Scholars report that 80 percent of on-the-job learning is informal (Marsick & Watkins, 1992). Yet little research exists that describes how those in the workplace can encourage informal learning through their interactions with potential learners. This is a case study drawing upon phenomenological methods—specifically, interpretive phenomenological analysis. The intention was to explore the interpersonal space among employees in order to understand how, if at all, the participant’s professional associates encouraged the participant to learn informally. Participants in this study were fourteen librarians. Semi-structured interviews were conducted to explore how participants said their choice to learn informally was influenced by their interactions with other people. Findings indicated that participants were the architects of their own informal learning. While participants drove their own learning, the positive regard of others played an important enabling role, particularly when participants were new or were attempting to transition to new roles, such as managing others. Participants did not focus on themselves; thus self-efficacy did not play a role in the topics they learned about. Instead, participants chose their topics and persisted in learning about the topics because they wanted to serve their customers in a way that enabled their lifelong library use. Given a backdrop of positive regard, participants initiated and participated in a variety of informal learning projects, often with learning being so embedded in the initiative or task that it was challenging to identify it as learning. Role modeling was important for the function of showing employees how to do things—and particularly for giving them ideas about how they could interact effectively with the public or with employees that they supervised.

This thesis focuses on (1) the statistical methodologies for the estimation of spatial data with outliers and (2) classification accuracy of disease diagnosis. Chapter I, Robust Estimation for Spatial Markov Random Field Models: Markov Random Field (MRF) models are useful in analyzing spatial lattice data collected from semiconductor device fabrication and printed circuit board manufacturing processes or agricultural field trials. When outliers are present in the data, classical parameter estimation techniques (e.g., least squares) can be inefficient and potentially mislead the analyst. This chapter extends the MRF model to accommodate outliers and proposes robust parameter estimation methods such as the robust M- and RA-estimates. Asymptotic distributions of the estimates with differentiable and non-differentiable robustifying function are derived. Extensive simulation studies explore robustness properties of the proposed methods in situations with various amounts of outliers in different patterns. Also provided are studies of analysis of grid data with and without the edge information. Three data sets taken from the literature illustrate advantages of the methods. Chapter II, Extending the Skill Test for Disease Diagnosis: For diagnostic tests, we present an extension to the skill plot introduced by Mozer and Briggs (2003). The method is motivated by diagnostic measures for osteoporosis in a study. By restricting the area under the ROC curve (AUC) according to the skill statistic, we have an improved diagnostic test for practical applications by considering the misclassification costs. We also construct relationships, using the Koziol-Green model and mean-shift model, between the diseased group and the healthy group for improving the skill statistic. Asymptotic properties of the skill statistic are provided. Simulation studies compare the theoretical results and the estimates under various disease rates and misclassification costs. We apply the proposed method in classification of osteoporosis data.