Where x w is the weighted average with x i as the daily intakes, and i is 1 – 5 (codes for days of the week) or 0,6 codes for weekend days corresponding to Sunday and Saturday respectively. The week-weighted average could be calculated as follows: Of course, for recalls without weekends this would just be a day of the week average and vice-versa. Rather than simply computing an average daily intake one may get more accurate results by weighting the days of the week and the weekend days differently.Ī weight of 5 for each week day and a weight of 2 for weekend days should allow us to calculate week-weighted averages for any number of recalls. The assumption here is that subjects will eat differently on different days with the greatest variability been between week days and weekend days. Of course, 7 day recalls will give more accurate estimates of this daily intake and the accuracy will increase if all the days of a week are used.
When expressing and comparing results of multi-day (3 day commonly) food recalls one tends to use the average daily intake as a measure of a subject’s intake. Report these results, write the other sections of the paper and you are done.īy on Main Appendices, Documentation, Food Data and calculations Compare your subjects to your control groups (subject variables) using statistical procedures and save the results.In either case you must make sure to merge on the Subject codes. This may be from a file produced externally from Candat or from the Candat Description file.
Merge the data variables that identify your subject variables.Apply exploratory statistics to all your variables and make sure the data seems reasonable.At this point your data is ready for statistical processing.
#DOS2USB 2015 CODE#
In most cases you will want to aggregate the data using Subject code and Date code as independent variables, an average weight code for the weighting variable (average will maintain the weight code for the day) and sum (which adds up all the contributions for that day) for the nutrient variables.
This makes sense because food databases do not spend much time analyzing nutrients that are not likely to exist in the food but they do not report them as having a value of zero (usually). In Candat summaries (means) missing values are considered a zero. Identify missing data as -1 (the Candat value at the food level.Read the Candat generated file into a statistical package.If you are not interested in data by meals or by food group or at the food detail level, leave those out of the Candat calculation. Generate the data in Candat you need in a compact way.Systematically then here are the procedures to follow for managing your data: These week-weighted averages are calculated in Candat but should be re-calculated in the statistical package so that you can make use of the proper variance calculations for weighted data. Where you have days of the week and weekend days you will probably want to make use week-weighted average daily intakes, where weekend days carry less weight than week days. Your study will probably want to make use of daily average intake data, as representative as possible of your subject’s usual intake.
#DOS2USB 2015 SOFTWARE#
The printed (text or PDF) file (hopefully you did not print to paper) can have all of the above information as well as basic statistics (for a quick perusal, not meant to be used instead of a statistical package).Ĭandat also produces computer readable files that can be directly read into statistical packages or spreadsheet software (such as Excel or Open or Libre Office Calc or ….).