NSG 6101 Week 6: sampling plan and research design

Everything in this post has to answer to the problem statement you open with, so write that first and keep it in view. The prompt does not ask you to define probability sampling; it asks whether your sampling plan is appropriate to your design and whether your sample reflects the population your own problem statement identified. That is a coherence question. If your problem statement concerns medication errors on night shift in acute care, then a convenience sample of day-shift nurses at one hospital is a mismatch you should name yourself rather than wait to be told. State the sampling method by its proper name — simple random, stratified, cluster, systematic, convenience, purposive, snowball — and then say what it buys you and what it costs. Probability methods support statistical generalisation; non-probability methods are often the only feasible option in clinical settings and should be defended on feasibility, not disguised as something stronger.

Sample size deserves a real answer rather than a number. Say what would drive it: an a priori power calculation for a quantitative design, or saturation for a qualitative one, and name the effect size or the saturation criterion you are assuming. Then handle generalisability honestly. To what population may findings be generalised, and what limits that — a single site, one specialty, a self-selected group, a response rate you cannot predict? Naming a limitation is not a weakness in this assignment; it is the assignment. For the design half, state the type plainly — descriptive, correlational, quasi-experimental, randomised controlled, phenomenological, grounded theory — and then do what the prompt asks: show that it flows from your problem, your theoretical framework, your literature review and your hypothesis. If your hypothesis predicts a causal effect and your design is descriptive, that gap is the most important thing in your post.