Differential effects of parental control on preschool-age adjustment depending on child effortful control

This study examined whether parental control differentially predicted children's effortful control and adjustment depending on children's levels of executive control and delay ability. Using longitudinal data, the study included 241 preschool-age children and their mothers. Fifty-seven percent of the sample was lower income and included 64% White, 10% Latino/Hispanic, 9% Black, 3% Asian-American, 2% Native or American Indian, and 12% multiple racial/ethnic backgrounds. Interactions between aspects of parental and child control at 3-years-old were tested as predictors of mother-reported child effortful control at 4.5 years and teacher-reported adjustment at 5.5 years. Children's early levels of executive control and delay ability moderated the relations of some forms of parental control with child outcomes, although several hypothesized interactions were nonsignificant. Low parental autonomy respect predicted higher externalizing for children initially high in executive control but predicted lower externalizing for children initially low in executive control. Conversely, high autonomy respect predicted higher impulsivity for children low in delay ability but higher social competence for children high in delay ability. Finally, low maternal negative control predicted higher internalizing for children low in delay ability, whereas it predicted higher internalizing for children high in delay ability. These findings suggest that in some cases the type and degree of parental control should match a child's level of effortful control to support their social, emotional, and behavioral adjustment optimally.     

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.     

Inequality Assessment by Probabilistic Development Indices

Social Indicators Research - This paper analyses the Human Development Index (HDI) time series from 2010 to 2017. An alternative index is studied, which combines the same components of the HDI by...     

Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non-OECD Countries with Focus on Time Trend Analysis

This study presents probabilistic analysis of dam accidents worldwide in the period 1911–2016. The accidents are classified by the dam purpose and by the country cluster, where they occurred, distinguishing between the countries of the Organization for Economic Cooperation and Development (OECD) and nonmember countries (non-OECD without China). A Bayesian hierarchical approach is used to model distributions of frequency and severity for accidents. This approach treats accident data as a multilevel system with subsets sharing specific characteristics. To model accident probabilities for a particular dam characteristic, this approach samples data from the entire data set, borrowing the strength across data set and enabling to model distributions even for subsets with scarce data. The modelled frequencies and severities are combined in frequency-consequence curves, showing that accidents for all dam purposes are more frequent in non-OECD (without China) and their maximum consequences are larger than in OECD countries. Multipurpose dams also have higher frequencies and maximum consequences than single-purpose dams. In addition, the developed methodology explicitly models time dependence to identify trends in accident frequencies over the analyzed period. Downward trends are found for almost all dam purposes confirming that technological development and implementation of safety measures are likely to have a positive impact on dam safety. The results of the analysis provide insights for dam risk management and decision-making processes by identifying key risk factors related to country groups and dam purposes as well as changes over time.     

Your money or your life: a new variation on the Heinz Dilemma

Millions of Medicare-age Americans are drug dependent, not because of addiction but because of common chronic health problems such as diabetes, heart failure, high blood pressure, and arthritis. Seniors are up in arms because drug company control of distribution and pricing of pharmaceuticals is eating away hard-earned nest eggs. Who cares? Where's the justice?     

An Empirical Investigation of Corporate Citizenship in Australia and Turkey *

This paper describes a study of corporate citizenship practices and its relationship to dimensions of internal and external corporate culture, customer loyalty and organizational commitment. Empirical data was collected from a cross‐industry sample in Turkey and Australia using a questionnaire form developed and validated for other cultures. It was shown that Australian and Turkish firms' practices differed significantly on a number of important variables, but also that it is possible to compare corporate citizenship practices in a way that is not culture‐specific.     

Restaurant Revenue Management at Chevys: Determining the Best Table Mix

Revenue management has been used in a variety of industries and generally takes the form of managing demand by manipulating length of customer usage and price. Supply mix is rarely considered, although it can have considerable impact on revenue. In this research, we focused on developing an optimal supply mix, specifically on determining the supply mix that would maximize revenue. We used data from a Chevys restaurant, part of a large chain of Mexican restaurants, in conjunction with a simulation model to evaluate and enumerate all possible supply (table) mixes. Compared to the restaurant's existing table mix, the optimal mix is capable of handling a 30% increase in customer volume without increasing waiting times beyond their original levels. While our study was in a restaurant context, the results of this research are applicable to other service businesses.