### Understanding the Structure of the NIS Data Dictionary
The NIS data dictionary lists every variable available in the dataset along with its definition, format, and valid values. Variables are grouped into sections such as patient demographics, hospital characteristics, diagnoses, procedures, and outcomes. Always note the **data year**, as variable names and definitions may change over time.
**Example:** The variable `AGE` represents patient age in years, but neonates may be coded differently in certain years.
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### Reading Variable Names, Labels, and Formats
Each variable has a short **variable name**, a **label** explaining what it represents, and a **format** (numeric, character, categorical). Understanding the format helps prevent analysis errors, such as treating categorical data as continuous.
**Example:** `FEMALE` is coded as `0 = Male` and `1 = Female`, so it should be analyzed as a categorical variable, not a continuous one.
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### Interpreting Coded Values and Missing Data
Many NIS variables use numeric codes instead of plain text, and missing values often have specific codes (such as `.` or `-9`). The data dictionary explains what each code means, which is critical before running statistics.
**Example:** For `RACE`, a value of `1` may represent White, while other numbers represent different racial groups, and missing race data must be handled carefully.
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### Using Clinical and Outcome Variables Correctly
Diagnosis and procedure variables (ICD-9/ICD-10) are stored across multiple columns, and outcome variables describe events during hospitalization. Always cross-check definitions to ensure accurate case identification and outcome measurement.
**Example:** `DIED = 1` indicates in-hospital mortality, while `LOS` gives length of stay in days, which can be compared across patient groups after applying weights.
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This careful reading of the NIS data dictionary ensures accurate variable selection, correct interpretation, and reliable research results.
