The different methods described in the scenarios represent various sampling techniques used in research. Let’s analyze each:

Scenario 1: The CEO’s intention to survey patient satisfaction after discharge indicates a desire to gather feedback from the entire population of discharged patients. This would ideally require a census, where every individual in the population is surveyed. However, given the impracticality of surveying all discharged patients, the CEO would likely use a sampling technique to select a representative subset.

Scenario 2: The case manager interviewing patients as they leave the hospital is an example of convenience sampling. This technique is easy and inexpensive but may not provide a representative sample as it relies on who happens to be available at the time.

Scenario 3: Distributing questionnaires to patients and requesting their return is a self-selection bias technique. This method may lead to a biased sample as only those who are motivated to complete and return the survey will be included.

Scenario 4: The CEO randomly selecting a set of patients for surveying represents simple random sampling. This technique ensures that every individual in the population has an equal chance of being selected, providing a more representative sample.

Scenario 5: Grouping patients by departments and selecting one from each is stratified sampling. This technique is useful when the population can be divided into distinct subgroups (strata). By sampling from each stratum, it ensures that all subgroups are represented in the sample.

In conclusion, the most representative sampling techniques among the options presented are simple random sampling and stratified sampling. These methods help to minimize bias and ensure that the sample is representative of the overall population.