WORKSHOP: Testing Multigroup and Multilevel Assessment Scale Equivalence across Nations and Cultures
Bibliographic record
Abstract
Over at least the past two decades, there has been a rapidly growing interest in cross-national comparisons. Although such comparisons traditionally have been the province of cross-cultural psychologists, a review of the extant literature reveals this investigative work now to be of substantial interest within mainstream psychology, as well as with researchers across a broad band of other disciplines. A notable outgrowth of this work has been the burgeoning number of assessment scales being translated into other languages for use in countries and cultures that differ from the one in which the original scale was developed and normed. Importantly, inherent in the conduct of multigroup comparisons across national/cultural groups are two critical assumptions: (a) that the translated assessment scale is operating equivalently across the groups; and (b) that given the hierarchical structure of multicultural data (i.e., individuals nested within countries), the scale is operating equivalently across individual and country levels. As with all statistical analyses, these assumptions must be tested. Based on several different CFA models within the framework of structural equation modeling (SEM), workshop participants will be walked through the hierarchical set of steps needed in testing both of these critically important assumptions. In broad terms, this workshop focuses on both the analytic procedures involved in testing for the equivalence of an assessment scale across national/cultural groups, and on the many and diverse issues contributing to complexities associated with these analyses. More specifically, the purpose of this workshop is twofold: (a) to present a nonmathematical introduction to the underlying rationale and basic concepts associated with tests for multigroup and multilevel equivalence, and (b) to discuss and illustrate by example, substantive, psychometric, and statistical issues that can impact these tests for equivalence when the groups under study represent different nations and cultures. To gain the most from this workshop, participants should have an understanding of basic SEM concepts. Workshop Agenda Detailed explanation and walk-through of several example applications in testing for measurement and structural equivalence of an assessment scale, as well as for latent mean differences across groups. Both the EQS 6.2 and Mplus 7.4 programs will be used in illustrating these applications. This session will be conducted in three sections as follows: (a) Explanation and walk-through of an Mplus application that tests for multilevel equivalence, as well as for differences in contextual variable effects. (b) Discussion and extensive exemplification of issues and complexities that most commonly lead to evidence of measurement and/or structural nonequivalence across multicultural groups. (c) Discussion, explanation, and possible solution to the commonly found problem related to tests for assessment scale equivalence when the number of groups is large (typically >5).
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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 | 0.152 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".