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In their authoritative literature review, Breen and Jonsson (2005) claim that ‘one of the most significant trends in the study of inequalities in educational attainment in the past decade has been the resurgence of rational-choice models focusing on educational decision making’. The starting point of the present contribution is that these models have largely ignored the explanatory relevance of social interactions. To remedy this shortcoming, this paper introduces a micro-founded formal model of the macro-level structure of educational inequality, which frames educational choices as the result of both subjective ability/benefit evaluations and peer-group pressures. As acknowledged by Durlauf (2002, 2006) and Akerlof (1997), however, while the social psychology and ethnographic literature provides abundant empirical evidence of the explanatory relevance of social interactions, statistical evidence on their causal effect is still flawed by identification and selection bias problems. To assess the relative explanatory contribution of the micro-level and network-based mechanisms hypothesised, the paper opts for agent-based computational simulations. In particular, the technique is used to deduce the macro-level consequences of each mechanism (sequentially introduced) and to test these consequences against French aggregate individual-level survey data. The paper's main result is that ability and subjective perceptions of education benefits, no matter how intensely differentiated across agent groups, are not sufficient on their own to generate the actual stratification of educational choices across educational backgrounds existing in France at the beginning of the twenty-first century. By computational counterfactual manipulations, the paper proves that network-based interdependencies among educational choices are instead necessary, and that they contribute, over and above the differentiation of ability and of benefit perceptions, to the genesis of educational stratification by amplifying the segregation of the educational choices that agents make on the basis of purely private ability/benefit calculations.

This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment.

Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve.

The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems.

Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.

In 1990, Salovey and Mayer presented a framework for emotional intelligence (EI). This marked the beginning of 20 years of academic research, development, and debate on the subject of EI. A significant amount of previous research has attempted to draw out the relationship between EI and leadership performance. EI has been a uniquely controversial area of the social sciences. EI is based on three simple yet fundamental premises. This manuscript reviews the definitions and models in the field of EI with special emphasis on the Mayer ability model and the connection between EI and leadership. The paper aims to discuss these issues.

This paper takes the form of a literature review.

EI appears to have a foothold in both our popular vernacular and our academic lexicon. However, it is not entirely clear what future form it will take.

This manuscript explores the current relationship between EI and leadership, discusses the various instruments and scales used to measure the construct, and examines the controversy and criticism surrounding EI. Finally, it illuminates some areas for additional research.

This chapter introduces a new theoretical framework for developing emotion-related abilities according to the emotional intelligence (EI) construct definition of Mayer, Salovey, and Caruso (2006). The awareness, reflection, and management (ARM) model has been devised and demonstrates a triadic cycle of emotional ARM relating to affect, cognition, and behavior. The ARM model constitutes an approach to nurture emotion-related abilities (ability EI) and responds to criticism raised by Zeidner, Matthews, and Roberts (2009). The ARM Theory was corroborated by both learning theory and schools of counselling (SOC). The potential to develop emotion-related abilities in emotional awareness, reflection and reasoning, coping and management is discussed.

This chapter examines EI, presents a history of EI including the various models, and a discussion of the three streams approach to classifying EI literature. The author advocates for the efficacy of the Stream One Ability Model (SOAM) of EI citing previous authors and literature. The commonly used SOAM instruments are discussed in light of recent studies. The discussion turns to alternate tests of the SOAM of EI including Situational Judgment Tests (SJTs). Recommendations include an analysis of SOAM instruments, a new approach to measurement, and increased use of SJTs to capture the four-branch ability model of EI.

The purpose of the present study was to evaluate the role of context in the identification of learning disabilities (LD) within the responsiveness-to-intervention (RTI) model. In Study 1, using a sample of students with and without LD (N=167) and data from a reading assessment, we tested whether the decision making regarding literacy disabilities is significantly different if we take into account variability within the schools and school characteristics. Initially a logistic multilevel model was fit to the data to assess prevalence rates of LD identification. The validity of these estimates was substantiated by bootstrapping the sample's parameters using 1,000 replications and by evidencing negligible bias parameters. Subsequently, the relationship between reading ability and LD identification was established by means of a multilevel model including random effects. The significant slopes linking reading to LD identification (i.e., fluency and overall reading ability ratings by teachers) were predicted by cross-level interactions involving schools' location (rural, urban, and suburban). The results of Study 1 demonstrated the moderating role of school context, as the slopes linking fluency and reading achievement to LD placement were moderated by the area in which a school was located. Study 2 was designed to present a relative discrepancy identification model by taking into account information from the school (i.e., district). Using 29 students from one district, whose writing ability was evaluated three times within the semester, comparisons were made between a specific low-ability student and the rest of his/her class. Through fitting a multilevel model in which within-student and between-student variance was assessed, Study 2 demonstrated that the specific pattern of responsiveness of a target student can be tested against the norm of his/her school district in order to have a more sensitive relative criterion of what constitutes both responsiveness and the norm. Thus, by utilizing a multilevel framework that involves school characteristics into our assessment we demonstrated that decision making is much more informative and likely more “accurate” under the RTI model. Certainly more research is needed to verify the usefulness and applicability of the proposed “relative slope-difference discrepancy model.”

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

Skills Approach leadership development suggests individuals can develop the skills, abilities, and behaviors needed to lead. However, it can be difficult to understand and apply these theoretical concepts. Movies provide a storytelling platform enabling learners to link theory to practice. This article uses 300 to explore skills-based leadership theories. 300 (2007) is an adaptation of Frank Miller’s novel depicting the Battle of Thermopylae (400 B.C.) between Greece and Persia. The movie tells the story of how King Leonidas led 300 Spartan warriors in battle against King Xerxes’ army in defense of Spartan land, values, and freedom. We provide background on Katz’s (1955) and Mumford, Zacaro, Harding, Jacobs, and Fleishman (2000) skills approaches. Then we describe how theoretical concepts associated with these approaches can be found in the movie. Finally, we provide suggestions for experiential learning activities which serve to help participants link and apply theory to practice.