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With the application of probability sampling in the 1930s, surveys became a standard tool for empirical research in social sciences, marketing, and official statistics. [1] The methods involved in survey data collection are any of a number of ways in which data can be collected for a statistical survey. These are methods that are used to ...
A probability-based survey sample is created by constructing a list of the target population, called the sampling frame, a randomized process for selecting units from the sample frame, called a selection procedure, and a method of contacting selected units to enable them to complete the survey, called a data collection method or mode. [10]
A visual representation of the sampling process. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population.
This category is for techniques for statistical sampling from real-world populations, used in observational studies and surveys. For techniques for sampling random numbers from desired probability distributions, see category:Monte Carlo methods.
Other sampling frames can include employment records, school class lists, patient files in a hospital, organizations listed in a thematic database, and so on. [1] [5] On a more practical levels, sampling frames have the form of computer files. [1] Not all frames explicitly list population elements; some list only 'clusters'.
In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies, different sample sizes may be allocated, such as in stratified surveys or experimental designs with multiple treatment groups.
An example of cluster sampling is area sampling or geographical cluster sampling.Each cluster is a geographical area in an area sampling frame.Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster.
Point sampling can be based on a two-stage scheme, sampling clusters in the first stage and sampling points in the second stage. Another option is a two-phase scheme of unclustered points: a large first-phase sample is selected. A stratification is conducted only for the first-phase sample and a stratified sample is chosen in the second phase.