Parent–professional interviews in child protection: Comparing viewpoints

Several studies have analysed the parent–professional relationship in the field of child protection. Although social workers tend to privilege interviews when working with families and view them as a means through which to build ties, few studies have focused on these interviews. To analyse how professionals and parents position themselves, we chose to focus on the content of these interviews and on the associated interactions. To this end, we recorded 13 parent–professional interviews after receiving the consent of the concerned parties. After analysing the interviews, we asked volunteer social workers and parents to analyse their own recorded interviews. Our results confirm the asymmetric nature of the parent–professional relationship. They show that professionals' viewpoints carry more weight than those of parents. They also underscore professionals' lack of openness and the strategies parents implement to cope with this power imbalance. Lastly, our results open new avenues for reflection on the evolution of practices that may enable professionals to better take into account families' viewpoints.     

Going solo: Findings from a survey of women aging without a partner and who do not have children

Greater longevity in the UK population has led to the increasing diversity of women experiencing aging in a multitude of ways. Internationally, gender inequalities in aging are still relatively invisible within both government policy and everyday life for particular groups of women. This article explores the concept of women growing older “solo”—by which we mean women who find themselves nonpartnered and aging without children as they move into later life. We report on the findings from a mixed-methods survey of 76 solo women in the UK aged 50 years and over, used to provide a broader overview of the issues and challenges they face as they move into later life. Qualitative data from the survey captured respondents’ perspectives about the links between their relationships status and well-being in later life and highlighted specific cumulative disadvantages emerging for some women as a result of their solo lifestyles. We discuss two key themes that were identified, “solo-loneliness” and “meaningful futures,” in conjunction with the relevant literature and make suggestions for future research within gender and aging studies that could enhance more positive approaches to solo lifestyles.     

Adjustment for nonresponse with variables from different sources: Bias correction and precision loss,with application to the Swiss European Social Survey 2012

ABSTRACT

Adjustment for nonresponse should reduce the nonresponse bias without decreasing the precision of the estimates. Adjustment for nonresponses are commonly based on socio-demographic variables, although these variables may be poorly correlated with response propensities and with variables of interest. Such variables nevertheless have the advantage of being available for all sample units, whether or not they are participating in the survey. Alternatively, adjustment for nonresponse can be obtained from a follow-up survey aimed at sample units which did not participate in the survey and from which the variables are designed to be correlated with response propensities. However, information collected through these follow-up surveys is not available for people in the sample who participated neither in the survey nor in its nonresponse follow-up. These two sets of variables when used in a nonresponse model for the Swiss European Social Survey 2012 differ only slightly with regard to their effect on bias correction and on the precision of estimates. The variables from the follow-up are performing slightly better. In both cases, the adjustment for nonresponse performs poorly.     

Application of the Hyper‐Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes

The hyper‐Poisson distribution can handle both over‐ and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation‐specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper‐Poisson distribution in analyzing motor vehicle crash count data. The hyper‐Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway‐highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness‐of‐fit measures indicated that the hyper‐Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper‐Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway‐Maxwell‐Poisson model previously developed for the same data set. The advantages of the hyper‐Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper‐Poisson model can handle both over‐ and underdispersed crash data. Although not a major issue for the Conway‐Maxwell‐Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model.     

Extension of the Application of Conway‐Maxwell‐Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion

The objective of this article is to evaluate the performance of the COM‐Poisson GLM for analyzing crash data exhibiting underdispersion (when conditional on the mean). The COM‐Poisson distribution, originally developed in 1962, has recently been reintroduced by statisticians for analyzing count data subjected to either over‐ or underdispersion. Over the last year, the COM‐Poisson GLM has been evaluated in the context of crash data analysis and it has been shown that the model performs as well as the Poisson‐gamma model for crash data exhibiting overdispersion. To accomplish the objective of this study, several COM‐Poisson models were estimated using crash data collected at 162 railway‐highway crossings in South Korea between 1998 and 2002. This data set has been shown to exhibit underdispersion when models linking crash data to various explanatory variables are estimated. The modeling results were compared to those produced from the Poisson and gamma probability models documented in a previous published study. The results of this research show that the COM‐Poisson GLM can handle crash data when the modeling output shows signs of underdispersion. Finally, they also show that the model proposed in this study provides better statistical performance than the gamma probability and the traditional Poisson models, at least for this data set.