jibs-data-analysis

Data Analysis & Cross-National Validity (jibs-data-analysis)

When to trigger

• Cross-country or multilevel data are collected and it is time to estimate and report
• You are unsure your estimator matches nesting (individuals in firms in countries) or a process design
• Reviewers will probe measurement equivalence, CMV, or (dynamic) endogeneity
• A reviewer cites a JIBS "From the Editors" methods editorial against your analysis

Establish cross-national measurement equivalence first

At JIBS, because constructs travel across countries and cultures, measurement equivalence is a first-order review concern, not an afterthought. Before testing hypotheses:

• Reliability & CFA. Report reliability (alpha/composite reliability) and a confirmatory factor analysis with fit (CFI, TLI, RMSEA, SRMR).
• Measurement invariance. Run multi-group CFA and report configural → metric → scalar invariance across countries; if scalar fails, report partial invariance or the alignment method and discuss what cross-country comparisons remain defensible.
• Construct & discriminant validity. AVE per construct; AVE > inter-construct squared correlations (or HTMT). Report the correlation matrix with reliabilities on the diagonal.
• Aggregation (multilevel). Justify any aggregation to the country/firm level with ICC(1), ICC(2), and r_wg(j).
• Qualitative designs. Where the design is case-based, "validity" becomes trustworthiness — a transparent data structure, an audit trail, and representative cross-case quotations.

Match the estimator to the cross-country/multilevel design

| Data structure / claim | Estimator |

|-----------------------------------------------------|--------------------------------------------------------------|

| Individuals/firms nested in countries | Multilevel / HLM (random intercepts/slopes); country-level Xs |

| Latent constructs across groups, mediation | Multi-group / multilevel SEM |

| Internationalization as a path-dependent process | Dynamic panel (system/diff GMM), firm FE, lagged DVs |

| Cross-border entry-mode / location choice | Logit/probit/multinomial; conditional/mixed logit |

| Cross-country count/limited DV | Poisson/negative binomial; Tobit/Heckman as fits |

Cluster standard errors to the country (or country-year) structure.

Common-method variance (CMV) — an active JIBS gatekeeper

JIBS routinely asks survey/same-respondent papers that appear to suffer from CMV to run validity checks and resubmit. A Harman single-factor test alone is not sufficient. Report the designed separations first (from jibs-methods), then statistical evidence: a marker-variable approach, an unmeasured latent method factor (CFA), or showing interaction/cross-level effects survive (these are hard to inflate by CMV). Cite the JIBS "From the Editors" CMV editorial.

Endogeneity and "dynamic endogeneity"

For internationalization-as-process designs, address dynamic endogeneity explicitly: past strategy, performance, and unobserved firm heterogeneity co-evolve. Execute the identification planned at design (instruments and first-stage strength, dynamic-panel GMM with instrument-count and Hansen/AR(2) diagnostics, DiD/natural experiment, or selection models) and discuss the assumptions. Reviewers anchor this to the JIBS endogeneity/dynamic-endogeneity editorials.

Reporting & robustness

• Mediation: bias-corrected bootstrap CIs (e.g., 5,000 resamples); conditional indirect effects for moderated mediation.
• Moderation/cross-level interactions: report the coefficient and plot simple slopes; report incremental variance.
• Effect sizes: report magnitudes, not only p-values (a JIBS p-value editorial cautions against p-value worship).
• Robustness: alternative country samples (drop influential countries), alternative distance measures, alternative specifications; sensitivity to endogeneity assumptions.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map:

[execution-with-mcp](../../../shared-resources/empirical-methods/execution-with-mcp.md). JIBS is international business — cross-country panels with confounded institutions; emphasize fixed effects, clustering, and endogeneity of location / entry choices.

• Many outcomes / specifications: romano_wolf (step-down FWER) or

benjamini_hochberg — report the adjusted threshold.

• OVB sensitivity: oster_delta / sensemakr.
• Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley;

multilevel data → cluster at the right level.

• Re-fit off one handle: audit_result(result_id) lists the missing checks and the

exact suggest_function for each.

• Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the appendix. See the

executed chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

Output format

【Measurement】reliability/CFA fit; invariance configural/metric/scalar (or partial/alignment) ...
【Estimator】multilevel / multilevel-SEM / dynamic-panel GMM / choice model; SE clustered by country ...
【CMV evidence】designed separation + marker/latent-method test (beyond Harman) ...
【Endogeneity / dynamic endogeneity】strategy executed; diagnostics; assumptions ...
【Mediation/Moderation】bootstrap CIs / simple slopes / incremental variance ...
【Robustness】country-drop, alt distance, alt specs ...
【FTE editorial alignment】 ...
【Next step】jibs-contribution-framing

Anti-patterns

• Pooling countries without testing measurement invariance.
• Single-factor (Harman) test as the sole CMV defense.
• OLS on country-nested data, ignoring non-independence.
• Internationalization-process regressions with no dynamic-endogeneity treatment.
• Reporting p-values with no effect sizes or practical magnitude.
• Treating cultural/institutional distance as a black-box covariate.