title: Measuring self-complexity: a critical analysis of Linville's H statistic.
abstract: The paper argues that the most commonly used measure of self-complexity, Linville's H statistic, cannot measure this construct appropriately. It first examines the mathematical properties of H and its relationships with five related indices: the number of self-aspects, the overlap among self-aspects, the average inter-aspect correlation, the ratio of endorsement, and the HICLAS attribute class number. Then, a demonstration study using simulations is reported. Three conclusions are drawn. H and the HICLAS attribute class number are similar in the way they are calculated. Both indices are highly related to the number of self-aspects, while their relationship to overlap is not monotonic. Overlap is affected by the ratio of endorsement and the average inter-aspect correlation but cannot represent the notion of redundancy among traits which directly determines Linville's H statistic. These conclusions are employed to explain the inconsistent findings relating self-complexity and adaptation and an alternative measurement approach is proposed.
authors.affiliation: Centre for Research in Pedagogy and Practice (CRPP), National Institute of Education, 1 Nanyang Walk, Singapore 637616. wenshu.luo@nie.edu.sg
authors.forename: Wenshu
authors.initials: W
authors.lastname: Luo
authors.forename: David
authors.initials: D
authors.lastname: Watkins
authors.forename: Raymond Y H
authors.initials: RY
authors.lastname: Lam
mesh_terms: D003071:Cognition; D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000706:statistics & numerical data*