NDLI: Using Genetic Algorithms in a Large Nationally Representative American Sample to Abbreviate the Multidimensional Experiential Avoidance Questionnaire

Content Provider	PubMed Central
Author	Sahdra, Baljinder K. Joseph, Ciarrochi Parker, Philip Luca, Scrucca
Copyright Year	2016
Abstract	Genetic algorithms (GAs) are robust machine learning approaches for abbreviating a large set of variables into a shorter subset that maximally captures the variance in the original data. We employed a GA-based method to shorten the 62-item Multidimensional Experiential Avoidance Questionnaire (MEAQ) by half without much loss of information. Experiential avoidance or the tendency to avoid negative internal experiences is a key target of many psychological interventions and its measurement is an important issue in psychology. The 62-item MEAQ has been shown to have good psychometric properties, but its length may limit its use in most practical settings. The recently validated 15-item brief version (BEAQ) is one short alternative, but it reduces the multidimensional scale to a single dimension. We sought to shorten the 62-item MEAQ by half while maintaining fidelity to its six dimensions. In a large nationally representative sample of Americans (N = 7884; 52% female; Age: M = 47.9, SD = 16), we employed a GA method of scale abbreviation implemented in the R package, GAabbreviate. The GA-derived short form, MEAQ-30 with five items per subscale, performed virtually identically to the original 62-item MEAQ in terms of inter-subscales correlations, factor structure, factor correlations, and zero-order correlations and unique latent associations of the six subscales with other measures of mental distress, wellbeing and personal strivings. The two measures also showed similar distributions of means across American census regions. The MEAQ-30 provides a multidimensional assessment of experiential avoidance whilst minimizing participant burden. The study adds to the emerging literature on the utility of machine learning methods in psychometrics.
Related Links	http://dx.doi.org/10.3389/fpsyg.2016.00189
Starting Page	189
File Format	PDF
ISSN	16641078
e-ISSN	16641078
Journal	Frontiers in Psychology
Volume Number	7
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2016-02-01
Access Restriction	Open
Rights Holder	Frontiers Media S.A.
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Psychology

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