The current study was designed to test two theoretically driven models of intimate partner violence IPV in a mixed-gender sample of adolescents. Research on these subtypes suggests they are associated with different patterns of emotional and behavioral regulation, Show more The current study was designed to test two theoretically driven models of intimate partner violence IPV in a mixed-gender sample of adolescents. Research on these subtypes suggests they are associated with different patterns of emotional and behavioral regulation, personality traits, and physiological reactivity, all of which may originate in childhood. Theories of the intergenerational transmission of IPV suggest that family violence, including exposure to parental IPV and child maltreatment, may influence the development of these variables. Additionally, there is little research on whether models of IPV function similarly for males and females, especially in adolescence. Thus, the current study was designed to test separate models of SCV and CCV, including the influence of childhood exposure to parental IPV and child maltreatment, on emotion regulation and behavioral self-control, personality traits, physiological reactivity, and IPV subtypes. These models were tested using a mixed-gender, late-adolescent sample. The results suggest that SCV and CCV have separate pathways and related individual characteristics, but can originate from common experiences of family violence and childhood maltreatment. The current study also found relatively equal distribution of both psychopathy traits and CCV across genders, which is different from previous research in these areas.
The use of LMMs is becoming increasingly widespread across many aspect of the Psychological and Life Sciences in place of more traditional models such as ANOVA which are based on the general linear model. LMMs work by modelling individual data points rather than aggregate data , can cope with unbalanced designed, missing data, a combination of categorical and continuous predictors, multiple random effects, participants and item covariates - and with GLMMs we can model data of different distribution types e. The philosophy begind G LMMs is relatively straightforward and can be thought of as an extension of the general linear model. You can download the slides in. We first read in the datafile we need. We are also going to use mutate to turn our subject and gender columns into factors. We can see from this output that the mean height of our Females is cm - this corresponds to the intercept of our model. To calculate the mean height of our Males, we add We interpret the parameter estimates slighly differently with continuous predictors compared to how we interpreted them when our predictor was categorical as in the previous example. The relationship between height and age across the lifespan is obviously not linear!
Not registered? Register here. Both genders wore flamboyant floral prints, slashed suits and carried handbags. The final ultra-glam looks were encrusted in sequins in zebra patterns, while the only difference between a pair of XL tiger print fur coats - one of which was modelled by Kaia Gerber - was that the mens version was slightly longer. Glamour for men?
Try out PMC Labs and tell us what you think. Learn More. Research suggests that child maltreatment predicts later violence, but it is uncertain whether the effects of victimization persist into adulthood or differ across gender.