sampling techniques - kedar belwalkar - roll no. 17

7/31/2019 Sampling Techniques - Kedar Belwalkar - Roll No. 17

1/15

SAMPLING TECHNIQUES

By Kedar Belwalkar

ITM SMBA13 Batch

Roll No. 17


2/15

SAMPLING

Sampling is the process of selecting a smallnumber of elements from a larger defined target

group of elements such that the information

gathered from the small group will allow judgments

to be made about the larger groups.


3/15

THE SAMPLING DESIGN PROCESS

Define the Population

Determine the Sampling Frame

Select Sampling Techniques

Determine the Sample Size

Execute the Sampling Process


4/15

CLASSIFICATIONOF SAMPLING TECHNIQUES

Sampling Techniques

Non-probability

Sampling Techniques

Probability

Sampling Techniques

Convenience

Sampling

Judgmental

Sampling

Quota

Sampling

Snowball

Sampling

Systematic

Sampling

Stratified

Sampling

Cluster

Sampling

Simple

Random

Sampling


5/15

CONVENIENCE SAMPLING

Convenience Sampling attempts to obtain a sample of convenientelements.

Often, respondents are selected because they happen to be in the

right place at the right time.

Easily accessible to researcher.

Allows researcher to study basic trends without complications.

Examples

Students, Patients in Clinic and members of social organizations

Mall intercept interviews without qualifying the respondents Department stores using charge account lists

People on the Street interviews


6/15

JUDGMENTAL SAMPLING

Judgmental sampling is a form of convenience sampling inwhich the population elements are selected based on the

judgment of the researcher.

Also Known as Purposive Sampling.

Test markets

Purchase engineers selected in industrial marketing

research

A TV researcher wants to do quick sampling of political

view.

Auditor knows which items are had problems in past and

which are higher risk to organization.

Expert witnesses used in court


7/15

QUOTA SAMPLING

Quota sampling may be viewed as two-stage restricted judgmentalsampling.

The first stage consists of developing control categories, or quotas, ofpopulation elements.

In the second stage, sample elements are selected based on

convenience or judgment.

Examples

A researcher in high street takes opinion of 100 people (50 Male & 50Female) for new taste of Maggi.


8/15

SNOWBALL SAMPLING

Snowball sampling, an initial group of respondents isselected, usually at random.

After being interviewed, these respondents are asked to

identify others who belong to the target population ofinterest.

Sample group grows like Rolling Snowball.

Examples

Researcher knows few Magicians but with their reference

sample size grows.


9/15

SIMPLE RANDOM SAMPLING

Each element in the population has a known and equal

probability of selection.

Each possible sample of a given size (n) has a known and

equal probability of being the sample actually selected.

This implies that every element is selected independently ofevery other element.

Example

We have a list of 40 heads of households. Each has a unique

number, 1 through 40. We want to select 10 households

randomly from this list. Using a random number table, we

select consecutive 2-digit numbers starting from the upper left


10/15

SYSTEMATIC SAMPLING

The sample is chosen by selecting a random starting point and thenpicking every ith element in succession from the sampling frame.

The sampling interval, i, is determined by dividing the population sizeN by the sample size n and rounding to the nearest integer.

When the ordering of the elements is related to the characteristic of

interest, systematic sampling increases the representativeness of thesample.

If the ordering of the elements produces a cyclical pattern, systematicsampling may decrease the representativeness of the sample.

For example, there are 100,000 elements in the population and a

sample of 1,000 is desired. In this case the sampling interval, i, is100. A random number between 1 and 100 is selected. If, forexample, this number is 23, the sample consists of elements 23, 123,223, 323, 423, 523, and so on.


11/15

STRATIFIED SAMPLING

A two-step process in which the population is partitioned into

subpopulations, or strata.

The strata should be mutually exclusive and collectively

exhaustive in that every population element should be

assigned to one and only one stratum and no populationelements should be omitted.

Next, elements are selected from each stratum by a random

procedure.

A major objective of stratified sampling is to increase precision

without increasing cost. The elements within a stratum should be as homogeneous as

possible, but the elements in different strata should be as

heterogeneous as possible.


12/15

STRATIFIED SAMPLING

Company Staff:male, full time: 90 male, part time: 18

female, full time: 9 female, part time: 63

Total: 180

Take a sample of 40 staff, stratified according to the above categories.

The first step is to find the total number of staff (180) and calculate the percentage in each

group.

% male, full time = 90 / 180 = 50% % male, part time = 18 / 180 = 10%

% female, full time = 9 / 180 = 5% % female, part time = 63 / 180 = 35%

This tells us that of our sample of 40,

50% should be male, full time. 10% should be male, part time.

5% should be female, full time. 35% should be female, part time.

50% of 40 is 20. 10% of 40 is 4.5% of 40 is 2. 35% of 40 is 14.


13/15

CLUSTER SAMPLING

The target population is first divided into mutually exclusive

and collectively exhaustive subpopulations, or clusters.

For each selected cluster, either all the elements are included

in the sample (one-stage) or a sample of elements is drawn

probabilistically (two-stage). Elements within a cluster should be as heterogeneous as

possible, but clusters themselves should be as homogeneous

as possible. Ideally, each cluster should be a small-scale

representation of the population.


14/15

CHOOSING NON-PROBABILITYVS PROBABILITY

SAMPLING

Conditions Favoring the Use of

Factors Nonprobabilitysampling

Probabilitysampling

Nature of research Exploratory Conclusive

Relative magnitude of samplingand nonsampling errors

Nonsamplingerrors arelarger

Samplingerrors arelarger

Variability in the population Homogeneous(low)

Heterogeneous(high)

Statistical considerations Unfavorable Favorable

Operational considerations Favorable Unfavorable


15/15

INFLATIONCauses, Measures & Anti Measures

By Kedar Belwalkar

ITM SMBA13 Batch

Roll No. - 17

sampling techniques - kedar belwalkar - roll no. 17

Documents