Handling Missing Values in Longitudinal Panel Data With Multiple Imputation

This article offers an applied review of key issues and methods for the analysis of longitudinal panel data in the presence of missing values. The authors consider the unique challenges associated with attrition (survey dropout), incomplete repeated measures, and unknown observations of time. Using simulated data based on 4 waves of the Marital Instability Over the Life Course Study (n = 2,034), they applied a fixed effect regression model and an event‐history analysis with time‐varying covariates. They then compared results for analyses with nonimputed missing data and with imputed data both in long and in wide structures. Imputation produced improved estimates in the event‐history analysis but only modest improvements in the estimates and standard errors of the fixed effects analysis. Factors responsible for differences in the value of imputation are examined, and recommendations for handling missing values in panel data are presented.     

The Challenges in Developing an Online Applied Statistics Program: Lessons Learned at Penn State University

Numerous professional fields have an increasing need for individuals trained in statistics and other quantitative analysis techniques. Today there exists great potential to fulfill this need by providing opportunities through online learning. However, to provide a high-quality education for returning adult professionals seeking advanced degrees in applied statistics online, many challenges need to be overcome. Based on our experience developing Penn State University’s online program in applied statistics, we discuss the evolution of the program’s curriculum, recruitment and development of online faculty, and meeting the requirements of students as important areas that require consideration in the development of an online program. We also highlight program evaluation strategies employed to ensure innovation and improvement in online education as cornerstones to a program’s success.     

Myths and Misconceptions About University Student Volunteering: Development and Perpetuation

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This paper examines myths and misconceptions about university student volunteering. Our study explored the experiences of...     

HEALTH NEEDS OF MIGRANT VIETNAMESE WOMEN IN SOUTH‐WEST BRISBANE: AN EXPLORATORY STUDY

This study was conducted in the southwest area of Brisbane, Australia, and is designed to explore and assess the health needs of Vietnamese migrant women. The needs of this group are becoming increasingly urgent due to ageing of the original immigrant refugee community and decreased capacity for support from their children and families. The study used a qualitative research strategy involving focus groups and in‐depth interviews with Vietnamese women between 18–65 years and interviews with Vietnamese health care providers. It shows that the women have had to deal with culture shock, low self‐esteem, lack of friends and relatives, unrecognized professional skills and most importantly in health terms, low socioeconomic status. Significant barriers to access health services and to improved health and well‐being were also identified, such as language difficulties, transportation, time, and knowledge about health education. Recognition of specific requirements of sub‐populations as well as broader socio‐economic and cultural determinants of health should be a guide to more effective planning and implementation of health promotion strategies. The changing needs, over time, of these sub‐populations should also be recognized.     

Health and welfare of immigrants and access to services in Australia

The main issues in relation to the provision of health and social welfare services for migrants in Australia are: (a) to ensure access and equity; (b) concern about the relatively high proportions of some groups from specific birthplaces requiring support; and (c) the complexity and costs of ethnically specific services. This article focuses on the major pensions and benefits paid by the Department of Social Security, particularly the age pension and unemployment benefit. It also examines the interrelationship between mortality, morbidity, private health insurance, use of health services and the proportions on health-related pensions. Linked with these issues is the debate about an appropriate level of immigration when the unemployment rate is high, as in the current recession, and when new arrivals have little prospect of finding a job. A second major concern is the cost of caring for elderly immigrant parents who have come to Australia under the family reunion program.     

Static Numbers to Dynamic Statistics: Designing a Policy-Friendly Social Policy Indicator Framework

In line with the economic crisis and rapid socio-demographic changes, the interest in ??social?? and ??well-being?? indicators has been revived. Social indicator movements of the 1960s resulted in the establishment of social indicator statistical frameworks; that legacy has remained intact in many national governments and international organisations. With this background, this research examines whether existing social indicator frameworks are valid and effective enough to address increasingly complex social issues. The authors argue that, despite some improvements, current social indicators fail to provide an effective framework and tool for measuring the progress of social welfare and also for developing or reforming social policy to cope with newly emerging social problems. While proposing a new social indicator framework based on the Organisation for Economic Co-operation and Development??s pressure-state-response (PSR) model, the paper argues that the new framework should be more than displaying static numbers but should use dynamic statistics revealing causes and effects and shedding light on social and policy changes.     

Orthodox BLUP versus h-likelihood methods for inferences about random effects in Tweedie mixed models

Recently, the orthodox best linear unbiased predictor (BLUP) method was introduced for inference about random effects in Tweedie mixed models. With the use of h-likelihood, we illustrate that the standard likelihood procedures, developed for inference about fixed unknown parameters, can be used for inference about random effects. We show that the necessary standard error for the prediction interval of the random effect can be computed from the Hessian matrix of the h-likelihood. We also show numerically that the h-likelihood provides a prediction interval that maintains a more precise coverage probability than the BLUP method.