Managing Clustered Data Using Hierarchical Linear Modeling

Abstract Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical m...

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Bibliographic Details
Published in:Journal of nutrition education and behavior 2012-05, Vol.44 (3), p.271-277
Main Authors: Warne, Russell T., PhD, Li, Yan, MS, McKyer, E. Lisako J., PhD, MPH, Condie, Rachel, BS, Diep, Cassandra S., MS, Murano, Peter S., PhD
Format: Article
Language:English
Subjects:
Biomedical Research - methods
Cluster Analysis
Data Analysis
Diet - statistics & numerical data
Eating Habits
Evaluation Methods
Feeding Behavior
Female
Food
Gastroenterology and Hepatology
hierarchical linear modeling
Humans
Infant
Internal Medicine
Linear Models
Male
multilevel models
Multivariate Analysis
Nutrition
nutrition behavior
Nutritional Sciences
Sampling
Statistical Analysis
Statistics
survey research
Violations
WIC program
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Summary:Abstract Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence assumption and lead to correct analysis of data, yet it is rarely used in nutrition research. The purpose of this viewpoint is to illustrate the benefits of hierarchical linear modeling within a nutrition research context.
ISSN:1499-4046
1878-2620
1878-2620
1708-8259
DOI:10.1016/j.jneb.2011.06.013