5. Using the numbers in the contingency table, calculate the percentage of antib
ID: 3203828 • Letter: 5
Question
5. Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria.
6. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested positive for candiduria.
7. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use.
RESEARCH DESIGNS APPROPRIATE FOR THE PEARSON x Research designs that may utilize the Pearson x include the randomized experimental, quasi-experimental, and comparative designs (Gliner, Morgan, & Leech, 2009). The vari- ables may be active, attributional, or a combination of both. An active variable refers to an intervention, treatment, or program. An attributional variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. Regardless of the whether the variables are active or attribution all variables submitted to X calculations must be measured at the nominal level. 409 Copyright O 2017, Elsevier Inc. All rights reserved. 410 EXERCISE 35 Calculating Pearson Chi-Square STATISTICAL FORMULA AND ASSUMPTIONS Use of the Pearson x involves the following assumptions (Daniel, 2000): 1. Only one datum entry is made for each subject in the sample. Therefore, if repeated measures from the same subject are being used for analysis, such as pretests and post- tests, X is not an appropriate test. 2. The variables must be categorical (nominal), either inherently or transformed to cat- egorical from quantitative values. 3. For each variable, the categories are mutually exclusive and exhaustive. No cells may have an expected frequency of zero. In the actual data, the observed cell frequency may be zero. However, the Pearson x2 test is sensitive to small sample sizes, and other tests such as the Fisher's exact test, are more appropriate when testing very small samples (Daniel, 2000; Yates, 1934) The test is distribution-free, or nonparametric, which means that no assumption has been made for a normal distribution of values in the population from which the sample was taken (Daniel, 2000)Explanation / Answer
Candiduria is a urinary tract infection.
One of the major causes of candiduria is antibiotic.
When the antibiotic does not build anti microbial resistance, one is likely to get candiduria.
Therefore, there is evidence of antibiotic resistance if users test positive for candiduria
And there is no evidence of antibiotic resistance if users test negative for candiduria
Replicating the contingency table below.
Used antibiotics
No recent use
Total
Resistant
8
7
15
Not resistant
6
21
27
Total
14
28
42
5. Percentage of antibiotic users who tested positive for candiduria
= (No of antibiotic users who showed evidence of resistance/total number of antibiotic users) * 100
= (8/14) * 100 = 57.14%
6. Percentage of non-antibiotic users who tested positive for candiduria
= (No of non recent users who showed evidence of resistance/total number of non recent users) * 100
= (7/28) * 100 = 25%
7.Percentage of veterans with candiduria who had a history of antibiotic use
= (No of veterans who showed evidence of resistance and used antibiotics /total number of veterans who showed evidence of resistance) * 100
= (8/15) * 100 = 53.33%
Used antibiotics
No recent use
Total
Resistant
8
7
15
Not resistant
6
21
27
Total
14
28
42
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