School Truants and Truancy Motivation Sorted out with Multidimensional Scaling

Abstract

A combination of established and novel multivariate techniques was applied to the problem of school truancy. Informants described 104 truants they were acquainted with by ranking the applicability of 73 motives and causes for absence from school. Individual rankings were analyzed in the context of a multidimensional representation of the motives, derived from sorting data provided by other informants. Each ranking was interpreted as a three-dimensional vector within this “similarity space” and as a profile of 10 weights indicating the contributions of 10 “hotspots” (broad themes summarizing the motives) located in the space. The broad spectrum of motivational profiles was subdivided using clustering techniques. Five main relatively homogeneous clusters emerged, of which two cover a previously recognized pattern of parent-condoned truancy, whereas the other three are related to recognized syndromes of adolescent delinquency. There may be considerable differences between these clusters in terms of developmental antecedents, prognosis, and most effective form of intervention.

Get full access to this article

View all access options for this article.

References

Bimler, D. L. , & Kirkland, J. (1998). Perceptual modelling of product similarities using sorting data. Marketing Bulletin, 9, 16-27.

Block, J. (1961). The Q-sort method in personality assessment and psychological research. Springfield, IL: Charles C Thomas.

Block, J. , Block, J. H. , & Keyes, S. (1988). Longitudinally foretelling drug usage in adolescence: Early childhood personality and environmental precursors. Child Development, 59, 336-355.

Block, J. , & Gjerde, P. F. (1988). Distinguishing between antisocial behavior and undercontrol. In D. Olweus , J. Block , & M. Radke-Yarrow (Eds.), Development of antisocial and prosocial behavior: Research, theories and issues. Orlando: Academic Press.

Carroll, J. D. (1972). Individual differences and multidimensional scaling. In R. N. Shepard , A. K. Romney , & S. B. Nerlove (Eds.), Multidimensional scaling: Theory and applications in the behavioral sciences, Vol. 1. New York: Seminar Press.

Caspi, A. , Block, J. , Block, J. H. , Klopp, B. , Lynam, D. , Moffitt, T. E. , & Stouthamer-Loeber, M. (1992). A “common-language” version of the California Child Q-set for personality assessment. Psychological Assessment, 4, 512-523.

Coxon, A.P.M. (1999). Sorting data: Collection and analysis. Newbury Park, CA: Sage.

Day, S. X. , & Rounds, J. (1998). Universality of vocational interest structure among racial and ethnic minorities. American Psychologist, 53, 728-736.

Eley, T. C. , Lichtenstein, P. , & Stevenson, J. (1999). Sex differences in the etiology of aggressive and nonaggressive antisocial behavior: Results from two twin studies. Child Development, 70, 155-168.

10.

Kirkland, J. , Bimler, D. , Drawneek, A. , McKim, M. , & Schölmerich, A. (2000). A quantum leap in the analyses and interpretation of attachment sort items. In B. Vaughn , E. Waters , G. Posada , and D. Teti (Eds.), Patterns of secure base behavior: Q-sort perspectives on attachment and caregiving in infancy and childhood. Hillsdale, NJ: Lawrence Erlbaum.

11.

McKeown, B. F. , & Thomas, D. B. (1988). Q methodology. In Quantitative Applications in the Social Sciences (Vol. 66). Newbury Park, CA: Sage.

12.

Miller, D. T. , & Norman, S. A. (1975). Actor-observer differences in perceptions of effective control. Journal of Personality and Social Psychology, 31, 503-515.

13.

Stephenson, W. (1953). The study of behavior: Q-technique and its methodology. Chicago: University of Chicago Press.

14.

Takane, Y. (1978). A maximum likelihood method for nonmetric multidimensional scaling: I. The case in which all empirical pairwise orderings are independent—theory. Japanese Psychological Research, 20, 7-17.

15.

Trochim, W.M.K. (1989). An introduction to concept mapping for planning and evaluation. Evaluation and Program Planning, 12, 1-16.