| Course | HS340 Epidemiology |
|---|---|
| Unit | Unit 3 |
| Paper type | Study design appraisal |
| Length | About 1,150 words, 7 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Health Science |
| Updated | October 2026 |
Free sample paper for HS340 Unit 3
One Question at the Lodge Door: A Unit 3 Study Design Appraisal of a Ski Helmet Survey That Reported a 40 Percent Lower Risk
[Student Name]
Purdue University Global
HS340: Epidemiology
Unit 3 Study Design Appraisal
[Instructor Name]
[Date]
The ski resort survey appraised here is a composite written for this sample; the meta-analysis and the methods papers cited are real.
The Study as Its Authors Described It
The composite authors interviewed adult skiers and snowboarders in the base lodge of one resort over a single February weekend. Each person was asked two questions. Did you wear a helmet on the slopes today? Have you had a head injury while skiing or snowboarding this season, defined as a blow to the head followed by headache, dizziness or a visit to a clinic? Of [1,100] respondents, [770] said they wore a helmet and [330] said they did not. A head injury this season was reported by [31] helmet wearers and [22] non-wearers.
The authors divided the two proportions, 4.0 percent against 6.7 percent, and reported a relative risk of 0.60, which they described as a 40 percent reduction in head injury risk among helmet users. They concluded that helmets prevent head injuries and that resorts should require them.
What Design Is This?
The paper calls itself a risk study, but a design should be classified by how its data were gathered, not by its title. Exposure and outcome were both measured at one moment, in one interview, among people who happened to be in the lodge. Nobody was followed forward, and nobody was selected because they had or had not been injured. That makes it a cross-sectional survey (Levin, 2006).
Cross-sectional data yield prevalence, not incidence. The correct measure here is a prevalence ratio: the proportion of helmet wearers reporting a head injury this season, divided by the proportion of non-wearers reporting one. Recomputed, it is 31 of 770, or 4.0 percent, over 22 of 330, or 6.7 percent, a ratio of 0.60. The prevalence difference is 2.6 percentage points. The arithmetic is the same as the authors' figure, but the name matters, because a prevalence ratio describes who reported injuries, not who went on to develop them.
The survey asked about helmets today and injuries this season, so in many respondents the injury came first, and an injury is one of the commonest reasons people start wearing a helmet.
The Timing Problem
The exposure question asked about one day. The outcome question asked about a whole season that had begun about ten weeks earlier. A skier who hit their head in December and bought a helmet afterward would appear in the February survey as a helmet wearer with a head injury. That makes helmets look less protective. A skier who has never been hurt and has never thought about a helmet appears as a non-wearer with no injury. The two kinds of misclassification pull in different directions, and the survey cannot say which is larger. What it can say is that helmet use today is not the exposure at the time of any injury it recorded.
Who Was Not in the Lodge
People with serious head injuries this season were unlikely to be skiing that weekend. Anyone hospitalized, told to rest after a concussion or frightened off the slopes is missing from the survey. If those people were mostly non-wearers, the survey understates the protective association; if helmets encourage faster skiing, as some have argued, the missing group could tilt the other way. Either way, the people who answered were the people still able to ski, which is a selection that the authors do not discuss.
How the Outcome Was Measured
Head injury was self-reported with a definition that mixes a mild bump followed by a headache with injuries that needed a clinic. Recall over ten weeks is imperfect, and minor knocks are easily forgotten. If helmet wearers were less likely to notice or remember minor bumps, because the helmet softened them, the survey would undercount injuries among wearers in a way that exaggerates protection. Again the direction is uncertain, which is the point: the measure is loose enough that a difference of 2.6 points could arise from how people remember as much as from what happened.
Confounding
Helmet wearers and non-wearers may differ in age, skill, the terrain they choose and how many days they ski. Someone who skis forty days a season has more opportunities for injury than someone who skis four, and frequent skiers may also be more likely to own a helmet. The survey did not ask about days skied or terrain, so it cannot adjust for exposure time, which in injury research is often the most important confounder of all (Grimes & Schulz, 2002).
What Better Evidence Shows
The question the authors care about has been studied with designs better suited to it. A meta-analysis of studies of skiers and snowboarders, most of them case-control studies comparing people with head injuries to injured people without head injuries, found that helmet wearers had a markedly lower risk of head injury, without a clear increase in neck injury (Russell et al., 2010). Those studies measure helmet use at the time of the injury, which is exactly what the lodge survey could not do.
So the authors' conclusion may well be right in direction. The problem is that their survey cannot show it, and its figure of 40 percent should not be quoted as an estimate of protection.
What the Survey Can Support
The survey can support a description: among adults in one resort's lodge on one weekend, those wearing a helmet that day were somewhat less likely to report a head injury earlier in the season. It can also support a measure of helmet use at that resort, about 70 percent of adults, which is useful to the resort and to anyone tracking use over time.
It cannot support the claim that helmets cut risk by 40 percent, and it cannot by itself support a mandate, though better evidence might.
