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The latent structure of the adult attachment interview: Large sample evidence from the collaboration on attachment transmission synthesis.

K Lee RabyMarije L VerhageR M Pasco FearonR Chris FraleyGlenn I RoismanMarinus H van IJzendoornCarlo SchuengelSheri MadiganMirjam OostermanMarian J Bakermans-KranenburgAnnie BernierKarin EnsinkAiri HautamäkiSarah MangelsdorfLynn E PriddisMaria S Wongnull null
Published in: Development and psychopathology (2020)
The Adult Attachment Interview (AAI) is a widely used measure in developmental science that assesses adults' current states of mind regarding early attachment-related experiences with their primary caregivers. The standard system for coding the AAI recommends classifying individuals categorically as having an autonomous, dismissing, preoccupied, or unresolved attachment state of mind. However, previous factor and taxometric analyses suggest that: (a) adults' attachment states of mind are captured by two weakly correlated factors reflecting adults' dismissing and preoccupied states of mind and (b) individual differences on these factors are continuously rather than categorically distributed. The current study revisited these suggestions about the latent structure of AAI scales by leveraging individual participant data from 40 studies (N = 3,218), with a particular focus on the controversial observation from prior factor analytic work that indicators of preoccupied states of mind and indicators of unresolved states of mind about loss and trauma loaded on a common factor. Confirmatory factor analyses indicated that: (a) a 2-factor model with weakly correlated dismissing and preoccupied factors and (b) a 3-factor model that further distinguished unresolved from preoccupied states of mind were both compatible with the data. The preoccupied and unresolved factors in the 3-factor model were highly correlated. Taxometric analyses suggested that individual differences in dismissing, preoccupied, and unresolved states of mind were more consistent with a continuous than a categorical model. The importance of additional tests of predictive validity of the various models is emphasized.
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