This thesis investigates the use of rules in the analysis of language mastery and human action, which are both viewed as social phenomena. The investigation is conducted through an examination of two analyses of the use of language in everyday social life and documents how each formulates a different understanding of rule-following in explaining linguistic and social action. The analyses in question are ‘Speech Act Theory' and 'Ethnomethodology'. The principal idea of speech act theory is that social action is rule-governed, and the theory attempts to explain the possibility of meaningful social interaction on that basis. The rigidities imposed by the notion of rule-governance frustrate that aim. The thesis then turns to an examination of ethnomethodology and conversation analysis and contrasts the notion of rule-orientation developed by that perspective. From that examination it becomes clear that what is on offer is not just a greater flexibility in the use of rules, but a restructuring of the concept of analysis itself. It is argued that re-structuring amounts to a reflexive conception of analysis. Its meaning and implications are enlarged upon through a close scrutiny of the later philosophy of Wittgenstein, particularly his concern with the nature of rule-following in his ‘Philosophical Investigations'. The thesis argues that his concern with rules was motivated by his insight that their use as ‘explanations’ of action said as much about the formulater of the rule as the activities the rules were held to formulate. The thesis concludes by outlining the meaning of this analytic reflexivity for social scientific findings.

Thesis (M.Ed.)--Regis University, Denver, Colo., 2007. / Title from PDF title page (viewed on Oct. 30, 2007). Includes bibliographical references.

Thesis (M. Ed.)--University of Hong Kong, 1998. / Includes bibliographical references (leaf 102-115).

Thesis (M.Ed.)--University of Hong Kong, 1998. / Includes bibliographical references (leaves 102-115). Also available in print.

The purpose of this study was to extend Glas and Vos's (1998) Bayesian procedure to the 3PL IRT model by using the MCMC method. In the context of fixed-length mastery tests, the Bayesian decision-theoretic procedure was compared with two conventional procedures (conventional- Proportion Correct and conventional- EAP) across different simulation conditions. Several simulation conditions were investigated, including two loss functions (linear and threshold loss function), three item pools (high discrimination, moderate discrimination and real item pool) and three test lengths (20, 40 and 60). Different loss parameters were manipulated in the Bayesian decision-theoretic procedure to examine the effectiveness of controlling false positive and false negative errors. The degree of decision accuracy for the Bayesian decision-theoretic procedure using both the 3PL and 1PL models was also compared. Four criteria, including the percentages of correct classifications, false positive error rates, false negative error rates, and phi correlations between the true and observed classification status, were used to evaluate the results of this study. According to these criteria, the Bayesian decision-theoretic procedure appeared to effectively control false negative and false positive error rates. The differences in the percentages of correct classifications and phi correlations between true and predicted status for the Bayesian decision-theoretic procedures and conventional procedures were quite small. The results also showed that there was no consistent advantage for either the linear or threshold loss function. In relation to the four criteria used in this study, the values produced by these two loss functions were very similar. One of the purposes of this study was to extend the Bayesian procedure from the 1PL to the 3PL model. The results showed that when the datasets were simulated to fit the 3PL model, using the 1PL model in the Bayesian procedure yielded less accurate results. However, when the datasets were simulated to fit the 1PL model, using the 3PL model in the Bayesian procedure yielded reasonable classification accuracies in most cases. Thus, the use of the Bayesian decision-theoretic procedure with the 3PL model seemed quite promising in the context of fixed-length mastery tests.

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Recent literature surrounding the process of recovery from work and work-related demands has included the supervisor as a key variable influencing how and if employees recover from work. Recovery represents the return of personal resources to their pre-work levels and is typically conceptualized as taking place during non-work time (e.g., at night after work, weekends, holidays). It is theorized to take place through four main recovery experiences: psychological detachment from work, relaxation, mastery experiences, and control over leisure time. This study extends the literature surrounding recovery experiences to include subordinate perceptions of supervisor recovery as a potential predictor of subordinate recovery and well-being. Participants (N=252) completed three time-lagged surveys and reported their perceptions of their supervisor’s recovery (Time 1), their own recovery (Time 2), and their feelings of vigor and fatigue (Time 3). Perceived supervisor recovery was empirically distinguishable from supervisor support for recovery and provided incremental validity in predicting subordinate recovery beyond this support perception. These results bolster the conclusion that supervisor recovery can spillover to directly influence subordinate recovery. / M.S. / Recent research has focused on how the supervisor may influence how and if subordinates are able to rest and recover from the demands they face at work. The process of recovery from work is usually thought to take place during time away from work (e.g., at night after work, weekends, holidays) and is thought to occur through four experiences: psychological detachment from work (cutting mental and physical ties with work), relaxation (positive mood and low effort), mastery experiences (building skills or hobbies), and control over leisure time (being able to decide how to spend one’s time). This thesis extends previous work to include subordinate perceptions of how his / her supervisor is recovering from work as a potential predictor of subordinate recovery and recovery related outcomes. Participants (N=252) completed three surveys over the course of four weeks and reported their perceptions of their supervisor’s recovery (Survey 1), their own recovery (Survey 2), and their feelings of vigor and fatigue (Survey 4). Subordinate perceptions of the mastery and control of their supervisor was related to subordinate mastery and control above and beyond existing measures of how the supervisor may influence subordinate recovery. These results suggest that the supervisor may be an important role model in how subordinates recover.

Purpose of this study was to propose a simple and effective method for cognitive diagnosis assessment (CDA) without heavy computational demand using Bayesian inference for binomial proportion (BIBP). In real data studies, BIBP was applied to a test data using two different item designs: four and ten attributes. Also, the BIBP method was compared with DINA and LCDM in the diagnosis result using the same four-attribute data set. There were slight differences in the attribute mastery probability estimate among the three model (DINA, LCDM, BIBP), which could result in different attribute mastery pattern. In Simulation studies, it was found that the general accuracy of the BIBP method in the true parameter estimation was relatively high. The DINA estimation showed slightly higher overall correct classification rate but the bigger overall biases and estimation errors than the BIBP estimation. The three simulation variables (Attribute Correlation, Attribute Difficulty, and Sample Size) showed impacts on the parameter estimations of both models. However, they affected differently the two models: Harder attributes showed the higher accuracy of attribute mastery classification in the BIBP estimation while easier attributes was associated with the higher accuracy of the DINA estimation. In conclusion, BIBP appears an effective method for CDA with the advantage of easy and fast computation and a relatively high accuracy of parameter estimation.