Sampling research methodology

8/13/2019 Sampling research methodology

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TYBMS Prof. Hemant Kombrabail

SAMPLING

SOME BASIC TERMS

1. Population In statistical usage the term population is applied to an finite or infinite

collection of indi!iduals. It has displaced the older term universe,"hich is deri!ed

from the universe of discourseof logic. It is practicall snonmous "ith aggregateand does not necessaril refer to a collection of li!ing organisms.

#. Census$ The complete enumeration of a population or groups at a point in time "ith

respect to "ell$defined characteristics such as population% production% traffic onparticular roads. In some connection the term is associated "ith the data collected

rather than the e&tent of the collection so that the term Sample 'ensus has a distinct

meaning. The partial enumeration resulting from a failure to co!er the "holepopulation% as distinct from a designed sample en(uir% ma be referred to as an

)incomplete census*.

+. Sample$ , part of a population% or a subset from a set of units% "hich is pro!ided b

some process or other% usuall b deliberate selection "ith the ob-ect of in!estigating

the properties of the parent population or set.. Sample survey , sur!e% "hich is carried out using a sampling method i.e. in "hich

a portion onl% and not the "hole population% is sur!eed./. Sampling unit$ 0ne of the units into "hich an aggregate is di!ided or regarded as

di!ided for the purposes of sampling% each unit being regarded as indi!idual and

indi!isible "hen the selection is made. The definition of unit ma be made on somenatural basis% for e&ample% households% persons% units of product% ticets% etc. 21 on

some arbitrar basis% e.g. areas defined b grid coordinates on a map. In the case of

multi-stage sampling the units are different at different stages of sampling% being

)large) at the first stage and gro"ing progressi!el smaller "ith each stage in theprocess of selection. The termsample unitis sometimes used in a snonmous sense.

3. Sampling Frame$ , list% map or other specification of the units% "hich constitute thea!ailable information relating to the population designated for a particular samplingscheme. There is a frame corresponding to each state of sampling in a multi$stage

sampling scheme. The frame ma or ma not contain information about the size or

other supplementar information of the units% but it should ha!e enough details so thata unit% if included in the sample% ma be located and taen up for in(uir. The nature

of the frame e&erts a considerable influence o!er the structure of a sample sur!e. It is

rarel perfect% and ma be inaccurate% incomplete% inade(uatel described% out of dateor sub-ect to some degree of duplication. 4easonable reliabilit in the frame is a

desirable condition for the reliabilit of a sample sur!e based on it. In multi$stage

sampling it is sometimes possible to construct the frame at higher stages during the

progress of the sample sur!e itself For example,certain first stage units ma beselected in the first instance% and then more detailed lists or maps be constructed b

compilation of a!ailable information or b direct obser!ation onl of the first$stage

units actuall selected5. Sampling esign$A.sample design is a definite plan for obtaining a sample from the

sampling frame. It refers to the techni(ue or the procedure the researcher "ould adopt

in selecting some sampling units from "hich inferences about the population is dra"n.Sampling design is determined before an data are collected.

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6. Statisti!"s# an parameter"s#$ , statistic is a characteristic of a sample% "hereas a

parameter is a characteristic of a population. Thus% "hen "e "or out certain measures

such as mean% median% mode etc from samples% then the are called statistic7s8 for thedescribe the characteristics of a sample. But "hen such measures describe the

characteristics of a population% the are no"n as parameter7s8. 9or instance% the

population mean 78 is a parameter% "hereas the sample mean 7:8 is a statistic. Toobtain the estimate of a parameter from a statistic constitutes the prime ob-ecti!e of

sampling analsis.

;. Sampling error $ That part of the difference bet"een a population !alue and anestimate thereof% deri!ed from a random sample% "hich is due to the fact that onl a

sample of !alues is obser!ed% as distinct from errors due to imperfect selection% bias in

response or estimation% errors of obser!ation and recording% etc The totalit of

sampling errors in all possible samples of the same si% then "e mean that there are ;/

chances in 122 7or .;/ in 18 that the sample results represent the true condition of thepopulation "ithin a specified precision range against / chances in 122 7or .2/ in 18 that

it does not. Precision is the range "ithin "hich the ans"er ma !ar and still beacceptable? confidence le!el indicates the lielihood that the ans"er "ill fall "ithinthat range% and the significance le!el indicates the lielihood that the ans"er "ill fall

outside that range. @e can al"as remember that if the confidence le!el is ;/>% then

the significance le!el "ill be 7122 A ;/8 i.e.% />% if the confidence le!el is ;;>% thesignificance le!el is 7122 A ;;8 i.e.% 1>% and so on. @e should also remember that the

area of normal cur!e "ithin precision limits for the specified confidence le!el

constitutes the acceptance region and the area of the cur!e outside these limits in either

direction constitutes the re-ection regions.1#. Sampling istri%ution $ @e are often concerned "ith sampling distribution in

sampling analsis. If "e tae certain number of samples and for each sample compute

