| Course | HLTH 8560E Advanced Analysis of Secondary Data |
|---|---|
| Week | Week 7 |
| Paper type | Analysis plan |
| Length | About 540 words, 4 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | PhD in Public Health |
| Updated | October 2026 |
Free sample paper for HLTH 8560E Week 7
Analysis Plan: Specifying Weights, Strata, Clusters, Variance Method and Reporting Rules for an NHANES Study of Depressive Symptoms and Poor Glycemic Control Before Any Model Is Run
Student Name
Walden University
HLTH 8560E: Advanced Analysis of Secondary Data
Instructor Name
Month Day, Year
Analysis Plan: Specifying Weights, Strata, Clusters, Variance Method and Reporting Rules for an NHANES Study of Depressive Symptoms and Poor Glycemic Control Before Any Model Is Run
Starting From How the Data Were Collected
NHANES does not draw people at random from a national list. It selects counties or groups of counties as primary sampling units within strata, then households, then persons, and it oversamples some age and race and ethnicity groups (Chen et al., 2020). A plan that ignores those steps would produce standard errors that are too small and estimates tilted toward oversampled groups. Every estimate below is therefore specified with its weight, design variables and variance method before any result is seen.
Design Elements
The weight is the examination weight divided by four, because the analysis pools four two-year cycles and both measures come from the examination visit. Strata are SDMVSTRA and clusters are SDMVPSU, the masked variance units released for this purpose. Variances use Taylor series linearization with clusters treated as sampled with replacement within strata, the approach used by the R survey package (Lumley, 2004). Degrees of freedom equal the number of clusters minus the number of strata; for the pooled file that is 62.
The analytic group is handled as a subpopulation. Records outside it stay in the design so that every stratum keeps its clusters; deleting them would understate variance (West et al., 2008).
Prespecified Estimates
The main comparison is the difference in the share with glycohemoglobin of 9.0% or more between adults with and without moderate or greater symptoms. No adjusted model is planned as the primary result, because the question is descriptive and an adjusted coefficient would invite a causal reading the cross-sectional design cannot support (Golden et al., 2008).
Table 1
Estimates, Population and Method
| Estimate | Population | Method | |
|---|---|---|
| Share with PHQ-9 score of 10 or more | Analytic group, n = 2,663 | Weighted proportion, Korn-Graubard interval | |
| Share with glycohemoglobin of 9.0% or more, by symptom group | Each symptom group | Weighted proportion, Korn-Graubard interval | |
| Difference in that share between groups | Analytic group | Linearized difference, t interval on 62 df | |
| Mean glycohemoglobin, by symptom group, and difference | Each symptom group | Weighted mean, t interval | |
| Share at under 7.0%, by symptom group | Each symptom group | Weighted proportion | |
| Same comparison within age band, sex and insulin use | Subgroups | As above, subject to the reliability rule | |
Proportions use Korn-Graubard intervals as recommended for NCHS survey estimates; means and differences use t intervals.
Reporting Rule
Every proportion must pass the National Center for Health Statistics presentation standards before it is printed as a stand-alone figure (Parker et al., 2017). An estimate is suppressed if its effective sample size is under 30, if its interval is wider than 30 percentage points, or if a wider-than-5-point interval is also more than 130% of the estimate. Degrees of freedom under 8 trigger a flag. Subgroup estimates are expected to fail most often, and a failed estimate is reported as suppressed with the reason rather than dropped silently.
Sensitivity Checks
Two checks are planned. The first rescores partial PHQ-9 records by prorating the answered items when at least seven are present. The second limits the analysis to the two most recent cycles to see whether pooling hides change over time.
References
Chen, T. C., Clark, J., Riddles, M. K., Mohadjer, L. K., & Fakhouri, T. H. I. (2020). National Health and Nutrition Examination Survey, 2015-2018: Sample design and estimation procedures (Vital and Health Statistics, Series 2, No. 184). National Center for Health Statistics.
Golden, S. H., Lazo, M., Carnethon, M., Bertoni, A. G., Schreiner, P. J., Diez Roux, A. V., Lee, H. B., & Lyketsos, C. (2008). Examining a bidirectional association between depressive symptoms and diabetes. JAMA, 299(23), 2751-2759. https://doi.org/10.1001/jama.299.23.2751
Lumley, T. (2004). Analysis of complex survey samples. Journal of Statistical Software, 9(8), 1-19. https://doi.org/10.18637/jss.v009.i08
Parker, J. D., Talih, M., Malec, D. J., Beresovsky, V., Carroll, M., Gonzalez, J. F., Jr., Hamilton, B. E., Ingram, D. D., Kochanek, K., McCarty, F., Moriarity, C., Shimizu, I., Strashny, A., & Ward, B. W. (2017). National Center for Health Statistics data presentation standards for proportions (Vital and Health Statistics, Series 2, No. 175). National Center for Health Statistics.
