A Deprivation Analysis for Andalusia (Spain): An Approach Based on Structural Equations

The study of deprivation, as a social indicator, is basic in the design and development of public policies because it allows decision makers to identify and analyse needy areas in order to improve their citizens’ well-being. The methodological approach proposed for the development of a new deprivation index is based on the Causal Theory whose conceptual model is analysed using Structural Equations. The domains selected for the deprivation index are: education, employment, income, housing, infrastructures and health. A structural equation model based on variance is the exploratory method used to obtain the indices pertaining to the above mentioned areas; the results obtained are seen to be quite reliable. There is a positive connection between the areas of education, employment and income while the relations between infrastructures and health are found to be negative. The results can be projected at a local level and show basic territorial deficiencies. The spatial units studied are the Andalusian (south of Spain) municipalities (770). The spatial projection of the indices obtained for the domains of deprivation highlights the existence of geographical areas which could be a potential target for public action.     

THE HARDER THE TASK,THE HIGHER THE SCORE: FINDINGS OF A DIFFICULTY BIAS

Studies have found that going first or last in a sequential order contest leads to a biased outcome, commonly called order bias (or primacy and recency). Studies have also found that judges have a tendency to reward contestants they recognize with additional points, called reference bias. Controlling for known biases, we test for a new type of bias we refer to as “difficulty bias,” which reveals that athletes attempting more difficult routines receive higher execution scores, even when difficulty and execution are judged separately. Despite some identification challenges, we add to the literature by finding strong evidence of a difficulty bias in gymnastics. We also provide generalizations beyond athletics. (JEL L10, L83, D81, J70, Z1)     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.     

Access is not enough: the impact of emotional costs and self-efficacy on the changes in African-American students’ ICT use patterns

Previous research has examined the impact of computing interventions to reduce digital inequity. However, few studies focus on factors such as inequalities to material access, Internet use patterns, and affective or emotional anxiety. This paper investigates the potential role of emotional costs and computer self-efficacy in the connection between computer use at home and students’ computer use patterns. Data for this research come from pretest and posttest surveys administered to fourth- and fifth-grade African-American students. The results reveal that students’ home computer use is a significant predictor of the change in their information and entertainment usage over the course of the intervention. Students’ emotional costs partially mediate the relationship between home computer use and information-oriented usage over the intervention period. The findings suggest that providing students access to digital devices is not enough to close the digital divide.     

Introductory Statistics Textbooks and the GAISE Recommendations

The six recommendations made by the Guidelines for Assessment and Instruction in Statistics Education (GAISE) committee were first communicated in 2005 and more formally in 2010. In this article, 25 introductory statistics textbooks are examined to assess how well these textbooks have incorporated the three GAISE recommendations most relevant to implementation in textbooks (statistical literacy and thinking; use of real data; stress concepts over procedures). The implementation of another recommendation (using technology) is described but not assessed. In general, most textbooks appear to be adopting the GAISE recommendations reasonably well in both exposition and exercises. The textbooks are particularly adept at using real data, using real data well, and promoting statistical literacy. Textbooks are less adept—but still rated reasonably well, in general—at explaining concepts over procedures and promoting statistical thinking. In contrast, few textbooks have easy-usable glossaries of statistical terms to assist with understanding of statistical language and literacy development. Supplementary materials for this article are available online.     

Social network effects on academic achievement

How peer groups contribute to educational outcomes has long interested researchers. However, the possibility that peer groups dominated by either low- or high-achieving youth can have substantively different effects on achievement has been largely ignored. In this paper, we show that while being embedded in a high-achieving network of friends is not associated with increased own achievement, being embedded in a low-achieving network is associated with decreased own achievement. In additional analyses, we present evidence that these associations are at least in part due to influence, as opposed to only selection effects or shared environment. We also examine whether the structure of the network in which a student is embedded might affect their educational achievement. We show that achieving at higher levels positively predicts how centrally located a student is in their network, but being more centrally located does not predict concurrent achievement. This finding suggests that the behavior of individuals is affecting the formation of network structure and not the reverse.     

The Role of the Government in Addressing Social Issues in India: The Perceptions of Indian Undergraduate and Graduate Social Work Students

Using a convenience sample of 318 undergraduate and graduate social work students (193 women and 125 men) from a public university in India, this study examined the differences between Indian social work students' perceptions toward the role of government in addressing social issues in India. Using Predictive Analytics Software (PASW) 18, several statistical procedures were conducted to analyze the relationship between social work students' demographic variables and their perceptions of the government's role in addressing social issues in India. The results indicated that older social work students were more likely to perceive that the government should ensure equal opportunities for citizens. Female social work students were more likely to perceive that the government should ensure housing for citizens. BSW students were more likely to perceive that the government should ensure employment of citizens. Macro social work students were more likely to perceive that the government should ensure equal opportunities, housing, food, employment, and health insurance for citizens. The findings of this study may help social work educational administrators and faculty in India to develop a curriculum that provides social work students with the opportunity to critically examine the complex relationship between age, gender, marital status, number of children, socioeconomic status, educational status, area of concentration, and the role of the government in addressing social issues. Doing so will prepare social work students and professionals to think and engage in a manner that will allow them to challenge and advocate for a change to the status quo.