Random Sampling

 Sampling

Measuring a small portion of something and then making a general statement about the whole thing is known as sampling.

• Sampling is a process of selecting a number of units for a study in such a way that the units represent the larger group from which they are selected.

Function

Since it is generally impossible to study an entire population (e.g. many individuals in a country, all college students, every geographic area etc.) researchers typically rely on sampling to acquire a section of the population to perform an experiment or observational study.

It is important that the group selected be representative of the population and not biased in a systematic manner.

For example, a group comprised of wealthiest individuals population in a given area probably would not accurately reflect the opinion of the entire population in that area.

Types of Sampling

A. Probability sampling:

A method of sampling that uses random selection so that all units/cases in the population have an equal probability of being chosen is known as probability or random sampling.

Advantages:

1. Easy to conduct.

2. High probability of achieving a representative sample.

3. Meets assumptions ofmany statistical procedure.

Disadvantages:

1. Identification of all members of the population can be difficult.

2. Contacting all members of the sample can be difficult.

B. Non-probability sampling:

Sampling that does not involve random selection and methods are not based on the10rationale of probability theory is non-probability or non-random sampling.

Simple Random Sampling

Simple random sampling is a probability sampling method.

Definition : A Simple Random Sample (SRS)  consists of n individuals from the population chosen such a way that every individual has the equal chance to be selected.

Advantage:

1. Simple to conduct.

2. Each unit has an equal chance of being selected.

Disadvantage :

1. Complete list of individuals in the universe is required.

 Systemic (Random) Sampling

There is a gap or interval between each between each selected species of the sample.

Selection of units is based on sample interval k starting from a determined point where k= N/n.

Steps:

1. Number the units  on your frame from 1 to N, and the population are arranged in the same way.

2. First sample drawn between 1 and K randomly. (Determine present/ random start.)

3. Afterwards, every k th must be drawn, untill the total sample has been drawn.

 Stratified Random Sampling

A stratified random sample is obtained by dividing the Population elements into non-overlapping groups, called strata and then selecting a random sample directly and independently from each stratum.

 A stratified SRS is a special case of stratified sampling that uses SRS for selecting units from each stratum.

Cluster Sampling

Cluster Sampling is defined as a sampling technique in which the elements of the population is divided in existing groups (cluster).

Then a sample from the cluster is selected randomly from the population.

sub-types:

1. Single stage cluster Sampling.
2. Double Stage cluster Samplimg.
3. Multiple Stage cluster Sampling.