HI560 Unit 6 Population Comparison Example

Reviewed by Chester Goodwin, MBA Health Care Data Analytics Purdue University Global Free custom sample in 24 to 48h

This HI560 Unit 6 population comparison asks how much of a composite outpatient center's 1.05-point excess in incomplete MRIs remains once differences in exams and patients are removed. Purdue Global HI 560, offered in the MS in Health Informatics, asks students in the sixth unit to compare populations fairly. The sample uses direct standardization across nine cells of exam group and age, showing the center higher in every cell but by less than the crude gap. Standardized rates differ by 0.70 points, and by 0.38 once slot length is added. The paper argues openly that slot length is a scheduling choice, reports both figures and says which question each answers.

CourseHI560 Health Care Data Analytics
UnitUnit 6
Paper typePopulation comparison
LengthAbout 890 words, 6 pages double-spaced
FormatAPA 7 student paper
SchoolPurdue University Global
ProgramMS in Health Informatics
UpdatedOctober 2026

Free sample paper for HI560 Unit 6

1

Like With Like: A Unit 6 Population Comparison Standardizing Two Sites' Incomplete MRI Rates by Exam Group, Age and Slot Length

[Student Name]

Purdue University Global

HI560: Health Care Data Analytics

Unit 6 Population Comparison

[Instructor Name]

[Date]

Thistlewood Health, its sites, every cell count and every rate are composite; the sources cited are real.

What this part is doingOpening with the crude gap and asking how much survives sets up the whole paper as a test, which is how a comparison of populations should read.
2

The Crude Gap and Why It Is Not Enough

Unit 5 found that 4.69 percent of Fennimore exams and 3.64 percent of hospital exams ended incomplete, a crude difference of 1.05 points. Unit 3 showed three ways the sites' exams differ: brain and neck studies make up a larger share of Fennimore's work, its patients are slightly younger, and close to 64 percent of its bookings use the shortest slot length. The first two describe what arrives at the door. The third is a choice the site makes.

Method

Direct standardization was chosen over a regression model because the imaging director can check the arithmetic cell by cell. Exams were cross-classified into nine cells by exam group (head-first neuro, torso, feet-first extremity) and age band (under 40, 40 to 64, 65 and older). Each site's rate within each cell was then applied to the combined population's mix of cells, giving the rate each site would have if both served the same exams and patients (Rothman et al., 2008). A logistic regression with the same terms gave a similar answer and is noted for completeness.

The Stratified Table

Each line gives the combined number of exams in the cell, then the hospital rate and the Fennimore rate.

1. Head-first neuro, under 40: 1,510 exams; hospital 4.9 percent, Fennimore 5.9.

2. Head-first neuro, 40 to 64: 2,940; 5.4 and 6.4.

3. Head-first neuro, 65 and older: 1,680; 5.8 and 6.9.

4. Torso, under 40: 1,600; 3.1 and 3.7.

5. Torso, 40 to 64: 4,140; 3.4 and 4.0.

6. Torso, 65 and older: 2,620; 3.8 and 4.5.

7. Extremity, under 40: 1,070; 1.4 and 1.6.

8. Extremity, 40 to 64: 1,630; 1.6 and 1.9.

9. Extremity, 65 and older: 650; 1.9 and 2.3.

Fennimore's rate is higher in all nine cells, but the gaps inside cells are smaller than the crude gap, because so much of Fennimore's volume falls in the head-first cells where every site struggles most. Two cells hold fewer than ten incomplete exams at one site, extremity exams aged 65 and older at Fennimore with 5 and extremity under 40 at Fennimore with 7, and their rates should be read loosely.

Every cell favors the hospital, yet each cell's gap is smaller than the crude one, which is the signature of a difference partly made by case mix.

What this part is doingShowing all nine cells before the summary rate lets a reader see where the adjustment comes from instead of taking one number on trust.
3

Standardized Rates

Standardized for exam group and age, the hospital's rate is 3.79 percent and Fennimore's is 4.48 percent, a difference of 0.70 points, 95 percent interval 0.10 to 1.29. Adding slot length as a third factor, with two levels, 30 minutes or less and longer, gives 18 cells and a difference of 0.38 points, interval minus 0.24 to 1.00.

What Should Be Adjusted

The core question is not computational. Exam group and age describe the exam and the patient before either reaches the scanner, so adjusting for them is uncontroversial: no one would want Fennimore judged worse simply because the neurology clinic sends it brain studies.

Slot length is different. Fennimore books short slots as a scheduling policy to increase throughput, and a technologist working against a 30-minute slot may have less time to coach an anxious patient or pause for a break. Adjusting for slot length removes whatever part of Fennimore's excess the short slots themselves cause. That is the right thing to do if the question is whether Fennimore's staff perform worse with comparable exams under comparable time; it is the wrong thing to do if the question is whether Fennimore, as it is run, completes fewer exams.

The paper therefore reports both figures and says which answers which question. The figure of 0.70 points describes how the two sites differ for comparable exams and patients, including the effect of their scheduling choices. The figure of 0.38 points describes how they differ when exams also had comparable time. For the imaging director's question about Fennimore as it operates, 0.70 is the headline. The difference between the two, about a third of a point, is the share plausibly attributable to slot length, and it suggests a lever the site controls.

