The Impact of Neighboring on Changes in Sense of Community Over Time: A Latent Transition Analysis

Sense of community (SOC) is associated with the quality of community life and the building of social capital. While its linkage to informal social behavior, such as neighboring, is inherent in discussions regarding theory, empirical evidence remains scarce. Moreover, the degree to which neighboring behavior influences SOC over time is largely unknown. Using a latent transition analysis, the effect of neighboring on SOC was investigated over a 5-year span from 2006 to 2011 among a sample of adults (n?=?165) in Arizona. Initially, a latent class analysis identified two SOC subgroups: Low SOC and High SOC. The likelihood of shifts in SOC class membership over 5 years was generally stable, with most individuals staying in the same group (82.3% Low SOC; 92.4% High SOC). Neighboring behavior and socio-demographic covariates impacted the likelihood that individuals changed classes, with 25.3% of Low SOC individuals transitioning to High SOC in 2011 and 55.4% of High SOC individuals moving to Low SOC in 2011. Specifically, having an income greater than $60,000 and visiting with neighbors lessened the likelihood of being in the Low SOC class in 2006; and length of residence and exchanging favors with neighbors lessened the likelihood of being in the Low SOC class in 2011. These findings have implications for both community design and community development practice. Design and development interventions that promote greater social interaction may help build and sustain SOC over time.

     

Properties of the weighted log‐rank test in the design of confirmatory studies with delayed effects

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms, and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds, and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log‐rank test allows a weighting for early, middle, and late differences through the Fleming and Harrington class of weights and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs and make an empirical evaluation in terms of power and type‐I error rate of the of the weighted log‐rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.     

Wage and Non-Wage Compensation Among Young Alcoholic and Heavy Drinking Women: A Preliminary Analysis

The association between drinking and selected job characteristics among women aged 24 to 31 is examined. Using the 1989 NLSY, women are classified as alcohol abusers or dependent based on DSM-III-R criteria or as heavy drinkers based on reported frequency of six or more drinks. Heavy drinking is negatively associated with wage and non-wage compensation. These effects diminish when human capital measures are controlled. Current alcoholism and current and past heavy drinking are strongly negatively associated with years of schooling. The association between alcoholism and job compensation and characteristics is not as strong as that seen for heavy drinking. It is not known if this is a consequence of errors in identifying alcoholic women in population-based surveys.     

GENDER AND RACE PATTERNS IN THE PATHWAYS FROM SPORTS PARTICIPATION TO SELF-ESTEEM

Athletics is the most prominent extracurricular activity in U.S. secondary schools in terms of student participation and school budgets. The latter is often justified on the grounds that healthy bodies produce healthy minds, that school sports boost school spirit, and that participation in school-based sports increases students' self-esteem. In this article we examine the interrelationships among participation in a school-based sport and the benefits assumed to be associated with it. Specifically, we test a model that postulates that school spirit, operationalized as attachment to school, and healthy bodies, operationalized as a sense of physical well-being, mediate the relationship between school sports and self-esteem. Data from the National Longitudinal Study of Adolescent Health on Caucasian and African American girls and boys were employed to test the model. School attachment and physical well-being absorbed the statistical effect of participating in a sport for all four gender-by-race groups. Among Caucasian girls a negative residual effect of sports participation was observed, which suggests that sports participation encapsulates multiple effects with contradictory influences. For African American girls school attachment by itself was not a significant mediator of the effect of sports participation on self-esteem. For all groups a sense of physical well-being was the more powerful mediator.     

A NOTE ON EVANESCENT PROCESSES

This note examines the connection between μ-invariant measures for the transition function of a continuous-time Markov chain and those of its q-matrix, Q. The major result establishes a necessary and aufficient condition for a convergent μ-invariant measure for Q to be μ-inhant for the minimal transition function, P, under the assumption that P is honest. This corrects Theorem 6 of Vere-Jones (1969) and the first part of Corollary 1 of Pollett (1986), both of which assert that the above conclusion holds in the absence of this condition. The error was pointed out by E.A. van Doom (1991) and the counterexample which be presented provides the basis for the present arguments. In determining where the error occurred in the original proof, we are able to identify a simple sufficient condition for μ-invariance.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

Don't mention Stonehenge!

It rises massive and magnificent above Salisbury Plain. The extraordinary monoliths of Stonehenge leave us as impressed today as ever—and as baffled. Archaeologists argue about how it was made. Can statisticians tell the answer? Chris Jones thinks they can be of considerable assistance.     

Female Victim Perceptions of the Causes of Male Spouse Abuse*

Victims of spouse abuse were interviewed concerning their views of why their husbands were abusive. It was hypothesized that by concentrating on one specific characteristic of this kind previously undocumented patterns of response would be elicited from the subjects. It was concluded that the women's reasons for the violence perpetrated by their spouses serve as justification for the abusers’actions and legitimize maintenance of the relationship.     

Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms

Summary.  In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.     

African American Children's Adjustment: The Roles of Maternal and Teacher Depressive Symptoms

This study examined the unique and interactive roles of mother and teacher depressive symptoms in child adjustment in 277 African American single mother‐headed families, as well as whether the associations differed depending on the age and gender of the child. Findings revealed a significant association between maternal depressive symptoms and child depressive symptoms in girls, but not boys. Moreover, the combination of higher levels of both mother and teacher depressive symptoms was associated with the highest level of child depressive symptoms and, for younger children, externalizing symptoms. The importance of considering the multiple social contexts in which children interact is discussed.