The present study is intended to be, as far as possible, an exhaustive examination of sociometric status groupings in regard to the way in which members of the group rate themselves and the others in their groups concerning happiness, problems and worry.

published_or_final_version / Business Administration / Master / Master of Business Administration

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A disproportionate number of Americans drive to work alone – at the individual state level, the figure ranges from a low of 58% in New York to 85% in Alabama. What factors explain this travel behavior and what sort of policy responses are required to effect changes? In responding to this question, we used multiple test beds and made the case for a demand side management approach to remedy parking problems particularly observed in cities central business districts. To this end, we provide an overview of travel behavior and information on policy levers by generating detailed profiles that capture the travel behavior of commuters in the Pacific states of the continental United States. Building off the result that revealed San Francisco as an outlier, we examine the efficacy of modifying parking rates, via elasticity measures, to influence the demand for parking by developing a two-stage panel data regression optimization model for managing parking in the City of San Francisco. A key contribution of the research is deriving these price elasticities of parking demand estimates using panel data methods. Coefficient estimates from the panel data regression are used to fit a linear prediction model that is the primary input to the optimization model. The balance of the thesis focuses on parking information by discussing the design and implementation of ParkPGH, a novel smart parking application that provides real time and predictive information on garage parking availability in downtown Pittsburgh. At its core is a predictive model that uses as input historical parking, weather and event data to provide estimates of available parking spaces. We provide an example of the model implementation using data from the Theater Square garage where we utilize neural network-based predictors and multiple net searches to generate both continuous and binary estimates of parking availability. Provision was made for the binary classifier given the need to reduce the possibility of Type II errors.

The problem of this study is that of developing a testing procedure for multiple-choice tests which would increase the relationship between test scores and a criterion. The procedure investigated in this research was one in which subjects took a multiple-choice test but were required to continue responding on each item until the correct answer was obtained. The total number of responses was used as the score on the test. The purpose of this research was to investigate the possibility of increasing predictability by changing the procedure of administering the test, rather than changing the test itself.