| Course | HD560 Health Education Evaluation and Research |
|---|---|
| Unit | Unit 7 |
| Paper type | Analysis approach |
| Length | About 840 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MS in Health Education |
| Updated | October 2026 |
Free sample paper for HD560 Unit 7
Models Chosen Before the Data Arrive: A Unit 7 Analysis Approach for the Within Earshot Hearing Aid Lottery
[Student Name]
Purdue University Global
HD560: Health Education Evaluation and Research
Unit 7 Analysis Approach
[Instructor Name]
[Date]
Within Earshot and its data are composite; the statistical and reporting sources cited are real.
Commitments Made in Advance
This plan is dated and shared with the agency's board and the university's IRB before the six-month data are collected. Any later change will be listed, with the reason for it, in a deviations table. Fixing analysis choices in advance prevents the results from steering the methods, a concern reflected in reporting standards for randomized studies (Schulz et al., 2010).
Primary Analysis
Question: is loneliness lower after six months among those offered a place than among those wait-listed?
Population: everyone randomized, analyzed in the group to which the lottery assigned them, whether or not they took up the offer. This intention-to-treat approach keeps the balance the lottery created.
Model: analysis of covariance, with six-month loneliness as the outcome and the group and baseline loneliness as predictors. Language of interview is added as a covariate because it was used to stratify the lottery. Couples randomized together are treated as clusters with robust standard errors. Adjusting for baseline in this way is generally more precise than analyzing change scores and handles chance imbalance at baseline (Vickers & Altman, 2001).
Reported as: the adjusted difference in mean loneliness with a 95 percent confidence interval.
Missing Data
Some participants will not complete the six-month assessment. The primary analysis uses multiple imputation, with baseline loneliness, hearing handicap, age, sex, language and group in the imputation model, assuming data are missing at random given those variables (Sterne et al., 2009). A sensitivity analysis will assume that wait-listed participants who were lost were lonelier than predicted, testing how far the result depends on that assumption.
Secondary Analyses
Hearing handicap at six months: the same model as the primary analysis.
Effect among those who used the program: an instrumental variable analysis using the lottery as the instrument estimates the effect among people who would take up the offer if given it (Angrist et al., 1996). It is labeled secondary, because it relies on stronger assumptions.
Device use: hours of daily use at one, three and six months described for the group offered the program, with the relation between use and change in hearing handicap shown in a scatterplot and a simple correlation. No causal claim is made from this.
Pathways: in the group offered the program, a regression of change in loneliness on change in hearing handicap and on drop-in attendance, added after the Unit 6 seminar. Labeled exploratory.
Subgroups
Results for Spanish-speaking participants, about 60 people, are reported descriptively with intervals, and no test of difference between language groups is made, because the subgroup is too small for a reliable comparison.
Qualitative Strand
Twenty participants offered the program are interviewed after six months: ten whose hearing handicap improved most and ten whose improved least, balanced by language. Interviews are recorded, transcribed, translated where needed and coded by two analysts who agree on a coding frame after the first four. Themes are used to explain patterns in the numbers, not counted.
The primary estimate will describe what happened to older adults who were offered this program as it is delivered, fittings, visits and drop-in hour included, and it will not by itself say how much of any change came from hearing better.
What the Primary Estimate Will Not Mean
A null result would not show that hearing aids have no effect on loneliness; it would show that this offer, at this size of sample, did not produce a detectable difference within six months. A positive result would not show that hearing caused the change; the pathway analyses address that only partly.
How Results Will Be Read
The evaluation will not use a single p value to declare success or failure. The interval for the primary difference will be compared with the 0.6-point difference the design was built to detect and with what the board considers meaningful, which it set at 0.5 points before the lottery. An interval lying wholly below zero would show benefit; one including zero but extending well into benefit would be described as uncertain; one tightly around zero would suggest little effect.
Reporting Every Prespecified Result
Every analysis named in this plan will be reported, including those that show nothing. The primary outcome will be reported first in every document, even if a secondary outcome looks more striking. Exploratory analyses will be labeled as exploratory in every table and paragraph where they appear, so that no reader mistakes a pattern found in the data for a finding the evaluation set out to test. A deviations table will list any analysis that changed, with its reason and the date of the change.
