Young people's educational aspirations: psychosocial factors and the home environment

Utilising data from Understanding Society (2010–2013), this study examined the contribution of young people's psychosocial and background factors and home environment to their educational aspirations in the UK. Young people's general well-being and self-efficacy emerged as good predictors of their educational aspirations as did some aspects of their home environment. Interestingly, filial dynamics such as emotional closeness to parents and cultural capital (e.g. participating in cultural events, discussing books) were better predictors of 10–15-year-olds’ aspirations than were more school-driven parent–child interactions (e.g. homework, extra-curricular activities). Furthermore, the findings from this study showed no shortage in young people's educational aspirations although interesting demographic trends emerged with certain groups (i.e. preadolescents, males) being less aspirant than middle adolescents and females. These findings have significant implications for family and educational policy, especially with regard to ‘raising aspirations’ and reducing early school leaving and, also, for reconsidering the role of the home environment as a web of emotionally and intellectually charged relationships between parents and children rather than an extension of the school day. Finally, discussions on young people's educational aspirations should not be polarised but informed by notions of opportunity (structure) and what young people make of it (agency).     

Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size

Principal axis factoring (PAF) and maximum likelihood factor analysis (MLFA) are two of the most popular estimation methods in exploratory factor analysis. It is known that PAF is better able to recover weak factors and that the maximum likelihood estimator is asymptotically efficient. However, there is almost no evidence regarding which method should be preferred for different types of factor patterns and sample sizes. Simulations were conducted to investigate factor recovery by PAF and MLFA for distortions of ideal simple structure and sample sizes between 25 and 5000. Results showed that PAF is preferred for population solutions with few indicators per factor and for overextraction. MLFA outperformed PAF in cases of unequal loadings within factors and for underextraction. It was further shown that PAF and MLFA do not always converge with increasing sample size. The simulation findings were confirmed by an empirical study as well as by a classic plasmode, Thurstone's box problem. The present results are of practical value for factor analysts.     

Walking,feeling, talking: the experience of public space in the historical center of Athens

Abstract

This article examines walking as a spatial-temporal practice as well as a transformative practice within public space. The historical center of Athens has recently undergone a major transformation of its public spaces, produced mostly by major pedestrianization projects within the context of a reworking of the archaeological touristic profile of the capital. This article aims to explore the plural facets of experiencing the city while walking, which have been neglected by Greek planning authorities. Temporality, rhythmicity and presence make walking a meaningful practice that goes beyond the objective perception of the trail. Drawing on the narratives of six citizens while walking, this article seeks to develop a vocabulary capable of informing public space design. The author, who takes a geographical, ethnographic perspective, aims to contribute through developing fieldwork methods and deepening the debate on public space planning by revealing walking as a place-anchored experience.     

Common Factor Analysis versus Principal Component Analysis: A Comparison of Loadings by Means of Simulations

Common factor analysis (CFA) and principal component analysis (PCA) are widely used multivariate techniques. Using simulations, we compared CFA with PCA loadings for distortions of a perfect cluster configuration. Results showed that nonzero PCA loadings were higher and more stable than nonzero CFA loadings. Compared to CFA loadings, PCA loadings correlated weakly with the true factor loadings for underextraction, overextraction, and heterogeneous loadings within factors. The pattern of differences between CFA and PCA was consistent across sample sizes, levels of loadings, principal axis factoring versus maximum likelihood factor analysis, and blind versus target rotation.