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Chapter III - Sampling for

Research

Steps in Sampling Design

Characteristics of a good sample designDifferent types of sample design

Random sampling

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Why Sample?

1.Population or target populationrefers to the entire group of people, events or things of

interest that the researcher wishes to investigate. Itincludes all relevant cases sharing some commoncharacteristic

For eg., if the CEO of a computer firm wants to knowthe kind of advertising strategies adopted by computer

firms in Pune, then all computer firms situated there willconstitute the population. If a banker is interested in investigating the saving

habits of blue-collar workers in an automobile industry,then all blue collar workers in automobile industriesacross the country will make up the population.

a population is a group of individuals or persons,objects, or items from which samples are taken formeasurement, for example, a population of doctors orprofessors, books or students.

Thus, a population is the total collection of elementsabout which we wish to make some inferences

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2. Element

is a single member of the population.If 1000 blue collar workers in a particular

organization happen to be the population ofinterest to a researcher, each single blue collarworker therein is an element.If 500 pieces of machinery are to be approved afterselecting a few, there would be 500 elements inthis population.3.Sampling Frameis a listing of all the elements in the populationfrom which the sample is drawn. The samplingframe is sometimes referred to as the populationframe or working population.The payroll of an organization would serve as thesampling frame if its members were to be studied.The telephone directory is also a sampling frame,a list of class students, a university register listing

all students, faculty and administrators during aparticular academic year or semester

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4. Sample

A sample is a sub-section of a population thatmirrors the traits of that population.

A statistical sample is a miniature picture orcross section of the entire group or aggregatefrom which the sample is taken. It is thereforethe reflection of the universe and bears all the

characteristics of the universe. A sample is a finite part of a statistical

population whose properties are studied to gaininformation about the whole (Webster).

When dealing with people, it can be defined as aset of respondents (people) selected from alarger population for the purpose of a survey.

Sample is a number of individual cases selected(drawn or pulled) from a larger population.

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5. Subject

A subject is a single member of thesample just as an element is a singlemember of a population. If a sample of50machines from a total of 500 machines is

to be inspected, then every one of the 50machines is a subject.

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What is sampling?

Basic idea of sampling is that by selecting some of

the elements in a population we may drawconclusions about the entire population.

We sample as a means to an end. To study agroup and be able to say something about it

without having to study every case in thepopulation, we must sample.

It is often the case that attempting to study every

case in the entire group will be toooverwhelming and/or costly. Furthermore, giventhe totality of constraints, we may end up with

more errors than we would through sampling.

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Relationship between population,sampling frame and sample

populationSampling frame

sample

Each is an element

Each is a subject

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SampleversusCensus

a. Census method deals with theinvestigation of the entire population.Under this, data is collected for each andevery unit of the universe. This method

provides more accurate and exactinformation as no unit is left out.

b. Sampling method involves the

selection of a small group which isrepresentative of the whole universe.

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Census vs Sample

There are some reasons for sampling

instead of doing a census: 1. Economy (lower cost) 2. Time factor (greater speed of data

collection- (eg, doctor during outbreak ofdisease)

3. The large size of many populations

4. Inaccessibility of some of thepopulation 5. Greater accuracy of results

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Steps in Sampling DesignResearcher has to consider the following points :

1. Type of Universe: can be finite or infinite.

finite- number of items is certain - population of a city,number of workers in a factory, children in a school

infinite-number of items is infinite, i.e., we cannot haveany idea about the total number of items - number ofstars in the sky, listeners of a specific radio programme

2. Sampling unit: Sampling unit may be geographicalone such as state, district, village etc., or a constructionunit such as a house, flat etc. or it may be a social unitsuch as a family, club, school etc. or an individual.

researcher will have to decide one or more of such unitsthat he has to select for his study.

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Steps in Sampling Design(contd)3. Sampling Frame (source list):

- is a list from which the sample is drawn. It contains

the names of all items of a universe (in case of finiteuniverse only). If source list is not available,researcher has to prepare it. Such a list has to becomprehensive, correct, reliable and appropriate. It isextremely important for the source list to be as

representative of the population as possible.

4. Size of sample:refers to the number of itemsto be selected from the universe to constitute asample. The size of the sample should neither

be excessively large, nor too small. It should beoptimum. An optimum sample is one whichfulfills the requirements of efficiency,representativeness, reliability and flexibility.

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Steps in Sampling Design(contd)

5. Parameters of interest: The characteristics of

interest will determine the sample size. There mayalso be some important sub-groups in the populationabout whom we would like to make estimates.

6. Budgetary constraints:Cost considerations from

the practical point of view, have a major impact upondecisions relating to not only the size of the samplebut also to the type of sample.

7. Sampling procedure:Finally, the researcher mustdecide the type of sample to be used, i.e., he must

decide about the technique to be used in selecting theitems of the sample. There are several sample designsout of which the researcher must choose one for hisstudy.

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Characteristics of a good sample

Representativeness:A sample must berepresentative of the population. In measurement

terms, a sample must be valid. The validity of asample depends upon its accuracy and precision. Accuracy: This is defined as the degree to which bias

is absentfrom the sample. An accurate (unbiased)sample is one which exactly represents the population.

It is free from any influence that causes any differencebetween sample value and population value. It shouldcontrol bias in every way.

Precision:The sample must yield precise estimate.Precision is measured by the standard error orstandard deviation of the sample estimate. The smallerthe standard error, the higher is the precision of thesample.

