whether they would take an intermittent or continuous approach to data collection. Discussions regarding audit frequency, whether frequent feedback to clinicians was required, how analysis would be undertaken and how the future activity would be guided by the results were undertaken prior to data collection. Sample type Whether you are collecting a sample for intermittent or continuous data collection, a key decision is whether to collect a random (probability) or judgement (non-probability) sample. Both types of sampling are appropriate in different circumstances and each has strengths and limitations to consider. Definitions and factors to consider are outlined in Table 2. Table 2: Sample type considerations What is it? Why use it? Considerations Random A process of Random sampling It may be hard to sampling taking a should be define a 18,31,35,36 sample so considered if: population from that each which to take a you need to infer member of random sample from the sample the given the dynamic to the whole population population nature of health has an equal care. you need chance of assurance the A but selection. results are important patient representative This removes group could be of the population bias and missed if sampling allows it is a is left to chance requirement of inferences to as part of random key stakeholders. be made sampling from the especially if small sample to samples are the whole chosen. population. There are different types of random sampling*. Consider seeking statistical advice regarding specific sampling needs. See examples in Box 2. Judgement A Consider judgement is a risk of sampling (also sampling when bias when using
whether they would take an intermittent or continuous approach to data collection. Discussions regarding audit frequency, whether frequent feedback to clinicians was required, how analysis would be undertaken and how the future activity would be guided by the results were undertaken prior to data collection. Sample type Whether you are collecting a sample for intermittent or continuous data collection, a key decision is whether to collect a random (probability) or judgement (non-probability) sample. Both types of sampling are appropriate in different circumstances and each has strengths and limitations to consider. Definitions and factors to consider are outlined in Table 2. Table 2: Sample type considerations What is it? Why use it? Considerations Random sampling 18,31,35,36 A process of taking a sample so that each member of the population has an equal chance of selection. This removes bias and allows inferences to be made from the sample to the whole population. Random sampling should be considered if: you need to infer from the sample to the whole population you need assurance the results are representative of the population it is a requirement of key stakeholders. It may be hard to define a population from which to take a random sample given the dynamic nature of health care. A but important patient group could be missed if sampling is left to chance as part of random sampling especially if small samples are chosen. There are different types of random sampling*. Consider seeking statistical advice regarding specific sampling needs. See examples in Box 2. Judgement sampling (also A Consider judgement sampling when is a risk of bias when using
SAQ127_National_QUM_Indicators_V14.docx