Data Processing & Data Analysis

Data Processing Requirements

Data Analysis Considerations

General Considerations

Guidance for Specific Research Objectives

  • If your research objective is to estimate solely the [glossary term:] mean intakes of a group, defining the mean for a proportion or [glossary term:] ratio appropriate to the particular objective requires further consideration. For example, data from multiple repeated administrations of food records can be used to estimate average per person or population ratios and proportions (Learn More about Ratios and Proportions). In addition, for studies that have collected multiple records, it is unnecessary to adjust for [glossary term:] within-person random error.
  • If your research objective is to estimate [glossary term:] usual dietary intake distributions for a group (e.g., to examine percentiles or to estimate the proportion above or below some threshold), clarification of whether the focus is to assess the distribution of habitual intake over the long run (Learn More about Usual Dietary Intake) or of single-day intake is required.
  • If your focus is the distribution of habitual intake for a group over the long run, statistical modeling must be conducted to account for [glossary term:] day-to-day variation in intakes over the various administrations of multiple or [glossary term:] n-day records. (Note that the repeat administration(s) can be done on a subsample rather than the entire sample.)
  • Several methods have been developed to appropriately analyze 24HR data to estimate usual distributions of intake, including the [glossary term:] National Cancer Institute (NCI) method [16-18]. This method also may be appropriate for multiple n-day food records, in which each n-day record is considered a single administration, although more research is needed to apply and evaluate the NCI method to food record data.

  • If your focus is the distribution of intake on a given day for a group, then distributions of dietary intake, including the mean, can be estimated without considering day-to-day variation.
  • If your research objective is to analyze the association between diet as an [glossary term:] independent variable and some other variable (e.g., diet at baseline and onset of cancer), and the food record is your main instrument, statistical modeling of data from multiple administrations of the food records will account for day-to-day variation, allowing greater [glossary term:] precision in the intake estimates and thus of their association with health [glossary term:] outcomes, and increasing statistical [glossary term:] power.
  • If your research objective is to analyze the association of an independent variable (e.g., socioeconomic status) and diet as the [glossary term:] dependent variable, statistical modeling to remove within-person random error is not necessary. However, averaging food records across administrations may increase the precision of the diet estimate and thus the statistical power to detect associations. In addition, variables known to affect quality of report (e.g., body mass index) should be included as [glossary term:] covariates in analyses.
  • If your research objective is to analyze a change in diet as a result of an intervention, the reactivity bias inherent in data collected using food records makes this method less desirable. Food records are desirable, however, to motivate and monitor participants trying to adhere to a dietary intervention.