Risk factors associated with and factors that influence intimate partner violence. A case study of sub-Saharan regions.
| dc.contributor.advisor | Ramroop, Shaun. | |
| dc.contributor.advisor | Habyarimana, Faustin. | |
| dc.contributor.author | Mhelembe, Talani Mabrow. | |
| dc.date.accessioned | 2022-10-31T06:55:24Z | |
| dc.date.available | 2022-10-31T06:55:24Z | |
| dc.date.created | 2021 | |
| dc.date.issued | 2021 | |
| dc.description | Masters Degree. University of KwaZulu-Natal, Pietermaritzburg. | en_US |
| dc.description.abstract | The reduction of intimate partner violence is critical to most societies' well-being and posterity, and for policymakers. However, in most cases, coming up with an accurate, intimate partner violence evaluation tool that focuses on vulnerable women, is a challenge for applied policy research. Intimate partner violence for women of conceptive age (15-49 years) has been measured utilizing the number of cases reported, and this approach has several underlying problems. Therefore, in this work, we came up with a rating scale from Demographic and Health Survey data as an alternate method to measure (Chapman & Gillespie, 2018) intimate partner violence, and examine different statistical methods suitable for identifying the associated factors. A generalized linear mixed model technique was utilized to elongate survey logistical regression to incorporate random effects, and account for variability amongst the primary sampling units. This was done to account for the complexity of the sampling design and the ordering of outcome variables. We have also utilized the generalized additive mixed model to ease the assumptions of normality and linearity intrinsic in linear regression models, in which categorical independent predictors were modeled by parametric model, continuous covariates, and interaction between the continuous and categorical variables by non-parametric models. Each of these models has inherent flaws and strengths. The choice of a statistical model depends on the objectives to be achieved. The findings from this current scientific setting revealed that the following determinants are the key factors influencing intimate partner violence: age of the woman's partner, marital status, region where the woman lives, age of the woman, media exposure, size of the family, polygamy, sex of the household head, wealth index, pregnancy termination status, body mass index, marital status, cohabitation duration, partner's desire for children, partner's education level, woman's working status, and woman's earnings compared to partner's earnings. | en_US |
| dc.identifier.uri | https://researchspace.ukzn.ac.za/handle/10413/21036 | |
| dc.language.iso | en | en_US |
| dc.subject.other | Generalized linear mixed model technique. | en_US |
| dc.subject.other | Categorical independent predictors. | en_US |
| dc.subject.other | Non-parametric models. | en_US |
| dc.subject.other | Generalized additive mixed model. | en_US |
| dc.subject.other | Logistic regression. | en_US |
| dc.title | Risk factors associated with and factors that influence intimate partner violence. A case study of sub-Saharan regions. | en_US |
| dc.type | Thesis | en_US |