Development and psychometric properties of the Attitudes Toward Intellectual Disability Questionnaire – Short Form
Bibliographic record
Abstract
Abstract Background Understanding public attitudes towards people with intellectual disability (ID) can help orient activities to promote the social inclusion of this group. The ATTitudes toward Intellectual Disability (ATTID) questionnaire is a validated 67‐item tool used to assess attitudes towards people with ID from a multidimensional perspective. It is based on a five‐factor model tapping into cognitive, emotional and behavioural components of attitudes. In order to facilitate international research, the goal of this study was to develop a short version that would retain the long form's psychometric properties. Methods Analyses were conducted on a sample of 1608 respondents who completed the full‐length ATTID. A four‐step test refinement procedure was used to reduce the number of items. The first two steps involved a Cronbach's alpha analysis. Items retained were then reviewed to assess face validity. Correlations between factors were calculated, and a factor analysis was performed to compare the original and short forms. Results The number of items in the ATTID was reduced from 67 to 35. The short form maintained good overall reliability. The correlational pattern between factors in both the long and short form is generally the same. The factor analysis of the short form showed a similar five‐factor structure with some loss of variance. Conclusions We recommend the short form be used when administration time is an issue, particularly in a research context. Replication studies with new samples are needed to further assess the psychometric properties of the ATTID‐Short Form.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".