Systematic Sampling

(noun)

Systematic sampling relies on arranging the target population according to some ordering scheme, a random start, and then selecting elements at regular intervals through that ordered list.

Related Terms

Examples of Systematic Sampling in the following topics:

  • Lab 2: Sampling Experiment

    • The student will demonstrate the simple random, systematic, stratified, and cluster sampling techniques.
    • In this lab, you will be asked to pick several random samples.
    • 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.

  • Sampling Techniques

    • Systematic sampling relies on arranging the target population according to some ordering scheme, a random start, and then selecting elements at regular intervals through that ordered list.
    • As long as the starting point is randomized, systematic sampling is a type of probability sampling.
    • Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy.
    • As described above, systematic sampling is an EPS method, because all elements have the same probability of selection.
    • In quota sampling the selection of the sample is non-random.

  • Summary

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

  • Random Sampling

    • Systematic and stratified techniques, discussed below, attempt to overcome this problem by using information about the population to choose a more representative sample.
    • Each sample would be combined to form the full sample.
    • Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.
    • Systematic sampling involves a random start and then proceeds with the selection of every kkth element from then onward.
    • Categorize a random sample as a simple random sample, a stratified random sample, a cluster sample, or a systematic sample

  • Variation in Samples

    • Doreen and Jung each take samples of 500 students.
    • Doreen uses systematic sampling and Jung uses cluster sampling.
    • Doreen's sample will be different from Jung's sample.
    • Even if Doreen and Jung used the same sampling method, in all likelihood their samples would be different.
    • Be aware that many large samples are biased.

  • 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.

  • How Well Do Probability Methods Work?

    • In earlier sections, we discussed how samples can be chosen.
    • Failure to use probability sampling may result in bias or systematic errors in the way the sample represents the population.
    • However, even probability sampling methods that use chance to select a sample are prone to some problems.
    • Recall some of the methods used in probability sampling: simple random samples, stratified samples, cluster samples, and systematic samples.
    • Random sampling eliminates some of the bias that presents itself in sampling, but when a sample is chosen by human beings, there are always going to be some unavoidable problems.

  • Chance Error and Bias

    • In statistics, a sampling error is the error caused by observing a sample instead of the whole population.
    • The variations in the possible sample values of a statistic can theoretically be expressed as sampling errors, although in practice the exact sampling error is typically unknown.
    • In sampling, there are two main types of error: systematic errors (or biases) and random errors (or chance errors).
    • Random sampling is used to ensure that a sample is truly representative of the entire population.
    • It results in a biased sample, a non-random sample of a population in which all individuals, or instances, were not equally likely to have been selected.

  • 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.
    • For example, in a survey sampling involving stratified sampling there would be different sample sizes for each population.
    • This can result from the presence of systematic errors or strong dependence in the data, or if the data follow a heavy-tailed distribution.
    • Sample sizes are judged based on the quality of the resulting estimates.

  • Properties of Sampling Distributions

    • For the case where the statistic is the sample mean, and samples are uncorrelated, the standard error is:
    • Where ss is the sample standard deviation and nn is the size (number of items) in the sample.
    • A statistical study can be said to be biased when one outcome is systematically favored over another.
    • This spread is determined by the sampling design and the size of the sample.
    • Larger samples give smaller spread.