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Data Entry

CANDAT prompts you for information in the form of a list of choices or tables. Lists are referred to as menus, not to be confused with menus used in restaurants. CANDAT will also prompt you for single field entries as in food codes, date of recalls, etc. An entry of a blank code or a 0 for date will terminate that process.

  1. There are two kinds of menu presented in CANDAT.
    1. The usual menu expects you to make a single choice. That is done by moving the selection bar (cursor) to the choice and pressing the enter key. Another way of accomplishing the same thing is to simply enter the first letter of the choice. Pressing the Esc (escape) key has the effect of choosing the last choice in the menu or of exiting menu selection without making a choice.
    2. The other menu gives you a series of Yes/No choices which you toggle by pressing the Y or N keys. You can also make all the choices Y or all N by pressing the Alt key with your selection of Y or N.
  2. There are two kinds of table entry in CANDAT.
    1. The usual table entry allows you to enter information within each cell of a table. Some cells will only allow numbers, others, both numbers and characters. Whichever is allowed will be obvious from the context. If you try to enter a character and it does not appear in the cell, the cell is probably expecting a number. Blank cells are considered filled with characters and you will not be allowed to move out of the cell until you insert a number. A 0 is usually sufficient. If this entry resulted in an extra line being added to the table, it can usually be removed using Alt F4 (see below). Some of these tables grow automatically as you try to enter another row. Pressing the Enter key in the last cell of the last row of a table activates this feature. You cannot simply move to a row that does not exist by using cursor controls. Other rows can be inserted and deleted in these “growing” tables by using Alt F3 to add lines and Alt F4 to delete lines. To exit after having entered your information, press the Esc key. You can move from cell to cell by pressing Tab (or back Tab) and by using the cursor control keys. You can also use Ctrl Home and Ctrl End to go to the first and last cells respectively.
    2. The other type of table entry allows editing of a full line at a time. This is usually a more “free form” type of entry and you sometimes need to be more careful of the layout of the information in this type of entry. To exit after having entered this information you must press Ctrl E or Ctrl Q. Ctrl E will save your entries, Ctrl Q will quit without saving your entries.

University of Guelph

I have used Candat for many years for the analysis of 24 hour recall data, and for the development and subsequent analysis of food frequency questionnaires for two different studies. The range of analytic options provided by Candat, and the fact that it is based on the latest version of the Canadian Nutrient File, are key features.
Assistance with the development of FFQs and translation of responses into the format that can be analyzed with Candat is available on an as needed and timely basis. Response is usually within 24 hours. And, there is no problem asking for help.
I have used Candat for years because of GLI’s knowledge and continuous efforts to make the software ever more powerful.

Susan Evers PhD
Professor
Department of Family Relations and Applied Nutrition
University of Guelph
Guelph, Ontario, N1G 2W1

First Nations Food, Nutrition and Environment Study

We are a multi-organization research team (University of Northern British Columbia, University of Ottawa, Université de Montréal, Assembly of First Nations and Health Canada) that has used CANDAT since 2008. So far, we have used CANDAT to enter over 5000 single and repeated recalls. The software is easy to learn and use. One of our students doing data entry mentioned that CANDAT was easier and more friendly to use than another program he had been using. We are able to incorporate traditional foods and recipes from our study easily into the database. The software developer is always open to suggestions from CANDAT users to facilitate data entry, such as viewing the food description when reviewing data entry and automatic repeat of past key word searches. Above all, the technical support provided by Godin London Incorporated (Gaetan) is always top-notch. He is prompt to answer questions and to solve problems. Recently, we requested a new feature which would enable users to view the food description when reviewing data entry and they were able to do this within a few days. Also, when validation of a dietary file with a more recent version of the Canadian Nutrient File was needed, they were able to write a program that allowed the file to be imported back into CANDAT (from SAS).

This was all done under the no-charge support provided with all CANDAT licences. I would highly recommend CANDAT to any nutrition researcher. Gaetan always strives to adapt CANDAT’s capabilities to suit its users.

Amy Ing, M.Sc.
Data Analyst
First Nations Food, Nutrition and Environment Study
www.fnfnes.ca

Statistics

Food nutrition research is a great source of data. In no time thousands and thousands of values can be generated and fed into a statistics program. Every nutrient is a variable. Other variables identify subjects, date, day of the week, height, weight, etc. All one needs to add to this is something which identifies the type of subject. Are the subjects just normal, overweight, diabetic, athletic, have cancer or some other illness, again, etc…?

There are good statistical packages into which data can be fed. SPSS, SAS come to mind immediately. There are also open source (read free) packages that can be used as well.

For students in nutrition wanting to explore research a good nutrient calculation software combined with a good statistical package is a must.

Teachers can also use these as the basis of a multi-term course in nutrition research.

All kinds of questions about nutrition need to be explained and clarified when one is doing research. There is no room for ambivalence.

Enjoy! it is a truly challenging field.

