Investigating the structure and measurement invariance of the Multigroup Ethnic Identity Measure in a multiethnic sample of college students.
Bibliographic record
Abstract
In this article, we evaluate the factor structure of the Multigroup Ethnic Identity Measure (MEIM; Phinney, 1992) and test whether the MEIM exhibits measurement invariance across ethnic groups taken from a diverse sample of students from 30 different colleges and universities across the United States (N = 9,625). Initial analyses suggested that a bifactor model was an adequate representation of the structure of the MEIM. This model was then used in subsequent invariance tests. Results suggested that the MEIM displayed configural and metric invariance across 5 diverse ethnic groups (i.e., White, Black, Hispanic, East Asian, and South Asian). There were indications that the MEIM displayed a similar factor structure with roughly equivalent factor loadings across diverse ethnic groups. However, there was little evidence of scalar invariance across these groups, suggesting that mean-level comparisons of MEIM scores across ethnic groups should be interpreted with caution. The implications of these findings for the interpretation and use of this popular measure of ethnic identity are discussed.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".