!arious statistical measures such as mean% standard de!iation% etc.% then "e can findthat each sample ma gi!e its o"n !alue for the statistic under consideration. ,ll such

!alues of a particular statistic% sa mean% together "ith their relati!e fre(uencies "ill

constitute the sampling distribution of the particular statistic% sa mean. ,ccordingl%"e can ha!e sampling distribution of mean% or the sampling distribution of standard

de!iation or the sampling distribution of an other statistical measure. It ma be noted

that each item in a sampling distribution is a particular statistic of a sample. The

sampling distribution tends (uite closer to the normal distribution if the number of

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samples is large. The significance of sampling distribution follo"s from the fact that

the mean of a sampling distribution is the same as the mean of the uni!erse. Thus% the

mean of the sampling distribution can be taen as the mean of the uni!erse.1+. Bias- Cenerall% an effect "hich depri!es a statistical result of representati!eness b

sstematicall distorting it% as distinct from a random error "hich ma distort on an

one occasion but balances out on the a!erage1. Biase sample $ , sample obtained b a biased sampling process% that is to sa% a

process "hich incorporates a sstematic component of error% as distinct from random

error "hich balances out on the a!erage Don$random sampling is often% though notine!itabl% sub-ect to bias% particularl "hen entrusted to sub-ecti!e -udgment on the

part of human beings

CENS&S S&R'E(AN) SAMPLE S&R'E(*'ensus sur!e means sur!e or complete enumeration of population "ith certain

ob-ecti!es. The go!ernment in India after e!er ten ears conducts such census sur!e.

The entire geographical area and entire population is co!ered in census sur!e. The datacollected are tabulated and published as census report. Such census data are used for

different purposes including economic planning and polic decisions. 'ensus sur!e is acostl and time$consuming acti!it and also needs huge organi


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amounts of purchase of each product categor% a!erage amount ept at home and the

lie8 and the population of interest is all households in a countr% the cost "ill

preclude a census being taen. Thus a sample is the onl logical "a of obtaining ne"

data from a population of this si


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FEAT&RES OF SAMPLING

718 Sampling is a small representati!e of the "hole. It is an effecti!e alternati!e to the

census sur!e.7#8 Sampling reduces the time% efforts and mone of the researcher on data collection

"ithout an ad!erse effect on its (ualit.

7+8 The sampling techni(ue is based on the assumption that random selection of samplefrom the uni!erse do possesses the same features and characteristics as that of the

uni!erse.

78 The findings of sample sur!e are accurate and reliable. The larger sample is better asthe results a!ailable are more accurate.

7/8 Sampling is used in data collection as "ell as for different purposes in our dail life.

738 The concept of sampling is (uite common and popular in mareting research as it

helps researchers to finali


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"2# Provies relia%le ata*The conclusions dra"n from the sample sur!e are reliable%

accurate and also applicable to the "hole populationFuni!erse. Sampling has no ad!erse

effect on the (ualit of data collected. It gi!es (ualit results "ith lesser !olume of "or.

"3# S!ienti$i! %ase* The concept of sampling has scientific bacing as it is based on the

la" of statistical regularit and the la" of inertia of large numbers.

"4# Fa!ilitates %etter supervision on ata !olle!tion*Sampling method is restricted tolimited number of respondents. Daturall effecti!e monitoring and super!ision on the

data collection "or is possible. This impro!es the (ualit of data collected.

LIMITATIONS OF SAMPLING MET0O)*

"1# Finings are not !ompletely a!!urate* The findings of sampling method are

reasonabl accurate but not completel accurate .The findings and conclusions dra"n

from sample sur!e ma be comparati!el less accurate compared "ith that a!ailablefrom the census techni(ue in "hich the entire population is co!ered.

"2# Finings may not %e relia%le* The findings ma notbe reliable if the sample

selected is too small or is not ade(uatel representati!e in character. In such cases the

conclusions dra"n ma be misleading and this ma affect the (ualit of research"or.

