thedixitjain Cross-National Data Analysis (jim-data-analysis)
When to trigger
- Multi-country data are in and the invariance battery must be run before anything else
- Country-level moderators need estimating (HLM, multi-group SEM, cross-level interactions)
- Latent means or path coefficients are about to be compared across countries
- A reviewer writes "the cross-national comparisons are not interpretable as reported"
Invariance first — nothing compares until it passes
Run the MGCFA ladder per construct, in order, and report every rung:
| Step | Constraint added | Licenses you to… |
|---|---|---|
| Configural | same factor structure | claim the construct exists everywhere |
| Metric | equal loadings | compare structural paths / correlations across countries |
| Scalar | equal intercepts | compare latent means across countries |
Decision rules: χ² difference plus practical criteria (ΔCFI ≤ .01; monitor ΔRMSEA). When full invariance fails: release the minimum number of parameters for partial invariance (at least two invariant items per construct, per the Steenkamp–Baumgartner protocol) and restate exactly which comparisons remain licensed. With many countries (8+), pairwise MGCFA explodes — use the alignment method and report the proportion of non-invariant parameters (≤25% rule of thumb). If scalar invariance dies and cannot be partially rescued, mean-comparison hypotheses are off the table; say so in the paper rather than burying it.
Also run: response-style controls (model ARS/ERS factors or covariates as planned in design) and per-country reliability/validity (CR, AVE, discriminant checks reported for each country, not pooled).
Match the estimator to the cross-national structure
| Data structure | Estimator | Reporting keys |
|---|---|---|
| Consumers nested in countries (10+ countries) | Multilevel / HLM with country-level predictors | ICC first; random slopes for moderated effects; group-mean vs. grand-mean centering stated |
| Few countries (2–4) | Multi-group SEM with invariance constraints | path-difference tests across groups, not eyeballed coefficients |
| Cross-level moderation | random-slope HLM or MSEM | the slope variance must be nonzero before a country variable can explain it |
| Firm export panels | FE / DiD (staggered-adoption estimators) / selection models | cluster at firm or country per the variation; pre-trends where causal |
| Country dyads (home–host) | dyadic models with distance variables | control both origin and destination effects |
| Meta-analytic | random-effects + meta-regression on country dimensions | between-study heterogeneity decomposed |
Small-N-countries warning: with fewer than ~10 countries, country-level regression coefficients are unstable — prefer multi-group contrasts, fixed effects, or Bayesian multilevel with informative priors, and never narrate 5 countries as a "test" of a continuous cultural dimension.
Report so the cross-national claim is auditable
- Invariance table is mandatory — steps, fit per step, Δ-statistics, decision. It is the first table JIM methods reviewers look for.
- Per-country descriptives and correlations (or a Web Appendix panel), not pooled-only.
- Standardized effects with CIs; for mediation, bootstrapped indirect effects per group or conditional on the country moderator.
- For every country difference claimed: the formal test of the difference (constrained vs. free model; interaction coefficient), never two separate significance verdicts.
- Translate the headline effect into a cross-border managerial magnitude: adaptation lift in market A vs. B, export-revenue elasticity, the country profile where the strategy flips sign.
Execution bridge (StatsPAI / Stata MCP)
Run the battery end-to-end instead of enumerating it. Full map: execution-with-mcp. For JIM's structures: multi-country panels via detect_design → recommend → fit with as_handle=true → audit_result; staggered policy variation via callaway_santanna / sun_abraham with honest_did_from_result; few-cluster (few-country) inference via wild_cluster_bootstrap; many-outcome hypothesis families via romano_wolf; OVB sensitivity via oster_delta / sensemakr; exhibits exported from the result handle (etable) so no number is retyped. HLM and measurement-invariance runs (MGCFA ladders, alignment) execute in lavaan/Mplus-lane scripts under resources/code/ — keep the per-step fit log as the audit trail; SEM path models follow the same handle-then-audit discipline.
Checklist
- Invariance ladder run and tabled per construct; licensed comparisons restated
- Partial invariance (≥2 invariant items) or alignment used where full invariance failed
- Response styles controlled; per-country reliability and validity reported
- Estimator matches nesting; ICC and centering choices stated for multilevel models
- Every claimed country difference backed by a formal difference test
- Small-N-countries limits acknowledged; no continuous-dimension claims from 4 countries
- Headline results carry cross-border managerial magnitudes
Anti-patterns
- Comparing raw scale means across countries with no scalar-invariance evidence
- "Significant in Germany, not in Brazil" offered as evidence of moderation
- Country dummies interpreted as cultural effects
- HLM with 6 countries and three country-level predictors
- Pooled reliability statistics hiding a collapsed construct in one country
- An invariance failure silently absorbed by dropping the offending country
Output format
【Invariance】per construct: configural/metric/scalar (full/partial/alignment) + decision rule
【Licensed comparisons】means / paths / neither — per construct
【Estimator】structure-matched model + clustering/centering choices
【Country-difference tests】formal Δ-tests for each claimed difference: done?
【Managerial magnitude】headline effect in cross-border decision units
【Robustness】response styles / small-N limits / identification threat addressed
【Next skill】jim-contribution-framing
Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-International-Marketing-Skills/skills/jim-data-analysis/SKILL.md