Second‐Generation Immigrants? The “2.5 Generation” in the United States*

Objective. This article takes issue with the way that second‐generation immigrants have been traditionally defined. In most studies, respondents are considered to be “second generation” if they are born in the United States and if at least one of their parents was born outside the United States. This article considers whether the experiences and outcomes of those with one U.S.‐born parent and one foreign‐born parent (the “2.5 generation”) are different from those with no U.S.‐born parents (the “2.0 generation”) and those with two native‐born parents (the “third generation”). Methods. The article analyzes data from the March Current Population Survey (CPS) from 1999 to 2001. Results. The evidence indicates that the 2.5 generation is a numerically significant population, and that it varies from other groups in age structure, racial identification, educational attainment, and income. Conclusions. In studying the U.S.‐born children of immigrants, scholars should avoid lumping together the 2.5 generation with those who have no native‐born parents. The members of the 2.5 generation also should be treated as distinct from those born in the United States to two native‐born parents.     

Civic Spaces: Mexican Hometown Associations and Immigrant Participation

The study of civic participation and social capital in the United States has, until recently, been silent on the role of immigrant-serving organizations. There is a new line of scholarship, which indicates that ethnic organizations are generally disadvantaged in relation to White mainstream organizations on factors such as resources and political visibility. Our fieldwork on Mexican hometown associations (HTAs) in Los Angeles shows that transnational associations are even more disadvantaged than ethnic organizations that primarily serve the native born. However, this marginality leaves some counterintuitive advantages, namely the creation of safe spaces where undocumented immigrants, recent immigrants, and those with limited English proficiency can get involved in civic and political activities. We explore the extent to which these dynamics vary by gender and immigrant generation, and over time as Mexican hometown associations increasingly turn their attention to political issues in the United States.     

Application of Multiple Imputation in Analysis of Data from Clinical Trials with Treatment Related Dropouts

This article develops a functional form of the generalized Poisson regression model that parametrically nests the Poisson and the two well known generalized Poisson regression models (GP-1 and GP-2). The proposed model is applied on the Malaysian motor insurance claim count data.     

A Numerical Method for Estimating the Variance of Age at Maximum Growth Rate in Growth Models

Studies on maturation and body composition mention age at peak height velocity (PHV) as an important measure that could predict adulthood outcome. The age at PHV is often derived from growth models such as the triple logistic fitted to the stature (height) data. Theoretically, for a well-behaved growth function, age at PHV could be obtained by setting the second derivative of the growth function to zero and solving for age. Such a solution obviously depends on the parameters of the growth function. Therefore, the uncertainty in the estimation of age at PHV resulting from the uncertainty in the estimation of the growth model, need to be accounted for in the models in which it is used as a predictor. Explicit expressions for the age at PHV and, consequently the variance of the estimate of the age at PHV, do not exist for some of the commonly used nonlinear growth functions, such as the triple logistic function. Once an estimate of this variance is obtained, it could be incorporated in subsequent modeling either through measurement error models or by using the inverse variances as weights. A numerical method for estimating the variance is implemented. The accuracy of this method is demonstrated through comparisons in models where explicit solution for the variance exists. The method of estimating the variance is illustrated by applying to growth data from the Fels study and subsequently used as weights in modeling two adulthood outcomes from the same study.     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

RETURN ON INVESTMENT AS A CRITERION FOR INVENTORY MODELS

In the past most inventory formulations have utilized cost minimization or profit maximization as an optimizing criterion. When viewed from the standpoint of the owner or investor, maximizing the return on investment (ROI) is an appropriate criterion for many types of inventories. This paper proposes ROI as a criterion for inventory models and derives optimal reorder rules for some common assumptions. An economic order quantity that differs greatly from the traditional formulas is discussed. The paper also enumerates the conditions under which ROI is an appropriate criterion and contrasts it to the traditional cost minimization and profit maximization criteria.     

Improving Intimate Partner Violence Services in Home Visiting: A Multisite Learning Collaborative Approach

Abstract

To improve intimate partner violence (IPV) service delivery, the Florida Maternal, Infant, and Early Childhood Home Visiting (MIECHV) Initiative’s continuous quality improvement (CQI) team implemented a Breakthrough Series model Learning Collaborative with eight program sites. Using mixed-methods, we: examined post-Collaborative changes in Florida MIECHV home visitors’ knowledge, system awareness, and confidence; identified strategies to address challenges in supporting families with IPV; and assessed post-Collaborative program improvements. The Collaborative included three interactive 2-day sessions, six webinars, and testing strategies using the Model for Improvement (Plan-Do-Study-Act). Three online surveys assessed changes influencing IPV screening, referrals, and safety planning. Thematic content analysis of qualitative data revealed existing barriers and strategies to address challenges. Home visitors reported accurate knowledge (change: 2.3%-34.8%), confidence (change: 31.8%-37.9%), and system awareness (change: 22.7%-53.5%) for all items from baseline to final survey. There was also an increase in rates of IPV screening (change: 88.0%-91.0%) and referrals (change: 43.0%-100.0%). Strategies for addressing challenges in supporting families experiencing IPV were identified at the individual (self-care, continuous education); interpersonal (reflective supervision, trauma-informed approach); organizational (defining success, supportive agency/program, policies); and community (collaboration with IPV service providers) levels. Lessons learned from the MIECHV IPV Learning Collaborative informed future CQI projects.     

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the Type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the Type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.     

Immigrant Incorporation and Political Participation in the United States1

This article examines several factors related to immigrant incorporation that have been ignored in previous studies of voting participation. We add various immigrant‐related variables to a model that controls for individual resources, social incorporation, institutional barriers and contexts of political mobilization. We find little support for straight‐line assimilationist theories of immigrant adaptation. We also find that coming from a repressive regime has no significant effect on voting and that living in areas with Spanish‐language ballots does not increase the likelihood of voting among first generation Latinos. Our results also suggest that antiimmigrant legislation has a positive effect on participation among first and second generation immigrants. Overall, the immigrant‐related variables introduced in our analysis add significantly to the existing theoretical knowledge on voting participation in the United States.     

A comparison of ordinal logistic regression models using Classical and Bayesian approaches in an analysis of factors associated with diabetic retinopathy

Estimating the risk factors of a disease such as diabetic retinopathy (DR) is one of the important research problems among bio-medical and statistical practitioners as well as epidemiologists. Incidentally many studies have focused in building models with binary outcomes, that may not exploit the available information. This article has investigated the importance of retaining the ordinal nature of the response variable (e.g. severity level of a disease) while determining the risk factors associated with DR. A generalized linear model approach with appropriate link functions has been studied using both Classical and Bayesian frameworks. From the result of this study, it can be observed that the ordinal logistic regression with probit link function could be more appropriate approach in determining the risk factors of DR. The study has emphasized the ways to handle the ordinal nature of the response variable with better model fit compared to other link functions.