What does disaggregated data analysis typically involve?

Disaggregated data analysis involves breaking down data into smaller, more specific segments to better understand the relationships among various factors. This approach allows researchers to analyze the interplay between different independent variables, such as age, gender, socio-economic status, and geographical location, that may influence juvenile delinquency rates. By focusing on these nuanced relationships, it becomes possible to identify patterns and trends that might not be visible when looking at aggregate data.

For example, rather than simply assessing a national crime rate, disaggregated analysis helps pinpoint how crime varies across different demographic groups or regions, providing deeper insights into the causes and correlates of juvenile delinquency.