Measurement invariance of the revised multigroup ethnic identity measure among a national sample of Native American and Alaska Native college students.
Bibliographic record
Abstract
OBJECTIVES: Developing a healthy ethnic-racial identity (ERI) is a vital developmental task for ethnically or racially marginalized individuals, promoting positive mental health and psychosocial adjustment. However, no prior study has evaluated whether one of the most widely used ERI measures, the Revised Multigroup Ethnic Identity Measure (MEIM-R), functions equivalently across Native American and Alaska Native (NA/AN) communities. METHOD: = 21) representing 122 communities, this study tested the factor structure, measurement invariance, and latent mean differences of the MEIM-R based on whether participants were raised on or away from their reservation and whether they identified as monoracial or Multiracial. RESULTS: Confirmatory factor analysis indicated that factor structure for the MEIM-R was not wholly consistent across demographic factors, requiring some residual covariances between items depending upon the group examined. Measurement invariance testing demonstrated full scalar invariance between participants raised on and away from their reservation and partial scalar invariance across monoracial and Multiracial identities. Latent means testing highlighted group differences in ERI exploration and commitment across these variables. CONCLUSION: These findings support that, at least in part, the meaning and configuration of ERI, as measured by the MEIM-R, differ slightly for NA/AN communities based on where individuals grew up and whether they identify as Multiracial. Scholars using the MEIM-R should account for these within-group differences when conducting research with NA/AN communities. Further research is necessary to holistically understand and operationalize important and salient aspects of ERI for NA/AN communities. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".