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Assessing Adult Attachment: Relation and Validity of two Dynamic-Maturational Model Approaches

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  • معلومة اضافية
    • بيانات النشر:
      PsychOpen GOLD/ Leibniz Institute for Psychology, 2018.
    • الموضوع:
      2018
    • Collection:
      LCC:Psychology
    • نبذة مختصرة :
      Assessing attachment is essential yet challenging. The Adult Attachment Interview (AAI) provides the best appraisal of adult attachment but is time-intensive and costly. Likewise, concerns have been raised regarding the Berkeley-AAI coding and classification method. Meanwhile, self-report measures of adult attachment are time-efficient and low-cost, but their validity is questionable. The Dynamic-Maturational Model approach to the AAI (DMM-AAI) and a novel self-report measure – the Attachment Relationship Questionaire (ARQ) – may offer a solution. However, additional investigations regarding the validity of DMM-AAI are needed and the ARQ’s psychometric properties have not be tested. The validity of the DMM approach to the AAI and the predictive relationship between the ARQ and DMM-AAI classification were examined for 212 participants living the UK. Results indicated a strong positive relationship between high numbered attachment classification on the DMM-AAI and psychological treatment status, χ²(6) = 56.07, p < .001; Cramer’s V = .371, p < .001. Binomial logistic regressions between the ARQ and DMM-AAI found both single-statement and multi-statement predictive models were statistically significant. However, the ARQ accounted for only a small amount of the variance (R² ≤ 0.15). In conclusion, the DMM-AAI demonstrated strong construct validity, whereas the ARQ is not useful for assessment of adult attachment. Further investigation with a revised version of the ARQ that addresses psychometric concerns is suggested.
    • File Description:
      electronic resource
    • ISSN:
      1981-6472
    • Relation:
      http://ijpr.psychopen.eu/article/view/318; https://doaj.org/toc/1981-6472
    • الرقم المعرف:
      10.5964/ijpr.v12i2.318
    • الرقم المعرف:
      edsdoj.89e1d5fe30b4b15901f47aa0ec59d4c