Questionnaire concepts

Questionnaire concepts:

A subject’s nutrient profile, whether obtained from food recall information or from food history (questionnaire) information, is obtained by a simple arithmetic calculation. Each nutrient is the product of the food quantity consumed and the nutrient’s concentration in that food. The subject’s consumption is the sum of these products over the foods consumed in one day. Simple.

Problems:
In a recall… using the right food code to represent the food consumed and estimating the quantity consumed as accurately as possible. For a recall there are thousands of food codes to choose from, one of those is likely to represent fairly accurately the actual food consumed. The quantity of that food consumed can also be fairly accurately recorded as reported.
An example of a recall record would be “I ate a banana for breakfast”. The corresponding coding would have the food code for bananas and quantity being typically “one medium banana”. Hence, nutrient profile and quantity fairly accurately recorded and yield fairly good nutrient information.

In a questionnaire very few questions (usually less than 200, sometimes as few as 100) are used to represent historical intake. Every question is matched to a food with a nutrient profile representing the consumption for that question. A single food taken from a database used in recalls is not likely to be indicative of  the group of foods represented by any one question in the questionnaire.

An example question could be “Do you consume soup?”. There are many soups with different nutrient profiles. Which one to use for the question as a nutrient profile? The soup code used  should be a “composite” of all the possible soups. Which composite to use? One formed of the relative use all the soups consumed in the population in question. This information can be obtained from a food recall study of the population. This “composite” food could then be taken as the food representing the question’s nutrient profile… a good estimate on a population basis, probably not so good on an individual basis.

Composite food calculations from food recalls:

  • Determine the unique food codes from all the recalls. Typically should be 400-600 food codes
  • Divide those codes into food groups corresponding to questions you would want in the questionnaire
  • Run the nutrient calculations on the recalls looking at food details by food group, sorted in descending order of food quantity in grams
  • Create a recipe from each food group using the main foods in that group and their corresponding quantities as recipe ingredients
  • Run the nutrient analysis on the recipe file and export each recipe and its nutrients per 100G to a food file
  • Use this food file in your nutrient calculations of the relevant questions

Of course, this assumes you have the software to do all these calculations and conversions automatically. Doing the calculations manually or using a spreadsheet would be very onerous indeed.

The question of quantity to record is a bit more difficult. Usually such a question asks “How often do you consume this soup? Per day? Per week? Per month?”. No problem here, just a mathematical calculation.
The problem is in the next part of the estimate, the portion size. If the portion size is indicated precisely as in .5 cup, 1 cup, 2 cups… again, no problem. The composite soup can have a weighted density based on the density of the soups making up the composite. Cup weights can then be precisely calculated.  250 ml x 1.06 G/ml would give us a cup weight of 265 G.

Technique A:
How does one estimate portion sizes when the portion is not so precisely indicated? As in, .5 of a cup or less, .5 cup to 2 cups, 2 cups or more? One logical estimate would be to take the mid-point of the ranges.
For minimal consumption to .5 cups, use .25 cup;
for .5 to 2 cups, use 1.25 cups; for 2 cups or more use 4 cups (maximal consumption assumed to be 6 cups).

Technique B:
Much more intensely computational… not using the composite weighted densities…
An alternative to the above would be to use population based estimates. For each of the range of consumption, .5 cup or less, .5 cup to 2 cups and 2 cups and more, establish the distribution of consumption and calculate the median or average value. The median value would probably be best as it would negate the effects of outlier consumption.

In the population there is no consumption of the composite soup. The distributions have to be calculated for each and every soup making up the composite. One median per soup! For example for the lower range, less than .5 cup, how does one obtain a composite median from the individual soup medians? A weighted average of medians? Based on what weighting factor? The relative weight of the total weight of the soup consumed in the population (used to get the weighted density of the composite) or the relative weight of the total weight consumed in the range less than .5 cup? I would guess the latter to be the better estimate.

How does one establish the cut-off weight for .5 cup. The best value would be obtained by using the density for the soup whose median is being calculated. If the soup has a density of 1.06, one would look at all consumption of that soup of .5 cup or less or of 265G/2 = 132 G or less. The range .5 and above would start at 133G…

Should the basis of the distribution of consumption be each consumption of soup or the total soup consumption for the day? This question may not seem relevant here (each consumption would be the best information for the typical portion size) but what about other foods, such as milk in all its possible consumption portion sizes (see below)?

Assumptions:
Technique B assumes that all consumptions recorded in the population recalls are based on portions that are cups. In soups this is probably reasonable. What about questions that ask questions about foods such as milk. “Do you consume milk?” If yes, how many times per day/week/month and how many glasses? Recalls will record all kinds of consumptions of milk. In cereal, in coffee or tea, in glasses or cups. Each one of these will be converted to Grams. The total of those consumptions, on a daily basis, or on a per consumption basis, may not reflect typical population median gram values for typical glass or cup portion sizes.
Estimates of portion sizes for questionnaire data should be based on recall data collected using those same portion sizes.

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