Applied Statistics Commons^™

Managing Clustered Data Using Hierarchical Linear Modeling, Russell Warne

Russell T Warne

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.

An Introduction To Item Response Theory For Health Behavior Researchers, Russell Warne

Russell T Warne

OBJECTIVE:

To introduce item response theory (IRT) to health behavior researchers by contrasting it with classical test theory and providing an example of IRT in health behavior.

METHOD:

Demonstrate IRT by fitting the 2PL model to substance-use survey data from the Adolescent Health Risk Behavior questionnaire (n=1343 adolescents).

RESULTS:

An IRT 2PL model can produce viable substance use scores that differentiate different levels of substance use, resulting in improved precision and specificity at the respondent level.

CONCLUSION:

IRT is a viable option for health researchers who want to produce high-quality scores for unidimensional constructs. The results from our example-although not …