Detect and Fix String-Encoded Numerics in Stata
Stata imports may store numeric-looking variables as strings, breaking analysis commands. Learn to detect the type with describe and convert safely using destring.
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Stata imports may store numeric-looking variables as strings, breaking analysis commands. Learn to detect the type with describe and convert safely using destring.
Learn how to use SPSS Syntax with Compute, If, and loops to automate survey cleaning steps such as outlier flagging and missing‑value replacement.
SPSS EM imputation retains cases under a Missing at Random assumption via iterative likelihood maximization, but single imputation understates uncertainty. Use convergence diagnostics and MIANALYZE pooling for proper inference.
How to pick the right Stata merge type (1:1, 1:m, m:1), validate keys with isid and duplicates tag, and read the _merge variable to catch mismatches before they corrupt your data.
Tired of re‑applying the same cleaning steps in SPSS each time a new dataset arrives? Learn how to capture those steps in plain‑text syntax, run them automatically, and version‑control your workflow for reproducible, error‑free analysis.
A decision guide for SPSS missing data handling: when listwise deletion is fine, when pairwise deletion helps, when EM imputation is worth it, and how to validate the result with reproducible syntax.
When a dataset contains missing values, selecting an imputation strategy can shape the validity of your analysis. This guide compares SPSS’s native Multiple Imputation, manual mean/median imputation, and Python integration, outlining trade‑offs and providing concrete syntax to help you decide.
Stata’s MERGE command fuses two datasets by key variables, producing a _merge variable that flags match status. This guide covers syntax, a worked example, pitfalls, and verification steps to ensure accurate data integration.