A Design That Would Fit the Question
If the resort wanted its own estimate, a case-control design at its first aid station would fit. Cases would be people treated for head injury; controls, people treated for other injuries that same day, an approach that matches cases and controls on being active on the slopes. Helmet use at the time of the injury would be recorded by staff, not recalled weeks later, along with age, ability level and terrain. That is the design used in many of the studies the meta-analysis pooled.
Conclusion
The lodge survey is a cross-sectional study reported as if it were a cohort. Its timing puts some injuries before the exposure, its sample excludes the most seriously hurt, its outcome is loosely measured and it ignores time on the slopes. Its prevalence ratio of 0.60 describes the lodge that weekend and nothing more.
References
Grimes, D. A., & Schulz, K. F. (2002). Bias and causal associations in observational research. The Lancet, 359(9302), 248-252. https://doi.org/10.1016/S0140-6736(02)07451-2
Levin, K. A. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24-25. https://doi.org/10.1038/sj.ebd.6400375
Russell, K., Christie, J., & Hagel, B. E. (2010). The effect of helmets on the risk of head and neck injuries among skiers and snowboarders: A meta-analysis. Canadian Medical Association Journal, 182(4), 333-340. https://doi.org/10.1503/cmaj.091080
The Purdue Global HS340 Unit 3 instructions
Unit 3 usually gives you a study, often a short summary with a reported measure, and asks you to identify its design, recalculate or rename its measure and judge whether the conclusion follows. Expect to write a few pages. The marks follow a design classification justified from the methods, the correct measure for that design, and specific threats to validity tied to the study's details rather than a generic list. Many sections reward comparing the study with stronger evidence on the same question and proposing a design that would fit. Recompute the headline figure from the counts provided. Cite a methods source for the design and, where possible, a review on the topic itself.
How the HS 340 Unit 3 example is put together
The appraisal opens by restating the study as its composite authors described it, with the counts and the claim. A section on design classifies it as cross-sectional from its data collection and recomputes the measure as a prevalence ratio with a prevalence difference. The highlighted sentence identifies the timing problem: helmets today, injuries this season. Separate sections then cover the missing seriously injured, the loose self-reported outcome and confounding by time on the slopes, each saying which way the bias could push. A section summarizes a real meta-analysis of skiers and snowboarders. The appraisal states what the survey can and cannot support, proposes a case-control design at the first aid station and ends with a one-paragraph verdict.
Reading the HS340 Unit 3 grading rubric
Design appraisals are usually scored on correct classification, correct measure, depth of the validity analysis, use of outside evidence and the quality of the conclusion. Classification and the validity analysis typically carry the most points, since naming the design wrongly leads to the wrong measure and the wrong criticisms. The measure earns credit when it is renamed and recalculated from the counts. Validity points reward specific mechanisms, such as reverse timing or missing participants, with their likely direction. Outside evidence is rewarded when a better study on the same question is cited fairly. The conclusion should state what the study supports in one sentence. Suggesting a fitting design often lifts an appraisal to the top level.
Common HS340 Unit 3 mistakes, and how to avoid them
Students often accept the authors' label for the design and then critique the wrong things. Another frequent error is calling a prevalence ratio a relative risk without comment. Some appraisals recite a catalog of biases and never tie one to the lodge, the skiers or the season. Others conclude that the study is worthless, which ignores what a cross-sectional survey can legitimately describe. Forgetting to recompute the figure leaves a grader unsure whether you checked the arithmetic. If your assigned study is different, perhaps a survey of runners or a workplace health screening, send the summary and we can help you classify it, rename its measure and find the threats that matter most.
Get an HS340 Unit 3 example written to your instructions
For HS340 Unit 3, send the study description or article the prompt names, the appraisal questions in their order, and the grading rubric. Within 24-48 hours the free first custom sample classifies that study from its methods, states which measure it is entitled to report, and points to the sentence where the design's limit shows. 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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HS340 Unit 3 questions, answered
How can a design be classified when the authors call it something else?
Read the methods for timing. If exposure and outcome were measured at one point, the study is cross-sectional; if participants were selected by outcome, it is case-control; if people free of the outcome were followed forward, it is a cohort. Authors sometimes use cohort loosely. An appraisal that explains its classification from the data collection will be credited even when it disagrees with the title.
Is a prevalence ratio less useful than a relative risk?
It answers a different question. A prevalence ratio compares how common a condition is in two groups at one time, which is useful for planning and for raising hypotheses. It cannot show that exposure came first, and for short illnesses it mixes new and older cases. The beach example reports 1.81 honestly as a prevalence ratio and treats it as a lead worth testing properly.
Does the appraisal need to calculate anything?
Usually a little. Recomputing the headline figure from the supplied counts shows that the numbers are sound and that only the interpretation is in question, which is a strong position for a critique. The example recalculates both prevalences and the ratio in three lines, then spends the rest of its length on design, where most of the unit's marks sit.
Where can I find a free HS340 Unit 3 sample paper?
This page holds the complete Unit 3 study design appraisal, testing a ski lodge helmet survey's claim of 40 percent lower risk.
Why does timing matter so much in a cross-sectional survey?
Because exposure and outcome are measured together, the survey cannot show which came first. Someone injured earlier may have started wearing a helmet afterward, which distorts the comparison.