The purpose of this study was to assess the effect of performance feedback on Athletic Trainers’ (ATs) perceived knowledge (PK) and likelihood to pursue continuing education (CE). The investigation was grounded in the theories of “the definition of the situation” (Thomas & Thomas, 1928) and the “illusion of knowing,” (Glenberg, Wilkinson, & Epstein, 1982) suggesting that PK drives behavior. This investigation measured the degree to which knowledge gap predicted CE seeking behavior by providing performance feedback designed to change PK. A pre-test post-test control-group design was used to measure PK and likelihood to pursue CE before and after assessing actual knowledge. ATs (n=103) were randomly sampled and assigned to two groups, with and without performance feedback. Two independent samples t-tests were used to compare groups on the difference scores of the dependent variables. Likelihood to pursue CE was predicted by three variables using multiple linear regression: perceived knowledge, pre-test likelihood to pursue CE, and knowledge gap. There was a 68.4% significant difference (t101= 2.72, p=0.01, ES=0.45) between groups in the change scores for likelihood to pursue CE because of the performance feedback (Experimental group=13.7% increase; Control group= 4.3% increase). The strongest relationship among the dependent variables was between pre-test and post-test measures of likelihood to pursue CE (F2,102=56.80, p<0.01, r=0.73, R2=0.53). The pre- and post-test predictive relationship was enhanced when group was included in the model. In this model [YCEpost=0.76XCEpre-0.34 Xgroup+2.24+E], group accounted for a significant amount of unique variance in predicting CE while the pre-test likelihood to pursue CE variable was held constant (F3,102=40.28, p<0.01,: r=0.74, R2=0.55). Pre-test knowledge gap, regardless of group allocation, was a linear predictor of the likelihood to pursue CE (F1,102=10.90, p=.01, r=.31, R2=.10). In this investigation, performance feedback significantly increased participants’ likelihood to pursue CE. Pre-test knowledge gap was a significant predictor of likelihood to pursue CE, regardless if performance feedback was provided. ATs may have self-assessed and engaged in internal feedback as a result of their test-taking experience. These findings indicate that feedback, both internal and external, may be necessary to trigger CE seeking behavior.

No description available.

The purpose of this study was to analyze the current and pre-service conditions that can affect student teachers' preparedness to pass the science portion of the EC-6 Texas Examinations for Educator Standards (TExES), one of the mandatory certification exam to become a teacher in Texas. Two types of prediction models were employed in this study: binomial logistic regression and multiple linear regression. The independent variables used in this study were: final grade in BIOL 1082, classification of students, transfer status, taken college biology, taken college chemistry, taken college physics, taken college environmental science, taken college earth science, attending college part-time, number of credits taken during the semester, first-generation college student, relatives with degree in education, and current GPA. The dependent variable of this study was the posttest score on science portion of the EC-6 TExES practice exam. A total of 170 preservice teachers participated this study. This study used students enrolled in BIOL 1082, who volunteered to take a Biology for Educators QualtricsTM survey and the EC-6 TExES practice exam in a pretest (start of semester) and posttest (end of semester) form. The findings of this study revealed that the single best predictor of preservice teachers' performance on the science portion of EC-6 TExES practice certification examination was the Grade in BIOL 1082.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20688号 / 工博第4385号 / 新制||工||1681(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 三ｹ田 均, 准教授 須崎 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.

Knowledge of construction duration is pertinent to a number of project planning functions prior to detailed design development. Funding, financing, and resource allocation decisions take place early in project design development and are significantly influenced by the construction duration. Currently, there is not an understanding of the project factors having a statistically significant relationship with highway construction duration. Other industry sectors have successfully used statistical regression analysis to identify and model the project parameters related to construction duration. While the need is seen for such work in highway construction, there are very few studies which attempt to identify duration-influential parameters and their relationship with the highway construction duration. This research identifies the project factors, known early in design development, which influence highway construction duration. The factors identified are specific to their respective project types and are those factors which demonstrate a statistically-significant relationship with construction duration. This work also quantifies the relationship between the duration-influential factors and highway construction duration. The quantity, magnitude, and sign of the factor coefficient yields evidence regarding the importance of the project factor to highway construction duration. Finally, the research incorporates the duration-influential project factors and their relationship with highway construction duration into mathematical models which assist in the prediction of construction duration. Full and condensed models are presented for Full-Depth Section and Highway Improvement project types. This research uses statistical regression analysis to identify, quantify, and model these early-known, duration-influential project factors. The results of this research contribute to the body of knowledge of the sponsoring organization (Virginia Department of Transportation), the highway construction industry, and the general construction industry at large. / Ph. D.

