| Course | PHE 505 Research Methods in Public Health |
|---|---|
| Module | Module 6 |
| Paper type | graduate research proposal milestone defining the question, hypotheses and variables |
| Length | About 1,010 words, 6 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MPH |
| Updated | October 2026 |
Free sample paper for PHE 505 Module 6
Sharpening the Question: Research Question, Hypotheses and Variables for a Study of Helmets and Scooter Head Injuries
[Student Name]
Southern New Hampshire University
PHE 505: Research Methods in Public Health
Final Project Milestone Three
[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.
Sharpening the Question: Research Question, Hypotheses and Variables for a Study of Helmets and Scooter Head Injuries
The literature review showed that head injuries are common among injured scooter riders and helmets rare, but that no study has estimated the protective effect of helmets after accounting for other factors. This milestone turns that gap into a precise research question, testable hypotheses and defined variables. Precision at this stage matters because every later choice, from sample size to analysis, follows from how the question is worded.
The Refined Research Question
The question is now stated in population, exposure, comparison and outcome terms. Population: adults aged 18 and older treated at either of the city's two emergency departments for an injury sustained while riding a shared electric scooter during the first three years of service. Exposure: not wearing a helmet at the time of the crash. Comparison: wearing a helmet. Outcome: any head injury diagnosed during the visit.
Written as one sentence: among adults treated in the city's emergency departments for injuries sustained while riding shared electric scooters, do riders who were not wearing a helmet have higher odds of head injury than riders who were wearing one, after adjusting for age, sex, alcohol involvement, time of day and collision with a motor vehicle?
The question meets the criteria Hulley et al. (2013) describe for a good question. It can be answered with existing records, it matters to a pending city decision and it adds a mid-sized city and adjusted estimates to a small literature.
Hypotheses
The primary hypothesis is directional, because the bicycle literature gives a clear expectation: helmeted cyclists have about half the odds of head injury of unhelmeted ones (Olivier & Creighton, 2017).
H1: Among injured adult riders, those not wearing a helmet will have higher adjusted odds of any head injury than those wearing a helmet. H0: There is no difference in adjusted odds of head injury by helmet use.
Two secondary hypotheses address severity and modification. H2: Helmet nonuse will be associated with higher adjusted odds of traumatic brain injury, a more severe subset of head injury. H0: No difference. H3: The association between helmet nonuse and head injury will be stronger among riders injured between 9 p.m. and 5 a.m. than among those injured during the day. H0: The association does not differ by time of day. H3 is exploratory and will be reported as such.
Variables and Definitions
Creswell and Creswell (2018) stress that variables must be defined so that another researcher could measure them the same way. Table 1 lists each variable.
Table 1. Variable definitions
| Variable | Role | Level | Definition and source |
|---|---|---|---|
| Head injury | Outcome | Binary | Any diagnosis code for injury to the head, scalp, skull or brain |
| Traumatic brain injury | Secondary outcome | Binary | Concussion, intracranial bleeding or skull fracture on codes or imaging |
| Helmet use | Exposure | Binary | Helmet documented in triage or physician notes; missing kept as a separate category |
| Age | Covariate | Continuous | Years at visit, from registration |
| Sex | Covariate | Binary | As recorded at registration |
| Alcohol involvement | Covariate | Binary | Blood alcohol above 0.05% or clinician note of intoxication |
| Time of day | Covariate and modifier | Binary | Arrival between 9 p.m. and 5 a.m. versus other hours |
| Motor vehicle collision | Covariate | Binary | Note or code indicating a collision with a vehicle |
Note. Definitions will be tested on 50 pilot records before the full review.
Assumed Causal Pathways
The question assumes that a helmet, if worn, reduces the force reaching the skull and brain in a fall, lowering the chance of head injury. Several factors could distort that relationship because they are linked to both helmet use and head injury. Alcohol may make riders less likely to wear a helmet and more likely to fall headfirst. Riding at night may be linked to both. Older riders may be more likely to wear helmets and more vulnerable to head injury. Collision with a vehicle raises injury severity and may be more common among commuters, who may wear helmets more often. These are the confounders the analysis will adjust for. Speed and distance traveled are likely confounders too, but records do not capture them, so they will be named as limitations.
Handling Missing Helmet Data
Helmet status will be missing for some patients. Treating missing as not wearing a helmet would bias results if clinicians record helmets mainly when present. The study will keep missing as its own category in descriptive results, analyze complete records in the main model and test whether conclusions change if missing values are assigned to either group.
How Each Hypothesis Will Be Tested
Each hypothesis maps to a specific analysis, which the methods milestone will develop. H1 will be tested with logistic regression of any head injury on helmet use, adjusted for the covariates, with results given as adjusted odds ratios and their 95% confidence intervals. H2 will use the same approach with traumatic brain injury as the outcome. H3 will add an interaction term between helmet use and nighttime arrival; because the study is unlikely to have much power for an interaction, its result will be described cautiously. All tests will be two-tailed, with alpha set at 0.05, and confidence intervals will be emphasized over p-values.
