SAMPLING METHODS

Senjuti

WHAT IS ‘SAMPLING’?

Procedure by which some members

of a population are selected as

representative of the entire

population.

The information gathered from the

small group will allow judgements to

be made about the larger groups.

SAMPLE

A sample is the segment of the population we

wish to study

A great deal of care is needed in selecting the

sample so we can say that the data we

obtain from it is meaningful .

For these reasons we use sampling methods.

RIMS

PGDM

METHODS OF SAMPLING

Several methods are used to ascertain a

particular aspect of the population, through

an unbiased sample drawn from the

population.

Probability Sampling

Non-probability Sampling

PROBABILITY

A probability sampling scheme is one in

which every unit in the population has a chance

(greater than zero) of being selected in the

sample, and this probability can be accurately

determined.

Ensures the sample is representative.

Can calculate sample error.

Can project results to whole population, with

allowance made for sampling error.

WHAT IS…………

Simple Random Sampling

Systematic Sampling

Stratified Sampling

Cluster Sampling

TYPES OF PROBABILITY SAMPLING

SIMPLE RANDOM SAMPLING(SRS)

A sample is selected such that each possible sample combination

has equal probability of being chosen.

Applicable when population is small, homogeneous & available.

It provides for greatest number of possible samples. This is

done by assigning a number to each unit in the sampling

frame.

A table of random number or lottery system is used to

determine which units are to be selected.

METHODS OF SELECTION OF A SIMPLE

RANDOM SAMPLING: Lottery Method

Table of Random numbers

Random number selections using

calculators or computers

EXAMPLEIn a lottery all the members of the

population have the equal chance of

being selected.

STRENGTHSEasily understood, result projectable.

Estimates are easy to calculate.

WEAKNESSESDifficult to construct sampling frame,

expensive, lower precision, no assurance of

repetitiveness.

SYSTEMATIC SAMPLING

It relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.

Entire population is numbered and elements are selected using a skip interval-e.g Every 3rd person in the list of Phone Directory.

Requires complete list of population of interest.

STRENGTHSCan increase representativeness.

Quick, efficient, saves time and cost.

WEAKNESSESNot entirely bias free, each item doesn’t

have equal chance to get selected.

Can decrease repetitiveness if there are

cyclical patterns

STRATIFIED SAMPLING Where population embraces a number of distinct

categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected.

Stratification means division into groups.

In this method the population is divided into a

number of subgroups or strata

From each stratum a simple random sample is

selected and combined together to form the required

sample from the population

FIGURE .1

STRENGTHS Stratifying our sample enable us to ensure that they are

included.

We divide the sample into groups in which we are interested

and then sample from the sub groups

WEAKNESSES First, sampling frame of entire population has to be

prepared separately for each stratum.

CLUSTER SAMPLING

It is an example of 'two-stage sampling' .

First stage a sample of areas is chosen;

Second stage a sample of respondents within those

areas is selected.

Population divided into clusters of homogeneous units,

usually based on geographical contiguity.

Sampling units are groups rather than individuals.

A sample of such clusters is then selected.

All units from the selected clusters are studied

STRENGTHSCuts down on the cost of preparing a sampling frame.

This can reduce travel and other administrative costs.

Disadvantages: sampling error is higher for a simple random sample of same size.

WEAKNESSES Imprecise.

Difficult to compute and interpret the result.