Validity of the Multigroup Ethnic Identity Measure for Māori, Pacific, Asian & European adolescents
Bibliographic record
Abstract
Abstract Background Ethnic identity is important for positive health and wellbeing outcomes, especially for Indigenous youth and adolescents from minoritised ethnic groups. The Multigroup Ethnic Identity Measure (MEIM) is widely used to examine ethnic identity as a general phenomenon across ethnic groups. However, evidence regarding its validity for adolescents from different ethnic backgrounds is mainly limited to the US context. This study investigated psychometric properties of the MEIM, for the first time, within a large ethnically diverse population-based sample in New Zealand. Methods We used data from the Growing Up in New Zealand study. Participants were 4500 12-year-olds and included 22.4% Māori (the Indigenous people of New Zealand), 16.7% Pacific, 14.8% Asian and 51.9% European young people. 45.7% were cisgender boy, 37.4% were cisgender girl, and 16.4% were non-binary, trans or unsure. We conducted factor analysis for the 12-item MEIM. Results Confirmatory factor analysis model fit tended to be best for a solution with two factors representing two ethnic identity subcomponents of Exploration and Affirmation/Belonging. A single ‘ethnic identity’ factor showed a slightly weaker model fit. Exploratory factor analysis revealed a 2-factor structure with a slightly different item composition as compared to the original MEIM subscales. The findings were largely comparable across ethnic groups. Conclusions The MEIM appears to be a valid measure for Māori, Pacific, Asian and European young people. However, nuances may exist due to unique contexts including structural factors, societal norms and challenges, opportunities and access to cultural engagement. Ethnic identity is strongly linked to health and wellbeing including quality of life, self-esteem and life satisfaction. Therefore, we recommend acknowledging nuances of ethnic identity during an important time of development within diverse cultural contexts, by applying subscales and subgroup analyses where possible. Key messages • The Multigroup Ethnic Identity Measure is valid and appropriate to use at age 12-years among diverse ethnic groups including Māori, Pacific, Asian and European. • Nuances of ethnic identity should be acknowledged as an important factor for health and wellbeing, especially during critical times of transition for adolescents within diverse cultural contexts.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".