Dietary Assessment Primer

Learn More about Statistical Modeling

In science, we often wish to measure phenomena that cannot be observed directly. One approach to overcoming this problem is statistical modeling, which estimates how variables are related to each other.

Directly capturing [glossary term:] usual dietary intake (Learn More about Usual Dietary Intakes) by individuals with an acceptable level of [glossary term:] accuracy and [glossary term:] precision is virtually impossible due to [glossary term:] measurement error (see Key Concepts about Measurement Error) inherent in self-reported intake data. This measurement error includes [glossary term:] day-to-day variation, which reflects that intakes change from day to day. To advance nutrition research, investigators have focused on how to analyze dietary data in a way that accounts for and minimizes the impact of error. Statistical modeling has been shown to be extremely helpful in this context.

Assessing an individual's usual intake remains an unsolved problem in the clinical setting. The challenges inherent in estimating usual intake at the individual level, together with recommendations, are outlined elsewhere.