Ethnic intermarriage in times of social change: The case of latvia

We gain insight into the dynamics of ethnic intermarriage in times of social change by studying marriages between Latvians and Russians (including Belarussians and Ukrainians) that occurred in Latvia before and after independence from the Soviet Union. Before independence, ethnic intermarriage was already rather common, involving about 17% of the marriages annually. Since independence, intermarriage between Russians and Latvians has increased substantially. Part of this increase can be explained by selective emigration, but at least half of it may be due to integrative processes. Although there were more marriages between Russian men and Latvian women before independence, the gender pattern reversed after independence. Intermarriage levels were the highest among the less educated, children of mixed couples, partners with similar educational levels, and people in the countryside.     

Precision,bias, and uncertainty for state population forecasts: an exploratory analysis of time series models

Many researchers have used time series models to construct population forecasts and prediction intervals at the national level, but few have evaluated the accuracy of their forecasts or the out-of-sample validity of their prediction intervals. Fewer still have developed models for subnational areas. In this study, we develop and evaluate six ARIMA time series models for states in the United States. Using annual population estimates from 1900 to 2000 and a variety of launch years, base periods, and forecast horizons, we construct population forecasts for four states chosen to reflect a range of population size and growth rate characteristics. We compare these forecasts with population counts for the corresponding years and find precision, bias, and the width of prediction intervals to vary by state, launch year, model specification, base period, and forecast horizon. Furthermore, we find that prediction intervals based on some ARIMA models provide relatively accurate forecasts of the distribution of future population counts but prediction intervals based on other models do not. We conclude that there is some basis for optimism regarding the possibility that ARIMA models might be able to produce realistic prediction intervals to accompany population forecasts, but a great deal of work remains to be done before we can draw any firm conclusions.     

Evaluating a multiple-family group access intervention for refugees with PTSD

The purpose of this study was to analyze the effects of a multiple-family group in increasing access to mental health services for refugees with posttraumatic stress disorder (PTSD). This study investigated a nine-session multiple-family group called Coffee and Families Education and Support with refugee families from Bosnia-Herzegovina in Chicago. Adults with PTSD (n = 197) and their families were randomly assigned to receive either the intervention or a control condition. The results indicated that a multiple-family group was effective in increasing access to mental health services and that depression and family comfort with discussing trauma mediated the intervention effect. Further well-designed studies of family interventions are needed for developing evidence-based interventions for refugee families.     

A Tale of Two Matrix Factorizations

In statistical practice, rectangular tables of numeric data are commonplace, and are often analyzed using dimension-reduction methods like the singular value decomposition and its close cousin, principal component analysis (PCA). This analysis produces score and loading matrices representing the rows and the columns of the original table and these matrices may be used for both prediction purposes and to gain structural understanding of the data. In some tables, the data entries are necessarily nonnegative (apart, perhaps, from some small random noise), and so the matrix factors meant to represent them should arguably also contain only nonnegative elements. This thinking, and the desire for parsimony, underlies such techniques as rotating factors in a search for “simple structure.” These attempts to transform score or loading matrices of mixed sign into nonnegative, parsimonious forms are, however, indirect and at best imperfect. The recent development of nonnegative matrix factorization, or NMF, is an attractive alternative. Rather than attempt to transform a loading or score matrix of mixed signs into one with only nonnegative elements, it directly seeks matrix factors containing only nonnegative elements. The resulting factorization often leads to substantial improvements in interpretability of the factors. We illustrate this potential by synthetic examples and a real dataset. The question of exactly when NMF is effective is not fully resolved, but some indicators of its domain of success are given. It is pointed out that the NMF factors can be used in much the same way as those coming from PCA for such tasks as ordination, clustering, and prediction. Supplementary materials for this article are available online.