Life change and spirituality among a college student cohort

OBJECTIVE: Because college marks a time when life-change is typically high, the authors designed this study to determine whether life-change was related to degree of spirituality, the "directing" component of health, among a college student cohort. PARTICIPANTS AND METHODS: The sample group, consisting of 180 northeastern US undergraduate college students, completed the 48-item Life Attitude Profile-Revised (LAP-R) and the Schedule of Recent Experience (SRE) in the fall semester of 2004. RESULTS: Findings indicate that college students who reported experiencing higher levels of life change, both positive and negative, also scored lower on spirituality. Nevertheless, these students had scores indicative of a higher desire to find spirituality, even though their motivation to do so was low. CONCLUSIONS: Although life changes among college students likely will remain high, lower spirituality can be enhanced; therefore, interested health educators are encouraged to help students increase their degree of spirituality.     

Immigration from Mexico into the Math/Science Pipeline in American Education*

Objectives. The purpose of this study was to explore generational differences in math/science enrollment and achievement among Mexican‐American students and the role of family and school contexts in these differences. Methods. We applied survey regression techniques to data from 12,020 adolescents in the National Longitudinal Study of Adolescent Health. Results. Native‐born Mexican‐American students had lower math/science enrollment than their peers, especially after differences in family and school contexts were taken into account. Mexican‐American immigrants had lower achievement when enrolled in such classes, but this was explained by their greater level of family and school disadvantages. Conclusions. Persistence and success in the math/science pipeline, a mechanism of social mobility in the modern economy, would likely be enhanced in the fast‐growing population of Mexican‐American students by improvements in family resources and school organization.     

Bias in linear model power and sample size due to estimating variance

Planning a study using the General Linear Univariate Model often involves sample size calculation based on a variance estimated in an earlier study. Noncentrality, power, and sample size inherit the randomness. Additional complexity arises if the estimate has been censored. Left censoring occurs when only significant tests lead to a power calculation, while right censoring occurs when only non-significant tests lead to a power calculation. We provide simple expressions for straightforward computation of the distribution function, moments, and quantiles of the censored variance estimate, estimated noncentrality, power, and sample size. We also provide convenient approximations and evaluate their accuracy. The results allow demonstrating that ignoring right censoring falsely widens confidence intervals for noncentrality and power, while ignoring left censoring falsely narrows the confidence intervals. The new results allow assessing and avoiding the potentially substantial bias that censoring may create.     

Seme distributions and their implications for an internal pilot study with a univariate linear model

In planning a study, the choice of sample size may depend on a variance value based on speculation or obtained from an earlier study. Scientists may wish to use an internal pilot design to protect themselves against an incorrect choice of variance. Such a design involves collecting a portion of the originally planned sample and using it to produce a new variance estimate. This leads to a new power analysis and increasing or decreasing sample size. For any general linear univariate model, with fixed predictors and Gaussian errors, we prove that the uncorrected fixed sample F-statistic is the likelihood ratio test statistic. However, the statistic does not follow an F distribution. Ignoring the discrepancy may inflate test size. We derive and evaluate properties of the components of the likelihood ratio test statistic in order to characterize and quantify the bias. Most notably, the fixed sample size variance estimate becomes biased downward. The bias may inflate test size for any hypothesis test, even if the parameter being tested was not involved in the sample size re-estimation. Furthermore, using fixed sample size methods may create biased confidence intervals for secondary parameters and the variance estimate.     

Two-Step Hypothesis Testing When the Number of Variables Exceeds the Sample Size

Medical images and genetic assays typically generate data with more variables than subjects. Scientists may use a two-step approach for testing hypotheses about Gaussian mean vectors. In the first step, principal components analysis (PCA) selects a set of sample components fewer in number than the sample size. In the second step, applying classical multivariate analysis of variance (MANOVA) methods to the reduced set of variables provides the desired hypothesis tests. Simulation results presented here indicate that success of the PCA in the first step requires nearly all variation to occur in population components far fewer in number than the number of subjects. In the second step, multivariate tests fail to attain reasonable power except in restrictive, favorable cases. The results encourage using other approaches discussed in the article to provide dependable hypothesis testing with high dimension, low sample size data (HDLSS).     

Parents’ Union Dissolution and Adolescents’ School Performance: Comparing Methodological Approaches

We use data from the National Longitudinal Study of Adolescent Health and the Adolescent Health and Academic Achievement Study to estimate how parents’ union dissolution influences changes in adolescents’ mathematics course work gains, overall grade point average, and course failure rates during a window of approximately 1 year (N =2,629). A primary purpose of this study is demonstrating the utility of propensity score matching techniques for studying topics such as ours that pose methodological challenges such as dealing with endogeneity and selection bias. We compare propensity score matching techniques to ordinary least squares (OLS) regression methods to show and discuss comparability of results obtained using these different procedures. Findings suggest that associations between parents’ union dissolution and achievement may be causal, regardless of method used.     

Extending the Box–Cox transformation to the linear mixed model

Summary.  For a univariate linear model, the Box–Cox method helps to choose a response transformation to ensure the validity of a Gaussian distribution and related assumptions. The desire to extend the method to a linear mixed model raises many vexing questions. Most importantly, how do the distributions of the two sources of randomness (pure error and random effects) interact in determining the validity of assumptions? For an otherwise valid model, we prove that the success of a transformation may be judged solely in terms of how closely the total error follows a Gaussian distribution. Hence the approach avoids the complexity of separately evaluating pure errors and random effects. The extension of the transformation to the mixed model requires an exploration of its potential effect on estimation and inference of the model parameters. Analysis of longitudinal pulmonary function data and Monte Carlo simulations illustrate the methodology discussed.     

I Want to Pretend I'm Eleven Years Younger: Subjective Age and Seniors' Motives for Vacation Travel

Subjective age–as distinct from chronologicalAge–is a relatively recent area of scientificinquiry within quality-of-life studies. Thepurpose of this empirical study was to seewhether the size of the gap between actual andself-perceived age could be predicted from aknowledge of a senior's reasons for wanting totravel on vacation, and his or her valuepriorities and sense of physical well-being. Asurvey of 356 Australian seniors ranging in agebetween 56 and 93 yielded data on theirsubjective age, measured in terms of (a) howold they felt; (b) the activities they wouldchoose to experience or enjoy while on avacation; and (c) the age they wished to betreated and recognized as–as well as theirpsychological motives for vacation travel,personal values, and self-rated health. Theresults of multivariate analyses indicate thatthe gap between actual age and subjective ageis a function of seniors' particular motivesfor travel and what they value in life, and,for certain aspects of subjective age, howhealthy they feel and their gender.