"3# )i$$i!ulties in t+e sele!tion o$ representative sample* There are man practicaldifficulties in the selection of representati!e sample. This ma defeat the !er purpose

of sampling..

"4# )ata !olle!tion i$$i!ult in t+e !ase o$ large sample* Gata collection becomesdifficult "hen large si


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, fe" e&amples are gi!en here.

If "e "ere to conduct a sur!e on the consumption of tea in Cu-arat% then thesespecifications might be as follo"s

7i8 lement House"i!es

7ii8 Sampling units Households% then house"i!es7iii8 &tent Cu-arat State

7i!8 Time anuar 1$12% 1;;;

If "e "ere to monitor the sales of a product recentl introduced b us% the populationmight be

7i8 lement 0ur product

7ii8 Sampling units 4etail outlets% super marets% then our product

7iii8 &tent Gelhi and De" Gelhi

7i!8 Time anuar 5$1% 1;;;

It ma be emphasi


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/ Is the frame up$to$date It could ha!e met all the criteria "hen compiled but could "ell

be deficient "hen it came to be used This could "ell be true of all frames in!ol!ing the

human population as change is taing place continuousl3 Ho" con!enient is it to use Is it readil accessible Is it arranged in a "a suitable for

sampling 'an it easil be re$arranged so as to enable us to introduce stratification and

to undertae multi$stage samplingThese are demanding criteria and it is most unliel that an frame "ould meet them all

De!ertheless% the are the factors to be borne in mind "hene!er "e undertae random

sampling

In mareting research most of the frames are from census reports% electoral registers% listsof member units of trade and industr associations% lists of members of professional

bodies% lists of d"elling unitsmaintained b local bodies% returns from an earlier sur!e

and large scale maps.

Step 3* Spe!i$y t+e sampling unit

The sampling unit is the basic unit containing the elements of the target population. The

sampling unit ma be different from the element. 9or e&ample,if one "anted a sample ofhouse"i!es% it might be possible to ha!e access to such a sample directl. Ho"e!er% it is

easier to select households as the sampling unit and then inter!ie" house"i!es in each of

the households.

,s mentioned in the preceding step% the sampling frame should be complete and accurate

other"ise the selection of the sampling unit might be defecti!e. It is necessar to get afurther specification of the sampling unit both in personal inter!ie"s and in telephone

inter!ie"s. Thus% in personal inter!ie"s% a pertinent (uestion isAof the se!eral persons

in a household% "ho should be inter!ie"ed If inter!ie"s "ere held during office timings"hen the heads of families and other emploed persons are a"a% inter!ie"ing "ould

under$represent emploed persons and o!er$represent elderl persons% house"i!es andthe unemploed. In !ie" of these considerations% it is necessar to ha!e a random processof selection of the adult residents of each household. 0ne method that could be used for

this purpose is to list all the eligible persons li!ing at a particular address and then select

one of them.

Step 4* Spe!i$y t+e sampling met+o

It indicates ho" the sample units are selected. 0ne of the most important decisions in this

regard is to determine "hich of the t"oAprobabilit and non$probabilit sampleAis to

be chosen.

In case of a probabilit sample% the probabilit or chance of e!er unit in the population

being in the sample is no"n. 9urther% the selection of specific units in the sample

depends entirel on chance. Do substitution of one unit for another is permissible. Thismeans that no human -udgment is in!ol!ed in the selection of a sample. In contrast% in a

non$probabilit sample% the probabilit of inclusion of an unit in the population in the

sample is not no"n. In addition% the selection of units "ithin a sample in!ol!es human-udgment rather than pure chance.

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In case of a probabilit sample% it is possible to measure the sampling error and thereb

determine the degree of precision in the estimates "ith the help of the theor ofprobabilit. This theor also enables us to consider% from amongst the !arious possible

sample designs% the one that "ill gi!e the ma&imum information per rupee. This is not

possible "hen a non$probabilit sample is used.

Probabilit sampling enables us to choose representati!e sample designs. It also enables

us to estimate the e&tent to "hich the results based on such a sample are liel to bedifferent from "hat "e "ould ha!e obtained had "e co!ered the population in our stud.

