Evaluating the invariance of the Multigroup Ethnic Identity Measure across foreign-born, second-generation and later-generation college students in the United States.
Bibliographic record
Abstract
OBJECTIVES: Past research has established that the Multigroup Ethnic Identity Measure (MEIM) exhibits measurement invariance across diverse ethnic groups. However, relatively little research has evaluated whether this measure is invariant across generational status. Thus, the present study evaluates the invariance of the MEIM across foreign-born, second-generation, and later-generation respondents. METHOD: A large, ethnically diverse sample of college students completed the MEIM as part of an online survey (N = 9,107; 72.8% women; mean age = 20.31 years; SD = 3.38). RESULTS: There is evidence of configural and metric invariance, but there is little evidence of scalar invariance across generational status groups. CONCLUSIONS: This study suggests that the MEIM has an equivalent factor structure across generation groups, indicating it is appropriate to compare the magnitude of associations between the MEIM and other variables across foreign-born, second-generation, and later-generation individuals. However, the lack of scalar invariance suggests that mean-level differences across generational status should be interpreted with caution. (PsycINFO Database Record
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".