The Reliability and Validity of the Korean Version of State Adult Attachment Measure
Bibliographic record
Abstract
ObjectivesZZAdult attachment is a relatively stable disposition, rooted in internal working models of self and relationship patterns. However, findings reported from recent research have suggested that levels of attachment anxiety, avoidance, and security are also affected by situational factors. The State Adult Attachment Measure (SAAM) was developed for the purpose of capturing temporary fluctuations in the sense of attachment security and insecurity. In this study, we examined the reliability and validity of the Korean version of the State Adult Attachment Measure (K-SAAM). MethodsZZK-SAAM, Experiences in Close Relationships Questionnaire-Revised (ECR-R), Relationship Questionnaire (RQ), Korean version of Positive Affective and Negative Affect Schedule (KPANAS), Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), Revised Dyadic Adjustment Scale (R-DAS), and Toronto Alexithymia Scale 20-K (TAS 20-K) were administered to 180 subjects in the community. Exploratory factor analyses and correlation analyses among related variables were conducted. ResultsZZScores on the K-SAAM demonstrated high internal consistency, with corrected item-total correlations from .56 to .87. Results of exploratory factor analysis yielded three reliable subscales measuring state levels of attachment-related anxiety, avoidance, and security. Results of additional analyses demonstrated both convergent validity and discriminant validity of the K-SAAM. ConclusionZZThe results reported here are highly supportive of the reliability, validity, and utility of the K-SAAM as a state measure of attachment. This new measure will allow clinicians to assess various temporary changes in attachment levels and to examine the efficacy of attachment-based psychotherapy. The K-SAAM has the potential to advance the field in understanding of the dynamics of adult attachment. J Korean Neuropsychiatr Assoc 2012;51:147-155
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".