'on!ersel% the use of probabilit sampling enables us to determine the sample si


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7b8 Don$Probabilit Sampling Method

"a# Pro%a%ility.Ranom Sampling Met+os

In the probabilit sampling methods% the sample units are selected at random. This means

the selection is hapha


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"%# Non/pro%a%ility Sampling Met+os

Here% sample units are selected in a non$random manner. The selection ma bepurposi!e. It ma be based on the con!enience or the -udgment of the researcher. The

selection is deliberate not random. !er item is not gi!en a definite chance of being

included in the sample. The non$probabilit sampling ) methods include con!eniencesampling% -udgment sampling% and (uota sampling. In these methods% the sample is

selected in a sub-ecti!e manner and the decision regarding sample is taen b the

researcher himself. The sample selected ma not be representati!e of the uni!erse tobe studied. The selection of sample ma be influenced b the sub-ecti!e consideration of

the person connected "ith research "or 7researcher8.

Don$probabilit sampling methods are also used in mareting research along "ithprobabilit methods. Such methods are sometimes preferred because the cost less per

obser!ation% re(uire less time and need relati!el little statistical sophistication in

planning the sample design and in the selection the respondents. Probabilit sampling

methods are more scientific and capable of ielding more representati!e samples thannon$probabilit sampling methods. Ho"e!er% there is no sampling method 7probabilit or

non$profitabilit8 that can be considered to be best in all situations. ,n suitable methodma be selected and used properl for promising results.

PROBABILIT( SAMPLING '.S NON/PROBABILIT( SAMPLING

Probabilit Sampling Don$Probabilit Sampling

Meaning 7i8 Probabilit sampling pro!ides

an e(ual chance of being

selected in the sample to each

element of the population.7ii8 , probabilit sample is one%

"here the selected units ha!esome specific chance of being

included in the sample.

7i8 Don$Probabilit sampling

does not pro!ide an e(ual chance

of being selected in the sample to

each element of the population.7ii8 , non$probabilit sample is

arbitraril selected.

Tpe of method It is a sstematic and modern

method of sampling

It is a traditional and rather

outdated method of sampling.

Selection of

sample

The sample is selected b chance

or at random

The sample is selected b choice

Selection process The selection process is

controlled ob-ecti!el so that theitems "ill be chosen strictl atrandom

The selection process is% at least

partiall% sub-ecti!e

Benefit It helps to select a trul

representati!e sample Here% theselection of sample items is

independent of the person

maing the stud 7researcher8

The sample selected ma or ma

not be a true representati!e of the"hole population as it is selected

as per the con!enience of the

researcher

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Dature of process It is a mechanical and

mathematical process

It is a mental

processFe&ercise of the researcher

"A# PROBABILIT( SAMPLING MET0O)S

"1# SIMPLE RAN)OM SAMPLING4andom sampling is one popular and e&tensi!el used sampling method In this method%

each and e!er unit of the population has an e(ual chance of being selected or included in

the sample 4andom selection does not mean hapha


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Advantages Of Simple Random Sampling Method

"1# Simpli!ity*Simple random sampling issimplest method of probabilit sampling andcan be used for different tpes of sur!es

7#8 S!ienti$i!*This method is scientific as there is e(ual opportunit to e!er unit for

selection as sample7+8 Truly representative !+ara!ter*The samples selected b this method are trul

representati!e in character.

"4# 9uality results*4andom sampling can beused effecti!el 7for (ualit results8 "henthe uni!erse to be studied is small and can be listed accuratel 7e. g. motor car o"ners in

a cit8

Limitations Of Simple Random Sampling Method

"1# )i$$i!ult :+en t+e universe is very large*In simple random sampling% the "hole list

of uni!erse is taen up for selection 0btaining the complete and up$to$date list of the

uni!erse is difficult It is difficult particularl "hen the uni!erse is !er large in number.

7#8 Costly* The cost for conducting sur!e b this sampling method is high as thesamples are selected at random and it is obligator to contact them and collect the

information7+8May prove ine$$i!ient*This method ma pro!e to be statisticall inefficient and

pro!ide a larger standard of error than the other tpes of sampling designs

78Aministrative i$$i!ulties* 4andom sampling in!ol!es administrati!e difficultiesas regards the selection of sample and follo"$up measures for the collection of

information

7/8May not%e $ully represente*The sample selectedma not befull representati!e

as the selection is from the "hole population and not from the groups that constitute thepopulation

