Managing clustered data using hierarchical linear modeling. Academic Article

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.

authors

• McKyer, Ellisa Lisako
• Murano, Peter

published proceedings

• J Nutr Educ Behav

altmetric score

• 1.5

author list (cited authors)

• Warne, R. T., Li, Y., McKyer, E., Condie, R., Diep, C. S., & Murano, P. S.

citation count

• 8

complete list of authors

• Warne, Russell T||Li, Yan||McKyer, E Lisako J||Condie, Rachel||Diep, Cassandra S||Murano, Peter S

publication date

• May 2012

publisher

• Elsevier  Publisher

keywords

• Biomedical Research
• Cluster Analysis
• Diet
• Feeding Behavior
• Female
• Humans
• Infant
• Linear Models
• Male
• Nutritional Sciences

PubMed Central ID

• 22236492

Digital Object Identifier (DOI)

• 10.1016/j.jneb.2011.06.013

start page

• 271

end page

• 277

volume

• 44

issue

• 3

URL

• http://dx.doi.org/10.1016/j.jneb.2011.06.013