Below is a completed IHP 340 Module 6 worksheet covering four scenarios, with a paired t test, a chi-square test of independence, a one-way ANOVA and a Mann-Whitney choice, each with test selection reasoning, reported output and interpretation. Searches like "ihp 340 module 6 assignment", "ihp340 module 6 methods identification worksheet" and "ihp 340 module 6 example" land here.
The IHP 340 Module 6 example, in full
Four Questions, Four Tests: Identifying Methods and Interpreting Results in Healthcare Scenarios
[Student Name]
Southern New Hampshire University
IHP 340: Statistics for Healthcare Professionals
Module Six Worksheet
[Instructor Name]
[Date]
The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.
Four Questions, Four Tests: Identifying Methods and Interpreting Results in Healthcare Scenarios
Summary of Choices
The table below shows the test chosen for each scenario and the deciding features.
Table 1
Methods Identified for Each Scenario
| Scenario | Outcome and level | Groups | Test chosen |
|---|---|---|---|
| 1. Nurse phone coaching and blood pressure | Systolic pressure, ratio | Same 12 patients measured twice | Paired t test |
| 2. Text versus letter reminders for shingles vaccine | Vaccinated or not, nominal | Two independent groups | Chi-square test of independence |
| 3. Ratings at three urgent care sites | Mean visit rating, treated as interval | Three independent groups | One-way ANOVA |
| 4. Length of stay after two hip surgery pathways | Days, ratio but strongly skewed | Two independent groups | Mann-Whitney U test |
Scenario 1: Nurse Phone Coaching and Blood Pressure
A composite primary care practice enrolled 12 adults with uncontrolled hypertension in eight weeks of weekly nurse phone coaching on home monitoring and medication use. Systolic pressure was measured at enrollment and at eight weeks. The outcome, systolic pressure in mmHg, is a ratio variable, and each patient is measured twice, so the samples are related. A paired t test is appropriate because it analyzes each patient's change, removing differences between patients (Daniel & Cross, 2018).
Output: mean reduction 7.1 mmHg, standard deviation of the differences 4.2, t = 5.86 with 11 degrees of freedom, p < .001, 95 percent confidence interval for the mean reduction 4.4 to 9.7 mmHg. Interpretation: systolic pressure fell significantly over the eight weeks, and the true average reduction is plausibly between about 4 and 10 mmHg. Because there was no control group, the design cannot separate coaching from other causes, such as regression toward the mean in patients enrolled because their readings were high.
Scenario 2: Reminders for Shingles Vaccine
A composite health system randomly assigned 800 patients aged 50 and older who had not received shingles vaccine to a text message reminder or a mailed letter. After 90 days, 168 of 400 in the text group (42.0 percent) and 131 of 400 in the letter group (32.8 percent) had been vaccinated. Both variables, reminder type and vaccinated or not, are nominal, so a chi-square test of independence is appropriate (McHugh, 2013). All expected cell counts were well above 5, satisfying the test's requirement.
Output: chi-square = 7.31 with 1 degree of freedom, p = .007. Interpretation: vaccination status is associated with reminder type; text reminders produced a vaccination rate about 9 percentage points higher. Because patients were randomized, this association can reasonably be read as an effect of the reminder method, which is not true of most chi-square results from observational data.
Scenario 3: Ratings at Three Urgent Care Sites
A composite urgent care company compared average patient ratings of the visit, from 0 to 10, across three sites using ten randomly selected days of survey averages from each. The outcome is a daily mean rating, treated as interval data, and there are three independent groups, so a one-way analysis of variance is appropriate; running three separate t tests would inflate the chance of a false positive (Kim, 2014).
Output: means 8.05, 7.16 and 7.85; F = 13.40 with 2 and 27 degrees of freedom, p < .001. Interpretation: at least one site differs from the others in mean rating. ANOVA does not identify which one, so a post hoc test is needed; the means suggest the second site is lower than the other two. The manager should investigate that site rather than conclude that all three differ.
