Disparity in key life outcomes is unfortunately ubiquitous. Happiness inequality refers to the extent to which individuals within a community differ in their levels of satisfaction and fulfillment. In this SROP, you will have the opportunity to choose from several topics related to happiness inequality and contemporary events: 1) the extent of happiness inequality by social groups (e.g., how does happiness differ by race and ethnicity?), 2) the temporal trends of happiness inequality by social groups (e.g., how does the level of disparity change over the course of COVID?), and 3) the impact of a major population event on happiness inequality, and 4) the consequences of happiness inequality (e.g., does greater happiness inequality predict collective actions?). In this SROP, we will make use of large existing datasets (over 5 million participants across the world). We will work together to narrow down the scope of the project, with a strong preference towards social issues/social groups of your interest. You will be involved in literature review, data analysis, and manuscript preparation. We will meet regularly to discuss progress and roadblocks to ensure that you have all the necessary resources and training to succeed. You will gain valuable skills in data management, statistical analysis, and data visualization. The resulting project will advance our understanding of how current social issues shape well-being disparity in our community.
My mentoring philosophy is to provide students with a generalist training, focusing on tackling ‚ ‘big’ questions while letting the data speak. x000D
Generalist training: Our happiness and well-being are inherently multifaceted, and a comprehensive understanding of well-being requires innovative thinking drawing from diverse disciplinary and cultural perspectives. x000D
Tackling ‘big’ questions: The on-going pandemic has shown us the value of evidence-based decision making in tackling societal issues. In addition to COVID, many pressing social issues play a role in shaping our happiness, and I believe these issues can be better addressed when grounded in data. x000D
Letting the data speak: To answer socially relevant questions with data, it is necessary to take steps to minimize researcher bias. I am therefore committed to mentoring students on various open science practices (e.g., pre-registration, sharing of research materials) to promote the transparency and veracity of our research.