Types of Probability Sampling

Once you know the characteristics of the target population and how the population will be identified, you next need to determine the specific probability sampling procedure by which individuals are selected for study inclusion. The three most common forms of probability sampling used in advertising research are simple random, systematic random, and stratified random samples.

Simple Random Samples.

Simple random samples are frequently used in advertising research. Here, each member of the population has an equal chance of being selected for inclusion in the research. By randomly selecting individuals from the sample universe, we can accurately estimate the behaviors of the entire target population.

Systematic Random Samples.

A variation of a simple random sample is a systematic random sample. Systematic random samples typically provide data identical to simple random samples with the added advantage of simplicity—no table of random numbers or coin toss is needed, and sample size can be firmly specified.

Count the number of elements on the list.
Determine the desired sample size.
Compute a skip interval.
Select a random place on the list to start.
Select each element at the appropriate skip interval.

Simple and Systematic Samples: Online Selection.

The prior discussion of simple and systematic samples assumed that a sample frame was available and that random sampling could be used to select individuals from the identified source, either through explicit selection or through techniques such as random digit dialing. There are times, however, that the sample frame reflects a set of behaviors and, as a result, not all individuals in the sample frame are known.

Stratified Random Samples

In the prior examples, simple and systematic random sampling techniques worked well. They were efficient and provided reliable generalizations about the total population. However, these forms of sampling worked well only because the universe was relatively homogeneous with respect to what was being measured. Simple and systematic random samples provide fewer reliable generalizations about the total population when significant differences among population subgroups are suspected. Stratified random sampling is a better choice versus simple and systematic random sampling whenever you have a situation where you think there is a large variation in what you are studying due to specific respondent characteristics.

Stratified sampling is accomplished a four-step process and described below for the social media usage universe:

First, one or more classification criteria that define the strata are identified. These classification criteria should define independent strata that do not overlap.
Second, each element in the sample frame is assigned to one and only one stratum.
Third, the total sample size is determined.
Fourth, independent random samples (using either simple or systematic sampling methods) are selected from each stratum in a way that results in the total sample size being achieved.

The fourth step, sampling from each stratum in order to achieve the desired final sample size, presents two options regarding the number of elements selected from each stratum. Either proportionate or disproportionate sampling may be used.

Proportionate stratified sampling selects individuals in proportion to their stratum’s size within the total target population.

Disproportionate stratified sampling selects a predetermined number of elements from each stratum despite the relative size of those strata.

	Erstellt von Roxanne V Springman vor etwa 7 Jahre

Important stuff from the book