Prediction of airline fares within the United States including Alaska & Hawaii is required for transportation mode choice modeling in impact analysis of new modes such as NASA's Small Airplane Transportation System (SATS). Developing an aggregate cost model i.e. a 'generic fare model' of the disaggregated airline fares is required to measure the cost of air travel. In this thesis, the ratio of average fare to distance i.e. fare per mile and average fare is used as a measure of this cost model. The thesis initially determines the Fare Class categories to be used for Coach and Business class for the analysis .The thesis then develops a series of 'generic fare models' using round trip distance traveled as an independent variable. The thesis also develops a set of models to estimate average fare for any origin and destination pair in the US. The factors considered by these models are: the round trip distance traveled between the origin (o) and destination (d), the type of fare class chosen by the traveler (first, business class and unrestricted coach class and restricted coach class), the type of airport (large hub, medium hub, small hub, or non hub), whether or not the route is served by a low cost airline and the airline market concentration between the o-d pair. The models suggest that competition at the destination airport is more critical than the competition at origin airport for coach class fares and vice a versa for business class fares. Models suggested in this thesis predict air fares with R-square values of 0.3 to 0.75. / Master of Science

The importance of bottomland hardwood (BLH) forests to support the economy through timber production and carbon sequestration is acknowledged; however, their full potential is yet to be explored. This study developed variable density yield models for BLH oak-gum-cypress forests along the US Gulf Coast and Lower Mississippi River Delta. The models, with an adjusted R2 of 98% for cubic foot growing stock volume and 77% for Doyle board foot sawlog volume, are expected to be valuable tools for landowners and managers seeking to make informed decisions about BLH forest management. A carbon stock model was also developed, and carbon sequestration was explored based on basal area increment. The results showed potential for carbon sequestration with an average carbon stock of 30.56 tons/acre and a maximum average discounted present value of carbon accumulation of $15.94/ton/acre/year. This provides valuable information to managers and landowners willing to participate in carbon credit markets.

Knowledge of battery health is very important. It provides insight into the capacity of a given system and allows the operators to plan ahead more efficiently. But measuring state of health (SoH) of a battery is difficult, and takes time. More importantly, the battery needs to be taken out of operation to be analysed correctly. This paper aims to evaluate a proposed linear regression method for predicting battery health, based on easily acquired operational data. The main predictor being voltage deviation, a characteristic of battery voltages during charge/discharge cycles. Using this method, the only time a battery would need to be extracted is to gather training data. Then, the model could be used for similar batteries to predict their SoH. Meaning those systems would never need to be halted, increasing productivity. The results of this paper is that the data used was not suitable for linear regression. There were problems with heteroskedasticity and non-normality of the residuals, but mainly the estimated parameter for the relationship between voltage deviation and SoH ran contrary to established theory. Which could not be overlooked. Therefore, the estimated models should not be used to predict SoH. To accomplish the goal of accurate SoH prediction, more research should be conducted and a better sample used.

The role of motivation and its relationship with desired outcomes has been studied in a variety of contexts as evidenced in the literature. Motivation, its origin, type, and its effect, has been theorized to range from non-existent to the main driving force behind all behavior. Self-determination theory, a more recent motivational theory, posits that motivation is a driving force of behavior; however, the amount of control one has to perform freely a given task determines whether this motivation is internally (autonomously) generated or externally (controlled) generated. The idea of motivation affecting outcomes is clearly evidenced in research geared toward finding the role of motivation on satisfaction of a given job, task, or assignment. This research reviewed studies that focused on motivation and its role on job satisfaction. A theoretical thread was postulated that intrinsic motivation is as good as, if not better in most instances, than extrinsic motivation in determining job satisfaction. Also, job satisfaction leads to greater lengths of tenure in a given job. Both of these statements were affirmed from a review of the literature. However, one question remains: what type of intrinsic motivation factors best correlate to job satisfaction (and its potential effect of improving tenure)? Therefore, the overall objective of this study is to determine whether various forms of intrinsic motivation correlate with an employee’s satisfaction with their job or career. The study was conducted using a survey method that incorporated the participation of 172 participants from two very similar psychiatric hospitals in the southeastern United States. Multiple linear regression was used to determine if any relationship existed between three intrinsic motivation factors (autonomy, competence, and relatedness) and job satisfaction. The results of this study suggest that positive relationships do exist between that of autonomy and relatedness intrinsic motivation factors and job satisfaction scores. The combined predictor factors (autonomy, competence, and relatedness) yielded an R2 = .145, indicating that almost 15% of the total job satisfaction scores can be explained by these three variables. Additional, exploratory regression analyses were conducted using experimental statements and selected demographic information. Conclusions and recommendations for future research are also given.