Pregnancy Acceptance,Parenting Stress,and Toddler Attachment in Low‐Income Black Families

Questionnaire items tapping feelings about pregnancy were administered to 173 young, low‐income primiparous Black mothers who either were pregnant or had delivered within the past year. A factor analysis indicated that 11 items together measured mothers’ acceptance of the pregnancies that resulted in the births of their first children. Links to mothers’ later parenting stress, warmth, and their toddlers’ attachment security were explored. Pregnancy acceptance was a negative predictor of one aspect of maternal parenting stress (distress resulting from feelings that parenting is burdensome) and a positive predictor of toddler attachment security. It did not, however, predict another aspect of parenting stress (feelings that interactions with children are not enjoyable) or maternal warmth.     

Integrated Likelihood Inference in Small Sample Meta‐analysis for Continuous Outcomes

This paper proposes the use of the integrated likelihood for inference on the mean effect in small‐sample meta‐analysis for continuous outcomes. The method eliminates the nuisance parameters given by variance components through integration with respect to a suitable weight function, with no need to estimate them. The integrated likelihood approach takes into proper account the estimation uncertainty of within‐study variances, thus providing confidence intervals with empirical coverage closer to nominal levels than standard likelihood methods. The improvement is remarkable when either (i) the number of studies is small to moderate or (ii) the small sample size of the studies does not allow to consider the within‐study variances as known, as common in applications. Moreover, the use of the integrated likelihood avoids numerical pitfalls related to the estimation of variance components which can affect alternative likelihood approaches. The proposed methodology is illustrated via simulation and applied to a meta‐analysis study in nutritional science.     

Women Left Behind? Poverty and Headship in Africa

Two stylized facts about poverty in Africa motivate this article: female-headed households tend to be poorer, and poverty has been falling in the aggregate since the 1990s. These facts raise two questions. First, how have female-headed households fared? Second, what role have they played in Africa’s impressive recent aggregate growth and poverty reduction? Using data covering the entire region, we reexamine the current prevalence and characteristics of female-headed households and ask whether their prevalence has been rising, what factors have been associated with such changes since the mid-1990s, and whether poverty has fallen equiproportionately for male- and female-headed households. Lower female headship is associated with higher gross domestic product. However, other subtle transformations occurring across Africa—changes in marriage behavior, family formation, health, and education—are positively related to female headship, resulting in a growing share of female-headed households. This shift has been happening alongside declining aggregate poverty incidence. However, rather than being left behind, female-headed households have generally seen faster poverty reduction. As a whole, this group has contributed substantially to the reduction in poverty despite their smaller share in the population.     

Modelling the role of variables in model-based cluster analysis

In the framework of cluster analysis based on Gaussian mixture models, it is usually assumed that all the variables provide information about the clustering of the sample units. Several variable selection procedures are available in order to detect the structure of interest for the clustering when this structure is contained in a variable sub-vector. Currently, in these procedures a variable is assumed to play one of (up to) three roles: (1) informative, (2) uninformative and correlated with some informative variables, (3) uninformative and uncorrelated with any informative variable. A more general approach for modelling the role of a variable is proposed by taking into account the possibility that the variable vector provides information about more than one structure of interest for the clustering. This approach is developed by assuming that such information is given by non-overlapped and possibly correlated sub-vectors of variables; it is also assumed that the model for the variable vector is equal to a product of conditionally independent Gaussian mixture models (one for each variable sub-vector). Details about model identifiability, parameter estimation and model selection are provided. The usefulness and effectiveness of the described methodology are illustrated using simulated and real datasets.