Presentation on Sampling

Group members1. Farman Ullah Khan2. Syed Tauseef Ali3. Tahir Aziz

MS finance 1st semester NUML university

Agenda of the presentation

• Sampling definition• Objective of Sampling• Definition of Sample • Examples of Sample• What is Population • Examples of Population• Reasons for Sampling• Difference between Sample and population

Different terminologies used in sampling• Element • Population• Target Population• Sample frame• Sample frame error • Sampling unit• Subject• Observation unit • Unit of Analysis • Statistics and Parameters

Standard symbols • N = Population size• n= Sample size• µ = Population mean • x’= Sample mean • σ= Population standard Deviation• S= Sample standard Deviation

Types of Sampling

• Probability Sampling or Random Sampling • Non Probability Sampling or non Random or Judgment sampling • Probability Sampling with Example• Non Probability Sampling • Biased Sample

Types of Probability sampling

• 1. Simple random sampling• 2. Systematic sampling• 3. Stratified sampling• 4. Cluster sampling

Types of non Probability

• Convenience sampling• Purposive sampling• Quota sampling• Snow Ball sampling• Sequential Sampling • Theoretical Sampling

Sampling Definition

• The procedure by which we select or draw sample from a given population is called Sampling.

• Objective :• The aim of Sampling is to get maximum

information about the population from which sample is drawn.

Definition of Sample Sample is the subset or portion of population.

Or A small group of people studied to collect

information to draw conclusion about the larger group

Example:1. 10 gram sugar is sample and 5000 sugar bags is population 2. 40 patient out 145 to be surveyed to judge their level of

satisfaction with treatment received

Population

• The population is the set of entities under study• The entire group of people, event or things of interest

that the researcher wishes to investigate.e.g.• If you want to know the average height of the

residents of Punjab, that is your population, ie, the population of Punjab, Pakistan.

• If company CEO want to know the kinds of marketing strategies adopted by firms in Islamabad, then all firm situated there will be population

Different terminologies used in sampling

Element:• An element is a single member of populationFor example:Approval of 1000 machinery parts after inspecting few, so there will

be ,1000 element in this population

Sample Unit:The element or set of element that is available for selection in some

stage of the sampling process.e.g.House hold ,individual in house hold like children under 5 year ,adult

men

Subject

A subject is a single member of the sample.e.g.If sample of 50 machines out of total 500 is to be

inspected then every one of 50 machine is a subject.

Reason for Sampling

• The reason of using sample rather collecting data from the entire population are clear.

• It is practically impossible to investigate several elements in population .

• It is also time consuming ,costly and other recoursese.g.In testing the life of bulbs, if we were to burn every

single bulb produced, there would be none left to sell.

Sampling Process

• Sampling is the selection of sufficient number of the right elements from the population, so that a study of the sample and an understanding of its properties or characteristic make it possible for us to generalize such properties or characteristic to the population elements.

Major Steps in Sampling

• Define the population• Determine the sample frame• Determine the sampling design• Determine the appropriate sample size• Execute the sampling process.

Define the population

• Sampling begin with precisely defining the target population.

• The target population population must be define in terms of elements ,geographical boundaries and time

e.g.The advertising agency interested in reading habits

of elderly people, the target population might be the Pakistan population aged 50 and over

Determine the sample frame

• Sample frame is a (Physical) representation of all the elements in the population from which the sample is drawn

• Sample frame is the actual set of units from which a sample has been drawn

e.g.Payroll of an organization if its member are to be studied.• a survey aimed at establishing the number of potential

customers for a new service in the population of New York City. The research team has drawn 1000 numbers at random from a telephone directory for the city, made 200 calls each day from Monday to Friday from 8am to 5pm and asked some questions.

Example

In this example, population of interest is all inhabitants of the city

the sampling frame includes only those New Your City dwellers who satisfy all the following conditions:

has a telephone;the telephone number is included in the directory;likely to be at home from 8am to 5pm from Monday to

Friday;not a person who refuses to answer all telephone

surveys.

Determine the sampling design

There are two types Sampling Design

• Probability Sampling• Non probability Sampling

Probability Sampling In probability sampling elements of population have some known,

non zero chance or probability of being selected as sample subject.

Non probability Sampling In non probability sampling element do not have a known or

predetermined chance of being selected as subject.

Determine the appropriate sample size

There are some factor affecting decision on sample size i.e.

• The research objective• The extent of precision desired• The acceptable risk in predicting that level of

precision.• The cost and time constraint

Types of sampling

Two types of Sampling1. Probability Sampling2. Non- Probability Sampling

Probability Sampling

• It is also called Random Sampling, 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.

Probability Sampling

• When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.

Non-Probability Sampling

It is also called Non-Random SamplingAny sampling method where some elements of

population have no chance of selection or It is a process in which the personal judgment determines which units of the population are selected for a sample.. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom.

