Body Image–Acceptance and Action Questionnaire–5: An Abbreviation Using Genetic Algorithms

Abstract

Body image concerns are typically linked with negative outcomes such as disordered eating and diminished well-being, but some people can exhibit psychological flexibility and remain committed to their valued goals despite being dissatisfied about their bodies. Such flexibility is most frequently measured by the Body Image-Acceptance and Action Questionnaire (BI-AAQ). This study used a recently validated, fully automated method based on genetic algorithms (GAs) on data from an American community sample (N=538, 71.5% female, Age: M = 40.87, SD = 13.5) to abbreviate the 12-item BI-AAQ to a 5-item short form, BI-AAQ-5. Validation tests were conducted on data from an independent community sample (N 762, 44.6% female, Age: M = 40.65, SD = 13.06). The short form performed comparably to the long form in terms of its factor structure and correlations with theoretically relevant constructs, including body image dissatisfaction, stigma, internalization of societal norms of appearance, self-compassion, and poor mental health. Further, preliminary analyses using structural equation modeling showed that body image flexibility, as measured by either the long or short form, was associated with almost all the criterion variables, even while controlling for a highly related construct of body image dissatisfaction. These results demonstrate the potential discriminant validity of both the long and short form of the BI-AAQ, and show that the BI-AAQ-5 is a suitable alternative to its long form. We discuss how psychological flexibility with respect to body image dissatisfaction can be conducive to positive functioning.

Using genetic algorithms to abbreviate the Mindfulness Inventory for Sport: A substantive-methodological synthesis

“…As described earlier, the algorithm first generated a population of shorter scales by randomly sampling items from the full scale. We chose a population size of 200 short scales based on recommendations from previous authors (Basarkod et al, 2018;Sahdra et al, 2016;Yarkoni, 2010).…”

“…Genetic algorithms were first introduced to solve pattern recognition problems (e.g., finding the best match for a visual pattern) and to computationally simulate biological 4 SHORT MINDFULNESS MEASURE processes (Holland, 1975). More recently, machine-learning techniques based on genetic algorithms have been used to create reliable and valid shortened measures of personality (Yarkoni, 2010), values (Sandy et al, 2014), psychopathy (Eisenbarth, Lilienfeld, & Yarkoni, 2015), experiential avoidance (Sahdra et al, 2016), and body image-acceptance (Basarkod et al, 2018). These algorithms function via principles of evolution and natural selection (Yarkoni, 2010).…”

“…In a randomised controlled trial comparing incentives, reminders, and shortened questionnaires, the only cost-effective strategy was an abridged questionnaire following a reminder (Glidewell et al, 2012). When time with clients is limited, short measures can reduce the insession time required for clients to complete measures, and they can increase compliance with measures assigned between sessions (Basarkod, Sahdra, & Ciarrochi, 2018). Overall, short measures can allow for more flexible, efficient, affordable, and accurate data collection.…”

“…Short measures also facilitate many core aims of science, including description, prediction, and causal inference. Short questionnaires allow for more constructs to be assed in the same battery (Basarkod et al, 2018). Assessing multiple constructs using poor empirical designs can be fraught, because it may increase the probability of spurious correlations or Type I error, but assessing multiple constructs may answer more interesting questions, when appropriate controls are in place (e.g., Bonferroni corrections for multiple comparisons).…”

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