akel, M., Khoury, C., Obeid, H., Obeid, S., y Hallit, S. (2021). Factors associated with violence against women in a representative sample of the Lebanese population: results of a cross-sectional study. Archives of women’s mental health, 24(1), 63-72.
Jabbi, A., Ndow, B., Senghore, T., Sanyang, E., Kargbo, J. C., y Bass, P. (2020). Prevalence and factors associated with intimate partner violence against women in The Gambia: a population-based analysis. Women & Health, 60(8), 912- 928.
OMS. (2020). Respeto a las mujeres: Prevención de la violencia contra las mujeres. Ginebra, Suiza: Organización
Mundial de la Salud.
OMS. (8 de marzo de 2021). Violencia contra la mujer [Artículo web]. https://www.who.int/es/news-room/fact-sheets/detail/violence-against-women.
Hernández, W. (2019). Factores asociados a la violencia de pareja contra mujeres: Un enfoque departamental desde los patrones de victimización. INEI.
Cassaretto, M., Dador, J., y Hernández, W. (2019). “Aló, tengo un problema”: Evaluación de impacto de la Línea 100 del Ministerio de la Mujer y Poblaciones Vulnerables. 67 ÍNDICE https://www.mimp.gob.pe/omep/pdf/evidencias/Hernandez2020.pdf
MIMP. MIMP Web site. [Página web]. Denunciar Violencia Familiar y Sexual.
Aldas, J., y Uriel, E. (2017). Análisis multivariante aplicado con R. Paraninfo.
Breiman, L. (2001). Random Forests. Machine Learning, 45, p. 5-32.
IBM. (s. f.). Random Forest [Artículo web]. https://www.ibm.com/cloud/learn/random-forest
Zumel, N., y Mount, J. (2020). Practical data science with R. Manning Publications.
IBM. (s. f.). Decision Trees. https://www.ibm.com/topics/decision-trees.
Efron, B., y Tibshirani, R. (1998). An introduction to the bootstrap. CRC Press LLC.
Seni, G., y Elder, J. (2010). Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. Morgan & Claypool.
Agüero, J., Aguilar, J., Cozzubo, A., Hernández, W., y Ledgard, D. (2022). El impacto de la pandemia por la covid-19 sobre la violencia familiar: Diferenciando víctimas, tipos de violencia y niveles de riesgo en el Perú. http://repositorio.grade.org.pe/bitstream/handle/20.500.12820/682/undp-rblac-PNUD_WckPapers_30.pdf?sequence=1&isAllowed=y
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.