Logistic Regression Results for Flood Adaptation Drivers, Wave 2020

Log-odds coefficients, robust standard errors, and odds ratios with 95% confidence intervals for predictors of flood adaptation measures. Mikhail Sirenko published this 10.5 KB Excel file on figshare under a CC-BY-4.0 license, last updated in March 2026. The results include Benjamini–Hochberg false discovery rate-adjusted significance markers and exclude households reporting prior adoption.

Use Cases

• Validate statistical models of flood resilience based on reported odds ratios and confidence intervals.
• Analyze the relationship between flood experience and adaptation measures based on the logistic regression coefficients.
• Assess the significance of predictors like financial incentives or risk awareness using the provided FDR-adjusted q-values.

Strengths

• Dataset includes multiple statistical outputs: coefficients, standard errors, p-values, FDR-adjusted q-values, and odds ratios with 95% CIs.
• Variance inflation factor checks indicated no multicollinearity concerns for the model.
• Published under a permissive CC-BY-4.0 license allowing reuse with attribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Mikhail Sirenko

Collection Method

Results from a logistic regression analysis, likely from a survey.

Time Range

Wave 2020

Freshness

Last updated 2026-03-18 17:27:08; freshness should be verified.

Geography

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