Matching Items (2)
Description
I began this thesis because I was confused about economics. I wondered why there were so many different models. I didn't understand how they fit together. I was also confused by the assumptions being made. For instance, the assumption that humans are rational utility-maximizers did not seem to agree with my own experiences. With my director Dr. Edward Schlee's help, my thesis has become an inquiry into the state of economic methodology, both in theory and in practice. The questions that drive this paper are: How do economists choose between theories? What is the purpose of economic theory? What is the role of empirical data in assessing models? What role do assumptions play in theory evaluation, and should assumptions make sense? Part I: Methodology is the theoretical portion of the paper. I summarize the essential arguments of the two main schools of thought in economic methodology, and argue for an updated methodology. In Part II: A case study: The expected utility hypothesis, I examine methodology in practice by assessing a handful of studies that seek to test the expected utility hypothesis. Interestingly, I find that there is a different between what economists say they are doing, and what they actually seem to be doing. Throughout this paper, I restrict my analysis to microeconomic theory, simply because this is the area with which I am more familiar. I intend this paper to be a guide for my fellow students and rising economists, as well as for already practicing economists. I hope it helps the reader better understand methodology and improve her own practice.
ContributorsKang, Dominique (Author) / Schlee, Edward (Thesis director) / Schoellman, Todd (Committee member) / Boerner, Rochus (Committee member) / Barrett, The Honors College (Contributor)
Created2013-05
Description
Network analysis is a key conceptual orientation and analytical tool in the social sciences that emphasizes the embeddedness of individual behavior within a larger web of social relations. The network approach is used to better understand the cause and consequence of social interactions which cannot be treated as independent. The relational nature of network data and models, however, amplify the methodological concerns associated with inaccurate or missing data. This dissertation addresses such concerns via three projects. As a motivating substantive example, Project 1 examines factors associated with the selection of interaction partners by students at a large urban high school implementing a reform which, like many organizational improvement initiatives, is associated with a theory of change that posits changes to the structuring of social interactions as a central causal pathway to improved outcomes. A distinctive aspect of the data used in Project 1 is that it was a complete egocentric network census – in addition to being asked about their own relationships, students were asked about the relationships between alters that they nominated in the self-report. This enables two unique examinations of methodological challenges in network survey data collection: Project 2 examines the factors related to how well survey respondents assess the strength of social connections between others, finding that "informant" competence corresponds positively with their social proximity to target dyad as well as their centrality in the network. Project 3 explores using such third-party reports to augment network imputation methods, and finds that incorporating third-party reports into model-based methods provides a significant boost in imputation accuracy. Together these findings provide important implications for collecting and extrapolating data in research contexts where a complete social network census is highly desirable but infeasible.
ContributorsBates, Jordan T (Author) / Maroulis, Spiro J (Thesis advisor) / Kang, Yun (Thesis advisor) / Frank, Kenneth A. (Committee member) / Arizona State University (Publisher)
Created2019