The Net Resource Distribution of Two Parent Low Income Families: A Regional Comparison*†

The purpose of this paper is firstly to investigate the distribution of two parent low income families in England and Wales and Scotland for see to what extent they differ, and secondly to examine the relative importance of certain contributing factors in determining the distributions. From an analysis of Family Expenditure Survey data it is found that although it is difficult to arrive at statistically verifiable conclusions about the nature of the distributions, interesting qualitative observations can be made.     

Collecting text messages from college students: Evaluating a novel methodology

Social interaction, particularly in older adolescents, increasingly involves computer‐mediated communication. Although studies of public computer‐mediated communication are increasingly common, studies of private text messaging remain rare. As approaches for obtaining such data evolve with technological advances, developmental scientists need designs in which to use such approaches that reduce sampling biases in both participants and text messages. In this study (n = 854; 46% male; 22% African American, 60% European American), we examined selection biases in the participant sample (i.e., factors associated with actual participation), procedural biases in the participant sample (i.e., factors related to failed data capture due to technological or procedural issues), and selection biases in the sample of text messages (i.e., based on self‐reported reasons for texting). Findings from our study suggest that studying human interaction directly through analysis of text message data is not only feasible, but also may be successfully undertaken with minimal biases regarding sample selection and text message selection among those who are engaged in research and engaged in text messaging outside of the study context. However, biases may occur depending on the type of platform (iPhone vs. Android) used by participants for texting.     

Educational inequality across three generations in Australia

The transfer of advantage and disadvantage across multiple generations is receiving increasing attention in the international literature; however, transfers of resources across multiple generations in Australian families are less well understood. Using a longitudinal dataset of Australian children, we have the opportunity to not only investigate the transfer of educational resources across three generations in Australia, but also investigate the gendered nature of these transfers, which has been a limitation of other studies. We find no evidence of individual grandparent education effects on numeracy and reading scores for grandchildren in Year 3, independent of parent educational attainment and other covariates. However, significant effects on numeracy and reading scores were observed for children in families where both the grandmother and grandfather in maternal and paternal grandparent sets had high educational attainment (a diploma or university qualification), and where either or both the mother and father had a university qualification. These results suggest that the contribution of grandparents to the academic achievement of grandchildren cannot be fully explained by the parent generation and that the concentration of human capital in families contributes to educational inequalities across multiple generations that can be observed by eight years of age.     

Parents at‐risk and their children: intersections of gender role attitudes and parenting practices

Existing research demonstrates that parent and child gender may influence important aspects of family relationships; however, most research in this area has been conducted with non‐clinical samples. As clinicians, it is important to consider how gender impacts family relationships, particularly among vulnerable families. This study examined the intersections of gender role attitudes and parenting practices among 34 parents involved with the child welfare system and referred for clinical intervention. Using a mixed methods design, themes regarding gender role beliefs and parenting practices were found through qualitative interviews with parents. Findings suggested that fathers felt responsible for financially providing for their families and expressed challenges in communicating with mothers, and mothers described challenges they face because of a lack of father involvement. Parents also discussed a perceived need to monitor daughters closely while fostering the independence of their sons. Results of the qualitative analyses were supported by quantitative findings indicating significant differences in harsh and inconsistent discipline practices and clear expectations for girls compared to boys. The discussion addresses implications for clinicians, including how a feminist family therapy perspective may help promote client influence over traditional gender norms by questioning gender role attitudes and exploring alternate narratives that may impact family dynamics.     

How do sexual harassment policies shape gender beliefs? An exploration of the moderating effects of norm adherence and gender

Sexual harassment laws have led to important organizational changes in the workplace yet research continues to document resistance to their implementation and backlash against the people who mobilize such laws. Employing experimental research methods, this study proposes and tests a theory specifying the mechanisms through which sexual harassment policies affect gender beliefs. The findings show evidence that sexual harassment policies strengthen unequal gender beliefs among men and women most committed to traditional gender interaction norms. I also find that men and women’s different structural locations in the status hierarchy lead to different, but related sets of concerns about the status threats posed by sexual harassment policies. By specifying the social psychological processes through which sexual harassment law affects beliefs about men and women, this study sets the stage for investigating ways to make laws designed to reduce inequality between social groups more effective.     

The relationship between neighborhood racial concentration and verbal ability: An investigation using the institutional resources model

Relatively few studies examine the relationship between racial residential segregation and educational or cognitive outcomes. Using data from the National Longitudinal Study of Adolescent Health and the institutional resources model of neighborhood effects, I investigate one account of how macrostructural arrangements between race, neighborhood segregation, and school quality interact to produce inequalities in test scores. Consistent with the institutional resources model, results suggest that school quality varies across neighborhoods based, in part, on their degree of racial concentration. Indeed, school quality and other school characteristics mediate the relationship between racial concentration and verbal skills, particularly among black males. These findings have implications not only for inequalities in cognitive skills among blacks across residential space, but also between blacks and whites given high levels of residential segregation in the United States. In sum, findings illustrate yet another way in which residential segregation contributes to, and not merely reflects, racial inequalities.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

People,Pipelines, and Probabilities: Clarifying Significance and Uncertainty in Environmental Impact Assessments

Determinations of significance play a pivotal role in environmental impact assessments because they point decision makers to the predicted effects of an action most deserving of attention and further study. Impact predictions are always subject to uncertainty because they rely on estimates of future consequences. Yet uncertainty is often neglected or treated in a perfunctory manner as part of the characterization, evaluation, and communication of anticipated consequences and their significance. Proposals to construct fossil fuel pipelines in North America provide a highly visible example; casual treatment of how uncertainty affects significance determinations has resulted in poorly informed stakeholders, frustrated industry proponents, and inconsistent choices on the part of public decision makers. Using environmental assessments for recent pipeline proposals as examples, we highlight five ways in which uncertainty is often neglected when determining impact significance and suggest that a mix of known methods, new guidelines, and appropriate oversight could greatly improve current practices.