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People are usually confronted with an emotion/deliberation dilemma during the course of judgement and decision making, such that an emotional want is at odds with a reasoning- based should/need. Such dilemmas can occur at a social level and be related to others’ welfare. Meanwhile, experience and activities in everyday life can often induce certain mood in people. This means a decision maker may be in a certain mood when confronted with an other-regarding emotion/deliberation dilemma. However, the potential role of mood in judgement and decision making in other-regarding emotion/deliberation dilemmas has been largely unexplored. The purpose of the current thesis is to explore the influence of mood on judgement and decision making in other-regarding emotion/deliberation dilemmas, by looking at whether and how mood affects socio-economic decisions and moral judgement. Treating such emotion/deliberation dilemmas with a dual-process approach, and building on theories of affect that suggest an informational function of mood and mood’s effect on information processing strategy, we propose that mood can affect judgements and decisions in other-regarding emotion/deliberation dilemmas through its informational value. Theoretical and empirical research suggests that mood can influence processing strategy by providing information about the situation (“affect-as-information” approach; Schwarz & Clore, 2007). Positive mood signals that the present situation is non-problematic and in turn encourages the use of a heuristic, top-down, reflexive processing strategy; in contrast, negative mood signals that the present situation is problematic and encourages the use of a more systematic, bottom-up, reflective processing strategy (Clore et al., 2001; Schwarz, 2000, 2002; Schwarz & Clore, 2007). Based on the affect-as-information theorising, we hypothesised that, when confronted with other-regarding emotion/deliberation dilemmas, positive mood would give rise to emotionally compelled judgements and decisions, whereas negative mood would give rise to more cognitively controlled judgements and decisions. To test this idea, three studies were conducted using two experimental paradigms — one pertaining to socio-economic decisions and the other to moral judgement — both of which are assumed to incorporate an other-regarding emotion/deliberation dilemma. Study 1 examined the influence of negative mood on socio-economic decisions using a paradigm called the “mini-ultimatum game” in which the divergence between emotion- and reasoning-based decisions corresponds to that between outcome-focused and intention-considered decisions. Studies 2 and 3 examined influences of positive and negative mood on moral judgement using a moral dilemma paradigm, in which an emotion/deliberation dilemma is posited by the fact that deontological (or, non-utilitarian) choices are at odds with utilitarian choices. Results of Study 1 show that mood did not affect decisions in the mini-ultimatum game. A closer inspection of the behavioural economics literature suggests that this might be caused by a contextual extremity such that outcome plays a dominant role over intention in determining socio-economic decisions. Main results of Studies 2 and 3 indicate that mood may affect moral judgement by providing information about individual moral disposition; thus, whether negative/positive mood is associated with more reasoning-/emotion-based (i.e., utilitarian/non-utilitarian) judgements may be subject to individual moral inclination. Moreover, a pattern that is contrary to the initial thesis hypothesis was obtained from Studies 2 and 3, such that utilitarian judgements tended to increase with positive mood but decrease with negative mood. This suggests that mood may influence moral judgement in a different way from affecting the use of a certain processing strategy. Taken together, our findings, adding to the line of research of judgement and decision making, suggest that mood may affect judgements and decisions in other-regarding emotion/deliberation dilemmas through its informational value, and that its influence (or absence of influence) may be determined by interaction with contextual and dispositional factors. Our findings are discussed in relation to interplays between mood and contextual and dispositional factors, and to mood effects on willpower and on processing scope. They are also thought to have possible implications for legal decision making.

