What is the result of recoding data in analysis?

Recoding data during analysis involves transforming existing values into new categories or groupings. This process is essential for various reasons, such as simplifying analysis, enhancing data interpretation, or preparing the data for specific statistical techniques. By mapping original values into new categories, analysts can facilitate clearer insights and patterns in the data.

For instance, if you have survey responses that include a range of ages, recoding those values into broader categories like "18-24", "25-34", etc., allows for easier comparisons and analysis of trends within those age groups. This practice also helps in managing outliers or consolidating categories with sparse data into more usable forms, ultimately enhancing overall analysis outcomes.

In contrast, creating entirely new datasets is not the focus of recoding; it is more about modifying existing data. Additionally, recoding does not eliminate the need for data validation, as validation is still essential to ensure accuracy and reliability in the recoded values. Additionally, while recoding may involve adjustments to data presentation, it does not inherently change the underlying database structure.