title: 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 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.affiliation: Department of Behavioral Science, Utah Valley University, Orem, UT 84058, USA. rwarne@uvu.edu
authors.forename: Russell T
authors.initials: RT
authors.lastname: Warne
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.forename: E Lisako J
authors.initials: EL
authors.lastname: McKyer
authors.forename: Rachel
authors.initials: R
authors.lastname: Condie
authors.forename: Cassandra S
authors.initials: CS
authors.lastname: Diep
authors.forename: Peter S
authors.initials: PS
authors.lastname: Murano
mesh_terms: D035843:Biomedical Research / Q000379:methods; D016000:Cluster Analysis; D004032:Diet / Q000706:statistics & numerical data*; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D007223:Infant; D016014:Linear Models; D008297:Male; D052756:Nutritional Sciences