Types of Sampling Technic

Types of Sampling:   There are divided by

Probability

          Simple Random

            Systematic Random

               Stratified Random

                   Cluster sampling

                       Multistage Sampling

Non-Probability

               Convenience Sampling

                     Purposive Sampling

                                 

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 the survey. Often, these folks have a strong interest in the main topic of the survey.
  
  Suppose, for example, that a news show asks viewers to participate in an on-line poll. This would be a volunteer sample. The sample is chosen by the viewers, not by the survey administrator.
• Convenience sample. A convenience sample is made up of people who are easy to reach.
  
  Consider the following example. A pollster interviews shoppers at a local mall. If the mall was chosen because it was a convenient site from which to solicit survey participants and/or because it was close to the pollster's home or business, this would be a convenience sample.

Probability Sampling Methods

The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population. This ensures that the statistical conclusions will be valid.

• Simple random sampling. Simple random sampling refers to any sampling method that has the following properties.
• The population consists of N objects.
• The sample consists of n objects.
• If all possible samples of n objects are equally likely to occur, the sampling method is called simple random sampling.

There are many ways to obtain a simple random sample. One way would be the lottery method. Each of the N population members is assigned a unique number. The numbers are placed in a bowl and thoroughly mixed. Then, a blind-folded researcher selects n numbers. Population members having the selected numbers are included in the sample.

Sampling With Replacement and Without Replacement

Suppose we use the lottery method described above to select a simple random sample. After we pick a number from the bowl, we can put the number aside or we can put it back into the bowl. If we put the number back in the bowl, it may be selected more than once; if we put it aside, it can selected only one time.
When a population element can be selected more than one time, we are sampling with replacement. When a population element can be selected only one time, we are sampling without replacement.

Stratified sampling.

 Stratification is the process of grouping members of the population into relatively homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded. Then random or systematic sampling is applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population.

A stratified sample is a probability sampling technique in which the  divides the entire  population into different subgroups, or strata based on some characteristic, and then randomly selects the final subjects proportionally from the different strata. The strata should also be collectively This type of sampling is used when the researcher wants to highlight specific subgroups within the population.

For example, to obtain a stratified sample of university students, the researcher would first organize the population by college class and then select appropriate numbers of freshmen, sophomores( second year), juniors, and seniors. This ensures that the researcher has adequate amounts of subjects from each class in the final sample.  

As a example, suppose we conduct a national survey. We might divide the population into groups or strata, based on geography - north, east, south, and west. Then, within each stratum, we might randomly select survey respondents.

• Systematic random sampling. With systematic random sampling, we create a list of every member of the population. From the list, we randomly select the first sample element from the first k elements on the population list. Thereafter, we select every kth element on the list. Where “K= (N/n)”
  
  
• Cluster sampling.
• Cluster sampling is a sampling technique used when "natural" groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a sample of the groups is selected. Then the required information is collected from the elements within each selected group. This may be done for every element in these groups or a subsample of elements may be selected within each of these groups. A common motivation for cluster sampling is to reduce the average cost per interview. Given a fixed budget, this can allow an increased sample size. Assuming a fixed sample size, the technique given more accurate results when most of the variation in the population is within the groups, not between them.
• With cluster sampling, every member of the population is assigned to one, and only one, group. Each group is called a cluster. A sample of clusters is chosen, using a probability method (often simple random sampling). Only individuals within sampled clusters are surveyed.
  
  Note the difference between cluster sampling and stratified sampling. With stratified sampling, the sample includes elements from each stratum. With cluster sampling, in contrast, the sample includes elements only from sampled clusters.
• Multistage sampling. With multistage sampling, we select a sample by using combinations of different sampling methods.
  
  For example, in Stage 1, we might use cluster sampling to choose clusters from a population. Then, in Stage 2, we might use simple random sampling to select a subset of elements from each chosen cluster for the final sample.