Are cross-cultural shyness comparisons valid? Testing invariance with multigroup CFA and the alignment method across eastern and western cultures.
Bibliographic record
Abstract
= 995, 30.8% male). In the first instance, we used the well-established multigroup confirmatory factor analysis (MGCFA) to show that there was measurement noninvariance in the one factor shyness scale across the two countries and the two countries by sex. We further examined the issue of noninvariance using the newer alignment method, an approach providing detailed information on noninvariance for each country model by parameter (across intercepts and loadings) as an alternative to the MGCFA restrictive assessment of whole scale construct validation. The findings suggested acceptable approximate invariance in the shyness scale to support an unbiased comparison of mean levels between the two countries and the two countries by sex. Chinese young adults had significantly higher mean levels of shyness than Canadian young adults. Despite some limited noninvariance, we were able to conclude that the underlying construct of shyness as measured in this study was equivalent across Chinese and Canadian cultures. Findings illustrated the difficulties and importance of first establishing fundamental measurement properties and equivalence in personality constructs before inferring cross-cultural universality in complex traits and characteristics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".