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Coping with dating violence as a function of violence frequency, severity, gender role beliefs and solution attribution: a structural modeling approach

Description
This study presents a structural model of coping with dating violence. The model integrates abuse frequency and solution attribution to determine a college woman's choice of coping strategy. Three hundred, twenty-four undergraduate women reported being targets of some physical abuse from a boyfriend and responded to questions regarding the abuse,…

This study presents a structural model of coping with dating violence. The model integrates abuse frequency and solution attribution to determine a college woman's choice of coping strategy. Three hundred, twenty-four undergraduate women reported being targets of some physical abuse from a boyfriend and responded to questions regarding the abuse, their gender role beliefs, their solution attribution and the coping behaviors they executed. Though gender role beliefs and abuse severity were not significant predictors, solution attribution mediated between frequency of the abuse and coping. Abuse frequency had a positive effect on external solution attribution and external solution attribution had a positive effect on the level of use of active coping, utilization of social support, denial and acceptance.
ContributorsBapat, Mona (Author) / Tracey, Terence J.G. (Thesis advisor) / Bernstein, Bianca (Committee member) / Green, Samuel (Committee member) / Arizona State University (Publisher)
Created2011
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Sensitivity analysis of longitudinal measurement non-invariance: a second-order latent growth model approach with ordered-categorical indicators

Description
Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection…

Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection of ordered-categorical indicators (e.g., Likert scale items). To evaluate longitudinal measurement invariance with ordered-categorical indicators, a set of hierarchical models can be sequentially tested and compared. If the statistical tests of measurement invariance fail to be supported for one of the models, it is useful to have a method with which to gauge the practical significance of the differences in measurement model parameters over time. Drawing on studies of latent growth models and second-order latent growth models with continuous indicators (e.g., Kim & Willson, 2014a; 2014b; Leite, 2007; Wirth, 2008), this study examined the performance of a potential sensitivity analysis to gauge the practical significance of violations of longitudinal measurement invariance for ordered-categorical indicators using second-order latent growth models. The change in the estimate of the second-order growth parameters following the addition of an incorrect level of measurement invariance constraints at the first-order level was used as an effect size for measurement non-invariance. This study investigated how sensitive the proposed sensitivity analysis was to different locations of non-invariance (i.e., non-invariance in the factor loadings, the thresholds, and the unique factor variances) given a sufficient sample size. This study also examined whether the sensitivity of the proposed sensitivity analysis depended on a number of other factors including the magnitude of non-invariance, the number of non-invariant indicators, the number of non-invariant occasions, and the number of response categories in the indicators.
ContributorsLiu, Yu, Ph.D (Author) / West, Stephen G. (Thesis advisor) / Tein, Jenn-Yun (Thesis advisor) / Green, Samuel (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2016