COACHES, CLIMATES, “FIELD” GOALS, AND EFFICACY: A “DE-CONSTRUCTION” OF THE MASTERY-APPROACH TO COACHING AND EXAMINATION OF RELATIONSHIPS TO PSYCHOSOCIAL OUTCOMES IN A YOUTH FOOTBALL PLAYER DEVELOPMENT PROGRAM.

In support of the achievement goal theory (AGT), empirical research has demonstrated psychosocial benefits of the mastery-oriented learning climate. In this study, we examined the effects of perceived coaching behaviors on various indicators of psychosocial well-being (competitive anxiety, self-esteem, perceived competence, enjoyment, and future intentions for participation), as mediated by perceptions of the coach-initiated motivational climate, achievement goal orientations and perceptions of sport-specific skills efficacy. Using a pre-post test design, 1,464 boys, ages 10-15 (M = 12.84 years, SD = 1.44), who participated in a series of 12 football skills clinics were surveyed from various locations across the United States. Using structural equation modeling (SEM) path analysis and hierarchical regression analysis, the cumulative direct and indirect effects of the perceived coaching behaviors on the psychosocial variables at post-test were parsed out to determine what types of coaching behaviors are more conducive to the positive psychosocial development of youth athletes. The study demonstrated that how coaching behaviors are perceived impacts the athletes’ perceptions of the motivational climate and achievement goal orientations, as well as self-efficacy beliefs. These effects in turn affect the athletes’ self-esteem, general competence, sport-specific competence, competitive anxiety, enjoyment, and intentions to remain involved in the sport. The findings also clarify how young boys internalize and interpret coaches’ messages through modification of achievement goal orientations and sport-specific efficacy beliefs.

RELATIONSHIP BETWEEN COLUMN DENSITY AND SURFACE MIXING RATIO FOR O3 AND NO2: IMPLICATIONS FOR SATELLITE OBSERVATIONS AND THE IMPACTS OF VERTICAL MIXING

Satellites have great potential for diagnosis of surface air quality conditions, though reduced sensitivity of satellite instrumentation to the lower troposphere currently impedes their applicability. One objective of the NASA DISCOVER-AQ project is to provide information relevant to improving our ability to relate satellite-observed columns to surface conditions for key trace gases and aerosols. In support of DISCOVER-AQ, this dissertation investigates the degree of correlation between O3 and NO2 column abundance and surface mixing ratio during the four DISCOVER-AQ deployments; characterize the variability of the aircraft in situ and model-simulated O3 and NO2 profiles; and use the WRF-Chem model to further investigate the role of boundary layer mixing in the column-surface connection for the Maryland 2011 deployment, and determine which of the available boundary layer schemes best captures the observations. Simple linear regression analyses suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere may be most meaningful for surface air quality under the conditions associated with the Maryland 2011 campaign, which included generally deep, convective boundary layers, the least wind shear of all four deployments, and few geographical influences on local meteorology, with exception of bay breezes. Hierarchical clustering analysis of the in situ O3 and NO2 profiles indicate that the degree of vertical mixing (defined by temperature lapse rate) associated with each cluster exerted an important influence on the shapes of the median cluster profiles for O3, as well as impacted the column vs. surface correlations for many clusters for both O3 and NO2. However, comparisons to the CMAQ model suggest that, among other errors, vertical mixing is overestimated, causing too great a column-surface connection within the model. Finally, the WRF-Chem model, a meteorology model with coupled chemistry, is used to further investigate the impact of vertical mixing on the O3 and NO2 column-surface connection, for an ozone pollution event that occurred on July 26-29, 2011. Five PBL schemes were tested, with no one scheme producing a clear, consistent “best” comparison with the observations for PBLH and pollutant profiles; however, despite improvements, the ACM2 scheme continues to overestimate vertical mixing.

Hierarchical Reconstruction Method for Solving Ill-posed Linear Inverse Problems

We present a detailed analysis of the application of a multi-scale Hierarchical Reconstruction method for solving a family of ill-posed linear inverse problems. When the observations on the unknown quantity of interest and the observation operators are known, these inverse problems are concerned with the recovery of the unknown from its observations. Although the observation operators we consider are linear, they are inevitably ill-posed in various ways. We recall in this context the classical Tikhonov regularization method with a stabilizing function which targets the specific ill-posedness from the observation operators and preserves desired features of the unknown. Having studied the mechanism of the Tikhonov regularization, we propose a multi-scale generalization to the Tikhonov regularization method, so-called the Hierarchical Reconstruction (HR) method. First introduction of the HR method can be traced back to the Hierarchical Decomposition method in Image Processing. The HR method successively extracts information from the previous hierarchical residual to the current hierarchical term at a finer hierarchical scale. As the sum of all the hierarchical terms, the hierarchical sum from the HR method provides an reasonable approximate solution to the unknown, when the observation matrix satisfies certain conditions with specific stabilizing functions. When compared to the Tikhonov regularization method on solving the same inverse problems, the HR method is shown to be able to decrease the total number of iterations, reduce the approximation error, and offer self control of the approximation distance between the hierarchical sum and the unknown, thanks to using a ladder of finitely many hierarchical scales. We report numerical experiments supporting our claims on these advantages the HR method has over the Tikhonov regularization method.