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The National Immunization Coverage Survey was carried out in 2016/17 by the National Primary Healthcare Agency in collaboration with the National Bureau of Statistics. Technical support was provided
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01
Identify the research question or hypothesis that requires a multilevel analysis.
02
Collect and organize data at multiple levels, such as individuals within groups or organizations.
03
Choose the appropriate statistical software for conducting multilevel analysis.
04
Specify the level of analysis for each variable in the dataset.
05
Run the multilevel analysis model to examine the relationships between variables at different levels.
06
Interpret the results and draw conclusions based on the findings of the multilevel analysis.

Who needs a multilevel analysis of?

01
Researchers and statisticians who are studying data that has a hierarchical structure, such as students within schools, employees within companies, or patients within hospitals.
02
Professionals working in fields such as education, healthcare, social sciences, or organizational management where relationships between variables at different levels need to be analyzed.
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A multilevel analysis is a statistical technique used to analyze data that has a hierarchical structure.
Researchers and analysts who are working with nested data or data that has a hierarchical structure may be required to conduct and file a multilevel analysis.
To fill out a multilevel analysis, one needs to have a good understanding of hierarchical data structures and statistical modeling techniques.
The purpose of a multilevel analysis is to examine how individual-level and group-level factors simultaneously influence outcomes.
In a multilevel analysis, researchers must report the variables at different levels and the relationships between them.
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