The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
The adult attachment interview and self‐reports of romantic attachment: Associations across domains and methods
Abstract
Two lines of research on adult attachment have emerged; both are based on Bowlby and Ainsworth's attachment theory, which in turn relies on evolutionary theory. Investigators in one tradition use the Adult Attachment Interview (AAI) to assess “state of mind with respect to attachment.” The AAI has been validated primarily by its ability to predict the attachment classification of an interviewee's child in Ainsworth's “strange situation.” Investigators in the second tradition use self‐report measures to assess romantic “attachment style.” The self‐report measures have been validated by their ability to predict features of romantic/marital relationships. Although the two constructs. state of mind and romantic attachment, are importantly different and so would not be expected to relate highly, some of their components, especially ability to depend on attachment figures, should be related if both stem from a person's attachment history. We report associations between components, or aspects, of the two measures. Overlap occurs mainly in the areas of comfort depending on attachment figures and comfort serving as an attachment figure for others. Implications of the findings for attachment theory and research, as well as for evolutionary psychology, are discussed.
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Metadata
- Authors
- SHAVER, P. R., BELSKY, J., & BRENNAN, K. A.
- Venue
- Personal Relationships
- Publisher
- Wiley
- Published
- 2000-03
- Volume
- 7
- Issue
- 1
- Pages
- 25-43
- ISSN
- 1350-4126 (ISSN-L)
- 1475-6811
- OpenAlex
- W2171824579
- Cited by
- 264
- References
- 66
- FWCI
- 13.60
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.