Matching Items (41)
Description
An emerging body of literature suggests that humans likely have multiple threat avoidance systems that enable us to detect and avoid threats in our environment, such as disease threats and physical safety threats. These systems are presumed to be domain-specific, each handling one class of potential threats, and previous research generally supports this assumption. Previous research has not, however, directly tested the domain-specificity of disease avoidance and self-protection by showing that activating one threat management system does not lead to responses consistent only with a different threat management system. Here, the domain- specificity of the disease avoidance and self-protection systems is directly tested using the lexical decision task, a measure of stereotype accessibility, and the implicit association test. Results, although inconclusive, more strongly support a series of domain-specific threat management systems than a single, domain- general system
ContributorsAnderson, Uriah Steven (Author) / Kenrick, Douglas T. (Thesis advisor) / Shiota, Michelle N. (Committee member) / Neuberg, Steven L. (Committee member) / Becker, David V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Designing studies that use latent growth modeling to investigate change over time calls for optimal approaches for conducting power analysis for a priori determination of required sample size. This investigation (1) studied the impacts of variations in specified parameters, design features, and model misspecification in simulation-based power analyses and (2) compared power estimates across three common power analysis techniques: the Monte Carlo method; the Satorra-Saris method; and the method developed by MacCallum, Browne, and Cai (MBC). Choice of sample size, effect size, and slope variance parameters markedly influenced power estimates; however, level-1 error variance and number of repeated measures (3 vs. 6) when study length was held constant had little impact on resulting power. Under some conditions, having a moderate versus small effect size or using a sample size of 800 versus 200 increased power by approximately .40, and a slope variance of 10 versus 20 increased power by up to .24. Decreasing error variance from 100 to 50, however, increased power by no more than .09 and increasing measurement occasions from 3 to 6 increased power by no more than .04. Misspecification in level-1 error structure had little influence on power, whereas misspecifying the form of the growth model as linear rather than quadratic dramatically reduced power for detecting differences in slopes. Additionally, power estimates based on the Monte Carlo and Satorra-Saris techniques never differed by more than .03, even with small sample sizes, whereas power estimates for the MBC technique appeared quite discrepant from the other two techniques. Results suggest the choice between using the Satorra-Saris or Monte Carlo technique in a priori power analyses for slope differences in latent growth models is a matter of preference, although features such as missing data can only be considered within the Monte Carlo approach. Further, researchers conducting power analyses for slope differences in latent growth models should pay greatest attention to estimating slope difference, slope variance, and sample size. Arguments are also made for examining model-implied covariance matrices based on estimated parameters and graphic depictions of slope variance to help ensure parameter estimates are reasonable in a priori power analysis.
ContributorsVan Vleet, Bethany Lucía (Author) / Thompson, Marilyn S. (Thesis advisor) / Green, Samuel B. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Envy may be an emotion shaped by evolution to resolve large resource disparities in zero-sum ancestral environments. Previous research has found evidence for two types of envy: benign envy, which drives greater effort and self-improvement; and malicious envy, which drives hostility toward the better-off target. We predicted that perceived resource scarcity would stoke either type, moderated by individual differences. Specifically, we predicted that high self-esteem would steer people toward benign envy and self-improvement, whereas narcissism would spark malicious envy. After completing the Rosenberg self-esteem scale and the Narcissism Personality Inventory (NPI-16), participants were randomly assigned to either read an article detailing severe cuts to university financial aid budgets (scarcity) or an article summarizing various forms of financial aid (control). Each article ended with the same envy-inducing paragraph about a particularly affluent scholarship-winner, after which participants completed a measure of both envy types, capturing feelings, appraisals, and behavioral tendencies. Results show that self-esteem predicts less malicious envy, while narcissism and scarcity predict more. Self-esteem and narcissism interact such that self-esteem dampens the effect of narcissism on malicious envy. Self-esteem predicted benign envy when narcissism was low, but not when it was high.
