Types of Sampling
The process of selecting a subset of units or individuals (a portion or sample) from a population of interest so that by examining the sample, we can generalize the results to the whole population.
 A shortcut method for investigating a whole population
 Data is gathered on a small part of the whole parent population or sampling frame, and used to inform what the whole picture is like
Advantages of sampling
1. Very accurate.
2. Economical in nature.
3. Very reliable.
4. High suitability ratio towards the different surveys.
5. Takes less time.
6. In cases, when the universe is very large, then the sampling method is the only practical method for collecting the data.
Disadvantages of sampling
1. Inadequacy of the samples.
2. Chances for bias.
3. Problems of accuracy.
4. Difficulty of getting the representative sample.
5. Untrained manpower.
6. Absence of the informants.
7. Chances of committing the errors in sampling.
Characteristics of the sampling technique
1. Much cheaper.
2. Saves time.
3. Much reliable.
4. Very suitable for carrying out different surveys.
5. Scientific in nature.
Types of Sampling:
1. Very accurate.
2. Economical in nature.
3. Very reliable.
4. High suitability ratio towards the different surveys.
5. Takes less time.
6. In cases, when the universe is very large, then the sampling method is the only practical method for collecting the data.
Disadvantages of sampling
1. Inadequacy of the samples.
2. Chances for bias.
3. Problems of accuracy.
4. Difficulty of getting the representative sample.
5. Untrained manpower.
6. Absence of the informants.
7. Chances of committing the errors in sampling.
Characteristics of the sampling technique
1. Much cheaper.
2. Saves time.
3. Much reliable.
4. Very suitable for carrying out different surveys.
5. Scientific in nature.
Types of Sampling:
Sampling Strategies and their Advantages and Disadvantages
Type of Sampling 
When to use it

Advantages

Disadvantages

Probability Strategies




Simple Random Sampling 
When the population members are similar to one another on
important variables

Ensures a high degree of representativeness

Time consuming and tedious

Systematic Sampling

When the population members are similar to one another on
important variables

Ensures a high degree of representativeness, and no need
to use a table of random numbers

Less random than simple random sampling

Stratified Random Sampling

When the population is heterogeneous and contains several
different groups, some of which are related to the topic of the study

Ensures a high degree of representativeness of all the
strata or layers in the population

Time consuming and tedious

Cluster Sampling

When the population consists of units rather than
individuals

Easy and convenient

Possibly, members of units are different from one another,
decreasing the techniques effectiveness

NonProbability Sampling




Convenience Sampling

When the members of the population are convenient to
sample

Convenience and inexpensive

Degree of generalizability is questionable

Quota Sampling

When strata are present and stratified sampling is not
possible

Insures some degree of representativeness of all the
strata in the population

Degree of generalizability is questionable

Types of Sampling
We may then consider different types of probability samples. Although there are a number of different methods that might be used to create a sample, they generally can be grouped into one of two categories: probability samples or nonprobability samples.Probability Samples
The idea behind this type is random selection. More specifically, each sample from the population of interest has a known probability of selection under a given sampling scheme. There are four categories of probability samples described below.Simple Random Sampling
The most widely known type of a random sample is the simple random sample (SRS). This is characterized by the fact that the probability of selection is the same for every case in the population. Simple random sampling is a method of selecting n units from a population of size N such that every possible sample of size an has equal chance of being drawn.Systematic Sampling
This method of sampling is at first glance very different from SRS. In practice, it is a variant of simple random sampling that involves some listing of elements  every nth element of list is then drawn for inclusion in the sample. Say you have a list of 10,000 people and you want a sample of 1,000.Creating such a sample includes three steps:
 Divide number of cases in the population by the desired sample size. In this example, dividing 10,000 by 1,000 gives a value of 10.
 Select a random number between one and the value attained in Step 1. In this example, we choose a number between 1 and 10  say we pick 7.
 Starting with case number chosen in Step 2, take every tenth record
Cluster Sampling
In some instances the sampling unit consists of a group or cluster of smaller units that we call elements or subunits (these are the units of analysis for your study). There are two main reasons for the widespread application of cluster sampling. Although the first intention may be to use the elements as sampling units, it is found in many surveys that no reliable list of elements in the population is available and that it would be prohibitively expensive to construct such a list. In many countries there are no complete and updated lists of the people, the houses or the farms in any large geographical region.Nonprobability Sampling
Social research is often conducted in situations where a researcher cannot select the kinds of probability samples used in largescale social surveys. For example, say you wanted to study homelessness  there is no list of homeless individuals nor are you likely to create such a list. However, you need to get some kind of a sample of respondents in order to conduct your research. To gather such a sample, you would likely use some form of nonprobability sampling.To reiterate, the primary difference between probability methods of sampling and nonprobability methods is that in the latter you do not know the likelihood that any element of a population will be selected for study.
There are four primary types of nonprobability sampling methods:
Availability Sampling
Availability sampling is a method of choosing subjects who are available or easy to find. This method is also sometimes referred to as haphazard, accidental, or convenience sampling. The primary advantage of the method is that it is very easy to carry out, relative to other methods. A researcher can merely stand out on his/her favorite street corner or in his/her favorite tavern and hand out surveys. One place this used to show up often is in university courses. Years ago, researchers often would conduct surveys of students in their large lecture courses.Quota Sampling
Quota sampling is designed to overcome the most obvious flaw of availability sampling. Rather than taking just anyone, you set quotas to ensure that the sample you get represents certain characteristics in proportion to their prevalence in the population. Note that for this method, you have to know something about the characteristics of the population ahead of time. Say you want to make sure you have a sample proportional to the population in terms of gender  you have to know what percentage of the population is male and female, then collect sample until yours matches. Marketing studies are particularly fond of this form of research design.Snowball Sampling
Snowball sampling is a method in which a researcher identifies one member of some population of interest, speaks to him/her, then asks that person to identify others in the population that the researcher might speak to. This person is then asked to refer the researcher to yet another person, and so on.Snowball sampling is very good for cases where members of a special population are difficult to locate. For example, several studies of Mexican migrants in Los Angeles have used snowball sampling to get respondents.