Pedestrians are vulnerable road users because they do not have any protection while they walk. They are unlike cyclists and motorcyclists who often have at least helmet protection and sometimes additional body protection (in the case of motorcyclists with body-armored jackets and pants). In the US, pedestrian fatalities are increasing and becoming an ever larger proportion of overall roadway fatalities (NHTSA, 2016), thus underscoring the need to study factors that influence pedestrian-injury severity and potentially develop appropriate countermeasures. One of the critical elements in the study of pedestrian-injury severities is to understand how injuries vary across age and gender ‒ two elements that have been shown to be critical injury determinants in past research. In the current research effort, 4829 police-reported pedestrian crashes from Chicago in 2011 and 2012 are used to estimate multinomial logit, mixed logit, and latent class logit models to study the effects of age and gender on resulting injury severities in pedestrian crashes. The results from these model estimations show that the injury severity level for older males, younger males, older females, and younger females are statistically different. Moreover, the overall findings also show that older males and older females are more likely to have higher injury-severity levels in many instances (if a crash occurs on city streets, state maintained urban roads, the primary cause of the crash is failing to yield right-of way, pedestrian entering/ leaving/ crossing is not at intersection, road surface condition is dry, and road functional class is a local road or street). The findings suggest that well-designed and well-placed crosswalks, small islands in two-way streets, narrow streets, clear road signs, provisions for resting places, and wide, flat sidewalks all have the potential to result in lower pedestrian-injury severities across age/gender combinations.

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.

The Strong Black Woman Schema (SBWS) refers to the collective believes, behaviors, resources and responses Black women are socialized to embody. The SBWS was developed as a positive counterimage to the negative stereotypes of Black women, such as the mammy or the jezebel, and is an important image among Black women. Observations suggest that the SBWS may affect how Black women experience and interpret stress and mental illness. I assert the SBWS may serve as one comprehensive explanation for the mental health outcomes observed for Black women. Qualitative and quantitative studies have identified a set of characteristics (i.e. strength, emotion regulation, caretaking) related to the schema. However, scales developed to measure the schema lack the ability to isolate adequately a unique typology for Black women. I argue that the SBWS is representative of a specific compilation of psychosocial resources (i.e. mastery, self-efficacy, resilience, self-esteem) representative of the cultural response to historical experiences of racism and sexism. I explore how the SBWS influences the reporting of depressive symptoms, depression and anxiety through a secondary data analysis of African American, Caribbean Black and White American women using data from the National Survey of American Life. Through a three part analysis, I answer the following questions: 1) Is a compilation of psychosocial measures an appropriate measure of the Strong Black Woman Schema? 2) What sociodemographic factors influence distinct typologies reflective of at least one uniquely Black form of the Strong Black Woman Schema? And 3) Does the Strong Black Woman Schema influence depressive symptons, depression, and anxiety? Results of this study clarify how socio-cultural aspects of oppression influence the mental health of Black women.

Research over the last few decades has consistently found that fathers are not routinely included in the provision of child welfare services. The current study examined whether ambivalent sexism on the part of child welfare workers was related to their beliefs about involving fathers. Ambivalent sexism theory posits that gender stereotypes include subjectively positive beliefs in addition to hostile beliefs that both serve to perpetuate patriarchal systems. Participants (N = 490) were currently front-line child welfare workers in the United States who completed an online survey assessing ambivalent sexism and beliefs about father involvement in child welfare cases. Ambivalent sexism was assessed using the Ambivalent Sexism Inventory and the Ambivalence Toward Men Inventory. The Dakota Father Friendly Assessment was modified to assess beliefs about father involvement in child welfare. Latent class analysis was used to empirically derive four sexism profiles. Results indicated that participants with profiles suggesting less sexist beliefs had more positive attitudes about father involvement and had a lower preference for working solely with mothers. Sexism profile was not related to participants’ stated father involvement behaviors such as conducting home visits when fathers are present, including fathers in case planning discussions, and recruiting fathers or paternal relatives as placement options. Implications for social work and child welfare practice include developing training that increases knowledge of fathers’ importance and increases workers’ comfort in providing services to men.

Direct marketing problems have been intensively reviewed in the marketing literature recently, such as purchase frequency and time, sales profit, and brand choices. However, modeling the customer response, which is an important issue in direct marketing research, remains a significant challenge. This thesis is an empirical study of predicting customer response to direct marketing and applies a Bayesian approach, including the Bayesian Binary Regression (BBR) and the Hierarchical Bayes (HB). Other classical methods, such as Logistic Regression and Latent Class Analysis (LCA), have been conducted for the purpose of comparison. The results of comparing the performance of all these techniques suggest that the Bayesian methods are more appropriate in predicting direct marketing customer responses. Specifically, when customers are analyzed as a whole group, the Bayesian Binary Regression (BBR) has greater predictive accuracy than Logistic Regression. When we consider customer heterogeneity, the Hierarchical Bayes (HB) models, which use demographic and geographic variables for clustering, do not match the performance of Latent Class Analysis (LCA). Further analyses indicate that when latent variables are used for clustering, the Hierarchical Bayes (HB) approach has the highest predictive accuracy.

