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- Genre: Doctoral Dissertation
Description
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.
ContributorsPirlott, Angela (Author) / Neuberg, Steven L. (Thesis advisor) / Kenrick, Douglas T. (Committee member) / Mackinnon, David P. (Committee member) / Shiota, Michelle N. (Committee member) / Arizona State University (Publisher)
Created2012
Description
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.
ContributorsKim, Han Joe (Author) / Mackinnon, David P. (Thesis advisor) / Tein, Jenn-Yun (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2017
Description
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.
ContributorsMiočević, Milica (Author) / Mackinnon, David P. (Thesis advisor) / Levy, Roy (Thesis advisor) / Grimm, Kevin (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2017