ContributorsDuarte, Jose L (Author) / Shiota, Michelle N. (Thesis advisor) / Kwan, Sau Y (Committee member) / Kenrick, Douglas (Committee member) / Arizona State University (Publisher)
Created2011
Description
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.
ContributorsMoser, Stephanie E (Author) / Aiken, Leona S. (Thesis advisor) / Shiota, Michelle N. (Committee member) / Kwan, Sau (Committee member) / Castro, Felipe (Committee member) / Arizona State University (Publisher)
Created2011
Description
An offender's expression of remorse plays an important role following relational transgressions, yet it is not well understood how the experience and expression of remorse relate to both victim responses to hurt and forgiveness in close relationships. This study uses a social functionalist framework to investigate the role of remorse in the forgiveness process and tests whether offender remorse experiences mediate the associations between victim responses to hurt and remorse expressions. Undergraduate participants (N=671) completed questionnaires about a time when they hurt a close relational partner and reported their partners' responses to hurt, their own experiences and expressions of remorse, and their perceptions of forgiveness. Results indicated that victims' sad communication positively predicted offenders' other-oriented and affiliation remorse experiences; victims' threatening communication positively predicted offenders' self-focused remorse experience; and victims' conciliatory communication and withdrawal positively predicted offenders' affiliation and self-focused remorse experiences. Results of the mediation analyses revealed that self-focused remorse fully mediated the relationship between victim threatening communication and low status behaviors; other-oriented remorse partially mediated the association between victim sad communication and apology/concern behaviors; and affiliation partially mediated the relationship between victim conciliatory communication and connection behaviors. Victims' withdrawal behaviors and offenders' use of compensation were not related. Finally, offenders' apology/concern and connection behaviors associated positively with perceptions of forgiveness, whereas low status behaviors negatively predicted forgiveness. Use of compensation following a hurtful event was not significantly related to forgiveness. Results are interpreted within the framework of evolutionary psychology and further validate the functional approach to studying emotion.
ContributorsGracyalny, Monica (Author) / Mongeau, Paul A. (Thesis advisor) / Guerrero, Laura K. (Committee member) / Shiota, Michelle N. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Female infertility can present a significant challenge to quality of life. To date, few, if any investigations have explored the process by which women adapt to premature ovarian insufficiency (POI), a specific type of infertility, over time. The current investigation proposed a bi-dimensional, multi-factor, model of adjustment characterized by the identification of six latent factors representing personal attributes (resilience resources and vulnerability), coping (adaptive and maladaptive) and outcomes (distress and wellbeing). Measures were collected over the period of one year; personal attributes were assessed at Time 1, coping at Time 2 and outcomes at Time 3. It was hypothesized that coping factors would mediate associations between personal attributes and outcomes. Confirmatory Factor Analysis (CFA), simple regressions and single mediator models were utilized to test study hypotheses. Overall, with the exception of coping, the factor structure was consistent with predictions. Two empirically derived coping factors, and a single standalone strategy, avoidance, emerged. The first factor, labeled "approach coping" was comprised of strategies directly addressing the experience of infertility. The second was comprised of strategies indicative of "letting go /moving on." Only avoidance significantly mediated the association between vulnerability and distress.
