Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/18164
Title: 
Measuring self-complexity: A critical analysis of Linville's H statistic
Authors: 
Keywords: 
Self-complexity
Linville’s H statistic
Number of self-aspects
Overlap
Ratio of endorsement
Average inter-aspect correlation
HICLAS attribute class number
Issue Date: 
2008
Citation: 
Luo, W., Watkins, D., & Lam, R. Y. H. (2008). Measuring self-complexity: A critical analysis of Linville's H statistic. Journal of Applied Measurement, 9(4), 357-373
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.
Description: 
This is the final draft, after peer-review, of a manuscript published in Journal of Applied Measurement. The published version is available online at http://jampress.org
URI: 
ISSN: 
1529-7713
Appears in Collections:Journal Articles

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