The impact of alcohol consumption on patterns of union formation in Russia 1998–2010: An assessment using longitudinal data

Using data from the Russian Longitudinal Monitoring Survey, 1998–2010, we investigated the extent to which patterns of alcohol consumption in Russia are associated with the subsequent likelihood of entry into cohabitation and marriage. Using discrete-time event history analysis we estimated for 16–50 year olds the extent to which the probabilities of entry into the two types of union were affected by the amount of alcohol drunk and the pattern of drinking, adjusted to allow for social and demographic factors including income, employment, and health. The results show that individuals who did not drink alcohol were less likely to embark on either cohabitation or marriage, that frequent consumption of alcohol was associated with a greater chance of entering unmarried cohabitation than of entering into a marriage, and that heavy drinkers were less likely to convert their relationship from cohabitation to marriage

A comparison of Bayes factor approximation methods including two new methods

Bayes factors (BFs) play an important role in comparing the fit of statistical models. However, computational limitations or lack of an appropriate prior sometimes prevent researchers from using exact BFs. Instead, it is approximated, often using the Bayesian Information Criterion (BIC) or a variant of BIC. The authors provide a comparison of several BF approximations, including two new approximations, the Scaled Unit Information Prior Bayesian Information Criterion (SPBIC) and Information matrix-based Bayesian Information Criterion (IBIC). The SPBIC uses a scaled unit information prior that is more general than the BIC’s unit information prior, and the IBIC utilizes more terms of approximation than the BIC. Through simulation, the authors show that several measures perform well in large samples, that performance declines in smaller samples, and that SPBIC and IBIC provide improvement to existing measures under some conditions, including small sample sizes. The authors illustrate the use of the fit measures with the crime data of Ehrlich and then conclude with recommendations for researchers