Impact of cluster sampling on scale psychometrics: simulation study and application to mental health survey
Bibliographic record
Abstract
Cluster sampling designs are frequently used in mental health surveys and prevention studies. The overall purpose of this thesis research is to investigate the impact of cluster sampling on scale psychometric properties and the psychometrics of a mental health assessment tool in Canadian culture. We conducted the simulation study to examine the impact on scale psychometrics of ignoring the non-independence of subjects within cluster. Results indicated that: (a) as the dependence among observations (i.e., ICC) increases, the model goodness of fit become worse or even not acceptable if we specified a single-level model for a multilevel data; (b) Single-level reliability estimates would consistently estimate reliability at both levels if the true reliability at both levels was the same or ICC is low; (c) Single-level reliability estimates would fall in the interval of true reliability at individual level and the true reliability at the school level. We also used data from Manitoba provincial Grade 5 mental health survey to examine the psychometrics of Strength and Difficulty Questionnaire (SDQ) as well as the influence of cluster sampling. Results indicate that the 5 factor structures identified in other cultures fit the Canadian sample well and the estimates of psychometrics (e.g., reliabilities) fell into reasonable range if we use the single level model. The study provides guidance for estimation of psychometrics with cluster sampling. Empirical analyses of psychometric properties of the Canadian SDQ provide supports for the usefulness of the SDQ as a screening tool for mental health of children and youth in the general Canadian population.
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 | 0.106 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".