2 resultados para Ethnic bias

em Universitat de Girona, Spain


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Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

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This study explored the ethnic identity among 331 emerging adults (144 mestizos and 187 indigenous) from the Intercultural University of Chiapas (México). Scholars suggest that ethnicity is much more salient for ethnic minority adolescents than for adolescents who are members of the ethnic majority. Our aim was to compare the results of the Multigroup Ethnic Identity Measure (MEIM) between the majority ethnic group and the minority group studied. Specifically, the following hypothesis was examined: adolescents who are members of the ethnic minority group (indigenous) will score significantly higher on ethnic identity than adolescents who are members of the ethnic majority group (mestizos). The results supported these hypothesis. We suggest that the effect of an intercultural educative model could explain these results