Psychometric Properties of the Defense Mechanisms Rating Scales-Self-Report-30 (DMRS-SR-30): Internal Consistency, Validity and Factor Structure
Bibliographic record
Abstract
Assessment of defense mechanisms has a longstanding history within the clinical psychology and psychopathology literature. Despite their centrality to clinical practice, there are few self-report measures that assess defenses and, those that do exist, have limitations in addressing individual defenses and levels of defensive functioning. To address this need, we investigated the psychometric properties of the Defense Mechanisms Rating Scale - Self-Report − 30 item (DMRS-SR-30) with a global, community sample of 1,539 participants who responded to an online survey about distress and coping. Exploratory factor analysis found a three-factor model for the DMRS-SR-30 – mature, mental inhibition and avoidance, and immature-depressive. Internal consistency was high for the Overall Defensive Functioning (ODF) and the three extracted factors with coefficient alphas ranging from .75 to .90. Examination of concurrent validity with a commonly used measure of defensive functioning found significant relationships in the predicted directions. The group of immature defenses had the strongest concurrent validity (r = .50). Finally, correlations with external criteria – including psychological distress and adverse childhood experiences – supported the convergent and discriminant validity of the DMRS-SR-30. The three factor structure of the DMRS-SR-30 has good psychometric properties. Limitations and directions for future research, as well as clinical implications, are described.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".