Being Called a ‘Streber’: The Roles of Personality and Competition in the Labelling of Academically Oriented Students

The current research investigated a phenomenon that has received little attention so far: the labelling of students who are characterised by a strong academic orientation. We analysed whether personality predicts being labelled a ‘Streber’ (literally a person who strives for success; German origin, similar to the English word ‘nerd’) and labelling others as Strebers. Besides individual characteristics, we examined the impact of the classroom context. In Study 1 (N = 317), eighth‐grade students nominated classmates who were considered to be Strebers and provided self‐ratings on how often they had labelled others as Strebers. In Study 2 (N = 358), using a round robin design, we had students rate each of their classmates on the extent to which the students perceived their classmates to be Strebers. Results showed that being labelled was associated with introversion and conscientiousness. Labelling others was related to extraversion, low conscientiousness and low agreeableness. Furthermore, the labelling and the expected relation between individual characteristics and labelling were stronger in high‐achieving than in low‐achieving classes. Results are discussed with respect to personality traits as potential risk factors in peer stigmatisation and the impact of the classroom context. Copyright © 2012 European Association of Personality Psychology     

A monotonically convergent algorithm for factals

Takane, Young, and de Leeuw proposed a procedure called FACTALS for the analysis of variables of mixed measurement levels (numerical, ordinal, or nominal). Mooijaart pointed out that their algorithm does not necessarily converge, and Nevels proposed a new algorithm for the case of nominal variables. In the present paper it is shown that Nevels' procedure is incorrect, and a new procedure for handling nominal variables is proposed. In addition, a procedure for handling ordinal variables is proposed. Using these results, a monotonically convergent algorithm is constructed for FACTALS of any mixture of variables.The authors are obliged to Jos ten Berge for stimulating comments on an earlier version of this paper. The research of H. A. L. Kiers has been made possible by a fellowship of the Royal Netherlands Academy of Arts and Sciences. The research of Y. Takane has been supported by the Natural Sciences and Engineering Research Council of Canada, grant number A6394, and by the McGill-IBM Cooperative Grant.