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The Involvement in Alcoholics Anonymous Scale - Short Form: Factor Structure & Validation.

Christopher R BeasleyOnawa LaBelleNoel A VestBradley OlsonMichael D SkinnerJoseph R FerrariLeonard A Jason
Published in: Substance use & misuse (2022)
Background : The aim of this research was to examine the psychometrics of a short form version of the multidimensional Involvement in Alcoholics Anonymous scale (IAA-SF) by assessing the factor structure, internal consistency, and predictive validity. While there are several existing measures of involvement in Alcoholics Anonymous, many are either unidimensional or are limited in their ability to gather variation in the level of involvement in the different dimensions of 12-step programs. Objective : To achieve our aim, we used exploratory and principal axis factor analysis, correlation, and logistic regression with two unique and diverse samples. Longitudinal data were collected from a northern Illinois sample of 110 post-treatment adults, and cross-sectional data were from a random sample of 296 recovery home residents in the United States. Results : Results from the first sample suggested three exploratory factors (Principles Involvement, Social Involvement, and Spiritual Involvement) that were concordant with the proposed conceptualization and were then confirmed in the second sample. A 2nd order factor of global involvement was also found. All subscales demonstrated good to excellent internal consistency and were moderately associated with AA affiliation. Global and social involvement predicted greater odds of abstinence 2 years later, but principles and spiritual involvement did not. Conclusion : Overall results suggest the IAA- SF is a valid and reliable 12-item instrument for assessing involvement in the AA program, and the differential prediction suggests potential utility for a multidimensional approach to 12-step involvement.
Keyphrases
  • healthcare
  • cross sectional
  • mental health
  • risk assessment
  • machine learning
  • palliative care
  • replacement therapy
  • big data