Sampling method refers to the way that observations are selected from a population to be in the sample for a sample survey.
Population Parameter vs. Sample Statistic
The reason for conducting a sample survey is to estimate the value of some attribute of a population.
Population parameter. A population parameter is the true value of a population attribute.
◾Sample statistic. A sample statistic is an estimate, based on sample data, of a population parameter.
Consider this example. A public opinion pollster wants to know the percentage of voters that favor a flat-rate income tax. The actual percentage of all the voters is a population parameter. The estimate of that percentage, based on sample data, is a sample statistic.
The quality of a sample statistic (i.e., accuracy, precision, representativeness) is strongly affected by the way that sample observations are chosen; that is., by the sampling method.
Probability vs. Non-Probability Samples
As a group, sampling methods fall into one of two categories.
Probability samples. With probability sampling methods, each population element has a known (non-zero) chance of being chosen for the sample.
◾Non-probability samples. With non-probability sampling methods, we do not know the probability that each population element will be chosen, and/or we cannot be sure that each population element has a non-zero chance of being chosen.
Non-probability sampling methods offer two potential advantages - convenience and cost. The main disadvantage is that non-probability sampling methods do not allow you to estimate the extent to which sample statistics are likely to differ from population parameters. Only probability sampling methods permit that kind of analysis.
Non-Probability Sampling Methods
Two of the main types of non-probability sampling methods are voluntary samples and convenience samples.
Voluntary sample. A voluntary sample is made up of people who self-select into...