West, B. T., Berglund, P., & Heeringa, S. G. (2008). A closer examination of subpopulation analysis of complex-sample survey data. The Stata Journal, 8(4), 520-531. https://doi.org/10.1177/1536867X0800800404
What the HLTH 8560E Week 7 instructions ask for
Students write the plan for their secondary analysis before producing results. For a complex survey, faculty expect the plan to name the weight, the strata and cluster variables, the variance method and the degrees of freedom. Each estimate should be listed with the population it describes and the method that produces it. The plan should explain how the analytic subgroup will be handled without deleting other records, and it should set a rule for which estimates are reliable enough to report. Most rubrics also look for a stated primary comparison and a reason for any model that is or is not used. Sensitivity checks that test key decisions from earlier weeks are valued. Write it tightly enough that a second analyst handed the same files would arrive at identical figures.
How this HLTH 8560E Week 7 example is built
The opening section explains the NHANES design and why ignoring it would distort standard errors, citing Chen and colleagues. The design section names the pooled examination weight, SDMVSTRA and SDMVPSU, linearized variances with clusters treated as sampled with replacement, the R survey package described by Lumley and 62 degrees of freedom, then cites West and colleagues on subpopulation analysis. Table 1 lists six estimates with population and method. A paragraph names the main comparison and explains, citing Golden and colleagues, why an adjusted model is not primary. The reporting rule section states the NCHS thresholds from Parker and colleagues and says suppressed estimates will be shown with reasons. A closing section describes two sensitivity checks.
Where the marks sit in the HLTH 8560E Week 7 rubric
Faculty grade the plan on whether the survey design is built in from the start. A plan that lists descriptive statistics and a regression without naming weights or design variables would produce wrong standard errors and earns little credit for method. Credit rises with each design element stated precisely and justified by the documentation. A table of prespecified estimates is valued because it shows the analysis was fixed before the results. Faculty look for a clear primary comparison and a reason for the modeling choice, since adjusted coefficients from cross-sectional data invite causal claims. A reporting rule set in advance earns strong credit, as it protects against selective presentation. Sensitivity checks tied to earlier decisions, such as handling partial PHQ-9 records, show continuity across the course.
HLTH 8560E Week 7 help from the desk
Analysis plans for survey data often read like plans for a clinical dataset, with no weights and t-tests run on raw records. Those analyses treat every respondent as equally likely to be sampled, which NHANES respondents were not. To write a stronger plan, start from the survey's design document and name the weight that matches your variables. Add the strata and cluster variables and state the variance method. Work out the degrees of freedom. List each estimate in a table with its population. Decide your main comparison and say why you will or will not fit a model. Write a reporting rule before you see any numbers, and plan one or two checks of your riskiest decisions. A survey analysis plan for your own question can be drafted to the same standard.
Get a HLTH 8560E Week 7 example written to your instructions
Bring the Week 7 assignment and rubric, the dataset you are using and the question your plan must answer. A plan specifying weights, design variables, variance method, reporting rules and sensitivity checks reaches you in 24-48h, the first free. Everything in it is written for a composite analysis, so the design fields in yours come from your file's own documentation.
More HLTH 8560E papers and PhD in Public Health samples
- HLTH 8560E Week 1: Dataset Orientation Post
- HLTH 8560E Week 2: Dataset Selection Memo
- HLTH 8560E Week 3: Codebook Review
- HLTH 8560E Week 4: Survey Round Discussion Post
- HLTH 8560E Week 5: Analytic Variable Map
- HLTH 8560E Week 6: Analytic Sample Derivation
- HLTH 8560E Week 8: Weighted Results Section
- HLTH 8560E Week 9: Recode Peer Replies
- HLTH 8560E Week 10: Data Constraints Memo
- HLTH 8560E Week 11: Journal Manuscript Draft
- HLTH 8551 Week 7: Study Alignment Table
- HLTH 8475 Week 7: Six-Month Process Evaluation
- HLTH 8402 Week 7: Public Meeting Engagement Analysis
- HLTH 8392 Week 7: Program Closure Difficult Message
HLTH 8560E Week 7 questions, answered
Do all public-use surveys need weighted analysis?
Those whose members had unequal chances of selection do, which covers most national health surveys. The documentation says which weight applies to which component. Unweighted analysis can be defended for describing respondents only, never for population claims. The plan should state which kind of claim each estimate supports, and use the weight whenever that claim concerns the population.
What does the NCHS presentation standard actually do?
It sets criteria for deciding whether an estimated proportion is reliable enough to publish, based on the effective sample size behind it and the width of its confidence interval. Estimates failing the criteria are suppressed or flagged. Adopting it in the plan tells a reader in advance which subgroup figures will appear, and prevents presenting a percentage built on too few respondents.
Can the plan change after results come in?
It can, but changes should be reported as changes, with the reason. A plan exists partly to separate the intended analyses from those the data suggested after the fact. Keeping the original plan in an appendix and marking later additions as exploratory preserves that distinction, and doctoral readers credit the candor.
Where can I find a free HLTH 8560E Week 7 sample paper?
The full Week 7 analysis plan is published here, specifying weights, strata, clusters, variance method, six prespecified estimates and an NCHS reporting rule for an NHANES depressive symptom and glycemic control study, with five references.
Why treat the analytic group as a subpopulation instead of deleting other records?
Variance in a complex survey comes from differences between clusters within strata. Deleting records outside the group can remove whole clusters from a stratum and understate the standard errors, so the full file stays in the design.