Sampling Concepts Nursing Research

Population  Population the group you are ultimately interested in knowing more about “entire aggregation of cases that meets a designated set of criteria".  Target population  Eligibility  Accessibility

Sampling  process of selecting a portion of the population to represent the entire population

 Sample: the people who actually participate in your study. The goal is to be representative of your population.

Rationale for sampling  More economical & efficient  Not necessary to gain data from entire population - its "almost always possible to obtain a reasonably accurate understanding of the phenomenon under investigation by securing information from a sample".

 Problem is sampling bias - "the over-representation or under-representation of some segment of the population in terms of characteristics relevant to the research question.  To assess risk of sampling bias, readers of research reports must consider the degree to which a population is heterogeneous with respect to key variables.  Want to generalize to the population

How to draw a sample  Non-probability sampling Convenience Snowball Quota Purposive  Probability sampling Simple random Systematic Stratified Cluster

Purposive Sampling (Non-probability sampling)  “People are selected because they are available, convenient or represent some characteristic the investigator seeks to study.” Creswell (2005), p.149

 Two popular approaches Convenience: selected because they are willing and available Snowball: researcher asks participants to identify others to become part of the sample.

 Quota Sampling – researcher identifies strata of the population & specifies the proportion of elements needed from various segments of the population Provides improvement over convenience sampling, but has some of the same problems. StrataPopulationConvenienceQuota Male100 (2%)5 (5%)20 (20%) Female400 (80%)95 (95%)80 (80%) Total500 (100%)100 (100%)

 Purposive Sampling (Judgmental) Researcher's knowledge is used to hand pick the cases to be included in the sample

Disadvantages of nonprobability sampling  Rarely representative of researcher's target population - not every element in the population has a chance of being included in the sample  Must be cautious about inferences and conclusions drawn from the data

Advantages of non-probability sampling  Convenient  Economical

Probability sampling Most rigorous form of sampling. Can claim representativeness because each person had an equal chance of being selected. Types  Simple random  Systematic  Stratified

Simple Random Sampling  Approximates drawing out of hat.  Selected one at a time; independent; without replacement; no further chance of being selected.  Difficult unless the list is short, has all units prenumbered, or is computerized so that numbering is easy.

So how do I do that?  Random number table  Computerized random number generator. SPSS Research Randomizer FormRandomizer You have a list of 900 nurses and you need a sample size of 100.

Systematic Sampling  Determine number on list.  Determine how many are to be selected from list.  Divide the number on the list by the number to be selected.  The start point is determined by choosing a random number that falls in the sampling interval.  Randomized start ensures chance selection process.  Start with the person selected in the randomly selected position you then sample by the sampling interval. Fowler

Huh?  You have a list of 900 patients with diabetes.  Need a sample of 100. =9 Random number generated: 2 Starting with the 2nd person on the list, you will select every 9th person.

Stratified sampling  All people in the sampling frame are divided into strata (categories). E.g. age, race, sex  A simple random or systematic sample is drawn from each strata.

Stratified sampling  You have a list of 900 diabetic adults who are seen at a university hospital diabetes center.  You want to make sure that all major ethnic groups are represented in the sample.  Strata= ethnicity.  Employ random or systematic sampling to each separate strata.

Cluster sampling  Used when population is large  There is a successive sampling of units.  Contains more sampling errors than stratified or simple, but is more economical & practical in large disbursed populations

How to choose a sampling method  List the research goals (usually some combination of accuracy, precision, and/or cost).  Identify potential sampling methods that might effectively achieve those goals.  Test the ability of each method to achieve each goal.  Choose the method that does the best job of achieving the goals. Taken from:

Sampling in Quantitative Studies  Power: statistical method used to determine sample size “Statistical power is the ability to detect a true difference when, in fact, a true difference exists in the population of interest.” McNamara (1994), p. 56  The larger the sample the more representative of the population it is likely to be.

 When expected differences between groups are large a large sample is not needed to ensure that differences will be revealed in statistical analysis  When expected differences are small a large sample is needed to show differences in statistical analysis  "A large sample cannot correct for a faulty sampling design".  Must assess both the size of the sample & the method by which the sample is selected.

Sampling in Qualitative Studies  Logic of Qualitative Sampling - Generalizibility not a guiding criteria  Types of Qualitative Sampling Volunteer Purposeful or Theoretical  Maximum variation  Homogeneous  Extreme/deviant case  Typical Case

 Sample Size Largely a function of the purpose of the inquiry, the quality of the informants, and the type of sampling strategy used. Guiding principle is data saturation.