In the present research, two interventions were developed to increase sun protection in young women. The purpose of the study was to compare the effects of intervention content eliciting strong emotional responses to visual images depicting photoaging and skin cancer, specifically fear and disgust, coupled with a message of self-efficacy and benefits of sun protection (the F intervention) with an intervention that did not contain an emotional arousal component (the E intervention). Further, these two intervention conditions were compared to a control condition that contained an emotional arousal component that elicited emotion unrelated to the threat of skin cancer or photoaging (the C control condition). A longitudinal study design was employed, to examine the effects of condition immediately following the intervention, and to examine sun protection behavior 2 weeks after the intervention. A total of 352 undergraduate women at Arizona State University were randomly assigned to one of the three conditions (F n = 148, E n = 73, C n = 131). Several psychosocial constructs, including benefits of sun protection, susceptibility to and severity of photoaging and sun exposure, self-efficacy beliefs of making sun protection a daily habit, and barriers to sun protection were measured before and immediately following the intervention. Sun protection behavior was measured two weeks later. Those in the full intervention reported higher self-efficacy and severity of photoaging at immediate posttest than those in the efficacy only and control conditions. The fit of several path models was tested to explore underlying mechanisms by which the intervention affected sun protection behavior. Experienced emotion, specifically fear and disgust, predicted susceptibility and severity, which in turn predicted anticipated regret of failing to use sun protection. The relationship between this overall threat component (experienced emotion, susceptibility, severity, and anticipated regret) and intentions to engage in sun protection behavior was mediated by benefits. The present research provided evidence of the effectiveness of threat specific emotional arousal coupled with a self-efficacy and benefits message in interventions to increase sun protection. Further, this research provided additional support for the inclusion of both experienced and anticipated emotion in models of health behavior.

For this thesis a Monte Carlo simulation was conducted to investigate the robustness of three latent interaction modeling approaches (constrained product indicator, generalized appended product indicator (GAPI), and latent moderated structural equations (LMS)) under high degrees of nonnormality of the exogenous indicators, which have not been investigated in previous literature. Results showed that the constrained product indicator and LMS approaches yielded biased estimates of the interaction effect when the exogenous indicators were highly nonnormal. When the violation of nonnormality was not severe (symmetric with excess kurtosis < 1), the LMS approach with ML estimation yielded the most precise latent interaction effect estimates. The LMS approach with ML estimation also had the highest statistical power among the three approaches, given that the actual Type-I error rates of the Wald and likelihood ratio test of interaction effect were acceptable. In highly nonnormal conditions, only the GAPI approach with ML estimation yielded unbiased latent interaction effect estimates, with an acceptable actual Type-I error rate of both the Wald test and likelihood ratio test of interaction effect. No support for the use of the Satorra-Bentler or Yuan-Bentler ML corrections was found across all three methods.

In the past, it has been assumed that measurement and predictive invariance are consistent so that if one form of invariance holds the other form should also hold. However, some studies have proven that both forms of invariance only hold under certain conditions such as factorial invariance and invariance in the common factor variances. The present research examined Type I errors and the statistical power of a method that detects violations to the factorial invariant model in the presence of group differences in regression intercepts, under different sample sizes and different number of predictors (one or two). Data were simulated under two models: in model A only differences in the factor means were allowed, while model B violated invariance. A factorial invariant model was fitted to the data. Type I errors were defined as the proportion of samples in which the hypothesis of invariance was incorrectly rejected, and statistical power was defined as the proportion of samples in which the hypothesis of factorial invariance was correctly rejected. In the case of one predictor, the results show that the chi-square statistic has low power to detect violations to the model. Unexpected and systematic results were obtained regarding the negative unique variance in the predictor. It is proposed that negative unique variance in the predictor can be used as indication of measurement bias instead of the chi-square fit statistic with sample sizes of 500 or more. The results of the two predictor case show larger power. In both cases Type I errors were as expected. The implications of the results and some suggestions for increasing the power of the method are provided.