Validation of the Preschool Attachment Rating Scales with child-mother and child-father dyads.
Saved in:
| Title: | Validation of the Preschool Attachment Rating Scales with child-mother and child-father dyads. |
|---|---|
| Authors: | Deneault, Audrey-Ann, Bureau, Jean-François, Yurkowski, Kim, Moss, Ellen |
| Source: | Attachment & Human Development. Oct2020, Vol. 22 Issue 5, p491-513. 23p. 4 Charts. |
| Subjects: | Attachment behavior, Child behavior, Father-child relationship, Research methodology, Mother-child relationship, Research evaluation, Inter-observer reliability, Research methodology evaluation, Descriptive statistics, Children |
| Abstract: | Growing evidence points to the theoretical and statistical advantages of continuous (rather than categorical) assessments of child-caregiver attachment. The Preschool Attachment Rating Scales (PARS) is a continuous coding system to assess preschool attachment that is complementary to the categorical MacArthur Preschool Attachment Coding System (PACS). The current study aims to evaluate the reliability and validity of the PARS to measure both child-mother and child-father attachment during the preschool period. Participants included 144 preschool-aged children (M = 46.89 months, SD = 8.77; 83 girls) and their parents. Results support the reliability and validity of the PARS: good inter-rater reliability, expected associations between scales, convergence with the PACS, and association with parental sensitivity and child externalizing problems. These findings support the application of continuous assessments of child-caregiver attachment in the preschool years. They also align with previous work on child-mother attachment, and present avenues for future research on child-father attachment. [ABSTRACT FROM AUTHOR] |
| Copyright of Attachment & Human Development is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
