Simple random sampling (SRS): defined as a sample in which all
elements or groups of elements have an equal and known nonzero chance
of being included.
- The simple random sample is the most basic and common type of
probability sample, and is incorporated into all the more elaborate
probability sampling designs.
- The probability of a sampling unit being selected is n/N, where n =
sample size and N = population size.
- Computer programs can used to generate random numbers to select the
sample.
Simple random sampling is utilized by most product/marketing serveys
as it is very simple to initiate (hence the name). Mail surveys also
utilize this method. It is generally used when randomness is a
priority.
Systematic sampling: this method consists of selecting every kth
sampling unit of the population after the first sampling unit is
selected at random from the first k sampling units.
- Systematic sampling is more convenient than simple random sampling.
- Systematic sampling requires only one random act to select the
starting point; simple random sampling requires the random selection
of every element to be included in the sample.
- It is easier to select every kth element from a list than to use a
table of random numbers.
- With systematic sampling, each element in the population has a 1/k
probability of being included in the sample.
- The sampling interval is the standard distance between elements
selected in the sample (k).
sampling interval = population size/sample size
- The sampling ratio is the proportion of elements in the population
that are selected.
sampling ratio = sample size/population size
Certain mailout home surveys also utilize this method of sampling. It
is generally used when simplicity is priority. There are, however,
disadvantages to this type of sampling; there may be a reoccuring
patter that has a period of every kth unit, causing bias in the
sample. Also, the order of the list may affect the randomness of this
type of sample.
Stratified random sampling: the population is divided into parts or
strata according to some characteristic (religion, race, social class,
etc.), and then a random sample is selected from each of the defined
strata.
- Sampling error: Stratified random sampling is a method for obtaining
a greater degree of representativeness, which decreases sampling
error.
- A larger sample produces a smaller sampling error that a small
sample.
- A homogeneous population produces samples with smaller sampling
errors than does a heterogeneous population.
- The function of stratification is to organize the population into
homogeneous subsets (with heterogeneity between subsets) and to select
the appropriate number of elements from each.
This method is generally used when there are certain variables that
must be kept constant within the sample.
Hope this helps.
-Tox-ga
Source: http://www2.chass.ncsu.edu/Judge/PS371/Lecture13.htm |