Software and Checking
Analyses run in R, with code written and checked by a second analyst before unblinding the group labels in the data file.
References
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444-455. https://doi.org/10.1080/01621459.1996.10476902
Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332. https://doi.org/10.1136/bmj.c332
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ, 338, b2393. https://doi.org/10.1136/bmj.b2393
Vickers, A. J., & Altman, D. G. (2001). Analysing controlled trials with baseline and follow up measurements. BMJ, 323(7321), 1123-1124. https://doi.org/10.1136/bmj.323.7321.1123
Reading the HD 560 Unit 7 instructions
The seventh unit typically asks students to describe how each type of data will be analyzed and why the methods fit the design. Prompts commonly ask for the main analysis, what happens when data are missing, any secondary or subgroup work and the qualitative analysis if there is one. State the population for each analysis and the model. Explain why the model suits the design, citing a methods source. Address missing data with a named approach and a sensitivity analysis. Label exploratory analyses clearly. Set rules for interpreting results before they arrive. Commit to reporting every prespecified analysis. A table pairing each question with its method usually anchors the plan.
Inside the HD560 Unit 7 example
The plan opens by dating its commitments and citing reporting standards. The primary analysis section names the population, an analysis of covariance adjusted for baseline and language, and treatment of couples as clusters. Missing data are handled by multiple imputation with a pessimistic sensitivity check. Secondary analyses cover hearing handicap, an instrumental variable estimate among those who would take up the offer, device use and pathways added after the seminar. Subgroups and the interview strand follow. The highlighted sentence states what the estimate can claim. Reading rules, a commitment to report every result and code checking close the plan.
Where the points sit in the HD560 Unit 7 rubric
Analysis plans in this course are judged on the fit between methods and design, clarity about populations and models, sound handling of missing data, honest labeling of secondary and exploratory work, and advance commitment. Fit is strongest where the writer shows why a method suits a randomized comparison with baseline data. Missing data plans score well when they name an assumption and test it. Labeling exploratory work prevents later overstatement and is noticed by graders. Reading rules set in advance show integrity. A clear table linking each question to its method and correct APA references round out the assessment. Some instructors ask whether the plan says what a null result would and would not mean, and plans that answer in advance tend to score well.
HD560 Unit 7 help: mistakes that cost points
A frequent problem is a plan that names a statistical test without saying which people are analyzed or what is adjusted for. Another is ignoring missing data or simply dropping incomplete cases. Some plans include many subgroup tests without considering power. Others describe qualitative analysis in one sentence. Writing the plan as if a stranger had to run it, with every choice stated, avoids most of these gaps. If your design or data differ, list what you will collect and when, and a writer can help you match each piece to a method and state its limits. Dating the plan before data arrive is a simple safeguard.
Get an HD560 Unit 7 example written to your instructions
Send the design, data types and sample size from your HD560 evaluation along with the Unit 7 prompt and rubric. Built on your instructions, the free first custom sample reaches you in 24-48h, matching each question to an analysis that fits the design and labeling exploratory work as exploratory. 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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HD560 Unit 7 questions, answered
What does intention to treat mean?
Analyzing everyone in the group they were assigned to, regardless of whether they participated fully. It preserves the balance randomization created and estimates the effect of offering the program. Excluding non-participants breaks that balance, since those who drop out usually differ in important ways from those who stay.
Why adjust for the baseline value instead of analyzing change?
In a randomized design, adjusting for baseline through analysis of covariance generally gives a more precise estimate than comparing change scores, and it handles regression toward the mean appropriately. Change scores are easier to explain, so some plans report them descriptively while using the adjusted model for the main result.
Does a qualitative strand need its own analysis plan?
Yes. Describe how interviewees will be selected, how interviews will be recorded and coded, who will code them, and how themes will be connected to the quantitative results. A sentence promising thematic analysis without those details reads as incomplete in most graduate sections. Two coders working from a shared codebook, with a stated way of settling disagreements, is a common and credible arrangement.
Where can I find a free HD560 Unit 7 sample paper?
This page carries a complete Unit 7 analysis approach for a hearing aid lottery, with methods fixed before outcomes are seen.
What is an instrumental variable estimate in a lottery?
It uses the lottery result to estimate the effect among people who would take up the program if offered. It relies on stronger assumptions than the main comparison and is usually secondary.