IHP 340 Module 1 Statistics in Practice Discussion example

Reviewed by Hattie Culpepper, MA Statistics for Healthcare Professionals Southern New Hampshire University Full sample paper Free custom sample in 24 to 48h

This complete IHP 340 Module 1 discussion post introduces statistics through something every health worker handles: a patient intake form. A composite urgent care front desk lead goes through seven fields on her clinic's form, classifies each as nominal, ordinal, interval or ratio, and shows how that choice limits which summary statistics make sense, including a common mistake with pain scores. It ends with a question for classmates. The clinic is invented; the measurement theory and textbook are real.

What this page holds

The complete IHP 340 Module 1 post on this page, about 380 words, classifies intake form variables by level of measurement, explains which statistics fit each level, flags the averaged pain score problem and ends by asking classmates to test a report from their own work. Searches like "ihp 340 module 1 assignment", "ihp340 module 1 statistics in practice discussion" and "ihp 340 module 1 example" land here.

The IHP 340 Module 1 example, in full

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Module One Discussion: Statistics in Your Healthcare Setting

Re: The intake form is a dataset

I lead the front desk at a composite urgent care center that sees about 90 patients a day, and until this week I thought of our intake form as paperwork. Reading this module, I realized it is a dataset: every field is a variable, and the clinic's monthly report is built from them.

Taking seven fields in turn: insurance type (commercial, Medicare, Medicaid, self-pay) is nominal, categories with no order. Reason for visit is also nominal. Pain score from 0 to 10 is ordinal, because the numbers are ranked but the spacing between them is not known to be equal. Arrival temperature in Fahrenheit is interval, with equal spacing but no true zero. Age, weight and minutes from check-in to provider are ratio variables, with a true zero, so a 60-minute wait really is twice a 30-minute wait. This classification goes back to Stevens (1946), who argued that the level at which something is measured determines which mathematical operations are permitted.

That is the practical point. For nominal data like insurance type, the right summary is a count or percentage. For ordinal data, the median and percentiles are appropriate. For interval and ratio data, means and standard deviations make sense (Daniel & Cross, 2018). Our monthly report follows this for most fields: it gives the percentage of self-pay visits and the mean wait time.

The one place it breaks the rule is the pain score, which the report averages to one decimal place. An average of 5.3 hides whether most patients rated 5 or whether half rated 1 and half rated 9, two very different clinics. To be fair, some statisticians argue that Stevens's categories are applied too rigidly and that the right summary depends on the question being asked (Velleman & Wilkinson, 1993). Even so, for a report meant to show how much pain our patients arrive with, the distribution tells managers more than a single average. A median with the share of patients scoring 7 or higher would describe our patients more honestly. My question for the group: find one number on a report at your workplace, and tell us what level of measurement it comes from and whether the summary fits.

What this page is doingThe post applies the levels of measurement to a real workplace document and draws a practical consequence, which shows understanding rather than recitation. The highlighted line identifies a specific misuse the writer can now recognize.
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References

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

Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677-680. https://doi.org/10.1126/science.103.2684.677

Velleman, P. F., & Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician, 47(1), 65-72. https://doi.org/10.1080/00031305.1993.10475938

How this IHP 340 Module 1 example is structured

The post uses one familiar document to teach a basic idea. The first paragraph introduces the writer and the form. The second walks through the fields and assigns each a level. The third explains why the level matters by showing which summaries are legitimate for each, and the fourth takes up the pain score, where the rule is most often broken. The post ends with a question inviting classmates to test the same idea on a form from their own workplace.

Get IHP 340 Module 1 written to your instructions

Send the IHP 340 Module 1 discussion prompt, the rubric and a little about your job in healthcare. A post built from your workplace is written within 24 to 48 hours, and 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.

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IHP 340 Module 1 questions, answered

What does IHP 340 Module 1 usually ask?

Introductory statistics courses for healthcare students typically open with a discussion about where statistics appear in the student's work or field, along with basic vocabulary such as variables, data types and levels of measurement. Check your discussion prompt for the specific question and response requirements.

What are the four levels of measurement?

Nominal data are categories with no order, such as insurance type. Ordinal data have an order but unequal or unknown spacing, such as a pain score. Interval data have equal spacing but no true zero, such as temperature in Fahrenheit. Ratio data have equal spacing and a true zero, such as weight or wait time in minutes.

Can I calculate an average of pain scores?

It is common in practice, but strictly a pain score is ordinal, so the distance between 2 and 3 may not equal the distance between 7 and 8. The median and the percentage of patients above a threshold are safer summaries. If a mean is reported, it should be interpreted cautiously.