Technical Adequacy of the Functional Assessment Checklist: Teachers and Staff (FACTS) FBA Interview Measure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it