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However, research now indicates clearly that the check-all and forced-choice formats produce different results both within and across visual and aural survey modes In mail and web surveys the forced-choice format encourages deeper processing of response options and endorsement of more items than the check-all format and it discourages satisficing response strategies often used in check-all questions (Smyth et al, 2006c; Thomas and Klein, 2006) Moreover, the forced-choice format produces similar results in web and telephone surveys (Smyth et al, 2006a)..

Table 11.6 HRS: Percentage of households needing tracking in 2004, and odds ratio estimates of need for tracking in 2004 from multivariate logistic regression model, by 2002 variables. Characteristic Age 550 50 59 60 69 70 79 80 89 90+ Sex Male Female Marital status Married Partnered Separated Divorced Widowed Never married Other Divorced in past 2 years Yes No Education Less than high school High School Some college College graduate Postgraduate Household assets Lowest quartile 2nd quartile 3rd quartile Highest quartile Type of dwelling unit One family house Two family house, duplex Apartment Mobile home, trailer Other Housing tenure Own Rent Other Has second home Yes No % Needing tracking 20.5 11.6 10.0 9.8 11.6 16.9 9.8 11.4 6.3 15.8 19.0 16.8 13.5 14.6 12.5 32.8 10.5 14.0 9.3 11.3 8.7 8.7 17.3 10.0 7.6 6.4 8.3 9.2 19.4 11.9 16.8 7.7 20.8 14.6 11.1 10.7 Predicted odds ratios (OR) 2.01 1.39 1.12 0.85 1.11 95 % Confidence interval for OR (0.89, 4.54) (1.11, 1.74) (0.94, 1.33) (0.69, 1.04) (0.82, 1.50)

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Bayes theorem indicates by precisely how much I should change my belief about A given some new evidence X Looking at the equation, p(A) is my prior belief about A before the new evidence, X, arrives p(X |A) is the likelihood that evidence X will be obtained given that A is indeed true p(A|X) is my subsequent, or posterior, belief about A after taking into account the new evidence All this will become clearer with a concrete example You will have been wondering why Bradley P Carlin, a professor of public health at the University of Minnesota, is so keen on Bayes theorem This is because public health is one of the many areas where Bayes theorem can be applied Consider the problem of breast cancer12 In particular consider the problem of the importance of routine screening.

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Most of what people eat is made up of carbohydrates, proteins, and fats. Other necessary ingredients in a person s diet are vitamins, minerals, and, in abundant quantities, water. Although everybody needs to pay attention to how much of each of these things they eat, in order to stay healthy people with diabetes need to be especially careful to ensure they get the right amounts of the right nutrients. The Canadian Diabetes Association (CDA) recommends that you divide your diet (based on energy, or calories) as follows: Carbohydrate: 45 60 percent Protein: 15 20 percent Fat: less than 35 percent Carbohydrates, proteins, and fats each provide different amounts of energy (measured in calories). The number of calories contained in 1 gram is

We know (this is the prior belief ) that by the age of 40, 1% of women will have breast cancer (p(A)) We also have a good test (this is the new evidence) for the presence of breast cancer mammography Eighty percent of women with breast cancer will get positive mammographies (p(X |A), correct hits), while only 96% of women without breast cancer will get positive mammographies (p(X | A), false positives) This is the likelihood that the evidence will be obtained given that our belief is true From these gures it seems obvious that routine screening for breast cancer should be a good thing.

So, if all women are screened, then what proportion of those with positive test results will actually have cancer: expressed mathematically as p(A|X) Given that the cancer test seems to be a good one, what would you believe about a woman who had just tested positive for breast cancer Most people believe it is highly likely that she has cancer Application of Bayes theorem shows that this assumption is wrong We can see this very easily if we forget about probabilities Instead, let s consider a group of 10,000 women Before screening, the 10,000 women can be divided into two groups:.

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