Stratified Sampling

Examples of Stratified Sampling in the following topics:

• Lab 2: Sampling Experiment

  • The student will demonstrate the simple random, systematic, stratified, and cluster sampling techniques.
  • Pick a stratified sample, by city, of 20 restaurants.
  • Pick a stratified sample, by entree cost, of 21 restaurants.
  • Pick a cluster sample of restaurants from two cities.
  • 1.14.7 Restaurants Stratified by City and Entree CostRestaurants Used in Sample

• Three sampling methods (special topic)

  • Stratified sampling is a divide-and-conquer sampling strategy.
  • The downside is that analyzing data from a stratified sample is a more complex task than analyzing data from a simple random sample.
  • The analysis methods introduced in this book would need to be extended to analyze data collected using stratified sampling.
  • Examples of simple random, stratified, and cluster sampling.
  • In the middle panel, stratified sampling was used: cases were grouped into strata, and then simple random sampling was employed within each stratum.

• Summary

  • Each member of the population has an equal chance of being selected- Sampling Methods

• Sampling Techniques

  • As long as the starting point is randomized, systematic sampling is a type of probability sampling.
  • Stratified sampling can increase the cost and complicate the research design.
  • In quota sampling, the population is first segmented into mutually exclusive subgroups, just as in stratified sampling.
  • In quota sampling the selection of the sample is non-random.
  • Accidental sampling (or grab, convenience, or opportunity sampling) is a type of non-probability sampling which involves the sample being drawn from that part of the population which is close to hand.

• Random Sampling

  • A random sample, also called a probability sample, is taken when each individual has an equal probability of being chosen for the sample.
  • Systematic and stratified techniques, discussed below, attempt to overcome this problem by using information about the population to choose a more representative sample.
  • Stratified sampling, which is discussed below, addresses this weakness of SRS.
  • Each sample would be combined to form the full sample.
  • Categorize a random sample as a simple random sample, a stratified random sample, a cluster sample, or a systematic sample

• Inferential Statistics

  • What is the sample?
  • Was the sample picked by simple random sampling?
  • Sometimes it is not feasible to build a sample using simple random sampling.
  • Since simple random sampling often does not ensure a representative sample, a sampling method called stratified random sampling is sometimes used to make the sample more representative of the population.
  • In stratified sampling, you first identify members of your sample who belong to each group.

• Random Samples

  • In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling (blocking).
  • Simple random sampling is a basic type of sampling, since it can be a component of other more complex sampling methods.
  • Further, for a small sample from a large population, sampling without replacement is approximately the same as sampling with replacement, since the odds of choosing the same individual twice is low.
  • Conceptually, simple random sampling is the simplest of the probability sampling techniques.
  • If these conditions are not true, stratified sampling or cluster sampling may be a better choice.

• Defining the Sample and Collecting Data

  • The stages of the sampling process are defining the population of interest, specifying the sampling frame, determining the sampling method and sample size, and sampling and data collecting.
  • There are various types of samples.
  • Examples of types of samples include simple random samples, stratified samples, cluster samples, and convenience samples.
  • Sampling errors and biases, such as selection bias and random sampling error, are induced by the sample design.
  • Non-sampling errors are other errors which can impact the results, caused by problems in data collection, processing, or sample design.

• Determining Sample Size

  • Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample.
  • The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.
  • In complicated studies there may be several different sample sizes involved.
  • For example, in a survey sampling involving stratified sampling there would be different sample sizes for each population.
  • Sample sizes are judged based on the quality of the resulting estimates.

• Samples

  • This process of collecting information from a sample is referred to as sampling.
  • The best way to avoid a biased or unrepresentative sample is to select a random sample, also known as a probability sample.
  • Several types of random samples are simple random samples, systematic samples, stratified random samples, and cluster random samples.
  • A sample that is not random is called a non-random sample, or a non-probability sampling.
  • Some examples of nonrandom samples are convenience samples, judgment samples, and quota samples.