Scenario 4: Length of Stay After Two Hip Surgery Pathways
A composite hospital compared length of stay for 25 patients on a new rapid recovery pathway after hip replacement with 25 on the standard pathway. Most stays were one to three days, but a few patients with complications stayed ten days or more, making the distributions strongly right-skewed. Although days is a ratio variable, the skew and small samples make the normality assumption of a t test doubtful. A Mann-Whitney U test, which compares ranks rather than means, is the safer choice, and results should be reported as medians with interquartile ranges. A single patient staying 20 days would pull a mean far more than a median, which is why the choice matters here.
Common Errors the Worksheet Avoids
Three mistakes are common in exercises like this. The first is treating paired data as independent, as would happen if the blood pressure readings in Scenario 1 were analyzed with an independent t test; that ignores the fact that each patient serves as his or her own comparison and usually produces a larger P value. The second is using several t tests instead of ANOVA when comparing three groups, which raises the chance of a false positive. The third is reporting a mean for a skewed outcome, which can make a pathway look worse or better because of a few extreme cases. Naming these errors shows why the chosen tests fit.
Writing the Results for a Manager
Each result can be reported in a sentence that a manager can use without reading the statistics. For Scenario 1: over eight weeks of nurse phone coaching, systolic pressure fell by an average of 7 mmHg, and the reduction was statistically significant, although the lack of a comparison group limits what can be concluded about cause. For Scenario 2: text reminders led to a vaccination rate of 42 percent compared with 33 percent for letters, a significant difference in a randomized comparison, which supports switching to text reminders. For Scenario 3: average patient ratings differ among the three sites, with the second site appearing lowest, and a follow-up comparison should confirm which differences are real before any action is taken. For Scenario 4: length of stay should be compared using medians, and the report should state how many patients on each pathway had long stays, since those cases drive both cost and patient experience.
The General Rule
The four scenarios illustrate a single rule: identify the level of the outcome variable, the number of groups and whether they are independent or related, and check the assumptions before choosing a test. Interpretation then goes beyond the P value to the size of the effect, the confidence interval where available and the limits of the design.
References
Daniel, W. W., & Cross, C. L. (2018). Biostatistics: A foundation for analysis in the health sciences (11th ed.). Wiley.
Kim, H.-Y. (2014). Analysis of variance (ANOVA) comparing means of more than two groups. Restorative Dentistry & Endodontics, 39(1), 74-77. https://doi.org/10.5395/rde.2014.39.1.74
McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23(2), 143-149. https://doi.org/10.11613/BM.2013.018
How this IHP 340 Module 6 example is structured
A worksheet is judged on consistency, so every scenario follows the same four steps: the question, the variables with their levels, the test and the reason for choosing it, and the interpretation. A summary table at the start shows all four choices together, which makes the decision logic visible. The scenarios are ordered to cover different designs: related samples, two categorical variables, three independent groups and a skewed outcome where a nonparametric test is safer. A closing section states the general rule the four cases illustrate.
Get IHP 340 Module 6 written to your instructions
Send the IHP 340 Module 6 worksheet, the rubric and any data or output provided. A completed worksheet with each method identified and interpreted comes back within 24 to 48 hours; the first is free. The paper above is an original model document written by our desk, not a submitted student paper and not an official Southern New Hampshire University document.
IHP 340 Module 6 questions, answered
What does the IHP 340 Module 6 worksheet ask?
The worksheet commonly presents several healthcare research scenarios and asks students to identify the variables and their types, choose the appropriate statistical method, and interpret provided or calculated output. The number and content of scenarios vary by course version, so follow the worksheet exactly.
How do I choose the right statistical test?
Start with the outcome variable's level of measurement, then ask how many groups are compared and whether the groups are independent or related. A quantitative outcome in two independent groups suggests an independent t test; in the same people measured twice, a paired t test; across three or more groups, ANOVA; and two categorical variables suggest a chi-square test.
When should I use a nonparametric test?
When the outcome is ordinal or a quantitative outcome is strongly skewed with small samples, so that the normality assumption of a t test is doubtful. Tests such as the Mann-Whitney U compare ranks rather than means and are less affected by extreme values.