Technical Adequacy of the Functional Assessment Checklist: Teachers and Staff (FACTS) FBA Interview Measure
Bibliographic record
Abstract
With the recent increase in the use of functional behavior assessment (FBA) in school settings, there has been an emphasis in practice on the development and use of effective, efficient methods of conducting FBAs, particularly indirect assessment tools such as interviews. There are both benefits and drawbacks to these tools, and their technical adequacy is often unknown. This article presents a framework for assessing the measurement properties of FBA interview tools and uses this framework to assess evidence for reliability and validity of one interview tool, the Functional Assessment Checklist: Teachers and Staff (FACTS; March et al., 2000). Results derived from 10 research studies using the FACTS indicate strong evidence of test—retest reliability and interobserver agreement, moderate to strong evidence of convergent validity with direct observation and functional analysis procedures, strong evidence of treatment utility, and strong evidence of social validity. Results are discussed in terms of future validation research for FBA methods and tools.
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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 | 0.059 | 0.176 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".