How to define and measure individuals’ well-being is important, as this has an impact on both research and society at large. This thesis concerns how to define and measure the self-reported well-being of individuals, which involves both theorizing as well as developing and applying empirical and statistical methods in order to gain a better understanding of well-being. The first paper critically reviews the literature on well-being. It identifies an individualistic bias in current approaches and accompanying measures related to well-being and happiness; for example, through an over-emphasis on the importance of self-centered aspects of well-being (e.g., the unprecedented focus on satisfaction with life) whilst disregarding the importance of harmony in life, interconnectedness and psychological balance in relation to well- being. It is also discussed how closed-ended well-being measures impose the researchers’ values and limit the ability of respondents to express themselves in regard to their perceived well-being. The second paper addresses concerns regarding this individualistic bias by developing the harmony in life scale, which focuses on interconnectedness and psychological balance. In addition, an open-ended approach is developed in the paper, allowing individuals to freely describe their pursuit of well-being by means of open-ended responses analyzed using statistical semantics (including techniques from artificial intelligence such as natural language processing and machine learning). The results show that the harmony in life scale and the traditional satisfaction with life scale form a two-factor model of well-being, where the harmony in life scale explains more unique variance in measures of psychological well-being, stress, depression and anxiety, but not happiness. It is further demonstrated that participants describe their pursuit of harmony in life using words related to interconnectedness (including words such as: peace, balance, cooperation), whereas they describe their pursuit of satisfaction with life using words related to independence (including words such as: money, achievement, fulfillment). It is concluded that the harmony in life scale complements the satisfaction with life scale for a more comprehensive understanding of subjective well-being. The third paper focuses on developing and evaluating a method for measuring and describing psychological constructs using open-ended questions analyzed by means of statistical semantics rather than closed-ended numerical rating scales. This semantic measures approach is tested and compared with traditional rating scales in nine studies, including two different paradigms involving reports regarding objective stimuli (i.e., the evaluation of facial expressions) and reports regarding subjective states (i.e., the self-reporting of harmony in life, satisfaction with life, depression and worry). The results indicate that semantic measures encompass higher, or competitive, levels of reliability and validity compared to traditional numerical rating scales. In addition, semantic measures appear to be better suited for differentiating between psychological constructs, such as harmony in life versus satisfaction with life as well as depression versus worry. In this thesis, the findings from these three papers are elaborated and integrated into two independent perspectives. The first perspective focuses on the theoretical and empirical differences between harmony in life and satisfaction with life within a context of societal and national progress. It is concluded that harmony in life complements satisfaction with life. The second perspective focuses on the open-ended, statistical semantics approach. It is proposed that statistical semantics may beneficially be used more widely as a research tool within psychological research.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.