A Limited Information Estimator for Dynamic Factor Models.

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework.

Diffusing innovative road safety practice: A social network approach to identifying opinion leading U.S. cities.

Objective: This study sought to identify opinion-leading U.S. cities in the realm of safe transportation systems by surveying road safety professionals and asking them to identify places that served as models for road safety.

Factors associated with adolescent and caregiver reported problems in using asthma medications.

Objectives: The purpose of this study was to: (a) describe the types of medication problems/concerns youth with asthma and their caregivers reported and (b) examine the association between sociodemographic characteristics and youth and caregiver reported medication problems/concerns.

Methods: English- and Spanish-speaking youth ages 11-17 with persistent asthma were recruited at four pediatric clinics. Youth were interviewed and caregivers completed questionnaires about reported asthma medication concerns/problems.

Patient-reported outcomes in cancer survivorship.

There are currently 33 million cancer survivors worldwide. With improvements in early cancer detection and treatments, patients are living longer - and it is well-recognized that many survivors develop short- and long-term physical, psychosocial and spiritual effects as a result of their diagnoses and treatments. There is increasing awareness of the importance of using patient-reported outcomes (PROs) to accurately assess these effects in cancer survivors.

A 6-year surveillance study of "Stingers" in NCAA American Football.

This study describes the epidemiology of "stinger" injuries in National Collegiate Athletic Association (NCAA) Men's Football. About 57 NCAA Men's Football programmes provided 153 team-seasons of injury data to the NCAA Injury Surveillance Programme (NCAA-ISP) during the 2009/2010-2014/2015 academic years. In the study period, 229 "stingers" were reported for an injury rate of 2.

The Ethics of Organ Donor Registration Policies: Nudges and Respect for Autonomy.

Governments must determine the legal procedures by which their residents are registered, or can register, as organ donors. Provided that governments recognize that people have a right to determine what happens to their organs after they die, there are four feasible options to choose from: opt-in, opt-out, mandated active choice, and voluntary active choice. We investigate the ethics of these policies' use of nudges to affect organ donor registration rates.

Masculinity and Race-Related Factors as Barriers to Health Help-Seeking Among African American Men.

Men's tendency to delay health help-seeking is largely attributed to masculinity, but findings scarcely focus on African American men who face additional race-related, help-seeking barriers. Building principally on reactance theory, we test a hypothesized model situating racial discrimination, masculinity norms salience (MNS), everyday racism (ERD), racial identity, sense of control (SOC), and depressive symptomatology as key barriers to African American men's health help-seeking. A total of 458 African American men were recruited primarily from US barbershops in the Western and Southern regions.

Whose stress is making me sick? Network-stress and emotional distress in African-American women.

Research on stress-related health outcomes in African-American women often neglects "network-stress": stress related to events that occur to family, friends, or loved ones. Data from the African-American Women's Well-Being Study were analyzed to examine self-stress and network-stress for occurrence, perceived stressfulness, and association with symptoms of psychological distress. Women reported a higher number of network-stress events compared with self-stress events.

Hypnosis and Guided Imagery Treatment for Gastrointestinal Disorders: Experience With Scripted Protocols Developed at the University of North Carolina.

Completely scripted treatment courses for verbatim interventions are uncommon in the field of clinical hypnosis. This approach was adopted for by a North Carolina research team for treating gastrointestinal disorders 20 years ago and has been used in hypnosis treatment of irritable bowel syndrome and ulcerative colitis, as well as in guided imagery treatment for functional abdominal pain. Treatment with these scripted protocols is delivered in a fixed series of sessions over a 2- or 3-month period.

Explaining religious differentials in family-size preference: Evidence from Nepal in 1996.

We examine how religio-ethnic identity, individual religiosity, and family members' religiosity were related to preferred family size in Nepal in 1996. Analyses of survey data from the Chitwan Valley Family Study show that socio-economic characteristics and individual experiences can suppress, as well as largely account for, religio-ethnic differences in fertility preference. These religio-ethnic differentials are associated with variance in particularized theologies or general value orientations (like son preference) across groups.

Longitudinal Multi-Trait-State-Method Model Using Ordinal Data.

Multi-trait multi-method (MTMM) models provide a way to assess convergent and discriminant validity when multiple traits are measured by multiple methods. In recent years, longitudinal extensions of MTMM models have been proposed in the structural equation modeling framework to evaluate whether and how the trait as well as method factors change over time. We propose a novel longitudinal ordinal MTMM model that can be used to effectively distinguish volatile "state" processes from "trait" processes that tend to remain stable and invariant over time.

Dynamic Factor Analysis Models With Time-Varying Parameters.

Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor model with vector autoregressive relations and time-varying cross-regression parameters at the factor level. Using techniques drawn from the state-space literature, the model was fitted to a set of daily affect data (over 71 days) from 10 participants who had been diagnosed with Parkinson's disease.

Sensitivity Analysis in Structural Equation Models: Cases and Their Influence.

The detection of outliers and influential observations is routine practice in linear regression. Despite ongoing extensions and development of case diagnostics in structural equation models (SEM), their application has received limited attention and understanding in practice. The use of case diagnostics informs analysts of the uncertainty of model estimates under different subsets of the data and highlights unusual and important characteristics of certain cases.

Abstract: Adequacy of Semiparametric Approximations for Growth Models with Nonnormal Random Effects.

Conventional growth models assume that the random effects describing individual trajectories are conditionally normal. In practice, this assumption may often be unrealistic. As an alternative, Nagin (2005) suggested a semiparametric group-based approach (SPGA) which approximates an unknown, continuous distribution of individual trajectories with a mixture of group trajectories.

Abstract: Parameter Influence In Structural Equation Models.

In applications of SEM, investigators obtain and interpret parameter estimates that are computed so as to produce optimal model fit in the sense that the obtained model fit would deteriorate to some degree if any of those estimates were changed. This property raises a question: to what extent would model fit deteriorate if parameter estimates were changed? And which parameters have the greatest influence on model fit? This is the idea of parameter influence. The present paper will cover two approaches to quantifying parameter influence.