Send the exact assignment or rubric from your course of study and a custom sample written to it lands in 24 to 48 hours, the first one free. D159 is WGU’s Evidence-Based Measures for Evaluating Healthcare Improvements course. It centers on choosing measures and evidence that can actually show whether an improvement project moved what it promised to move. Searches like "d159 task 2 assignment example", "D159 sample paper", and "D159 task samples" land on this page.
What D159 is really about
Evaluation is the stage where an improvement project either proves itself or quietly disappears, and D159 is built around that difference. You work with outcome, process and balancing measures, decide what data already exists and what has to be collected, set a baseline, choose how often to look, and settle in advance what result would count as success and what would count as harm. The evidence side matters too, since the literature that predicted the effect also tells you roughly how large it should be and how long it takes to appear, which is what makes a sensible measurement interval possible. Statistics stay practical here, closer to run charts and simple comparisons than to inference for its own sake.
Sitting at the end of one continuous project, this course inherits everything already decided, and the evaluator is checking whether your measures answer the promise made earlier rather than some new promise. Submission timing is yours, as it is throughout the program: finish it and send it in. The evaluator reads each rubric aspect and returns anything not yet met with a note, most often on the link between a measure and the outcome it is supposed to prove, and revision at that stage is narrow work. Your field experience documentation, including hours and anything signed by a preceptor, stays entirely with you and is never drafted here. The two drawers below hold the forms this written work usually takes.
What D159’s tasks ask for
Expect to be asked for the outcome you intend to move, the measure that represents it, where the data lives, who pulls it and how often, the baseline you are comparing against, and how results get back to the people whose work produced them. Balancing measures usually earn an aspect of their own, because a change that improves one figure while wrecking another has not improved anything. Say what you would conclude at several possible results, including the one where nothing moved, since a plan that can only report success is not an evaluation. Where the analysis is descriptive, say so plainly instead of dressing it in tests the data cannot support.
Why D159 tasks come back for revision
The characteristic failure here is an evaluation that could not see its own result. A project meant to cut catheter associated infections is evaluated with a staff satisfaction survey and an attendance record for the teaching session, both of which will look fine whether or not a single infection was prevented. A subtler version keeps the right measure and ruins the timing: an outcome that moves over months, sampled once at thirty days, or a rare event counted on one floor where the expected reduction is smaller than ordinary variation between reporting periods. Before the plan is written, set the prediction and the measure side by side and ask what number would change, by how much, and by when. If no honest answer exists, the measure is wrong or the denominator is too small to carry it.
The D159 drawers
D159 Task 1 example
Often one part selects measures and data sources tied to the predicted improvement. On request, free, 24-48h.
D159 Task 2 example
The second typically lays out collection, analysis and how results reach the people affected. On request, free, 24-48h.
Your course of study shows something else?
Western Governors University revises courses; task counts and deliverables shift between terms. Send what your classroom shows and the desk matches it exactly.
Using a D159 sample the right way
What a finished evaluation plan teaches fastest is discipline about the link between prediction and measurement. Trace how the writer states an expected effect, chooses a measure that could show it, sets an interval long enough for it to appear, and names the result that would count as failure. Then build yours on your own project and your own data. Send the rubric and task instructions and the sample is written against those aspects, first one free, back inside 24-48h. If a submission was returned, tell us which aspect fell short and the sample will be pointed at it.
How these samples are written
Method, in one line: aspects first, structure from the aspects, artifacts consistent, format exact. Both C-code and D-code spellings resolve here because students search both. Your free request is drafted against what your degree plan actually shows.
D159 questions, answered
How do I know my measure can detect the change I predicted?
Work out the arithmetic before you commit. Take the current rate, apply the effect the literature predicts, and see what the new number would be across the period you plan to observe. If the difference is one event, or smaller than the normal swing between reporting periods, you need a longer window, a bigger denominator or a process measure that moves sooner.
Do I need statistical tests?
Usually not, and reaching for them can hurt. Most improvement work is judged on a baseline, a series of observations over time and a clear rule for what counts as a real shift. Say which comparison you are making and why it is fair. Claiming significance from a handful of observations is a faster route to a returned aspect than plain description ever is.
Can you write the sample if I only have the rubric?
Yes, though it will be better with two or three lines about the project the evaluation belongs to, since measures only make sense against a specific change. Send whatever you have. The first sample is free, returns within 24-48h, and is written to the aspects in your rubric rather than to a general version of the course.