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Using genetic algorithms in a large nationally representative American sample to abbreviate the Multidimensional Experiential Avoidance Questionnaire
Sahdra, Baljinder Kaur Ciarrochi, Joseph Parker, Phillip David Scrucca, Luca
Sahdra, Baljinder Kaur
Ciarrochi, Joseph
Parker, Phillip David
Scrucca, Luca
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.
Keywords
genetic algorithms, experiential avoidance, abbreviation, measurement, psychometrics
Date
2016
Type
Journal article
Journal
Frontiers in Psychology
Book
Volume
7
Issue
189
Page Range
1-14
Article Number
ACU Department
Institute for Positive Psychology and Education
Faculty of Education and Arts
School of Behavioural and Health Sciences
Faculty of Health Sciences
Faculty of Education and Arts
School of Behavioural and Health Sciences
Faculty of Health Sciences
Relation URI
Source URL
Event URL
Open Access Status
Open access
License
CC BY 4.0
File Access
Open