Jocelyn H. Bolin

Understanding the Occupational Stress of Collegiate Track and Field Coaches during the Championship Season

The purpose of this study was to investigate sources of occupational stress for NCAA Division I and Division III track and field coaches during the championship season. The Administrative Stress Index (ASI) measured the perceived occupational stressors, and the Personal Resource Questionnaire (PRQ): Part 2 assessed perceived social support. A total of 67 male and female (44.51 + 10.92 yrs.) experienced (14.75 + 10.00 yrs.) coaches participated. Results indicated that a weak to moderate significant correlation exists between the three subscales of the ASI. A significant negative correlation was found between the PRQ and task-based stress (r = −.244, p < .05). When all three predictors and the interaction of years of experience were entered into the model, the social support (β = −0.259 p = .04) and NCAA Division (β = −0.243 p = .052) were significant predictors of task-based stress. As social support increased, task-based stress decreased.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Supervised classification in the presence of misclassified training data: a Monte Carlo simulation study in the three group case

Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.

Group membership prediction when known groups consist of unknown subgroups: a Monte Carlo comparison of methods.

Classification using standard statistical methods such as linear discriminant analysis (LDA) or logistic regression (LR) presume knowledge of group membership prior to the development of an algorithm for prediction. However, in many real world applications members of the same nominal group, might in fact come from different subpopulations on the underlying construct. For example, individuals diagnosed with depression will not all have the same levels of this disorder, though for the purposes of LDA or LR they will be treated in the same manner. The goal of this simulation study was to examine the performance of several methods for group classification in the case where within group membership was not homogeneous. For example, suppose there are 3 known groups but within each group two unknown classes. Several approaches were compared, including LDA, LR, classification and regression trees (CART), generalized additive models (GAM), and mixture discriminant analysis (MIXDA). Results of the study indicated that CART and mixture discriminant analysis were the most effective tools for situations in which known groups were not homogeneous, whereas LDA, LR, and GAM had the highest rates of misclassification. Implications of these results for theory and practice are discussed.

Interdependent Tripartite Efficacy Perceptions and Individual Performance: Case Study of a Boys' Basketball Team.

Tripartite efficacy refers to the beliefs of the individuals within a dyad regarding personal abilities (self-efficacy), the partner’s abilities (other-efficacy), or relation-inferred self-efficacy. This efficacy model has recently gained popularity in sports research (Jackson, Whipp, & Beauchamp, 2013), although there has not been any longitudinal research on efficacy beliefs and performance within this complex intra-dyad tripartite efficacy model. In a case study, we examined six individual players on a high school basketball team to explore any longitudinal changes in these tripartite efficacy beliefs through a season of play. On seven data collection periods, players completed the Basketball-Tripartite Efficacy Measure, and their game performance statistics were analyzed with an objective basketball individual performance formula. We found similar variations between participants’ other-efficacy beliefs and the dyad partner’s basketball performance score as well as between self-efficacy and individual performance score. Observational data from this case study lend some support to spiraling of self-efficacy and performance from repeated successes or failures and to perceived efficacy-performance plateaus that have been previously demonstrated in controlled experimental research. Importantly, this study suggests the presence of other-efficacy beliefs in their relationship to other-performance and to spiraling relationships between other-efficacy beliefs and other-performance, which have not been demonstrated previously.

Estimation of Random Coefficient Multilevel Models in the Context of Small Numbers of Level 2 Clusters

Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the treatment of multilevel data specifically when there is random coefficient variation in small samples. The methods being compared are fixed effects modeling (the industry standard in business and managerial sciences), multilevel modeling using restricted maximum likelihood (REML) estimation (the industry standard in the social and behavioral sciences), multilevel modeling using the Kenward–Rogers correction, and Bayesian estimation using Markov Chain Monte Carlo. Results indicate that multilevel modeling does have an advantage over fixed effects modeling when Level 2 slope parameter variance exists. Bayesian estimation of multilevel effects can be advantageous over traditional multilevel modeling using REML, but only when prior probabilities are correctly specified. Results are presented in terms of Type I error, power, parameter estimation bias, empirical parameter estimate standard error, and parameter 95% coverage rates, and recommendations are presented.

Empowering future educators through environmental sustainability

Purpose – The purpose of this paper is to provide information regarding the ways in which the authors’ College’s faculty use paper within their pedagogical practice. A related purpose was to ascertain faculty interest in professional development initiatives related to reducing paper usage through technological affordances. Design/methodology/approach – A survey was developed and administered to the faculty within the university’s Teachers College. The survey was built to assess faculty pedagogical use of paper. Both quantitative and qualitative data were collected via the survey. Findings – The results suggested that digital resources are widely used by faculty, and students are encouraged to conserve paper. However, many faculty are uncomfortable with the complete elimination of paper. Originality/value – Modeling and promoting environmentally sustainable pedagogical practice is imperative within institutions charged with preparing the next generation of educators.