Printable Samples and Surveys Worksheets

Here is why samples and surveys are important: Surveys allow researchers to conclude without needing to know about every member of a group. Samples provide the required data.

Both quantitative and qualitative data are important. Quantitative data is information that can be conveyed in the form of numbers, or quantities. Qualitative data describes other, non-numeric qualities of the individuals or objects being studied.

A person’s age, weight and current heart rate are all quantitative data points. A person’s middle name, occupation and birthplace are all qualitative pieces of data.

The five basic survey sampling methods are:

Random sampling, where survey samples are taken from the population according to unpredictable selection criteria. Rolling a pair of dice or picking names out of a hat would allow for random survey sampling.

Systematic sampling, where samples are selected according to a set, unvarying pattern. Questioning every third member of a class or every fifth guest at a party would be a systematic sampling method.

Cluster sampling, where a population is first broken up into subgroups according to location or some other characteristic. Every member of some number of these clusters then gets sampled.

Stratified sampling, where characteristics of interest are used to define subgroups, which are called “strata.” Each stratum will then be sampled several times corresponding to its size relative to the others, using a different survey sampling method.

Convenience sampling, where survey samples are taken as they become available until enough have been acquired. Analyzing the first 50 responses to a survey sent to every member of a group would be convenience-based sampling.

These five basic sampling methods can also be used in combination, and often are. There are four common ways to conduct random sampling. Several of which include elements of other sampling methods.

There are four distinct types of random sampling:

Simple random sampling, where samples are chosen by chance from a single list identifying all the members of a population.

Cluster random sampling, where the clusters to be sampled are selected randomly.

Stratified random sampling, where the members of each stratum to be sampled are chosen at random.

Systematic random sampling, where one or more of the parameters of the system to be used are randomly determined.

Each general type of sampling has its own distinctive strengths and appropriate use cases. Random sampling is the most common and versatile. Systematic sampling often proves to be the simplest option. Convenience sampling can be beneficial where ease of use is more important than statistical significance.

Cluster sampling is most often employed when easily identifiable subgroups occur naturally within a population. Finally, stratified sampling can be used to make sure samples are taken from all subgroups of interest.