ContributorsDriscoll, Mary (Author) / Davis, Mary C. (Thesis advisor) / Aiken, Leona S. (Committee member) / Luecken, Linda J. (Committee member) / Zautra, Alex J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful cross-group comparisons, failure to attend to possible sources of latent class heterogeneity in the form of class-based differences in factor structure has the potential to compromise conclusions with respect to observed groups and may result in misguided attempts at instrument development and theory refinement. The present studies examined the sensitivity of two widely used confirmatory factor analytic model fit indices, the chi-square test of model fit and RMSEA, to latent class differences in factor structure. Two primary questions were addressed. The first of these concerned the impact of latent class differences in factor loadings with respect to model fit in a single sample reflecting a mixture of classes. The second question concerned the impact of latent class differences in configural structure on tests of factorial invariance across observed groups. The results suggest that both indices are highly insensitive to class-based differences in factor loadings. Across sample size conditions, models with medium (0.2) sized loading differences were rejected by the chi-square test of model fit at rates just slightly higher than the nominal .05 rate of rejection that would be expected under a true null hypothesis. While rates of rejection increased somewhat when the magnitude of loading difference increased, even the largest sample size with equal class representation and the most extreme violations of loading invariance only had rejection rates of approximately 60%. RMSEA was also insensitive to class-based differences in factor loadings, with mean values across conditions suggesting a degree of fit that would generally be regarded as exceptionally good in practice. In contrast, both indices were sensitive to class-based differences in configural structure in the context of a multiple group analysis in which each observed group was a mixture of classes. However, preliminary evidence suggests that this sensitivity may contingent on the form of the cross-group model misspecification.
ContributorsBlackwell, Kimberly Carol (Author) / Millsap, Roger E (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2011
Description
This project uses a functional approach to understand how members of stigmatized groups perceive emotional expressions on others' faces. The project starts from the premise that different groups are seen to pose different threats to others, and thus different groups face prejudices colored by different, specific negative emotions. For example, prejudice toward Black men is driven largely by fear, whereas prejudice toward obese people is driven largely by disgust. Members of these groups may thus come to be "expert" in perceiving fear or disgust in others' faces, depending on the specific emotional prejudices others feel toward their group. Alternatively, members of these groups may be biased to over- or under-perceive these emotional expressions on others' faces. I used a functional approach to predict that, if a Black man believes that seeing others' fear expressions will be useful to him, he will tend to overperceive fear on others' faces, whereas if an obese man believes that seeing others' disgust expressions will be useful to him, he will tend to overperceive disgust on others' faces. If, however, it is not considered useful to perceive these prejudicial emotions on others' faces, Black men and obese people will tend to underperceive these emotional expressions. This study recruited Black men, overweight men, and a group of comparison men. All participants completed an emotion detection task in which they rated faces on whether they expressed fear, disgust, or no emotion. Participants were randomly assigned to complete this emotion detection task either before or after a questionnaire designed to make salient, as well as to measure, participants' beliefs about others' prejudices and stereotypes of their group. Finally, participants completed a set of measures tapping predicted moderator variables. Results suggested that a) Black men tend to be less sensitive perceivers of both fear and disgust on others' faces than are other groups, unless prejudice is salient, and b) variables that would guide the functionality of perceiving others' prejudicial emotional expressions (e.g., belief that prejudice toward one's group is justified, belief that group status differences are legitimate, belief that one can manage stigmatizing interactions, stigma consciousness, and emotion-specific metastereotypes of one's group) do predict differences among Black men in perceiving these emotions on others' faces. Most results for overweight participants were null findings. The results' implications for the psychology of detecting prejudice, and emotional expressions more broadly, are discussed.
ContributorsNeel, Rebecca (Author) / Neuberg, Steven L. (Thesis advisor) / Shiota, Michelle N. (Committee member) / Becker, D. Vaughn (Committee member) / Kenrick, Douglas T. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Including a covariate can increase power to detect an effect between two variables. Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase the power to detect a relation between two variables has not been investigated. The first study identified situations where empirical and analytical power of two tests of significance for a single mediator model was greater than power of a bivariate significance test. Results from the first study indicated that including a mediator increased statistical power in small samples with large effects and in large samples with small effects. Next, a study was conducted to assess when power was greater for a significance test for a two mediator model as compared with power of a bivariate significance test. Results indicated that including two mediators increased power in small samples when both specific mediated effects were large and in large samples when both specific mediated effects were small. Implications of the results and directions for future research are then discussed.
ContributorsO'Rourke, Holly Patricia (Author) / Mackinnon, David P (Thesis advisor) / Enders, Craig K. (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2013
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013