Resiliencia y rendimiento escolar en adolescentes de Lima y de Santiago de Chile de sectores vulnerables

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Abstract

Resumen El objetivo del estudio es proponer instrumentos de medición de la resiliencia relacionada al rendimiento escolar, válida para adolescentes y jóvenes de sectores sociales vulnerables de zonas urbanas de dos países latinoamericanos. El método de investigación está basado en el análisis del comportamiento estadístico de los ítems y dimensiones de la resiliencia de la escala SV-RES en 764 adolescentes de zonas urbanas vulnerables de Lima-Perú y 1 145 de Santiago de Chile, con alto y bajo rendimiento escolar de sectores sociales de alta vulnerabilidad social. Se realizó Análisis Factorial Exploratorio (AFE) y el análisis de validez convergente y discriminante. Los resultados sugieren nuevas dimensiones de la resiliencia vinculada al rendimiento académico, diferenciadas para adolescentes de Lima y de Santiago de Chile.

Translated abstract

Abstract The aim of the study is to propose measuring instruments resilience related to school performance valid for adolescents from vulnerable social groups in urban areas of two South American countries. The research method is based on analysis of the statistical results of the items and dimensions of the resilience of the SV-RES Scale in 764 adolescents from vulnerable urban areas of Lima, Peru and 1,145 from Santiago de Chile with high and low academic performance of sectors with social vulnerability. Exploratory Factor Analysis (AFE), convergent and discriminant validity analysis was performed. The results suggest new different dimensions of resilience linked to academic performance on adolescents of Lima and Santiago de Chile.

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The Hull Method for Selecting the Number of Common Factors.

Urbano Lorenzo-Seva, Marieke Timmerman, Henk Kiers (2011)

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an extensive simulation study in which the simulated data are based on major and minor factors. The study compares the method with four other methods such as parallel analysis and the minimum average partial test, which were selected because they have been proven to perform well and/or they are frequently used in applied research. The Hull method outperformed all four methods at recovering the correct number of major factors. Its usefulness was further illustrated by its assessment of the dimensionality of the Five-Factor Personality Inventory ( Hendriks, Hofstee, & De Raad, 1999 ). This inventory has 100 items, and the typical methods for assessing dimensionality prove to be useless: the large number of factors they suggest has no theoretical justification. The Hull method, however, suggested retaining the number of factors that the theoretical background to the inventory actually proposes.