Contextual identity experiencing facilitates resilience in Native American academics

This article discusses the socio-cultural dynamics that interact with ethno-racial identity experiencing in a previously under-researched group. A qualitative interdisciplinary study with 40 Native American academics from 28 mainstream universities across the U.S. served as a case example with findings that contrasted with historically influential theoretical frameworks postulating identity confusion and conflicts at the intersection of one’s mainstream education and profession versus one’s ethno-racial community grounding. Instead of feeling pressure to identify with only one worldview, the contextual, dynamic identities associated with the inclusive and flexible self-concept of tribal participants allowed them to in turn take advantage of two divergent cultural meaning systems pertaining to their distinct socio-cultural contexts. These shifts were experienced as not endogenous but rather exogenous variables, which did not cause the historically theorized issues. Participants felt their tribal identities instead facilitated meaningful integration of the existing incongruences, which resulted in unexpectedly resilient subjective experiencing.     

Nenparametric Two-Group Classification: Concepts and a SAS-Based Software Package

This article introduces BestClass, a set of SAS macros, available in the mainframe and workstation environment, designed for solving two-group classification problems using a class of recently developed nonparametric classification methods. The criteria used to estimate the classification function are based on either minimizing a function of the absolute deviations from the surface which separates the groups, or directly minimizing a function of the number of misclassified entities in the training sample. The solution techniques used by BestClass to estimate the classification rule use the mathematical programming routines of the SAS/OR software. Recently, a number of research studies have reported that under certain data conditions this class of classification methods can provide more accurate classification results than existing methods, such as Fisher's linear discriminant function and logistic regression. However, these robust classification methods have not yet been implemented in the major statistical packages, and hence are beyond the reach of those statistical analysts who are unfamiliar with mathematical programming techniques. We use a limited simulation experiment and an example to compare and contrast properties of the methods included in Best-Class with existing parametric and nonparametric methods. We believe that BestClass contributes significantly to the field of nonparametric classification analysis, in that it provides the statistical community with convenient access to this recently developed class of methods. BestClass is available from the authors.