While the positive impact of goals on task performance is well documented (Locke & Latham, 1990), there still is little understanding of the way in which people set and revise personal goals over time. Research has found that people change their personal goals over time, often to reduce performance-goal discrepancies (Campion & Lord, 1983; Donovan & Williams, 2003; Vancouver et al, 2001) although how such goal revision proceeds over time has not been clearly conceptualized. This dissertation proposes that personal goal revision proceeds through the process of goal calibration by the mechanism of Bayesian updating. People use performance and performance variance information to better estimate future performance and revise their current personal goals to reflect such information. Bayesian updating has been shown to be a process that influences how people make estimations of a number of factors (Griffiths & Tenenbaum, 2006) and thus should have a similar impact on personal goals. This was tested with 155 college students performing a temporary worker hiring task and setting task related personal goals. Support was found for a goal calibration model better fitting actual participant personal goals compared to a Goal Setting Model, although unexpectedly a constant growth model offered the best fit. The reasons for these results and implications for future work on personal goal setting is discussed.
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