Managing missing data plays a fundamental role in statistical analysis, critically contributing to preserving the accuracy, precision, and reliability of results.
Neglecting this consideration could lead to partial or misleading conclusions.

This is why understanding the importance of addressing missing data is essential.
 
During the webinar, we delve into these aspects, focusing on:

• What missing values are

• Types of missing information:

  • Missing Completely at Random (MCAR)

  • Missing at Random (MAR)

  • Missing Not at Random (MNAR)

• Replacement methodologies

• Practical example with Statistics Data Analysis powered by SPSS