Infant formula samples: perinatal sources and breast-feeding outcomes at 1 month postpartum.

The purpose was to describe sources of infant formula samples during the perinatal period and assess their associations with breast-feeding outcomes at 1 month postpartum. Subjects included expectant mothers who anticipated breast-feeding at least 1 month. Infant feeding history and sources of formula samples were obtained at 1 month postpartum. Associations between sources and breast-feeding outcomes were assessed using partial correlation. Of the 61 subjects who initiated breast-feeding, most were white (87%), married (75%), college-educated (75%), and planned exclusive breast-feeding (82%). Forty-two subjects (69%) continued breast-feeding at 1 month postpartum. Subjects received formula samples from the hospital (n = 40; 66%), physician's office (n = 10; 16%), and mail (n = 41; 67%). There were no significant correlations between formula samples from the hospital, physician's office, and/or mail and any or exclusive breast-feeding at 1 month (P > .05). In addition to the hospital, a long-standing source of formula samples, mail was also frequently reported as a route for distribution. The lack of statistically significant associations between formula samples and any or exclusive breast-feeding at 1 month may be related to small sample size and unique characteristics of the group studied.

Fixed Effects or Mixed Effects Classifiers? Evidence From Simulated and Archival Data.

This study seeks to compare fixed and mixed effects models for the purposes of predictive classification in the presence of multilevel data. The first part of the study utilizes a Monte Carlo simulation to compare fixed and mixed effects logistic regression and random forests. An applied examination of the prediction of student retention in the public-use U.S. PISA data set was considered to verify the simulation findings. Results of this study indicate fixed effects models performed comparably with mixed effects models across both the simulation and PISA examinations. Results broadly suggest that researchers should be cognizant of the type of predictors and data structure being used, as these factors carried more weight than did the model type.

Sleep in NCAA Division I collegiate athletes: Relations with self-compassion, stress, and rumination.

The purpose of the present study was to investigate whether the benefits of self-compassion on sleep difficulties extend to a sample of National Collegiate Athletic Association (NCAA) Division I college athletes, and to examine the unique contributions of two possible pathways that have been separately evidenced to underlie the self-compassion and sleep difficulties association in non-athlete populations - namely perceived stress and rumination. A total of 412 NCAA Division I college athletes (M = 19.88, SD = 1.50) from 16 different West, Midwest, South, and North-eastern universities completed the Self-Compassion Scale, Perceived Stress Scale-10, Rumination-Reflection Questionnaire, and the Pittsburgh Sleep Quality Index. Findings indicated that self-compassion is associated with fewer sleep difficulties in college athletes, and that lower levels of perceived stress and rumination are independent mechanisms through which self-compassion is associated with sleep difficulties. Further, a comparison of the indirect effects revealed that perceived stress had a significantly stronger indirect effect on the relation between self-compassion and sleep difficulties than rumination. This study provides initial support for self-compassion training as a potentially relevant and important resource for promoting mental health and sleep health in the context of collegiate sport.

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.

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.

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.

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.