SAS offers various tools and techniques to address data quality issues:
1. Data Cleaning
Data cleaning involves detecting and correcting errors and inconsistencies. SAS procedures like `PROC SORT`, `PROC FREQ`, `PROC MEANS`, and `PROC UNIVARIATE` can identify anomalies. Functions like `IF-THEN` statements and `ARRAY` can be used to correct data errors.
2. Handling Missing Data
SAS can manage missing data using techniques such as imputation, deletion, or using models that accommodate missing values. Procedures like `PROC MI` and `PROC MIANALYZE` are specifically designed for multiple imputation.
3. Data Transformation and Standardization
To ensure consistency, data transformation and standardization are crucial. SAS functions like `FORMAT`, `INPUT`, and `PUT` help in converting data into a standard format. `PROC TRANSPOSE` and `PROC SQL` are useful for reshaping and standardizing datasets.
4. Duplicate Detection and Removal
Duplicate records can be identified and removed using SAS procedures like `PROC SORT` with the `NODUPKEY` option. Additionally, `PROC SQL` can be utilized to identify duplicates by querying the dataset.
5. Outlier Detection
Outliers can be detected using statistical methods such as z-scores and interquartile range (IQR). SAS procedures like `PROC UNIVARIATE` and `PROC MEANS` can help identify and handle outliers.
