A retrospective comparative study examined whether longer antibiotic treatment courses were associated with increased antimicrobial resistance in patients with spinal cord injury (Lee et al., 2014). Using urine cultures from a sample of spinal cord–injured veterans, two groups were created: those with evidence of antibiotic resistance and those with no evidence of antibiotic resistance. Each veteran was also divided into two groups based on having had a history of recent (in the past 6 months) antibiotic use for more than 2 weeks or no history of recent antibiotic use.
The data are presented in Table 35-1. The null hypothesis is: “There is no difference between antibiotic users and non-users on the presence of antibiotic resistance.”
|Antibiotic Use||No Recent Use|
The computations for the Pearson χ2 test are as follows:
|Used Antibiotics||No Recent Use||Totals|
χ 2 =n[(A)(D)−(B)(C)] 2 (A+B)(C+D)(A+C)(B+D)
χ 2 =42[(8)(21)−(7)(6)] 2 (8+7)(6+21)(8+6)(7+21)
χ 2 =666,792158,760
χ 2 =4.20
Step 4: Locate the critical χ2 value in the χ2 distribution table (Appendix D) and compare it to the obtained χ2 value.
The obtained χ2 value is compared with the tabled χ2 values in Appendix D. The table includes the critical values of χ2 for specific degrees of freedom at selected levels of significance. If the value of the statistic is equal to or greater than the value identified in the χ2 table, the difference between the two variables is statistically significant. The critical χ2 for df = 1 is 3.84, and our obtained χ2 is 4.20, thereby exceeding the critical value and indicating a significant difference between antibiotic users and non-users on the presence of antibiotic resistance.
Furthermore, we can compute the rates of antibiotic resistance among antibiotic users and non-users by using the numbers in the contingency table from Step 1. The antibiotic resistance rate among the antibiotic users can be calculated as 8 ÷ 14 = 0.571 × 100% = 57.1%. The antibiotic resistance rate among the non-antibiotic users can be calculated as 7 ÷ 28 = 0.25 × 100% = 25%.
The following screenshot is a replica of what your SPSS window will look like. The data for subjects 24 through 42 are viewable by scrolling down in the SPSS screen.
The following tables are generated from SPSS. The first table contains the contingency table, similar to Table 35-1 above. The second table contains the χ2 results.
The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). A Pearson χ2 analysis indicated that antibiotic users had significantly higher rates of antibiotic resistance than those who did not use antibiotics, χ2(1) = 4.20, p = 0.04 (57.1% versus 25%, respectively). This finding suggests that extended antibiotic use may be a risk factor for developing resistance, and further research is needed to investigate resistance as a direct effect of antibiotics.
c. For each variable, the categories are mutually exclusive and exhaustive. It was not possible for a participant to belong to both groups, and the two categories (recent antibiotic user and non-user) included all study participants.
8. The study design in the example was a retrospective comparative design (Gliner et al., 2009).
A retrospective comparative study examining the presence of candiduria (presence of Candida species in the urine) among 97 adults with a spinal cord injury is presented as an additional example. The differences in the use of antibiotics were investigated with the Pearson χ2 test (Goetz, Howard, Cipher, & Revankar, 2010). These data are presented in Table 35-2 as a contingency table.
Name: _______________________________________________________ Class: _____________________
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1. Do the example data in Table 35-2 meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.
Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.
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