Impaired Self-Awareness in Pathological Gamblers

Lack of self-awareness of one’s decisions remains an understudied and elusive topic in the addiction literature. The present study aimed at taking a first step towards addressing this difficult subject through the use of a combination of behavioral procedures. Here, we explored the association between a metacognitive process (the ability to reflect and evaluate the awareness of one’s own decision) and poor performance on the Iowa Gambling Task (IGT) in a group of pathological gamblers (PG; n = 30), and in a comparison group (n = 35). This metacognitive process was assessed during the IGT with the post-decision wagering procedure, while a number of potential confounds (i.e., reward/loss sensitivity, dual-tasking) were controlled for. Results showed that: (1) Initial performance enhancement of the control group on IGT occurred without explicit knowledge of the task, thus confirming its implicit character; (2) compared to controls, performance of PG on the IGT failed to increase during the task; (3) taking into account increased reward sensitivity and decreased loss sensitivity as well as poorer dual-tasking in pathological gamblers, PG tended to exhibit a bias in evaluating their own performance on the IGT by maximizing their wagers independently of selecting advantageous decks. Our findings suggest that biased metacognition may affect pathological gamblers, leading to disadvantageous post-decision wagering, which is in turn linked to impaired decision making under ambiguity. Perhaps this deficit reflects the impaired insight and self-awareness that many addicts suffer from, thus providing a novel approach for capturing and measuring this impairment, and for investigating its possible causes.     

Combinatorial design of a minimum cost transfer line

The problem of equipment selection for a production line is considered. Each piece of equipment, also called unit or block, performs a set of operations. All necessary operations of the line and all available blocks with their costs are known. The difficulty is to choose the most appropriate blocks and group them into (work)stations. There are some constraints that restrict the assignment of different blocks to the same station. Two combinatorial approaches for solving this problem are suggested. Both are based on a novel concept of locally feasible stations. The first approach combinatorially enumerates all feasible solutions, and the second reduces the problem to search for a maximum weight clique. A boolean linear program based on a set packing formulation is presented. Computer experiments with benchmark data are described. Their results show that the set packing model is competitive and can be used to solve real-life problems.     

Gender Gaps in Educational Outcomes Among Children of New Migrants: The Role of Social Integration from a Comparative Perspective

This article reviews current research on gender gaps in educational outcomes among children of new migrants (first, 1.5, and second generations) from a comparative cross‐national perspective. The article examines potential explanations for why gendered differences in educational outcomes among immigrants (vis‐à‐vis non‐migrants) continue to occur, focusing on individual, schooling, and institutional factors. For example, while gender gaps in expectations and aspirations as well as some cognitive outcomes are increasing (reading ability is considerably higher among females) and shrinking in specific subjects (e.g. math and science ability is slightly higher among boys), that process often has been less explored within and across immigrant vis‐à‐vis non‐immigrant students. We show that in some countries, gender gaps among disadvantaged ethnic minorities (in relation to other groups) continue to persist and are exacerbated by institutional factors. These disadvantages vary not only across different educational systems and at different rates over time but also are based on students' social background. Overall, we argue that these educational inequalities are key to understanding not only the socioeconomic and future political adaptation of migrants and their children but also future social policy developments in the European Union context.     

Experimentation order with good properties for 2 k factorial designs

Randomizing the order of experimentation in a factorial design does not always achieve the desired effect of neutralizing the influence of unknown factors. In fact, with some very reasonable assumptions, an important proportion of random orders achieve the same degree of protection as that obtained by experimenting in the design matrix standard order. In addition, randomization can induce a large number of changes in factor levels and thus make experimentation expensive and difficult. De Leon et al. [Experimentation order in factorial designs with 8 or 16 runs, J. Appl. Stat. 32 (2005), pp. 297–313] proposed experimentation orders for designs with eight or 16 runs that combine an excellent level of protection against the influence of unknown factors, with the minimum number of changes in factor levels. This article presents a new methodology to obtain experimentation orders with the desired properties for designs with any number of runs.     

Design of experimental studies for measurement and evaluation of the determinants of Job Satisfaction

In this paper the authors examine the methodology of data analysis and collection usually applied in Job-Satisfaction studies and more generally in Well-being research. In this field, data is collected through questionnaires and analysed using multivariate statistical techniques, a process which is commonly known as non-experimental research. The article points out some of the problems in this procedure and proposes an alternative experimental methodology which is applied to evaluate Job-Satisfaction over a Dutch random sample. Essentially it consists of the implementation of factorial designs in questionnaire construction. The use of fractional factorials is also discussed.     

The turning arcs: a computationally efficient algorithm to simulate isotropic vector-valued Gaussian random fields on the d-sphere

Statistics and Computing - Random fields on the sphere play a fundamental role in the natural sciences. This paper presents a simulation algorithm parenthetical to the spectral turning bands method...     

Space-time estimation of a particle system model

Let X be a discrete time contact process (CP) on ?², as defined by Durrett and Levin (1994, Stochastic spatial models: a user's guide to ecological applications. Philosophical Transactions of the Royal Society of London Series B, 343, 329–350). We study the estimation of the model based on space-time evolution of X, that is, T + 1 successive observations of X on a finite subset S of sites. We consider the maximum marginal pseudo-likelihood (MPL) estimator and show that, when T→∞, this estimator is consistent and asymptotically normal for a non-vanishing supercritical CP. Numerical studies confirm these theoretical ones.     

Using Economic Freedom Indexes as Policy Indicators: An Intercontinental Example

We use time-series cross-section analysis to provide empirical validation for the existence of a specific American ethos and a specific European ethos with respect to economic policy. In our innovation, economic policy is proxied by “economic freedom” from the Fraser Institute database and constitutional “political institutions” are proxied by variables from the Database of Political Institutions (from the World Bank). Our results suggest that, once we control for political and institutional differences, the United States and Europe still pursue different economic policies.

Goodness-of-Fit Tests for the Skew-Normal Distribution When the Parameters Are Estimated from the Data

In this article, tests are developed which can be used to investigate the goodness-of-fit of the skew-normal distribution in the context most relevant to the data analyst, namely that in which the parameter values are unknown and are estimated from the data. We consider five test statistics chosen from the broad Cramér–von Mises and Kolmogorov–Smirnov families, based on measures of disparity between the distribution function of a fitted skew-normal population and the empirical distribution function. The sampling distributions of the proposed test statistics are approximated using Monte Carlo techniques and summarized in easy to use tabular form. We also present results obtained from simulation studies designed to explore the true size of the tests and their power against various asymmetric alternative distributions.     

Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings

We evaluate the effects of college choice on earnings using Swedish register databases. This case study is used to motivate the introduction of a novel procedure to analyse the sensitivity of such an observational study to the assumption made that there are no unobserved confounders – variables affecting both college choice and earnings. This assumption is not testable without further information, and should be considered an approximation of reality. To perform a sensitivity analysis, we measure the departure from the unconfoundedness assumption with the correlation between college choice and earnings when conditioning on observed covariates. The use of a correlation as a measure of dependence allows us to propose a standardised procedure by advocating the use of a fixed value for the correlation, typically 1% or 5%, when checking the sensitivity of an evaluation study. A correlation coefficient is, moreover, intuitive to most empirical scientists, which makes the results of our sensitivity analysis easier to communicate than those of previously proposed methods. In our evaluation of the effects of college choice on earnings, the significantly positive effect obtained could not be questioned by a sensitivity analysis allowing for unobserved confounders inducing at most 5% correlation between college choice and earnings.