# Exercise 35: Calculating Pearson Chi-Square

Exercise 35: Calculating Pearson Chi-Square

The following questions refer to the section called “Data for Additional Computational Practice” in Exercise 35 of Grove & Cipher, 2017.

1.     Do the example data in Table 35-2 meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.

A.   Yes, the data meet the 2 assumptions.

B.    No, the data do not meet the 2 assumptions.

C.    Yes, the data meet the 3 assumptions.

D.   No, the data do not meet the 3 assumptions.

2.     Compute the χ2 test. What is the χ2 value?

A.     11.93

B.     12.93

C.     13.93

D.     14.93

3.     Is the χ2 significant at α = 0.05? Specify how you arrived at your answer.

A.   Yes, by comparing it with the critical value.

B.    No, by comparing it with the critical value.

4.     If using SPSS, what is the exact likelihood of obtaining the χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?

A.     0.1%

B.     0.5%

C.     1%

D.     5%

5.     Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria.

A.    15.5%

B.    25.9%.

C.    47.6%

D.    0%

6.     Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested positive for candiduria.

A.   15.5%

B.    25.9%.

C.    47.6%

D.   0%

7.     Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use.

A.   0%

B.    10%.

C.    15%

D.   100%

8.     Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had no history of antibiotic use.

A.   0%

B.    10%.

C.    15%

D.   100%

9.     Write your interpretation of the results as you would in an APA-formatted journal.

10.  Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

A.    The sample size was adequate to detect differences between the two groups because a significant difference was found, p = 0.001.

B.    The sample size was not adequate to detect differences between the two groups because no significant difference was found, p >0.05. [checkout]