Types of Probability Sampling

1. Simple Random Sampling It is also known as unrestricted Random Sampling. It is selected in such a way that each observation of

population has an equal chance of selection to be included in a sample or the selection of each observation have same chance to select from population. e.g Lottery method.

A simple Random Sample is drawn by one of the following devices ,

1. Tickets numbered from 1 to N for N units of the population are placed in a basket and then units of the sample are drawn one by one.

Simple Random Sampling

2. Random number tables have been constructed and published to draw random samples.

3. Computers are used to draw random samples. • Two concepts of Simple Random Sampling• Sampling schemes may be without replacement

('WOR' - no element can be selected more than once in the same sample) or with replacement ('WR' - an element may appear multiple times in the one sample).

Example

• For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water (e.g. if we eat the fish), this becomes a WOR design.

Stratified Random Sampling

• In stratified Sampling, the population is divided into relatively homogeneous sub-populations. These sub-populations are non overlapping and together they comprise the whole population. These sub-populations are called Strata and the process is called stratification. Simple Random Samples are then drawn independently from each stratum and combined into a single sample. The whole procedure is known as stratified Random Sampling.

Systematic Random Sampling

A Sampling technique in which the first unit is selected with the help of random numbers the remaining can be selected automaticallyaccording to some pre-designed pattern is known

as Systematic Random Sampling. Example; A simple example would be to select every 10th

name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10')

Cluster Sampling

In cluster Sampling the population is divided into many sub-groups, each with only few elements , having heterogeneity

within sub-groups and homogeneity between sub-groups. These sub- groups are called clusters and the process is called clustering. Each cluster is taken as a sampling unit of the population. A random sample of these clusters are then drawn ,these procedure is called clustering sampling.eg First we select group like ten teams and then we choose three teams among them.

Biased Sample

• It is a process in which all participants are not equally balanced or objectively represented. It is also known as Weighted Sample.

Non Probability Sampling

Definition of Non Probability Sampling

“In Non probability sample designs, the elements in the population do not have any probability attached to their being chosen as sample subject”.

Limitation of non probability sampling

•Each element in the population has not an equal chance of being selection in the sample.•The findings from the study of sample can not be generalized over the population.•Subjects are selected as the researcher finds it more convenient

So, why non probability sampling?

• It is used in the initial stage of your research mainly for exploratory research

• It helps to get hold of the topic or understand the research topic by having some basic information

• It is used to have some preliminary information • When you have limited time you use non probability sampling

• When you are interested more in finding the solution to the

problem rather than the generalizability.

Types of Non Probability Sampling

1. Convenience Sampling

2. Purposive Sampling

a) Judgment Sampling or Expert opinion Sampling

b) Quota Sampling

3. Snow ball Sampling

4. Sequential Sampling

5. Theoretical Sampling

Convenience Sampling

Definition of convenience sampling “As its name implies convenience sampling refers to the

collection of information from members of the population who are conveniently available to provide it”.

• Example 1: Ali from a Geo TV channel with a camera man and a microphone in his hand asking about the a particular issue

• Here Ali will interview any person that he finds and is willing to be interviewed

Example 2:

• Marketing team of Pepsi wants a research about “what the people prefer Pepsi or Coke and their experience about Pepsi”.

• Here that marketing team will interview any person nearby some superstores at their convenience

Purposive sampling

Instead of collecting information from most readily or conveniently available we prefer to obtain information from those who meet some criteria or have some knowledge related to our topic.

• Here we set some criteria to chose a subject • Only those will be included in the sample who meets the

criteria

Purposive sampling has further two types

1. Judgment sampling or expert opinion sampling

2. Quota sampling

Judgment sampling or expert opinion sampling

• Judgment sampling involves the choice of subjects who are most advantageously placed or in the best position to provide the information required.

• Most of the times these people are at the top possessions

Example:

• If we are interested in the skills needed for a women to rise to the top possession (the president, wise president or executive)

• Here in this example we will take a sample of only those women who are at the top possessions

• We are taking feed back from only those women who are at the top level

• There is a bias but we have no other choice• Less generalizable but good to solve the current problem

Quota samplingA second type of the purposive sampling

ensures that certain groups are properly represented in the study through the assignment of the quota.

• Here the researcher first selects the groups and then he provides the quota to each group to ensure the representation of each group in the sample

Example:

If we want to study the “working habits of the managers in corporation”

• Here there are many layers in the management and different numbers of manager in different levels

• Many at lover level and less at the top • To provide them a fair chance of representation we

provide the quota to each level.

Snow ball sampling• Snow ball sampling (also called network,

chain referral or reputational sampling) is a method for identifying and sampling cases in the network. It is based on an analogy to a snowball which begins small but becomes larger as it is rolled on wet snow and it pick up additional snow.

• This method of sampling is used when we can not find our subjects to conduct our study

Example:

• If we are interested in some characteristic of underworld criminals so first of all we have to find a single criminal, we will get information from him and will ask to give 4 names of his friends like him, then we will contact those four and from those four we will ask from each to give four other names.

End

Any Questions

Thanks…….