SAMPLING METHODS

Sampling

Sampling refers to the statistical process of selecting and studying the characteristics of a

relatively small number of items from a relatively large population of such items,, to draw

statistically valid inferences about the characteristics about the entire population.

Methods

• There are two broad methods of sampling used by researchers.

Non-random (or non-

probability) sampling Random (or probability)

sampling.

Random sampling(Probability sampling)

• In this technique, each member of the population has an equal chance of being selected as

subject.

• The entire process of sampling is done in a single step with each subject selected

independently of the other members of the population.

Types of random sampling methods

Methods

simple random

sampling,

systematic sampling,

stratified sampling, and

cluster sampling.

Simple random sampling

• Simple random sampling ensures that each possible sample has an equal probability of being

selected, and each item in the entire population has an equal chance of being included in the

sample.

• The entire process of sampling is done in a single step with each subject selected

independently of the other members of the population.

• There are many methods to proceed with simple random sampling. The most primitive and

mechanical would be the lottery method.

Conti…

• Each member of the population is assigned a unique number. Each number is placed in a

bowl or a hat and mixed thoroughly.

• The blind-folded researcher then picks numbered tags from the hat. All the individuals

bearing the numbers picked by the researcher are the subjects for the study.

• Another way would be to let a computer do a random selection from your population. For

populations with a small number of members, it is advisable to use the first method but if the

population has many members, a computer-aided random selection is preferred.

Systematic Sampling

• In systematic random sampling, the researcher first randomly picks the first item or subject

from the population. Then, the researcher will select each n'th subject from the list.

• The procedure involved in systematic random sampling is very easy and can be done

manually.

• The results are representative of the population unless certain characteristics of the

population are repeated for every n'th individual, which is highly unlikely.

Conti…

The process of obtaining the systematic sample is much like an arithmetic progression.

• Starting number :

The researcher selects an integer that must be less than the total number of individuals in the

population. This integer will correspond to the first subject.

• Interval :

The researcher picks another integer which will serve as the constant difference between any

two consecutive numbers in the progression.

• The integer is typically selected so that the researcher obtains the correct sample size

• For example, the researcher has a population total of 100 individuals and need 12 subjects.

He first picks his starting number, 5.

• Then the researcher picks his interval, 8. The members of his sample will be individuals 5, 13,

21, 29, 37, 45, 53, 61, 69, 77, 85, 97.

Stratified Sampling

• Stratified sampling is a probability sampling technique wherein the researcher divides the entire

population into different subgroups or strata, then randomly selects the final subjects

proportionally from the different strata.

• It is important to note that the strata must be non-overlapping. This completely negates the

concept of stratified sampling as a type of probability sampling.

• Equally important is the fact that the researcher must use simple probability sampling within the

different strata.

• The most common strata used in stratified random sampling are age, gender, socioeconomic status,

religion, nationality and educational attainment.

Cluster Sampling

• In cluster sampling, instead of selecting all the subjects from the entire population right off, the

researcher takes several steps in gathering his sample population.

First, the researcher selects groups or clusters, and then from each cluster, the researcher selects

the individual subjects by either simple random or systematic random sampling. The researcher

can even opt to include the entire cluster and not just a subset from it.

• The most common cluster used in research is a geographical cluster. For example, a researcher

wants to survey academic performance of high school students in Spain. He can divide the entire

population (population of Spain) into different clusters (cities).

Conti…

• Then the researcher selects a number of clusters depending on his research through simple

or systematic random sampling.

• Then, from the selected clusters (randomly selected cities) the researcher can either include

all the high school students as subjects or he can select a number of subjects from each

cluster through simple or systematic random sampling.

• The important thing to remember about this sampling technique is to give all the clusters

equal chances of being selected.

• Types of cluster sample.

1. ONE-STAGE CLUSTER SAMPLE

2. TWO-STAGE CLUSTER SAMPLE

Non random sampling (Non-probability sampling)

• Non probability sampling is also known by different names such as deliberate sampling,

purposive and judgement sampling.

• It is that sampling procedure which does not afford any basis for estimating the probability

that each item in the population has of being included in the sample.

• It does not allow the study's findings to be generalized from the sample to the population.

• When discussing the results of a non-probability sample, the researcher must limit his/her

findings to the persons or elements sampled.

Non-random sampling methods

Methods

Convenience sampling

Purposive sampling

Judgment sampling Quota sampling Snowball

sampling

Convenience sampling

• Convenience sampling is a non-probability sampling technique where subjects are selected

because of their convenient accessibility and proximity to the researcher.

• The subjects are selected just because they are easiest to recruit for the study and the

researcher did not consider selecting subjects that are representative of the entire

population.

Conti…

• In all forms of research, it would be ideal to test the entire population, but in most cases, the

population is just too large that it is impossible to include every individual.

• This is the reason why most researchers rely on sampling techniques like convenience

sampling, the most common of all sampling techniques. Many researchers prefer this

sampling technique because it is fast, inexpensive, easy and the subjects are readily available.

• http://rchsbowman.wordpress.com/2009/08/16/statistics-notes-sampling-techniques-2/

Purposive sampling

• In purposive sampling we sample with a purpose in mind.

• In purposive sampling, the researcher employs his or her own "expert” judgment about who

to include in the sample frame.

• Prior knowledge and research skill are used in selecting the respondents or elements to be

sampled.

• We usually would have one or more specific predefined groups we are seeking .

• Used for situations for reaching a target sample quickly.

Judgement sampling

• A form of convenience sampling in which the population elements are purposively selected

based on the judgement of the researcher.

• It is low cost, convenient and quick.

• It is useful if broad population inferences are not required.

Quota Sampling

• Quota sampling is a non-probability sampling technique wherein the assembled sample has

the same proportions of individuals as the entire population with respect to known

characteristics, traits or focused phenomenon.

• In addition to this, the researcher must make sure that the composition of the final sample to

be used in the study meets the research’s quota criteria.

• The main reason why researchers choose quota samples is that it allows the researchers to

sample a subgroup that is of great interest to the study. If a study aims to investigate a trait or

a characteristic of a certain subgroup, this type of sampling is the ideal technique.

• Quota sampling also allows the researchers to observe relationships between subgroups. In

some studies, traits of a certain subgroup interact with other traits of another subgroup.

Snowball sampling

• In snowball sampling, you begin with identifying someone who meets the criteria for

inclusion in your studies

• You then ask them to recommend others who they may know who also meet the criteria.

• It is useful when you are trying to reach populations that are inaccessible or hard to find.

THANK YOU….

Names Roll nos.

ABHISHEK SOBALKAR 3

AKSHAY SHITOLE 9

ANKITA GAUTAM 15

DARSHAN JAIN 21

DISHA DESAI 27

HITESH MADNANI 33

JASIM SHAIKH 39