In this dissertation, we investigate three distinct but interrelated problems for nonparametric analysis of clustered data and multivariate data in pre-post factorial design. In the first project, we propose a nonparametric approach for one-sample clustered data in pre-post intervention design. In particular, we consider the situation where for some clusters all members are only observed at either pre or post intervention but not both. This type of clustered data is referred to us as partially complete clustered data. Unlike most of its parametric counterparts, we do not assume specific models for data distributions, intra-cluster dependence structure or variability, in effect addressing the so-called nonparametric Behrens-Fisher problem. A nonparametric measure of effect size is proposed. By constructing hypotheses based on the nonparametric effect size measure, the proposed test can eventually provide meaningful and interpretable probabilistic comparisons of treatments. The method accommodates continuous, ordered categorical and ordinal data seamlessly. The second project focuses on nonparametric methods for multivariate data in a pre-post design where some of the samples are partially complete. This type of data can also be viewed as missing data where all the variables are missing at either pre or post intervention. Here also we derive asymptotic theory for estimating the vector of nonparametric effect size measures. The Wald-type statistic is proposed for large sample size and ANOVA-type statistic is proposed for small sample size. Apart from asymptotic evaluations, the Wald-type and ANOVA-type statistics are also shown to have good finite-sample performance in a variety of settings and missing patterns by a simulation study. The methods are further extended to the arbitrary missing pattern situation. The third project is motivated by the Asthma Randomized Trial of Indoor Wood Smoke (ARTIS). The study involved a three-arm placebo controlled randomized trial on homes with wood burning stoves in a pre-post intervention design. The active treatments were aimed at improving functional, emotional and activity symptoms of children with asthma. In this project, nonparametric procedures are developed for the general pre-post clustered data collected in a factorial layout. Compared with the first project, the methods in this project are able to make comparisons across multiple treatments and between intervention periods. Here also, both complete and partially complete clusters are allowed. Simulation studies provide evidence that our method perform reasonably well in both large sample and small-sample settings. Therefore, the proposed nonparametric methods provide a new way of analyzing non-metric clustered data in factorial designs with repeated measures and is also a powerful competitor of the parametric mixed effects model for continuous outcomes. Simulation studies provide evidence that our methods perform well in a wide variety of settings that involve small samples. Real datasets from two randomized trials are used to illustrate the application of the methods.

The current dissertation mainly discusses about the polymers anchoring behavior in liquid crystal cells in two aspects: surface interaction and bulk interaction. The goal of the research is to understand the fundamental physics of anchoring strength and apply the knowledge to liquid crystal display devices. Researchers proposed two main contributors to the surface anchoring strength: the micro grooves generated by external force and the polymer chain’s alignment. Both of them has experimental proofs. In the current study, explorations were made to understand the mechanisms of surface anchoring strength and easy axis of surface liquid crystal provided by rubbed polymer alignment layer. The work includes not only the variation of the alignment layer itself such as thickness(Chapter 3) and polymer side chain (Chapter 5), but also the variation of external conditions such as temperature (Chapter 4) and rubbing condition (Chapter 6). To determine the polar and azimuthal anchoring strengths, Rapini-Papoular’s expression was applied. However, it was discovered that higher order terms may be required in order to fit the experimental result or theoretically predict unique anchoring behaviors (Chapter 2, Chapter 6). SEM and AFM technologies were introduced to gather the actual structures of polymer alignment layer and extrapolate the alignment of liquid crystal in a micro scale. The result shows that the anchoring strength can be adjusted by the layer thickness, side chain structure, while the easy axis direction can be adjusted by a second rubbing direction. In addition, different anchoring conditions combined with liquid crystal's elastic energy can generate quite different forms of liquid crystals (Chapter 7).In the study of bulk alignment, the main contrition from the current dissertation is applying the understanding of anchoring behavior to optimizing actual switchable devices. Conventional PDLC performance can be tuned with the knowledge of the polymer and the liquid crystal’s interaction and proper selection of the materials (Chapter 5). Also, innovative micro structure can be designed and achieved, like the encapsulated PSCT (Chapter 8) which combines the merits of PDLC and PSCT. Such structure involves two separate polymer constructions. One is to encapsulate large droplets of cholesteric liquid crystal, while the other is inside the droplet to stabilize the cholesteric liquid crystal at transparent state. Thus the mixture has large viewing angle due to similarity to PSCT and is applicable to roll-to-roll manufacture.