Modeling population and subject-specific growth in a latent trait measured by multiple instruments over time using a hierarchical Bayesian framework

Psychometric growth curve modeling techniques are used to describe a person’s latent ability and how that ability changes over time based on a specific measurement instrument. However, the same instrument cannot always be used over a period of time to measure that latent ability. This is often the case when measuring traits longitudinally in children. Reasons may be that over time some measurement tools that were difficult for young children become too easy as they age resulting in floor effects or ceiling effects or both. We propose a Bayesian hierarchical model for such a scenario. Within the Bayesian model we combine information from multiple instruments used at different age ranges and having different scoring schemes to examine growth in latent ability over time. The model includes between-subject variance and within-subject variance and does not require linking item specific difficulty between the measurement tools. The model’s utility is demonstrated on a study of language ability in children from ages one to ten who are hard of hearing where measurement tool specific growth and subject-specific growth are shown in addition to a group level latent growth curve comparing the hard of hearing children to children with normal hearing.KEYWORDS: Bayesian hierarchical models, psychometric modeling, language ability, growth curve modeling, longitudinal analysis     

Maternal mortality: What can we learn from stories of postpartum haemorrhage?

Death from pregnancy is rare in developed countries such as Australia but is still common in third world and developing countries. The investigation of each maternal death yields valuable information and lessons that all health care providers involved with the care of women can learn from. The aim of these investigations is to prevent future maternal morbidity and mortality.Obstetric haemorrhage remains a leading cause of maternal death internationally. It is the most common cause of death in developing countries. In Australia and the United Kingdom, obstetric haemorrhage is ranked as the 4th and 3rd most common cause of direct maternal death respectively. In a number of cases there are readily identifiable factors associated with the care that the women received that may have contributed to their death. It is from these identifiable factors that both midwives and doctors can learn to help prevent similar episodes from occurring.This article will identify some of the lessons that can be learnt from the recent Australian and UK maternal death reports. This paper presents an overview of the process and systems for the reporting of maternal death in Australia. It will then specifically focus on obstetric haemorrhage, with a focus on postpartum haemorrhage, for the 12-year period, 1994–2005. Vignettes from the maternal mortality reports in Australia and the United Kingdom are used to highlight the important lessons for providers of maternity care.     

The evidence base for social support interventions in child and family practice: Emerging issues for research and practice

This paper examines the evidence base for interventions which seek to enhance social support resources for families with children at risk of adverse developmental outcomes. Substantive and methodological issues related to the implementation of social support interventions are discussed and a research agenda for the development, testing and evaluation of such interventions is presented.     

Maternal Employment,Community Contexts,and the Child‐Care Arrangements of Diverse Groups

Integrating family and child data from the Early Childhood Longitudinal Study–Birth Cohort with contextual data from the census, this study examined associations among maternal employment, aspects of communities related to child‐care supply and demand, and the early care and education arrangements of 4 year olds in Mexican‐origin, Black, and White families. Children with employed mothers were more likely to be in informal care arrangements than in early childhood education, regardless of racial/ethnic background. For children in Mexican‐origin families, selection into informal care over early childhood education was more likely in zip codes with greater demand for care as measured by higher female employment. Utilization of parent care versus early childhood education was also more likely for children in Mexican‐origin and Black families in zip codes with higher female employment. Constraints associated with maternal employment thus hindered children from enrolling in early childhood education, and community contexts posed challenges for some groups.     

Do the Constructs of the FACES IV Change Based on Definitions of “Family?” A Measurement Invariance Test

The Family Adaptability and Cohesion Scale (FACES) IV does not provide instructions about which family members respondents should think about while answering questions. This study examined which family members respondents thought about while completing the FACES IV, and if this changed measurement invariance and population heterogeneity of the measure. Using a sample of n = 511 individuals, a latent class analysis showed three distinct classes: Nuclear Family, Family of Origin, and All of the Above. The FACES IV demonstrated measurement invariance across classes on the majority of subscales; however, population heterogeneity tests suggested that the means and variances of the subscales varied across classes. The findings suggest further examination of how the measure functions with unique family constellations is warranted.     

Feasibility of Implementation of a Parenting Intervention with Karen Refugees Resettled from Burma

Parents and children exposed to war and relocation have high rates of negative relational and mental health outcomes. This study tested the feasibility of implementing an adapted evidence‐based parenting intervention for contexts of trauma and relocation stress. Eleven Karen refugee caregivers from Burma participated in the intervention. Participants and a focal child completed ethnographic interviews as well as structured assessments at baseline and follow‐up. Caregivers reported changes in their teaching, directions, emotional regulation, discipline, and child compliance. Children reported changes in these areas and in positive parent involvement. Caregivers reported higher mental health distress immediately after the intervention, potentially due to increased awareness. Researchers made personalized referrals for counseling services as needed. Children reported a decrease in mental health symptoms.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.