Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.
In: PLoS ONE, Jg. 11 (2016-11-28), Heft 11, S. 1-19
Online
academicJournal
Zugriff:
The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function. [ABSTRACT FROM AUTHOR]
Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.
|
---|---|
Autor/in / Beteiligte Person: | Schroeders, Ulrich ; Wilhelm, Oliver ; Olaru, Gabriel |
Link: | |
Zeitschrift: | PLoS ONE, Jg. 11 (2016-11-28), Heft 11, S. 1-19 |
Veröffentlichung: | 2016 |
Medientyp: | academicJournal |
ISSN: | 1932-6203 (print) |
DOI: | 10.1371/journal.pone.0167110 |
Schlagwort: |
|
Sonstiges: |
|