Confirmatory factor analysis of the Maltreatment and Abuse Chronology of Exposure (MACE) scale: Evidence for essential unidimensionality
Bibliographic record
Abstract
The Maltreatment and Abuse Chronology of Exposure (MACE) scale is a retrospective self-report scale that measures 10 distinct types of childhood maltreatment. Despite its increasing use, the factor structure of the MACE has not been thoroughly investigated. As such, the viability of the MACE total and subscale scores are uncertain. The current study investigated the factor structure of the MACE in order to quantify the reliability of its total and subscale scores. Two independent samples of participants (N=1051 & N=582) who completed the MACE were included in this study. Using confirmatory item response models, we tested one-factor and several bifactor models of participants’ responses. We used model-based indices to estimate the reliability of the MACE total and subscale scores, and to quantify the essential unidimensionality of the scale. We found the MACE total score was a reliable and valid measure of overall childhood maltreatment. In contrast, although we found MACE subscale scores exhibited adequate reliability, the vast majority of their reliable variance reflected general maltreatment and not any particular type of maltreatment as intended. We also found that the MACE is essentially unidimensional and some of its items exhibit differential item functioning by gender. Our results provide support for a one-factor structure of the MACE, as well as continued use of the MACE total score. Our results caution against the use of MACE subscale scores, which have little practical use insofar that they provide little unique, reliable information above and beyond the total score.
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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 | 0.044 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".