IHP 340 Module 7 Chi-Square Discussion example

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This complete IHP 340 Module 7 discussion post applies the chi-square test of independence to a question a community health center actually faces: are patients who receive a ride voucher more likely to complete a specialist referral? A composite referral coordinator sets up the contingency table, checks expected counts, reports the test and then explains why a significant association in this case falls short of proving the vouchers work. The health center is invented; the statistical and research sources are real.

What this page holds

Here is a finished IHP 340 Module 7 discussion post, about 350 words, with a 2 by 2 contingency table, expected counts, a chi-square result of 7.18 (p = .007), an interpretation, a confounding problem and a question for classmates. Searches like "ihp 340 module 7 assignment", "ihp340 module 7 chi-square discussion" and "ihp 340 module 7 example" land here.

The IHP 340 Module 7 example, in full

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Module Seven Discussion: Chi-Square Tests in Healthcare

Re: Do ride vouchers get patients to the specialist?

I coordinate referrals at a composite community health center, and missed specialist appointments are one of our biggest problems. Transportation is a well-documented barrier to health care access, particularly for low-income patients (Syed et al., 2013). Last year we began offering rideshare vouchers to patients who said they had no reliable way to get to an appointment. Our question was whether patients who received a voucher were more likely to complete the referral.

Both variables are categorical: voucher (yes or no) and referral completed within 60 days (yes or no), which makes this a chi-square test of independence (McHugh, 2013). Of 120 patients with a voucher, 84 completed the referral (70 percent). Of 150 without a voucher, 81 completed it (54 percent). The null hypothesis is that completion is independent of voucher status. If that were true, we would expect about 73 voucher patients and 92 non-voucher patients to complete their referrals. Every expected count was above 46, well over the minimum of 5 the test requires.

The result was chi-square = 7.18 with 1 degree of freedom and p = .007, so we reject the null hypothesis: referral completion is associated with receiving a voucher, and completion was 16 percentage points higher in the voucher group. For a center that refers about 1,200 patients a year to specialists, a difference of that size would mean many more completed cardiology, gastroenterology and eye appointments, if it holds.

But this association cannot tell us that the vouchers caused the difference. Vouchers went to patients who asked about transportation, and patients who ask may be more organized or more motivated to attend than those who do not, even though they also reported more transportation barriers. Those two influences push in opposite directions, and our data cannot separate them (Daniel & Cross, 2018). The chi-square test only answers whether the variables are related, not why. My question for the group: if you had to design next year's voucher program so that it could show whether vouchers work, how would you decide who gets one?

What this page is doingThe post sets up the table, checks the expected-count assumption, reports the test correctly and then limits the conclusion because of how vouchers were assigned. The highlighted line states the key interpretive point.
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References

Daniel, W. W., & Cross, C. L. (2018). Biostatistics: A foundation for analysis in the health sciences (11th ed.). Wiley.

McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23(2), 143-149. https://doi.org/10.11613/BM.2013.018

Syed, S. T., Gerber, B. S., & Sharp, L. K. (2013). Traveling towards disease: Transportation barriers to health care access. Journal of Community Health, 38(5), 976-993. https://doi.org/10.1007/s10900-013-9681-1

How this IHP 340 Module 7 example is structured

The post follows the logic of the test in order. It opens with the practical question and the two categorical variables. The observed counts are given, followed by the hypotheses and the expected counts that the test compares them with. The result and its meaning come next, and the last paragraph explains the design problem that limits the conclusion and poses a question to classmates about how to fix it.

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IHP 340 Module 7 questions, answered

What does IHP 340 Module 7 usually cover?

Later modules in healthcare statistics commonly introduce tests for categorical data, especially the chi-square test of independence and goodness of fit, and ask students to apply one to a healthcare question and interpret the result. Check your discussion prompt for whether you must supply your own data.

What are expected counts in a chi-square test?

They are the counts you would expect in each cell of the table if the two variables were unrelated, calculated as the row total times the column total divided by the grand total. The test statistic measures how far the observed counts differ from these expected counts.

Does a significant chi-square result show cause and effect?

No. It shows that two categorical variables are associated. Whether one causes the other depends on the study design; in observational data, a third factor may explain the association. Randomized assignment is the strongest way to support a causal conclusion.