What this part is doingArguing openly over what to adjust for, and naming the question each figure answers, is what separates a thoughtful comparison from a mechanical one.
4

The Bridge From Crude to Adjusted

1. Crude difference: 1.05 points.

2. Minus 0.35 for exam group and age: 0.70 points.

3. Minus 0.32 for slot length: 0.38 points.

Limits

Three factors could not be measured. Weight is missing on 17 percent of Fennimore exams, so body size, which affects comfort in the bore, was left out. Anxiety itself is captured only by a booking question asked unevenly across sites. And the analytic file excludes exams scheduled with sedation, which happen only at the hospital, so any patients most likely to struggle who were sedated in advance are absent from the hospital's side. Dewey et al. (2007) found that claustrophobic reactions vary with exam type and patient characteristics in ways a single file seldom captures fully, and pooled studies put claustrophobia among the most frequent reasons scans stop (Munn et al., 2015), so the unmeasured share could be meaningful.

5

References

Dewey, M., Schink, T., & Dewey, C. F. (2007). Claustrophobia during magnetic resonance imaging: Cohort study in over 55,000 patients. Journal of Magnetic Resonance Imaging, 26(5), 1322-1327. https://doi.org/10.1002/jmri.21147

Munn, Z., Moola, S., Lisy, K., Riitano, D., & Murphy, F. (2015). Claustrophobia in magnetic resonance imaging: A systematic review and meta-analysis. Radiography, 21(2), e59-e63. https://doi.org/10.1016/j.radi.2014.12.004

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology (3rd ed.). Lippincott Williams & Wilkins.

What the HI560 Unit 6 assignment asks for

Unit 6 usually asks you to adjust a comparison between groups for differences in who they contain. Expect to explain the method, present stratified results, report adjusted rates with intervals and discuss which factors belong in the adjustment. Seven pages with a stratified table and a summary figure is common. Direct standardization is a good choice when readers need to check the arithmetic; regression can appear as a supporting analysis. Show every stratum before the summary so readers see where the adjustment comes from. Flag strata with small counts. The most important part is often the argument about what to adjust for: characteristics of patients are uncontroversial, but factors the organization chooses need careful handling.

How this HI560 Unit 6 example is built

The comparison opens by restating the crude gap and three differences between the sites. A method section explains direct standardization to the combined population and why it was preferred over regression. Nine numbered cells give combined counts and each site's rate, and the highlighted sentence observes that every cell favors the hospital by less than the crude gap. Standardized rates follow, 4.48 and 3.79 percent after exam group and age, with a second result once slot length is added. The core section argues that slot length is a scheduling decision, reports both figures and assigns each to a question, making 0.70 the headline. A three-step bridge from 1.05 to 0.38 and a limits section on weight, booking data and the sedation exclusion close the paper.

HI560 Unit 6 rubric: what earns full points

Population comparisons are generally graded on method, accuracy, presentation of strata and the reasoning about adjustment. Reasoning about adjustment often carries the most points at the graduate level: graders want to see why each factor was included and what including it means. Method earns credit when it is explained clearly and suited to the audience. Accuracy is judged on whether standardized rates follow from the stratified table. Presentation of strata, including small-cell flags, adds credit. Limits should name unmeasured factors specifically. A stratified table, one summary figure and correct citations account for the remaining marks. Comparisons that adjust for every available variable without discussion lose points.

HI560 Unit 6 help: mistakes that cost points

The most common problem here is adjusting for everything available without asking whether each factor belongs. Another is reporting only the adjusted figure, which hides how much case mix explained. Some students skip the stratified table, so readers cannot check the work. Small cells are often left unflagged. Limits sections tend to be generic instead of naming the specific factors missing from the data, such as body size or an anxiety measure. Name each one and say which way it might push the result. If your comparison involves different groups, such as clinics or payers, share your strata and the prompt, and a population comparison can be built from your own data.

Get an HI560 Unit 6 example written to your instructions

What groups does your Unit 6 prompt compare, and which case mix variables does the file hold? Pass along the data, the instructions and the rubric. The first sample carries no charge, comes back in 24-48h, and shows stratified rates before any adjusted figure, with each adjustment choice defended in a sentence. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.

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HI560 Unit 6 questions, answered

Is logistic regression required for a population comparison?

Only where the assignment calls for it. Stratification and standardization answer the same question transparently and suit a management reader. If regression is used, report adjusted differences or predicted rates, not only odds ratios, and check that the model's answer resembles the stratified picture. A model that contradicts the strata deserves a closer look before anyone reports it.

Where can I find a free HI560 Unit 6 sample paper?

The complete Unit 6 comparison appears on this page, with nine stratified cells, two standardized results and the argument over which belongs in the headline.

Is direct or indirect standardization better for two sites?

Direct standardization works well when both sites have enough cases in each stratum, because it gives each site a rate on the same population mix that readers can check cell by cell.

Should a comparison adjust for scheduled slot length?

Only with care. Slot length is a choice the site makes, so adjusting for it removes any effect the short slots cause. The sample reports both figures and explains which question each answers.

Is logistic regression required for a population comparison?

Not always. Standardization is easier for managers to check, and regression can be reported as a supporting analysis that gives a similar answer.