Sampling Random Sampling Bias Alternate Sampling Strategies

Sampling Before data can be collected, the population must be defined in terms of: population demographics size geography/location relevant characteristics* Decide whether to gather data from a subset, or sample, of the population, or from the whole group. A sample may be drawn if: sample the population is too large (more than 500 members), or the population is inaccessible

Random Sampling Theory: all members of the population have an equal opportunity to be selected In theory, the best way to ensure that findings apply to the entire population

Random Sampling Nonrandom samp- ling is appropriate in some forms of research Sampling error: variation in the sample due to random fluctuation Sampling bias hap- pens when the sample does not reflect or represent the population Use a table of random numbers

Simple Random Sampling Usually completed by using a computer- generated Table of Random Numbers (p of the coursepack) Theoretically ensures that the sample will be fairly representative of the population All 3 rd grade students in Cincinnati Sample: a subset of 3 rd grade students in Cincinnati

Stratified Random Sampling The sample is selected so that identified characteristics in the population are represented proportionally in the sample

Stratified Random Sampling Subjects are randomly selected from characteristic subgroups, rather than from the popu- lation in general

Cluster Random Sampling Clusters, or groups, of subjects are selected from a population made up of groups, or clusters All individuals in a selected group are included in the sample Not all groups in the population are included in the sample All 3 rd grade class- rooms in Cincinnati Sample: subset of 3 rd grade classrooms in Cincinnati

Systematic Random Sampling The first subject is individually selected The population is ordered in some way The designation of the first subject determines the entire sample

Alternate Sampling Sometimes the population under study is just not available. A purposeful sample may be selected, in that case, based on the logic that the sample is statistically representative. A purposeful sample may also be drawn if it can be shown that the sample contains information-rich cases. That is, it must be shown that the sample contains subjects with more information than the rest of the population.

Alternate Sampling Strategies Purposeful, or alternate sampling strategies include:alternate sampling strategies comprehensive sampling maximum variation sampling extreme case sampling typical case sampling homogeneous sampling