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While the Affordable Care Act (ACA) promised to reduce inequalities in insurance coverage between Latinos and non-Latinos by expanding coverage, it also excluded a large portion of noncitizen immigrants. Past research has demonstrated that among Latinos, further inequalities have developed between citizens and noncitizens after the ACA took effect, but it is unclear if this pattern is unique to Latinos or is evident among non-Latinos as well. I use data from the 2011 to 2016 waves of the National Health Interview Survey (NHIS) (n = 369,386) to test how the relationship between citizenship status (native citizen, naturalized citizen, or noncitizen) and insurance coverage changed after the ACA, adjusting for health, demographic, and socioeconomic factors. I disaggregate the analysis by ethnicity to test whether this change differs between Latinos and non-Latinos. The analysis finds that after the ACA, naturalized citizens across ethnic groups moved toward parity with native citizens in health insurance coverage while the benefits of the ACA for noncitizens were conditional on ethnicity. For non-Latinos, lacking citizenship became less disadvantageous for predicting insurance coverage while for Latinos, lacking citizenship became even more disadvantageous in predicting insurance coverage. This bifurcation among noncitizens by ethnicity implies that while the ACA has strengthened institutional boundaries between citizens and noncitizens, this distinction is primarily affecting Latinos. The conclusion offers considerations on how legal systems of stratification influence population health processes.

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.

Introduction. This study examined the association between self-rated physical and oral health, cigarette smoking, and history of criminal justice contact (i.e., never arrested; arrested, but never incarcerated; or incarcerated in reform school, detention, jail, or prison) among African American men and women. Methods. We conducted descriptive statistical, linear regression, and multinomial regression analyses of the African American subsample (n = 3,570) from the National Survey of American Life (2001–2003). Results. Overall, African American women reported lower arrest rates and histories of incarceration than African American men. Additionally, we found that criminal justice contact was associated with lower self-rated physical health and oral health and higher levels of smoking for both men and women. African American women who had been arrested and detained in facilities other than jail had more chronic health problems than their male counterparts. Furthermore, having been arrested or spent time in a reform school, detention center, jail, or prison significantly increased the odds of African American men being a current smoker. Lastly, among African American women, those who had any level of criminal justice contact were likely to be current smokers and former smokers compared to those without a history of criminal justice contact. Conclusion. Addressing the health of African Americans with criminal justice contact is a critical step in reducing health disparities and improving the overall health and well-being of African American men and women. Furthermore, attention to differences by gender and specific types of criminal justice contact are important for a more precise understanding of these relationships.

Exercises are key components of assessment centers (ACs). However, little is known about the nature and determinants of AC exercise performance. The traditional exercise paradigm primarily emphasizes the need to simulate task, social, and organizational demands in AC exercises. This chapter draws on trait activation theory in proposing a new AC exercise paradigm. First, we develop a theoretical framework that addresses the complexity of situational characteristics of AC exercises as determinants of AC performance. Second, we argue for planting multiple stimuli within exercises as a structured means of eliciting candidate behavior. Third, we show how the new paradigm also has key insights for the rating part of ACs, namely, in selecting dimensions, designing behavioral checklists, screening assessors, and training assessors. Finally, the impact of this new AC exercise paradigm is anticipated on important AC outcomes such as reliability, internal/external construct-related validity, criterion-related validity, assessee perceptions, and feedback effectiveness.

In addition to individual differences, variations in visitors’ motivations may result from temporal variance. The leisure ladder model (LLM) is one of the most representative motivation models, which proposes patterns for an individual's temporal dynamic nature. This study attempts to examine empirically the ageing and experiential variations of the theme park visitors’ motivations, which underlie the model. Using stratified and systematic sampling techniques, survey data were collected from visitors to four leading theme parks in Taiwan – an Asian island nation. Limited support for the ageing variation was found and its changing pattern was also recognized in this study. A relatively more discriminating scale to measure the extent of visitor's experience was also suggested.

It is generally accepted that repeat visitation represents an attractive, cost-effective market segment for most destinations. Given such importance, an analytical model is proposed and tested. Our model prescribes a direct, causal relationship between pull motivations, travel satisfaction, intention of repeat visit, and repeat-visit behavior. Two hundred and two survey questionnaires are collected using a systematic sampling technique. Factor Analysis is employed to reduce 14 motivational attributes to five factors. The results of the logistic regression analysis reveal that the factor ‘local food and beverages’ and tourists’ overall satisfaction level are insignificant predictors of repeat visitation. However, the rest of the variables such as climate, attractions, and facilities are significant. Implications of the results for both researchers and practitioners are discussed.