Changing Policy Environments in Europe and the Resilience of the Third Sector

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - In the article, we analyse the impact of changing policy environments on the development of the third sector in Europe....     

On the Spectral Decomposition in Normal Discriminant Analysis

This article enlarges the covariance configurations, on which the classical linear discriminant analysis is based, by considering the four models arising from the spectral decomposition when eigenvalues and/or eigenvectors matrices are allowed to vary or not between groups. As in the classical approach, the assessment of these configurations is accomplished via a test on the training set. The discrimination rule is then built upon the configuration provided by the test, considering or not the unlabeled data. Numerical experiments, on simulated and real data, have been performed to evaluate the gain of our proposal with respect to the linear discriminant analysis.     

Conceptual Framework for Marriage Education Programs for Stepfamily Couples with Considerations for Socioeconomic Context

Stepfamily couples face unique challenges that put them at higher risk for dissolution than non-stepfamily couples. Risks for stepfamily couples are magnified in the context of lower economic resources, making low-income stepfamily couples a key target population for intervention and for community education programs. Existing programs and services for couples often do not address the unique situations of stepfamilies. This article builds on previous reviews that have offered information on important research themes and assessment of available curricula for stepfamily couples and presents a more comprehensive conceptual model to guide program design and services for stepfamily couples inclusive of specific objectives, as well as conditions and antecedents affecting content and prioritized needs. The goal is to enhance efforts to strengthen more vulnerable couples in complex families.     

Determining Latent Classes of Cumulative Family Structure Experiences in Emerging Adulthood

ABSTRACT

Using a sample of emerging adults (N = 536), this study examined detailed cumulative family structure spanning birth through emerging adulthood. Latent class analysis was used to determine profiles of cumulative family structure experiences based on measures of family structure measured at 5 time points, duration in current family structure, the number of parent relationship transitions, and the number of sibling transitions. Two distinct stable groups, 2 distinct mostly stable groups, and 1 highly unstable group emerged. Additionally, we explored 2 demographic variables as predictors of class membership, and class-level differences on current levels of well-being with a sample of emerging adults.     

Knowledge integration in family SMEs: an extension of the 4I model

This article explores the process of learning and strategic change in small and medium-sized family firms (family SMEs). Organizational learning theory posits that knowledge must be integrated throughout the firm to facilitate strategic renewal. This process occurs in a particular way in family SMEs, according to their specific characteristics. In such firms the family’s role, the company size and the lack of formal procedures and systems strongly affect the process of learning and change. The study applies the 4I model of organizational learning to three case studies and offers empirical support for this model. The article has theoretical implications for variants of the 4I model as applied to family SMEs, and implications for practice related to the family’s role in starting and perpetuating the process of learning and change.     

Estimating percentile-specific treatment effects in counterfactual models: a case-study of micronutrient supplementation, birth weight and infant mortality

Summary.  Clinical trials of micronutrient supplementation are aimed at reducing the risk of infant mortality by increasing birth weight. Because infant mortality is greatest among the low birth weight (LBW) infants (2500 g or under), an effective intervention increases the birth weight among the smallest babies. The paper defines population and counterfactual parameters for estimating the treatment effects on birth weight and on survival as functions of the percentiles of the birth weight distribution. We use a Bayesian approach with data augmentation to approximate the posterior distributions of the parameters, taking into account uncertainty that is associated with the imputation of the counterfactuals. This approach is particularly suitable for exploring the sensitivity of the results to unverifiable modelling assumptions and other prior beliefs. We estimate that the average causal effect of the treatment on birth weight is 72 g (95% posterior regions 33–110 g) and that this causal effect is largest among the LBW infants. Posterior inferences about average causal effects of the treatment on birth weight are robust to modelling assumptions. However, inferences about causal effects for babies at the tails of the birth weight distribution can be highly sensitive to the unverifiable assumption about the correl-ation between the observed and the counterfactuals birth weights. Among the LBW infants who have a large causal effect of the treatment on birth weight, we estimate that a baby receiving the treatment has 5% less chance of death than if the same baby had received the control. Among the LBW infants, we found weak evidence supporting an additional beneficial effect of the treatment on mortality independent of birth weight.     

John U. Ogbu: a personal recollection1 2

The article presents a portrait of the late anthropologist John U. Ogbu through the author’s personal recollections of him as a social scientist, a teacher, a colleague and a longtime friend.     

Identification of latent class Markov models with multiple indicators and correlated measurement errors

Statistical Methods & Applications - A necessary condition for identification of latent class models is that the number of unknown independent parameters must not be greater than the number of...