Dataset for the Deez Alexithymia Scale (DAS-21): Adult Community Sample
Bibliographic record
Abstract
This dataset contains anonymized item-level and scored responses for the Deez Alexithymia Scale (DAS-21), a 21-item self-report psychometric instrument developed to assess alexithymia as a multidimensional construct. Alexithymia refers to persistent difficulties in identifying and describing emotions, a tendency toward externally oriented thinking, and reduced emotional engagement. The DAS-21 was designed to extend traditional measurement approaches by incorporating emotional detachment as an explicit and theoretically relevant dimension, making the scale suitable for research, educational, and applied psychological contexts. The dataset is based on a community adult sample (N = 300) and includes demographic variables alongside full item-level responses to all DAS-21 items. Participants are represented by anonymous identifiers to ensure confidentiality. Demographic variables include age (in years), gender (coded numerically), and education level (coded to reflect highest level of completed education). No personally identifiable information is included, and no clinical diagnoses are implied or recorded in the dataset. Each DAS-21 item is rated using a 5-point Likert-type response format ranging from 1 (Strongly disagree) to 5 (Strongly agree). Higher scores reflect greater levels of alexithymic traits. In addition to raw item responses, the dataset includes computed subscale scores corresponding to the four theoretical dimensions of the DAS-21: Difficulty Identifying Feelings (DIF), Difficulty Describing Feelings (DDF), Externally Oriented Thinking (EOT), and Emotional Detachment (ED). A total alexithymia score is also provided, calculated as the sum of all 21 items. No reverse-scored items are used, reducing scoring complexity and potential user error. The dataset is structured to facilitate transparency, reproducibility, and secondary analysis. It may be used for psychometric evaluation, scale comparison studies, methodological demonstrations, educational purposes, and exploratory research on emotional awareness and affective processing. Researchers may also use the dataset to test alternative factor structures, examine measurement invariance, or illustrate scoring procedures in teaching and training contexts. While the dataset reflects realistic response distributions consistent with self-report research, it should be interpreted as non-diagnostic and not as a substitute for clinical assessment. All data are fully anonymized, and participation was voluntary. The dataset does not contain sensitive personal information, health records, or identifying variables. Overall, this dataset supports open science practices by providing a clear, well-documented resource for the study of alexithymia. By combining item-level data with scored subscales and transparent documentation, the dataset offers a valuable foundation for future research on emotional processing, neurodiversity, and educational implications associated with alexithymic traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.070 | 0.061 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".