Tests, models and the handling of missing values, all fixed before any data exist in a stepped-wedge scribe trial, fill this HS840 analysis plan written for Unit 8. Searches like "hs 840 unit 8 assignment example", "hs840 unit 8 sample" and "hs840 unit 8 example" land here.
What a finished HS840 Unit 8 data analysis plan looks like
Eight pages written as a statistical analysis plan. It opens with the estimand: the average difference in after-hours documentation time per eight scheduled hours between clinic-periods with and without scribe access, among all enrolled clinicians regardless of how much they used the tool. Descriptive statistics come next, specified down to which summaries appear in the first table. The primary model is a linear mixed model on log-transformed time, with fixed effects for exposure and each two-month period, and random intercepts for clinic and for clinician within clinic. Secondary analyses cover the burnout index, a model allowing the effect to grow with time since access, and a per-protocol analysis by actual use. The missing data section distinguishes absent log data from missing surveys and treats each differently. A table maps every hypothesis to its test.
How a HS840 Unit 8 example is structured
The plan moves from what is being estimated to how, which keeps the statistics subordinate to the question. Defining the estimand first settles arguments that otherwise surface after results arrive, such as whether light users count. Model choices are justified from the design and the data: calendar period effects because exposure and time are confounded in a stepped wedge, nested random intercepts because clinicians sit within clinics, and a log transformation because documentation time is right-skewed with a long tail. Missing data is handled by mechanism. Log data are missing mainly through departures and system outages, which the model accommodates under a missing-at-random assumption; survey nonresponse may relate to burnout itself, so multiple imputation is paired with a sensitivity analysis that shifts imputed values. The plan states it will be registered before any outcome data are examined.
Estimand before model
Clinic-period difference in after-hours time with versus without access, all enrolled clinicians included, stated in a sentence that settles who counts before anyone sees results.
Why period effects are mandatory
In a stepped wedge, later periods carry more exposure; without fixed effects for calendar period, any secular drift in documentation time would masquerade as the tool's effect.
Skewed time, nested people
Log-transformed outcome, back-transformed to a ratio for reporting, with random intercepts for clinic and clinician; a gamma model named as the alternative if residuals misbehave.
Two kinds of missing
Absent log data through departures or outages, handled by the mixed model; missing burnout surveys, imputed twenty times with a delta-adjusted sensitivity analysis in case the most exhausted stop answering.
Hypotheses mapped to tests
A single table pairs each hypothesis with its outcome, model, covariates and threshold, so a reader can check that nothing was chosen after the data arrived.
Where marks go in HS840 Unit 8
A test that appears with no reason attached is the recurring weakness graders cite in analysis plans; 'a t-test will be used' says nothing about why the data and design permit it. Doctoral plans are expected to derive each choice from the outcome's measurement scale, its distribution and the structure of the design. Clustered and repeated data make this unit unforgiving: an analysis that ignores clinics or treats repeated periods as independent overstates precision, and a stepped-wedge analysis without time effects is biased. Missing data should be addressed by mechanism, with the assumption stated and a sensitivity analysis planned, rather than by deleting incomplete cases. Specifying the estimand earns credit because it prevents later ambiguity. Plans that commit to registration before data are seen show awareness of analytic flexibility and its risks.
Get a HS840 Unit 8 example written to your instructions
Analysis plans rest on everything before them: the design, measures and hypotheses from earlier units, plus the Unit 8 prompt, the rubric and any software your course requires. With those, the plan defines its estimand, derives each test from design and data, and treats missing values by mechanism with a sensitivity check. 24-48h, and free for a first custom sample.
HS840 Unit 8 questions, answered
What is an estimand, and do I need one?
It is a precise statement of the quantity a study aims to estimate: population, comparison, outcome, and how events such as stopping the intervention are handled. Many doctoral methods courses now expect one, following the ICH E9(R1) guidance on trials. The sample's estimand includes all enrolled clinicians regardless of use, which decides the primary analysis before any data exist.
Is deleting incomplete cases ever acceptable?
Occasionally, when very little is missing and the missingness plausibly has nothing to do with the outcome. More often it biases results and wastes information. The sample uses methods that retain partial data and states its missing-at-random assumption. If your plan uses complete cases, justify it with the expected missingness and add a sensitivity analysis.
Should the plan include code?
Not usually in the body. The plan specifies models in words and notation; code belongs in an appendix or repository if the course asks for it. The sample names the software and packages so the analysis can be reproduced, and it describes each model precisely enough that a statistician could write the code from the text alone.