This study investigates the degree to which the cumulative risk index predicted school readiness in a Head Start population. In general, the reviewed studies indicated the cumulative risk model was efficacious in predicting adverse developmental outcomes. This study built on this literature by investigating how child, parent, and family risk factors predicted school readiness in Head Start children using two statistical models. Specific aims of this study included identifying 1) to what degree multiple predictors contributed to school readiness and 2) to what degree the cumulative risk index contributed to school readiness. Participants included 176 Head Start children ages 3 to 5 years. Data were analyzed using multivariate regression to determine if the cumulative risk model was a stronger predictor of school readiness than any risk factor in isolation. Hierarchical regression was also utilized to determine if individual risk factors contributed anything above and beyond the sum, the cumulative risk index. Multiple regression analysis revealed that older age and previous enrollment in Head Start predicted higher scores, while low income predicted lower scores, as did taking the test in Spanish. Analysis also revealed that higher scores on the cumulative risk index predicted lower test scores. The analysis revealed that the individual risk factors did not contribute to the model above and beyond the cumulative risk index. Adding the individual risk factors did not account for more variance than using gender, age, and the cumulative risk index as the only predictors. Similarly, the cumulative risk index did not account for more variance than using age and gender as the only predictors. The current study adds empirical support to the continued use of the cumulative risk model in predicting adverse developmental outcomes.

This study investigates the degree to which the cumulative risk index predicted school readiness in a Head Start population. In general, the reviewed studies indicated the cumulative risk model was efficacious in predicting adverse developmental outcomes. This study built on this literature by investigating how child, parent, and family risk factors predicted school readiness in Head Start children using two statistical models. Specific aims of this study included identifying 1) to what degree multiple predictors contributed to school readiness and 2) to what degree the cumulative risk index contributed to school readiness. Participants included 176 Head Start children ages 3 to 5 years. Data were analyzed using multivariate regression to determine if the cumulative risk model was a stronger predictor of school readiness than any risk factor in isolation. Hierarchical regression was also utilized to determine if individual risk factors contributed anything above and beyond the sum, the cumulative risk index. Multiple regression analysis revealed that older age and previous enrollment in Head Start predicted higher scores, while low income predicted lower scores, as did taking the test in Spanish. Analysis also revealed that higher scores on the cumulative risk index predicted lower test scores. The analysis revealed that the individual risk factors did not contribute to the model above and beyond the cumulative risk index. Adding the individual risk factors did not account for more variance than using gender, age, and the cumulative risk index as the only predictors. Similarly, the cumulative risk index did not account for more variance than using age and gender as the only predictors. The current study adds empirical support to the continued use of the cumulative risk model in predicting adverse developmental outcomes.

The cumulative risk model is used to explain the coexistence of small gender differences and large health disparities between husbands and wives. Specifically, the current model incorporates conflict (a risk factor), support (a protective factor), and coping (a moderator of the conflict-stress link) to predict cortisol slopes for newlywed husbands and wives. One hundred and seventy-two couples completed both global and daily measures of protective factors (empathy, responsiveness, and perceived support), risk factors (withdrawal, loyalty, self-silencing, and negativity), and coping (self-distraction, substance use, emotional support, and rumination). For the six days that participants provided daily reports of these constructs, participants also provided waking and evening saliva samples for later determination of salivary cortisol levels. I hypothesized that men would incur more protective factors than would women, and that these protective factors would be associated with steeper cortisol slopes (i.e., healthy cortisol slopes.) Further, I hypothesized that women would incur more cumulative risks than would men, and that these risks would be associated flatter cortisol slopes (i.e., unhealthy cortisol slopes). Finally, I hypothesized that the association between cumulative risk and cortisol slopes would be moderated by coping, such that theoretically-effective coping strategies would blunt the impact of cumulative risks whereas ineffective coping strategies would exacerbate the impact of cumulative risks. Support for these hypotheses was mixed. Women did incur fewer cumulative protective factors than did men; however, there were no gender differences in cumulative risks for this highly satisfied newlywed sample. The impact of both cumulative protection and cumulative risk on cortisol slopes differed for men and women. Coping moderated the impact of cumulative risk on daily cortisol slopes, but again these patterns were different for men and women. Future work must continue to isolate gender differences in relationship processes to understand resulting health implications. With further refinement, the proposed model can provide a more holistic explanation of gendered health disparities, and perhaps identify ways that women and men can experience more equivalent health benefits from romantic relationships. / text