Other Questions Weighed
Asking whether helmets reduce the chance of crashing would require data on all riders, injured or not, which do not exist locally. Asking whether a helmet rule would change behavior would need data from after a rule took effect. Both are worth studying later; neither can be answered with the records available now.
Scope and Limits of the Question
The question concerns injured riders, not all riders, so it can estimate how helmets relate to the type of injury among those who crash, not how they relate to the chance of crashing. Riders who were not hurt badly enough to seek care are excluded, which the STROBE statement would require to be reported as a source of selection (von Elm et al., 2007). The question also addresses shared scooters only, so findings may not apply to privately owned ones. Within those limits, it is the question local decision makers most need answered.
The methods milestone will build on these definitions.
References
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.
Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D. G., & Newman, T. B. (2013). Designing clinical research (4th ed.). Lippincott Williams & Wilkins.
Olivier, J., & Creighton, P. (2017). Bicycle injuries and helmet use: A systematic review and meta-analysis. International Journal of Epidemiology, 46(1), 278-292. https://doi.org/10.1093/ije/dyw153
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. The Lancet, 370(9596), 1453-1457. https://doi.org/10.1016/S0140-6736(07)61602-X
What the PHE 505 Module 6 instructions ask for
PHE 505 Milestone Three asks you to state your research question, hypotheses and variables, usually in three to four pages of APA 7. Refine your question into a single sentence that names the population, exposure or intervention, comparison and outcome. State a primary hypothesis and any secondary hypotheses, each with its null form, and explain why you expect the direction you propose. Define every variable with its role, level of measurement and data source, ideally in a table. Explain the assumed causal pathway and the confounders, plan for missing data and state what the question can and cannot answer. Show how each hypothesis will be tested. Keep wording consistent.
How this PHE 505 Module 6 milestone three example is built
This PHE 505 milestone refines a question about helmets and head injury among adults treated after shared scooter crashes in a Midwestern university city. Hulley and colleagues' criteria check the question, and Olivier and Creighton's bicycle meta-analysis justifies a directional primary hypothesis. Secondary hypotheses address brain injury and whether the association is stronger at night. A table defines eight variables with levels and record sources, following Creswell and Creswell. Alcohol, night riding, age and vehicle collision are reasoned through as confounders, missing helmet data get their own plan and von Elm and colleagues' STROBE statement frames the scope. Each hypothesis is linked to a test, and set-aside questions are explained. Scope is stated honestly.
Where the PHE 505 Module 6 rubric puts the points
The PHE 505 Milestone Three rubric generally rewards a precise, answerable research question with defined population, exposure, comparison and outcome, clearly stated hypotheses with null forms and a rationale, operational definitions for every variable with levels of measurement, attention to confounding and missing data and an honest statement of scope. The strongest milestones keep the question, hypotheses and variables perfectly aligned. Graders mark down questions that are still topics, hypotheses that cannot be tested with the stated data and variables left undefined. Tables and accurate APA 7 citations complete stronger submissions. Linking each hypothesis to a planned test shows alignment. Exploratory hypotheses should be labeled. Missing data plans are a plus.
PHE 505 Module 6 help: the mistakes that cost points
Research question milestones for PHE 505 often lose points when the question names no comparison, hypotheses are missing their null forms or variables are listed without definitions. If your study is different, send the prompt, your literature review and your working question, and the milestone will sharpen the wording, write hypotheses with nulls and a rationale and define each variable in a table. Knowing your data source helps define variables realistically. Our PHE 505 question milestones also plan for confounders and missing data, so the methods milestone in Module Eight can be built directly on them. Each hypothesis is paired with a planned test. Exploratory aims are labeled.
Get PHE 505 Module 6 written to your instructions
Send the PHE 505 Milestone Three prompt, your literature review and working question. The milestone will sharpen the question, write hypotheses with null forms and a rationale, define variables in a table, reason through confounders and plan for missing data. It takes about two days, 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.
More PHE 505 papers and related MPH samples
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- PHE 505 Module 4 Milestone Two: The Literature Review
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- PHE 330 Module 1 Discussion: Why the Warning Flags Are Ignored
- PHE 489 Module 8 Discussion: Looking Back on the Whole Degree
- PHE 101 Module 8 Discussion: Closing Reflection on the Values of Public Health
PHE 505 Module 6 questions, answered
Where can I find a free PHE 505 Module 6 Milestone Three sample?
This page carries a full PHE 505 Module 6 Milestone Three with a refined question, hypotheses and defined variables for an e-scooter helmet study.
What is a null hypothesis?
A statement that there is no association or difference, which the study tests against the alternative hypothesis.
What does it mean to operationally define a variable?
To state exactly how it will be measured, including its level of measurement and data source, so others could repeat it.
What is a confounder?
A factor linked to both the exposure and the outcome that can distort the apparent relationship between them.
Should exploratory hypotheses be included?
They can be, if labeled as exploratory so readers know they were not the study's main test.