Nutrient Adequacy Assessment Using Food Consumption Surveys (1986) / Chapter Skim
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8. Modeling of Sources of Variability and Biases
8. Modeling of Sources of Variability and Biases
Pages 66-78
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From page 66... ...
Some of the errors will be due to random sampling variation. The magnitude of these errors can be determined directly from the data, and their impact on prevalence estimates can be determined with statistical theory.
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From page 67... ...
For example, let Al k denote the amount of food eaten by the ith individuai on the jth day of the kth food item category. When a random components model is used to model the errors resulting from a random sample, Aijk = Ok + Iik + Di k' where pk is the population mean amount of the ith food item eaten in one day and Iik is the difference between the average amount of the kth food item eaten by individual i and the population mean.
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From page 68... ...
and includes sampling and reporting error only. FCT denotes error in estimate resulting from food composition tables, and includes sampling and reporting error.
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From page 69... ...
~ _ Random error in food reporting enters into intraindividual variation. Because the adjustment of the intake distribution described in Chapter 4 separates interindividua1 variation from intraindividual variation, this type of intraindividual reporting error will have no effect on the estimation of prevalence.
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From page 70... ...
Let FRijk denote the difference between the mean nutrient content Fk and the actual amount of nutrient in the kth food eaten by the ith individual on the jth day. The variable FRijk is assumed to be randomly distributed with a mean of zero and a variance of REFRY.
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From page 71... ...
Ideally, the recorded nutrient intake equals the true amount of food eaten multiplied by the true nutrient content of the foods and summed over all food items. Hence, the actual amount of nutrient intake for the ith individual on the jth day could be expressed as Nij = £(kk + Iik + Dijk)
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From page 72... ...
Where the estimates of prevalence calculated in the two approaches differ, this should only be slight; however, in such a case the estimate obtained with the ~~onparametric approach is the one of choice. As indicated in Appendix C, prevalence estimates based on the parametric approach are derived from the population means of interindividual variation of nutrient intake, which are obtained from an analysis of variance (ANOvA)
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From page 73... ...
. Hence, the population mean that is being estimated is the true population mean EFk~k plus the value X, which is a realization of the error terms coming from the food tables.
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From page 74... ...
Using methodology similar to that described in Appendix E (using standard error instead of standard deviation) , the subcomu~ittee obtained a rough approximation of the standard error in the mean nutrient consumed in a sample diet as a result of random sampling of foods from the food composition table.
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From page 75... ...
TABLE 8-2. Magnitude of Expected Effect of Random Underand Overreporting in Population Dataa Distribution of Deviations Between Coefficient Recorded and True Intake (% of Subjects of Variation Exhibiting Deviation)
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From page 76... ...
were further adj usted to incorporate or to remove the ef feet of a component of random variation. his was done by using ratios of standard deviations of the derived distribution of usual intakes.
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From page 77... ...
bValues are the apparent prevalences of inadequate intake computed by the probability approach. Mean requirement of protein taken as 43 g/day and mean requirement of vitamin C taken as 46.2 mg/day.
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From page 78... ...
Only if it could be argued that the random error greatly exceeds the real variation, after day-to-day variability had been factored out, would the magnitude of the error be totally unacceptable for the purpose of survey data interpretation. Again the subcommittee emphasizes that this phenomenon is quite different from systematic under- or overreporting across individuals.
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