Indiana University-Purdue University Indianapolis (IUPUI) / Rates of vaping among high school students have increased significantly over the past decade. Prior research has found significant associations between youth vaping and substance use. However, little is known about how vaping is associated with various patterns of polysubstance use and asthma in youth. We aimed to identify how youth are best categorized into classes based on co-occurring vaping and polysubstance use behaviors, how these classes are associated with youth background and demographic characteristics, and if these classes significantly predict asthma outcomes. Our sample consisted of nationally representative data from the 2017 and 2019 waves of the Youth Risk Behavior Survey (N = 28,442). We utilized Latent Class Analysis, multinomial logistic regression analyses, and binary logistic regression analyses to examine relationships between youth vaping, polysubstance use, and asthma. Three latent classes of substance use were identified: Polysubstance Users, Lifetime Alcohol and Vape Users, and Abstainers. Age, gender, grade and race were all significantly associated with odds of membership in the Polysubstance Users class, compared to the Abstainers class. Sexual identity was not associated with class membership. Membership in the Polysubstance Users class was significantly associated with higher odds of asthma, as compared to membership in the other two classes. These findings indicate that recent vaping is associated with high probabilities of recent polysubstance use. They also suggest that youth with high probabilities of vaping and polysubstance use are at significantly high risk for asthma compared to other classes vi of youth users and non-users. We recommend that future youth intervention strategies be tailored differently toward different classes of substance use and vaping. Future research should examine how the classes of vaping and substance use that we identify emerge in youth and what social factors (e.g., peer behavior, parental connectedness, etc.) influence their development.

No description available.

Although the application of segmentation is a topic of central importance in marketing literature and practice, managers tend to rely on intuition and on traditional segmentation techniques based on socio-demographic variables. In the airline industry, it is regarded as common sense to separate between business and economy passengers. However, the simplicity of this segmentation logic no longer matches the ever more complex and heterogeneous choices made by customers. Airline companies relying solely on flight class as the segmentation criterion may not be able to customize their product offerings and marketing policies to an appropriate degree in order to respond to the shifting importance and growing complexity of customer choice drivers, e.g. flexibility and price as a result of liberalization in the airline industry. Thus, there is a need to re-evaluate the traditional market segmentation criterion. By analyzing the stated preference data of more than 5800 airline passengers, we show that segmenting into business and leisure (a) does not sufficiently capture the preference heterogeneity among customers and (b) leads to a misunderstanding of consumer preferences. We apply latent class modeling to our data and propose an alternative segmentation approach: we profile the identified segments along behavioral and socio-demographic variables. We combine our findings with observable consumer characteristics to derive pronounced fencing mechanisms for isolating and addressing customer segments receptive for tailored product packages.

Objectives: To evaluate the effects of an appearance-focused intervention to reduce the risk of skin cancer by decreasing indoor tanning, examine potential heterogeneity in tanning across this time, and correlate the subtypes with predictors collected at baseline. Design: Randomized controlled trial with 379 female college students measured at 6 monthly time points. Main Outcome Measure: Self-reported indoor tanning frequency. Results: The intervention was effective at decreasing tanning over the period between the fall and spring. Longitudinal latent class analysis found 3 patterns of tann1ers among the treatment individuals: abstainers, moderate tanners, and heavy tanners. These classes appeared in both the treatment and control conditions, and the intervention had a harm reduction effect by reducing levels of exposure within the moderate and heavy tanner classes. Participant age and self-reported tanning patterns were found to be predictive of class membership. Conclusions: This research suggests that brief intervention approaches can be effective at reducing risk for skin cancer and illustrates several ways in which these protective effects can be enhanced.

Background For the 1.2 million older adults residing in U.S. nursing homes, little is known about their experience with physical frailty and cognitive impairment, two critical interrelated aging conditions. Methods Minimum Data Set 3.0 was used. Physical frailty was measured by FRAIL-NH and cognitive impairment by Brief Interview for Mental Status and Cognitive Performance Scale. Demographic and clinical characteristics were adjusted accordingly. Aim 1 described the prevalence of physical frailty and cognitive impairment and longitudinally examined the association between two conditions with the non-proportional odds model. Aim 2 used latent class analysis to identify physical frailty subgroups and estimated their association with cognitive impairment using multinomial logistic regression. Aim 3 fitted group-based trajectory models to identify physical frailty trajectories and cognitive impairment trajectories and quantified the association between the two sets of trajectories. Main Results Around 60% of older residents were physically frail and 68% had moderate/severe cognitive impairment, with improvement and worsening observed in both conditions, particularly in the first three months. Older residents with moderate/severe cognitive impairment were consistently and increasingly more likely to be frail. Three physical frailty subgroups were identified at admission. Greater cognitive impairment was associated with higher odds to belong to “severe physical frailty”. Five physical frailty trajectories and three cognitive impairment trajectories were identified over the first six months. One in five older residents were “Consistently Frail” and “Consistently Severe Cognitive Impairment”. Conclusion Findings emphasized the need for care management tailored to the heterogeneous presentations and progression trajectories of physical frailty and cognitive impairment.