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      Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm

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      PLoS ONE

      Public Library of Science

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          Abstract

          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.

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          Most cited references 10

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          Construct validity in psychological tests.

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            The attack of the psychometricians

            This paper analyzes the theoretical, pragmatic, and substantive factors that have hampered the integration between psychology and psychometrics. Theoretical factors include the operationalist mode of thinking which is common throughout psychology, the dominance of classical test theory, and the use of “construct validity” as a catch-all category for a range of challenging psychometric problems. Pragmatic factors include the lack of interest in mathematically precise thinking in psychology, inadequate representation of psychometric modeling in major statistics programs, and insufficient mathematical training in the psychological curriculum. Substantive factors relate to the absence of psychological theories that are sufficiently strong to motivate the structure of psychometric models. Following the identification of these problems, a number of promising recent developments are discussed, and suggestions are made to further the integration of psychology and psychometrics.
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              On the consistency of individual classification using short scales.

              Short tests containing at most 15 items are used in clinical and health psychology, medicine, and psychiatry for making decisions about patients. Because short tests have large measurement error, the authors ask whether they are reliable enough for classifying patients into a treatment and a nontreatment group. For a given certainty level, proportions of correct classifications were computed for varying test length, cut-scores, item scoring, and choices of item parameters. Short tests were found to classify at most 50% of a group consistently. Results were much better for tests containing 20 or 40 items. Small differences were found between dichotomous and polytomous (5 ordered scores) items. It is recommended that short tests for high-stakes decision making be used in combination with other information so as to increase reliability and classification consistency. (c) 2007 APA, all rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 November 2016
                2016
                : 11
                : 11
                Affiliations
                [1 ]Department of Educational Science, University of Bamberg, Bamberg, Germany
                [2 ]Department of Psychology and Education, Ulm University, Ulm, Germany
                Universita degli Studi di Catania, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: US OW GO.

                • Formal analysis: US OW GO.

                • Methodology: US OW GO.

                • Software: US OW GO.

                • Visualization: US OW GO.

                • Writing – review & editing: US OW GO.

                Article
                PONE-D-16-28223
                10.1371/journal.pone.0167110
                5125670
                27893845
                © 2016 Schroeders et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 2, Pages: 19
                Product
                Funding
                The author(s) received no specific funding for this work.
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                This paper uses data from the National Educational Panel Study (NEPS): Starting Cohort 4-9th Grade, doi: 10.5157/NEPS:SC4:4.0.0. From 2008 to 2013, NEPS data were collected as part of the Framework Programme for the Promotion of Empirical Educational Research funded by the German Federal Ministry of Education and Research (BMBF). As of 2014, the NEPS survey is carried out by the Leibniz Institute for Educational Trajectories (LIfBi) at the University of Bamberg in cooperation with a nationwide network. This data can be used after users have registered and signed a data use agreement (for more information please see https://www.neps-data.de/tabid/444/language/en-US). The authors confirm that these are third party data and that they did not receive any special access privileges that others would not have.

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