Size:A good sample must be adequate in size in orderto be reliable. The sample size should be such that theinferences drawn are accurate to a reasonable level ofconfidence.

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Different Types of Sampling design

There are two types:

Probability sampling Non-probability sampling

Probability Sampling

the elements in the population have someknown chance or probability of being selected assample subjects. This method provides ascientific technique of drawing samples from apopulation according to some laws of chance inwhich each unit has some definite pre assignedprobability of being chosen in the sample.Examples of probability sampling are

A grain of rice in a bowl

Lottery any chit can be picked

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Probability Sample

A sample selected in such a way that every element

has an equal chanceof being selected.- based on probability theory and the ability to

later use inferential statistics to compute thelikelihood that sample characteristics arerepresentative of the population.

- allow for computation of the confidence thatthe sample and findings drawn from it are

representative of the larger population.- are used when the representativeness of a

sample is of importance in the interests of widergeneralisability

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Types of probability sampling

random sampling

stratified random sampling systematic random sampling cluster (area) random sampling multi-stage random sampling

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Probability Sampling (contd)

Simple Random Sample:A probability sample in whichevery member of a study population has been given anequal chance of selection.

A simple random sample is free from sampling bias.

Stratified Random Sample:Stratification means divisionof the universe into groups according to thegeographical, sociological or economic characteristics.

A probability sample in which the study population isdivided into smaller groups or strata on the basis ofsome important characteristic. Simple random samplesare then selected from each stratum.

P b bilit S li ( td)

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Probability Sampling (contd)Systematic random sampling- sample taken from list prepared on a systematic

arrangement either on basis of alphabetical order or some number or any othermethod. In this, only first sample unit is selected at random and remainingunits are automatically selected in a definite sequence at equal spacing fromone another.For eg., 100 students in a class. You want a sample of 20 from these 100.Youhave their names listed on a piece of paper may be in alphabetical order. Forsystematic random sampling, divide 100 by 20, you will get 5. Randomly selectany number between 1 and five. Suppose the number picked is 4, that will bethe starting number. So student number 4 has been selected. From there youwill select every 5th name until you reach the last one, number one hundred.You will end up with 20 selected students.

Cluster Sample: In this the population is grouped. Categories are not defined.Eg. If a bank has 15000 credit holders but wants to study only 450, it makes100 clusters of 150 each. Then picks any three clusters to complete the sample.

Multi-stage Sample:- generally used in selecting sample from very large area.As name suggests multi stage refers to sampling technique carried out invarious stages. Here the population is regarded as made of a number ofprimary units, each of which is further composed of a number of secondarystage units which is further composed of third stage units and so on till thedesired sampling unit is reached.

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Non-probability samples

A sample that has been drawn in a way that

doesnt give every member of the population aknown chanceof being selected.

Types of Non-probability samples

accidental, haphazard, or conveniencesampling

purposive sampling or judgment sampling

quota sampling snowball sampling

Types of Non probability Samples:

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Types of Non-probability Samples:

1. Accidental, haphazard or Convenience sampling- known asunsystematic or opportunistic sampling. Sample selected

according to convenience of researcher Convenience inrespect of availability of data, accessibility of units etc.

called accidental or haphazard sampling because it uses who

ever happens to be available. Random sampling is sacrificed

to save time, money, and effort. The physical and

organizational proximity of participants drives this type of

sampling; eg., you decide to interview the first ten people you

meet tomorrow morning. It saves time, money and effort.

poorest way of getting samples, has lowest credibility and

yields information-poor cases.

constitutes the most common methods of sampling. includes

the traditional "person on the street" interviews conducted

frequently by television news programs to get a quick

(although non-representative) reading of public opinion.

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Types of Non-probability Samples: (contd)

2. Purposive or judgement samplingwe sample with apurposein mind. When theresearcher deliberately selects certain units forstudy from the universe, it is called deliberate

sampling or judgement sampling.- selection is done on the judgement of theresearcher and nothing is left to chance.- Purposive samples consist of people whom you

feel are important to the study because ofspecific personal traits, where they live, thework they do, or their involvement in aparticular issue.

3 Q t S li

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3. Quota Sampling Quota sampling capitalizes on the first come,

first served principle. It is a practical and

convenient method. It is also relativelyinexpensive. However, the sample may not berepresentative of the universe and theinferences drawn may no be amenable tostatistical treatment.

Quota sampling is a special type of stratifiedsampling. Firstly, the population is stratified onsome basis, preferably on the characteristics ofthe population under study. Then the

interviewers are simply given quotas to be filledfrom the different strata, with some restrictionson how they are to be filled.

In quota sampling, we select people non-randomly according to some fixed quota

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4. Snow-ball sampling

In snowball sampling what you do is toget hold of one and he/she will tell youwhere the others are or can be found.When you find those others they will tell

you where you can get more others andthe chain continues.

involves a process of chain referrals. Youbegin with a small group of people andask them who the others are whom youmight want to interview.

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Non-Probability Sample When to Use

Non-probability samples can be used effectively in a wide

variety of circumstances. . .

When a group that represents the target populationalready exists.

When it is impossible or overly difficult to obtain a list ofnames for sampling. (Example homeless.)

When research is exploratory in nature and all of the casesof interest may not be identified ahead of time.

It is critical to recognize that you cannotgeneralize withany knowndegree of accuracy from a non-probabilitysample. In other words, the data represent only thoseunits of analysis you actually studied.