"2# STRATIFIE) SAMPLING*

In stratified sampling% the units included in the sample constitute roughl the same

population in "hich the are present in the total population

Stratified sampling is also calledproportional random sampling. In this sampling% the

population is first subdi!ided into certain mutuall e&clusi!e groups or strata Such groups

ma be formed on the basis of geographical area F si


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2$/22 /%222 /2 5/

/21$1222 %222 2 #2

1221$#222 +%222 +2 #2

#221$+222 #%222 #2 #/

+221 O 1%222 12 12

1/%222 1/2 1/2

In the abo!e e&ample% the population consists of 1/%222 households% di!ided into fi!e

strata on the basis of monthl income. 'olumn 7+8 of the table sho"s the sample% i.e.%number of households selected from each stratum. The sample constitutes one per cent of

the population. , sample of this tpe% "here each stratum has a uniform sampling

fraction% is called a proportionate stratified sampling. If% on the contrar% the strata ha!e!ariable sampling fractions% the sample is called a disproportionate stratified sample. The

figures gi!en in column 78 of the abo!e table sho" a disproportionate stratified sample.

It "ill be seen that the sampling fraction !aries from one stratum to another. Thus% fore&ample% it is 2.21/ for the monthl income 4s 2$/22 and 2.21 for the stratum% 4s +221O.

It ma he noted that a stratified random sample "ith a uniform sample fraction results in

greater precision than a simple random sample. But% this is possible onl "hen theselection "ithin strata is made on a random basis. 9urther% a stratified proportionate

sample is generall con!enient on account of practical considerations%

There are some other considerations in fa!or of the stratified random sample. The

researcher ma be interested in the results for separate strata rather than for the entire

population. , simple random sample "ill not sho" results b strata as it presents onl anaggregati!e picture. ,nother consideration is that it ma be administrati!el e&pedient to

split the population into strata. Yet another consideration is that one can use different

procedures for selecting samples from !arious strata. If the data are more !ariable in anparticular strata% a larger sampling fraction should be taen in that stratum. This "ould

result in greater o!erall precision

This method reduces the sampling error and it is a more accurate and representati!esampling method Daturall% it is treated as an impro!ement o!er simple random

sampling. It pro!ides information about different components of the total population Nse

of stratified sampling also leads to administrati!e con!eniences In order to use a stratifiedsample% some information regarding the population and its strata should be a!ailable to

the researcher

The process of stratified random sampling differs from simple random sampling Insimple random sampling% sample items are chosen at random from the entire uni!erse

"hile in stratified random sampling% a separate random sample is chosen from each

stratum Stratified random sampling is used in order to increase the precision of samplingestimates.

"3# S(STEMATIC RAN)OM SAMPLING*

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In sstematic random sampling method% the units of a population are first listed and the

sample is selected as per a "ell$defined sstem. The sample is dra"n b selecting e!er

nth item is the sampling frame% %n%is determined on the basis of the desired si


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,rtificial clusters ma be formed% as is generall done in area sampling "here grids ma

be determined on the maps. Third,se!eral le!els of clusters ma be used in an one

sample design. Thus% in a cit sur!e% localities or "ards% streets and households ma beselected in "hich case localities or "ards are the clusters at the first le!el and streets at

the second le!el and households "ould be the units.

'luster sampling method is less costl as the e&penditure on tra!eling of inter!ie"ers is

minimi


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called primar sampling units 7PSNs8. ach of these PSNs consists of a number of

second$stage units. 9irst% a sample is taen of the PSNs% and then a sample is taen of the

second$stage units. This process continues until the selection of the final sampling units.It ma be noted that at each stage of sampling% a sample can be selected "ith or "ithout

stratification.

,n illustration "ould mae the concept of multi$stage sampling clear. Suppose a sample

of /222 urban households from all o!er the countr is to be selected. In such a case% the

first stage sample ma in!ol!e the selection of districts. Suppose #/ districts out of sa/22 districts are selected. The second stage ma in!ol!e the selection of cities% sa four

from each district. 9inall% /2 households from each selected cit ma be chosen. Thus%

one "ould ha!e a sample of /222 urban households% arri!ed at in three stages. It is

ob!ious that the final sampling unit is the household.

In the absence of multi$stage sampling of this tpe% the process of the selection of /222

urban households from all o!er the countr "ould be e&tremel difficult. Besides% such a

sample "ould be !er thinl spread o!er the entire countr and if personal inter!ie"s areto be conducted for collecting information% it "ould be an e&tremel costl affair. In !ie"

of these considerations a sampling from a "idel spread population is generall based onmulti$stage.

