The bioecological perspective emphasizes that proximal processes are the primary mechanisms driving development. Proximal processes can be influenced by a single microsystem or multiple microsystems collectively, which is defined as the mesosystem. Language brokering and ethnic-racial socialization are important proximal processes in U.S. Latinx families, and are likely to be influenced by the ethnic-racial compositions within families’ ecologies. In this dissertation, I examined these important family processes in relation to the ecologies that Latinx adolescents and/or parents are situated within.Paper 1 examined the developmental trajectories of everyday, school, and official business language brokering, and the influences of neighborhood ethnic-racial compositions on language brokering. Findings showed that language brokering trajectories differed across situations. Neighborhood ethnic-racial compositions influenced language brokering trajectories. Overall, this study addressed the gap in foundational understanding of how language brokering unfolds across developmental time and highlighted neighborhood as a key context shaping the development of language brokering. In paper 2, I used a person-centered approach to capture the ethnic-racial exposures within Latina mothers’ mesosystems using activity space methods, and identified four distinct profiles of mothers navigating distinct activity spaces. Mothers' activity space profiles directly impacted adolescents' ethnic-racial identity. Latina mothers’ activity spaces also influenced adolescent ethnic-racial identity through changes in mothers' cultural socialization. This study offered a holistic view of the varied ethnic-racial exposures encountered by different groups of Latina mothers in their daily activities. This study broadens the existing research by examining Latina mothers' mesosystemic exposures, moving beyond the examination of ethnic-racial socialization and identity within singular microsystems. Both studies centered on Latinx families in emerging immigrant gateways, allowing for the examination of ethnic-racial compositions within a single microsystem and mesosystems at levels not observable in established immigrant gateways. Collectively, the two papers contribute to a comprehensive understanding of U.S. Latinx family processes within ecological contexts over time.

This project studied a four-variable single mediator model, a single mediator model: X (independent variable) to M (mediator) to Y (dependent variable), and a confounder (U) that influences M and Y. Confounding represents a threat to the causal interpretation in mediation analysis. For instance, if X represents random assignment to control and treatment conditions, the effect of X on M and the effect of X on Y have a causal interpretation under certain reasonable assumptions. However, the randomization of X does not allow for a causal interpretation of the M to Y effect unless certain confounding assumptions are satisfied. The aim of this project was to develop a significance test and an effect size comparison for two sensitivity to confounding analyses methods: Left Out Variables Error (L.O.V.E.) and the correlated residuals method. Further, the project assessed the accuracy of the methods for identifying confounding bias by simulating data with and without confounding bias.

Perception of the future self (i.e., future self-identification) is an important indicator of outcomes over time and during different life-stages (e.g., adolescence, emerging adulthood, retirement). Although recent research established that future self-identification is comprised of three distinct but interrelated factors (i.e., relatedness, positivity, and vividness of the future self), the current research was the first to consider the stability of that factor structure (i.e., factorial invariance) over extended time and over the course of a major life-stage transition. Using a longitudinal design, this research investigated (1) longitudinal factorial invariance as young adults transitioned into, and became established in, their college education and (2) explored differences in factor stability across demographic groups (i.e., sex; college generation status). Results indicated that as students progressed through their first three semesters of college, future self-identification had a stable factor structure over the short-term. However, from the first week of college to when students were established in college, strong factorial invariance (i.e., invariance of the item intercepts) did not hold. In general, there were not differences in future self-identification factor structure by sex. However, from the first year of college to the second year, strict invariance was not supported (i.e., the item residual variances were not invariant between men and women). This sex difference appeared during the first stage of the transition into college and diminished as students became established in their college career. Finally, complete factorial invariance was established between first-generation and continuing-generation college students suggesting that the future self-identification factor structure did not differ based on college generation status. Findings provide crucial information regarding the validity of mean comparisons of future self-identification across a transition into a life-stage and across demographic groups. Future research may build on this foundation to better understand the sources of factorial non-invariance.

Time-to-event analysis or equivalently, survival analysis deals with two variables simultaneously: when (time information) an event occurs and whether an event occurrence is observed or not during the observation period (censoring information). In behavioral and social sciences, the event of interest usually does not lead to a terminal state such as death. Other outcomes after the event can be collected and thus, the survival variable can be considered as a predictor as well as an outcome in a study. One example of a case where the survival variable serves as a predictor as well as an outcome is a survival-mediator model. In a single survival-mediator model an independent variable, X predicts a survival variable, M which in turn, predicts a continuous outcome, Y. The survival-mediator model consists of two regression equations: X predicting M (M-regression), and M and X simultaneously predicting Y (Y-regression). To estimate the regression coefficients of the survival-mediator model, Cox regression is used for the M-regression. Ordinary least squares regression is used for the Y-regression using complete case analysis assuming censored data in M are missing completely at random so that the Y-regression is unbiased. In this dissertation research, different measures for the indirect effect were proposed and a simulation study was conducted to compare performance of different indirect effect test methods. Bias-corrected bootstrapping produced high Type I error rates as well as low parameter coverage rates in some conditions. In contrast, the Sobel test produced low Type I error rates as well as high parameter coverage rates in some conditions. The bootstrap of the natural indirect effect produced low Type I error and low statistical power when the censoring proportion was non-zero. Percentile bootstrapping, distribution of the product and the joint-significance test showed best performance. Statistical analysis of the survival-mediator model is discussed. Two indirect effect measures, the ab-product and the natural indirect effect are compared and discussed. Limitations and future directions of the simulation study are discussed. Last, interpretation of the survival-mediator model for a made-up empirical data set is provided to clarify the meaning of the quantities in the survival-mediator model.

Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of the single mediator model with manifest and latent variables have been studied using simulation studies. However, the single mediator model with latent variables in the Bayesian framework with various accurate and inaccurate priors for structural and measurement model parameters has yet to be evaluated in a statistical simulation. This dissertation outlines the steps in the estimation of a single mediator model with latent variables as a Bayesian structural equation model (SEM). A Monte Carlo study is carried out in order to examine the statistical properties of point and interval summaries for the mediated effect in the Bayesian latent variable single mediator model with prior distributions with varying degrees of accuracy and informativeness. Bayesian methods with diffuse priors have equally good statistical properties as Maximum Likelihood (ML) and the distribution of the product. With accurate informative priors Bayesian methods can increase power up to 25% and decrease interval width up to 24%. With inaccurate informative priors the point summaries of the mediated effect are more biased than ML estimates, and the bias is higher if the inaccuracy occurs in priors for structural parameters than in priors for measurement model parameters. Findings from the Monte Carlo study are generalizable to Bayesian analyses with priors of the same distributional forms that have comparable amounts of (in)accuracy and informativeness to priors evaluated in the Monte Carlo study.

Research methods based on the frequentist philosophy use prior information in a priori power calculations and when determining the necessary sample size for the detection of an effect, but not in statistical analyses. Bayesian methods incorporate prior knowledge into the statistical analysis in the form of a prior distribution. When prior information about a relationship is available, the estimates obtained could differ drastically depending on the choice of Bayesian or frequentist method. Study 1 in this project compared the performance of five methods for obtaining interval estimates of the mediated effect in terms of coverage, Type I error rate, empirical power, interval imbalance, and interval width at N = 20, 40, 60, 100 and 500. In Study 1, Bayesian methods with informative prior distributions performed almost identically to Bayesian methods with diffuse prior distributions, and had more power than normal theory confidence limits, lower Type I error rates than the percentile bootstrap, and coverage, interval width, and imbalance comparable to normal theory, percentile bootstrap, and the bias-corrected bootstrap confidence limits. Study 2 evaluated if a Bayesian method with true parameter values as prior information outperforms the other methods. The findings indicate that with true values of parameters as the prior information, Bayesian credibility intervals with informative prior distributions have more power, less imbalance, and narrower intervals than Bayesian credibility intervals with diffuse prior distributions, normal theory, percentile bootstrap, and bias-corrected bootstrap confidence limits. Study 3 examined how much power increases when increasing the precision of the prior distribution by a factor of ten for either the action or the conceptual path in mediation analysis. Power generally increases with increases in precision but there are many sample size and parameter value combinations where precision increases by a factor of 10 do not lead to substantial increases in power.

This study examined the influence of the traditional values held by Mexican heritage parents on the intention of their adolescent children to use drugs. Specifically, the study tested a mediation model in which the traditional cultural values of parents were hypothesized to influence adolescent drug use intentions indirectly by influencing ethnic identify and adolescent perceptions of parental injunctive norms against drug use. Parents reported on traditional cultural values and expectations for their child. Adolescents reported perceived reaction from parents if they used drugs (parental injunctive norms), ethnic identity, and their intention to use drugs in the future. Two direct effects were observed: parental values on expectations and parental injunctive norms on adolescent drug use intentions. Two paths were also moderated by the sex of the adolescent. The path from parent values to parent expectations was significantly stronger for adolescent girls than boys; the path from ethnic identity affirmation to drug intentions was protective for boys but not for girls. The negative relationship between perceived parental reaction and adolescent drug use intentions suggests that anti-drug norms communicated by parents had a protective influence and can deter youth from using drugs. The results of the current study did not support the hypothesized mediational model, but did provide additional support for the importance of parental influence on adolescents' plans and ideas about using alcohol, cigarettes, and marijuana. More research is necessary to examine the influence of culture and the mechanisms by which cultural values impact Mexican heritage adolescents' intentions to use drugs and subsequent use.

Traditional perspectives on sexual prejudice typically focus on the distinction between heterosexual ingroup and homosexual outgroup. In contrast, I focus on an affordance-management paradigm which views prejudices as resulting not from ingroup/outgroup relations, but instead from perceptions of the threats and opportunities posed by members of different groups. Past research has demonstrated that non-heterosexual target groups are perceived to pose a variety of threats, including threats to the socialization of young children, of child molestation, of disease, and to values. My research, however, suggests sexual prejudices arise for college students from beliefs that certain sexual orientation groups pose threats of unwanted sexual interest. For young adults, mating concerns are salient and should define relevant threats and opportunities--including those that might drive prejudices. For individuals with different active motivations, however, different threats and opportunities and threats are salient, and so the threats driving sexual prejudices may also differ. I extend my past research to consider how activating different fundamental goals (e.g., disease avoidance, parenting) alters patterns of sexual prejudice. I posit that activating disease concerns will increase prejudice specifically toward non-heterosexuals associated with disease (gay and bisexual me)--but not other non-heterosexuals (lesbians and bisexual women)--whereas activating offspring care will increase prejudice toward all non-heterosexual target groups, as all are perceived to pose socialization threats. To test this, heterosexual participants were randomly assigned to a parenting or disease-avoidance goal activation, or control condition, and then rated their general negativity towards heterosexual, bisexual, and homosexual male and female targets. They also rated their perceptions of the extent to which each target posed unwanted sexual interest, socialization, and disease threats. Contrary to predictions, activating parenting and disease avoidance systems failed to affect sexual prejudices. Furthermore, although the pattern of observed data was largely consistent with previously observed patterns, women's attitudes towards gay men in the control condition were more negative than that found in previous studies, as were men's attitudes towards bisexual and lesbian women. Multiple mechanisms underlie sexual prejudices, and research is needed to better understand the circumstances under which alternative mechanisms are engaged and have their effects.