Les progrès dans la réalisation de la classification quantitative de la psychopathologie

Shortcomings of approaches to classifying psychopathology based on expert consensus have given rise to contemporary efforts to classify psychopathology quantitatively. In this paper, we review progress in achieving a quantitative and empirical classification of psychopathology. A substantial empirical literature indicates that psychopathology is generally more dimensional than categorical. When the discreteness versus continuity of psychopathology is treated as a research question, as opposed to being decided as a matter of tradition, the evidence clearly supports the hypothesis of continuity. In addition, a related body of literature shows how psychopathology dimensions can be arranged in a hierarchy, ranging from very broad "spectrum level'' dimensions, to specific and narrow clusters of symptoms. In this way, a quantitative approach solves the "problem of comorbidity'' by explicitly modeling patterns of co-occurrence among signs and symptoms within a detailed and variegated hierarchy of dimensional concepts with direct clinical utility. Indeed, extensive evidence pertaining to the dimensional and hierarchical structure of psychopathology has led to the formation of the Hierarchical Taxonomy of Psychopathology (HiTOP) Consortium. This is a group of 70 investigators working together to study empirical classification of psychopathology. In this paper, we describe the aims and current foci of the HiTOP Consortium. These aims pertain to continued research on the empirical organization of psychopathology; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic; and the development of novel and comprehensive models and corresponding assessment instruments for psychopathology constructs derived from an empirical approach. (C) 2020 Published by Elsevier Masson SAS

Socioeconomic segregation between schools in the United States and Latin America, 1970–2012

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Patterns of Cross-National Variation in the Association Between Income and Academic Achievement

In a recent paper, Reardon found that the relationship between family income and children’s academic achievement grew substantially stronger in the 1980s and 1990s in the United States. We provide an international context for these results by examining the income–achievement association in 19 other Organisation for Economic Co-operation and Development countries using data from the Progress in International Reading Literacy Study and the Programme for International Student Assessment. First, we calculate and compare the magnitude of “income achievement gaps” across this sample of countries. Second, we investigate the association between the size of a country’s income achievement gap, its income inequality, and a variety of other country characteristics. We find considerable variation across countries in income achievement gaps. Moreover, the U.S. income achievement gap is quite large in comparison to this sample of countries. Our multivariate analyses show that the income achievement gap is positively associated with educational differentiation, modestly negatively associated with curricular standardization, and positively associated with national levels of poverty and inequality

Socioeconomic inequality in access to high-status colleges: A cross-country comparison

This paper considers the relationship between family background, academic achievement in high school and access to high-status postsecondary institutions in three developed countries (Australia, England and the United States). We begin by estimating the unconditional association between family background and access to a high status university, before examining how this relationship changes once academic achievement in high school is controlled. Our results suggest that high achieving disadvantaged children are much less likely to enter a high-status college than their more advantaged peers, and that the magnitude of this socio-economic gradient is broadly similar across these three countries. However, we also find that socio-economic inequality in access to high-status private US colleges is much more pronounced than access to their public sector counterparts (both within the US and when compared overseas)

Private schooling, educational transitions, and early labour market outcomes: Evidence from three Anglophone countries

This article considers the extent to which private-state school differences in post-secondary outcomes can be explained by family background, secondary school achievement, or neither of the above. We find that privately educated children’s more advantaged family backgrounds and higher levels of school achievement are the main reasons why this group is more likely to enter university and work in professional jobs. However, even after accounting for family background and high school achievement, non-trivial private-state school differences in later lifetime outcomes remain. Empirical evidence is presented for three industrialized nations (Australia, England, and the United States), with broadly similar patterns of association observed within each

Predicting university entry using machine-based models and solutions

Increasingly, governments and grant bodies around the world are funding large databases of longitudinal data on young people as they transition from adolescence into adulthood. They are often put together by multidisciplinary teams including economists, sociologists, educators and psychologists and have led to considerable advancements in theory within these fields. Nevertheless, aspects of these databases remain underutilized. In particular, belying their conception, research flowing from these databases tends to be discipline-specific and consists of a small subset of variables. This is consistent with a dominant focus in social science research on explanatory science at the cost of predictive science. However, advances in machinelearning algorithms mean that there are possibilities to leverage the broad multidisciplinary nature of these databases to build models that can be used to predict important transition outcomes like university entry. We illustrate various approaches, using over 100 variables from the Longitudinal Study of Australian Youth (LSAY) collected when participants (N = 6,363) were 15 years of age to predict university entry three years later. We also consider what insights the various approaches provide to theory. While not a replacement for rigorous testing of causal explanations, machinelearning approaches provide a powerful additional tool for developmental researchers with important real-world applications