Business Research MethodsMade By: Abdul Basit

Sampling

Process of choosing a representative portion of the entire population is called Sampling.

Population The entire group of

people,events,or things of interest that the researcher wishes to investigate.

Example: For example, you might be

interested in the laundry detergent preferences of Pakistani women who live

in urban areas. This group of people is the population

whose preferences you will study.

Element An element is a single

member of the population. Example: If in an organization the

researcher wants to study the profile data of

workers in population , then each

worker is an element in this population.

Sample A sample is a subset of the

population. It contains some members selected from it.

Example: The Population of GCUF

students is 600,only 200 GCUF are included as the target population and only 100 students are chosen as samples for the actual study.

Sampling Unit The sampling unit is the element or

set of elements that is available for selection in some stage of sampling process.

Example: In a Sampling Unit samples are city

blocks,households,and individuals within

households.

Subject A subject is a single

member of the sample.

Example of Sampling Elements

Parameters The characteristics of the population. Such as the population mean, the the population variance etc.

Representativeness We calculate the sample statistics so

that these can be used as estimates of the

population parameters.

Reasons for Sampling

Sampling is used because Save time and moneyAccurate measurementWide surveyScientific researchReduce the demands on resources i.e.

cost of investigationWhen results are quickly required

The Sampling Process

1.Define the Population Sampling Process begins with

defining the target population. The population must be defined in terms of elements, geographical boundaries and time.

Example: For an advertising agency interested

in reading habits of elderly people, the target population might be the population aged 50 and over.

2. Determine the sample frame

The sample frame is the list of all elements in the population from which the sample is drawn.

Example:

Telephone book directoryVoter listRandom digit dialing This is essential for probability

sampling.

3.Determine the Sample design

There are two major types of sampling design: probability and non probability sampling.

In probability sampling, the elements in the population have some known, non-zero chance or probability of being selected as sample subjects.

In non probability sampling, the elements do not have a known or predetermined chance of being selected as subjects.

4.Determine the sample size

Determining the sample size will be based on six factors such as:

The research objective;Level of Accuracy desiredThe amount of variability in the

population itself;Cost and time to generate sampleYour knowledge of the size of

populationExperience with the risk of sampling

5.Execute the sample process The final step in the sample process

involves execution of the operational sampling plan.

It is important that this step include adequate checking to make sure that specified procedures are implemented.

Probability Sampling Probability sampling

involves the selection of elements from the population using random in which each element of the population has an equal and independent chance of being chosen.

Types of Probability Sampling

Simple Random Sampling:

In which every element in the population has a known and equal chance of being selected

as a subject.

Example: If a sample of 100 students is to be

selected from a population of 1000 students, then it is know to every one that each student has 1000/100 i.e. 1 chance in 10 being selected.

•Stratified Random Sampling Stratified random

sampling involves dividing up the population into smaller groups, and randomly sampling from each group.

Types:• Proportionate• Disproportionate

Example: Randomly select 1 to 5 numbers as like 4,7,13,19 and 21.

Note, one element is selected from each column.

•Restricted/Complex Probability Sampling As an alternative to the simple

random sampling design, several complex probability sampling designs can be used. These probability sampling procedures offer a viable, and sometimes more efficient, alternative to the unrestricted design. The five most common complex……

next all probability sampling types under it.

•Systematic Sampling The systematic sampling design

involves drawing every nth element in the population starting with a randomly

chosen element between 1 and n.

Example There are 260 houses and a sample of

35 households is desired. We have to sample every nth house starting from a random number from 1 to 7.Let us say that the random sample number was 7,then houses numbered 7,14,21,28, and so on, would be sampled until 35 houses were selected.

• Cluster Sampling Cluster samples are used when

population is divided into groups or clusters.Then,a random sample of clusters is drawn and for each selected cluster either all the elements or a sample of elements are included in the sample.

Types Of Cluster SamplingSingle Stage Cluster Sampling

Multi Stage Cluster Sampling

And other specific type isArea Sampling

•Single Stage Sampling In which involves the division of the

population into convenient clusters , randomly choosing required number of clusters as sample subjects, and investigating all the elements

in each of the randomly chosen clusters.

• Multi Stage Sampling Involves choosing sample using more

than two sampling techniques. This type is rarely used of the complexity of its application. Its requires

a lot of effort,time,and cost.

•Area Sampling It is a method of cluster sampling and in

connection With selection of sampling area with help

of maps. Area sampling is less expensive than most

other probability sampling designs.

Example: The city of Karachi can be divided on

the basis of municipal wards of zone. A random selection of this is made within each of the areas selected; a sub sample of locality or sample of residence is taken & then investigated.

Double Sampling This plan is resorted to when further

information is needed from which some information has already been collected for the same study. A sampling design where initially a sample is used in a study to collect some preliminary information of interest, later a subsample of this primary sample is used to examine the matter in more detail, is calls double sampling.

Double Sampling Simple Definition:

The same sample or a subset of the sample is studied twice is called Double Sampling.

Non Probability Sampling In no probability sampling

designs, the elements in the population do not have any probabilities attached to their being chosen as sample subject.

•Convenience Sampling Convenience sampling refers to the collection

of information from members of the population

who are conveniently available to provide it. It involves the non random selection of subjects who are conveniently available.

Example: A Pepsi contest was held in shopping mall

visited by many shoppers. Those inclined to take the test might form the sample for the study of how many

people prefer Pepsi over Coke or product X to product Y.Such sample is a Convenience sampling

Purposive Sampling This is necessarily useful when a group of subjects is needed to participate in a pretest of newly developed instruments or when a group of experts is desirable to validate research information.

Types: • Judgment sampling• Quota sampling

•Judgment Sampling Judgment sampling involves

the nonrandom selection of elements

based on the researcher’s judgment

and knowledge about the population.

Example: A TV researcher wants a quick sample of

opinions about a political topic. He stops what seems like people in the street to get their views.

•Quota Sampling Quota sampling, a second type

of purposive sampling, ensures that certain groups are adequately represented in the study through

the assignment of a quota. Generally, the quota is fixed for each subgroup based on the total numbers of each group in the population.

Example:A sample of 40 students can be selected from a

group of 200 students comprising of 120 boys and 80

girls. to make the sample representative, the group

of 40 should include 24 boys and 16 girls (i.e.

120:80=3:2).

Table. Probability and non probability sampling designs

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