Psychological and Interpersonal Implications of Believing that Everything is One: Identity, Personality, Values, and Worldviews

For thousands of years, people from a variety of philosophical, religious, spiritual, and scientific perspectives have believed in the fundamental unity of all that exists, and this belief appears to be increasingly prevalent in Western cultures. The present research was the first investigation of the psychological and interpersonal implications of believing in oneness. Self-report measures were developed to assess three distinct variants of the belief in oneness – belief in the fundamental oneness of everything, of all living things, and of humanity – and studies examined how believing in oneness is associated with people’s self-views, attitudes, personality, emotions, and behavior. Using both correlational and experimental approaches, the findings supported the hypothesis that believing in oneness is associated with feeling greater connection and concern for people, nonhuman animals, and the environment, and in being particularly concerned for people and things beyond one’s immediate circle of friends and family. The belief is also associated with experiences in which everything is perceived to be one, and with certain spiritual and esoteric beliefs. Although the three variations of belief in oneness were highly correlated and related to other constructs similarly, they showed evidence of explaining unique variance in conceptually relevant variables. Belief in the oneness of humanity, but not belief in the oneness of living things, uniquely explained variance in prosociality, empathic concern, and compassion for others. In contrast, belief in the oneness of living things, but not belief in oneness of humanity, uniquely explained variance in beliefs and concerns regarding the well-being of nonhuman animals and the environment. The belief in oneness is a meaningful existential belief that is endorsed to varying degrees by a nontrivial portion of the population and that has numerous implications for people’s personal well-being and interactions with people, animals, and the natural world.

Rhetoric, Roman Values, and the Fall of the Republic in Cicero's Reception of Plato

This dissertation seeks to identify what makes Cicero’s approach to politics unique. The author's methodology is to turn to Cicero’s unique interpretation of Plato as the crux of what made his thinking neither Stoic nor Aristotelian nor even Platonic (at least, in the usual sense of the word) but Ciceronian. As the author demonstrates in his reading of Cicero’s correspondences and dialogues during the downward spiral of a decade that ended in the fall of the Republic (that is, from Cicero’s return from exile in 57 BC to Caesar’s crossing of the Rubicon in 49 BC), it is through Cicero's reading of Plato that the former develops his characteristically Ciceronian approach to politics—that is, his appreciation for the tension between the political ideal on the one hand and the reality of human nature on the other as well as the need for rhetoric to fuse a practicable compromise between the two. This triangulation of political ideal, human nature, and rhetoric is developed by Cicero through his dialogues "de Oratore," "de Re publica," and "de Legibus."

Methods for Imputing Missing Values and Synthesizing Confidential Values for Continuous and Magnitude Data

Abstract

Continuous variable is one of the major data types collected by the survey organizations. It can be incomplete such that the data collectors need to fill in the missingness. Or, it can contain sensitive information which needs protection from re-identification. One of the approaches to protect continuous microdata is to sum them up according to different cells of features. In this thesis, I represents novel methods of multiple imputation (MI) that can be applied to impute missing values and synthesize confidential values for continuous and magnitude data.

The first method is for limiting the disclosure risk of the continuous microdata whose marginal sums are fixed. The motivation for developing such a method comes from the magnitude tables of non-negative integer values in economic surveys. I present approaches based on a mixture of Poisson distributions to describe the multivariate distribution so that the marginals of the synthetic data are guaranteed to sum to the original totals. At the same time, I present methods for assessing disclosure risks in releasing such synthetic magnitude microdata. The illustration on a survey of manufacturing establishments shows that the disclosure risks are low while the information loss is acceptable.

The second method is for releasing synthetic continuous micro data by a nonstandard MI method. Traditionally, MI fits a model on the confidential values and then generates multiple synthetic datasets from this model. Its disclosure risk tends to be high, especially when the original data contain extreme values. I present a nonstandard MI approach conditioned on the protective intervals. Its basic idea is to estimate the model parameters from these intervals rather than the confidential values. The encouraging results of simple simulation studies suggest the potential of this new approach in limiting the posterior disclosure risk.

The third method is for imputing missing values in continuous and categorical variables. It is extended from a hierarchically coupled mixture model with local dependence. However, the new method separates the variables into non-focused (e.g., almost-fully-observed) and focused (e.g., missing-a-lot) ones. The sub-model structure of focused variables is more complex than that of non-focused ones. At the same time, their cluster indicators are linked together by tensor factorization and the focused continuous variables depend locally on non-focused values. The model properties suggest that moving the strongly associated non-focused variables to the side of focused ones can help to improve estimation accuracy, which is examined by several simulation studies. And this method is applied to data from the American Community Survey.

Effects of Mindful Acceptance and Reappraisal Training on Maladaptive Beliefs About Rumination

© 2016, Springer Science+Business Media New York.The present study examined the relative effects of mindful acceptance and reappraisal on metacognitive attitudes and beliefs in relation to rumination and negative experiences. A small but growing literature has compared the effects of these strategies on immediate emotional experience, but little work has examined the broader, metacognitive impact of these strategies, such as maladaptive beliefs about rumination. One hundred and twenty-nine participants who reported elevated symptoms of depression were randomly assigned to receive brief training in mindful acceptance, reappraisal, or no training prior to undergoing an autobiographical sad mood induction. Participants rated their beliefs in relation to rumination and negative experiences before and after instructions to engage in mood regulation. Results showed that relative to reappraisal or no training, training in mindful acceptance resulted in greater decreases in maladaptive beliefs about rumination. The study suggests that training in mindful acceptance promotes beneficial changes in metacognitive attitudes and beliefs relevant to depression, and contributes to a greater understanding of the mechanisms through which mindfulness-based interventions lead to positive outcomes.