Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)
Exploratory and Bayesian Structural Equation Modeling Approaches
Abstract
Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.
References
2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al. Journal of Management, 41, 1561–1577. doi: 10.1177/0149206315591075
(2015). Differential changes in self-reported aspects of interoceptive awareness through 3 months of contemplative training. Frontiers in Psychology, 5, 1504. doi: 10.3389/fpsyg.2014.01504
(2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111–150. doi: 10.1207/S15327906MBR3601_05
(2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews Neuroscience, 3, 655–666. doi: 10.1038/nrn894
(2015). Improving transparency and replication in Bayesian statistics: The WAMBS-checklist. Psychological Methods, Advance online publication. doi: 10.1037/met0000065
(2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105, 399–412. doi: 10.1111/bjop.12046
(2015). Interoception, contemplative practice, and health. Frontiers in Psychology, 6, 763. doi: 10.3389/fpsyg.2015.00763
(2013). Factor analyses of the Hospital Anxiety and Depression Scale: A Bayesian structural equation modeling approach. Quality of Life Research, 22, 2857–2863. doi: 10.1007/s11136-013
(2015). Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach. Journal of Occupational Health, 57, 353–358. doi: 10.1539/joh
(2013). On the embodiment of emotion regulation: Interoceptive awareness facilitates reappraisal. Social Cognitive and Affective Neuroscience, 8, 911–917. doi: 10.1093/scan/nss089
(2013). Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling. Psychological Assessment, 25, 496–508. doi: 10.1037/a0030676
(2016).
(Exploratory structural equation modelling and Bayesian estimation . In N. NtoumanisN. D. MyersEds., An introduction to intermediate and advanced statistical analyses for sport and exercise scientists (pp. 172–194). London, UK: Wiley.1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409–426. doi: 10.1007/BF02291366
(2014). Interoceptive sensitivity, body weight and eating behavior in children: A prospective study. Frontiers in Psychology, 5, 1003. doi: 10.3389/fpsyg.2014.01003
(1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111, 490–504. doi: 10.1037/0033-2909.111.3.490
(2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11, 320–341. doi: 10.1207/s15328007sem1103_2
(2010). A new look at the big five factor structure through exploratory structural equation modeling. Psychological Assessment, 22, 471–491. doi: 10.1037/a0019227
(2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110. doi: 10.1146/annurev-clinpsy-032813-153700
(2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling, 16, 439–476. doi: 10.1080/10705510903008220
(2009). Body awareness: Construct and self-report measures. PLoS One, 4, e5614. doi: 10.1371/journal.pone.0005614
(2012). The multidimensional assessment of interoceptive awareness (MAIA). PLoS One, 7, e48230. doi: 10.1371/journal.pone.0048230
(2015). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling, , 1–24. doi: 10.1080/10705511.2014.961800
(2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335. doi: 10.1037/a0026802
(2004). Point and interval estimation of reliability for multiple-component measuring instruments via linear constraint covariance structure modeling. Structural Equation Modeling, 11, 342–356. doi: 0.1207/s15328007sem1103_3
(2012). Next steps in Bayesian structural equation models: Comments on, variations of, and extensions to Muthén and Asparouhov (2012). Psychological Methods, 17, 336–339. doi: 10.1037/a0027130
(2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8, 23–74. doi: 10.3389/fpsyg.2015.00993
(2015). Interoception and stress. Frontiers in Psychology, 6, 993. doi: 10.3389/fpsyg.2015.00993
(2015). Psychometric properties of the multidimensional assessment of interoceptive awareness (MAIA) in a Chilean population. Frontiers in Psychology, 6, 120. doi: 10.3389/fpsyg.2015.00120
(2014). A gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85, 842–860. doi: 10.1111/cdev.12169
(2013). Facing off with Scylla and Charybdis: a comparison of scalar partial and the novel possibility of approximate measurement invariance. Frontiers in Psychology, 4, 1–15. doi: 10.3389/fpsyg.2013.00770
(2011). Why psychologists must change the way they analyze their data: The case of psi: Comment on Bem (2011). Journal of Personality and Social Psychology, 100, 425–432. doi: 10.1037/a0022790
(2015). Bayesian estimation and inference: A user’s guide. Journal of Management, 41, 390–420. doi: 10.1177/0149206313501200
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