The number of stages in a multi$stage sampling !aries depending on con!enience and thea!ailabilit of suitable sampling frames at different stages. 0ften% one or more stages can

be further included in order to reduce cost. Thus% in our earlier e&ample% the final stage of

sampling comprised /2 households from each of the four selected cities. Since this "ould

in!ol!e the selection of households all o!er the cit% it "ould turn out to be (uitee&pensi!e and time consuming if personal inter!ie"s are to be conducted. In such a case%

it ma be ad!isable to select t"o "ards or localities in each of the four selected cities and

then to select #/ households from each of the # selected "ards or localities. Thus% the costof inter!ie"ing as also the time in carring out the sur!e could be reduced considerabl.

It "ill be seen that an additional stage comprising "ards or localities has been introducedhere. Thus the sample has become a four$stage sample

1ststage districts

#ndstage cities

+rdstage localitiesthstage households

9rom the preceding discussion it should be clear that a multi$stage sample results in theconcentration of field"or. This in turn% leads to sa!ing time% labor and mone. There is

another ad!antage in its use. @here a suitable sampling frame co!ering the entire

population is not a!ailable% a multi$stage sample can be used.

"8# M&LTI/P0ASE SAMPLING

, multi phase sample should not be confused "ith a multi$stage sample The formerin!ol!es a design "here some information is collected from the entire sample and

additional information is collected from onl a part of the original sample Suppose a

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sur!e is undertaen to determine the nature and e&tent of health facilities a!ailable in a

cit and the general opinion of the people. In the first phase a general (uestionnaire can

be sent out to ascertain "ho amongst the respondents had at one time or other used thehospital ser!ices. Then% in the second stage% a comprehensi!e (uestionnaire ma be sent

to onl these respondents to ascertain "hat the feel about the medical facilities in the

hospitals. This is a t"o$phase or double sampling.

The main point of distinction bet"een a multi$stage and a multi$phase sampling is that in

the former each successi!e stage has a different unit of sample "hereas in the latter theunit of sample remains unchanged though additional information is obtained from a sub$

sample.

The main ad!antage of a multi$phase sampling is that it effects econom in time% moneand effort. In our earlier e&ample% if a detailed (uestionnaire is sent out to a large sample

comprising indi!iduals% the "ould not be able to pro!ide the necessar information.

Second% more time "ill be re(uired. 9inall% it "ill be far more e&pensi!e to carr out the

sur!e% especiall "hen personal inter!ie"s are in!ol!ed.

";# REPLICATE) SAMPLING

4eplicated sampling implies a sample design in "hich Et"o or more sub$samples are

dra"n and processed completel independent of each otherE It "as first introduced bQMahalnobisE in 1;+3% "ho used the term inter$penetrating sub$samples.

In replicated sampling% se!eral random sub$samples are selected from the populationinstead of one full sample. ,ll the sub$samples ha!e the same design and each one of

them is a self$contained sample of the population. 9or e&ample% tae the case of a random

sample of 122 households. This sample ma be di!ided into% sa% 12 e(ual sub$samples to

be assigned to 12 inter!ie"ers. Thus% each inter!ie"er ma be re(uired to collectinformation from 12 households.

, replicated sample is particularl chosen on account of the con!enience it affords in thecalculation of standard error. In man comple& sample designs% the calculation of

standard error becomes too laborious. Selecting a replicated sample design can

considerabl reduce this difficult. Ho"e!er% in modem times "hen computers are beingincreasingl used% the ease in calculating standard error has made it some"hat less

important. ,part from this ad!antage% there are certain other ad!antages of replicated

sampling. 9irst% if the si


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,part from the abo!e limitations% replicated samples ha!e other disad!antages If personal

inter!ie"s are to be conducted% replicated samples turn out to be costlier Jie"ise%

tabulation costs "ould be higher than in the case of a single large sample 9inall%replicated samples are more comple& to administer.

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sampling is more suitable in e&plorator research% "here the focus is mainl on getting

ne" ideas and insights into a gi!en problem.

Advantages of Convenience Sampling

7a8 It is profitabl used in pre$testing of (uestionnaires

7b8 It eeps the researcher free of tension.7c8 It allo"s the respondents to ans"er (uestions in leisure.

Disadvantages of Convenience Sampling

7a8 Sampling could be non$representati!e of the population e.g.% students li!ing in college

to"n ma not represent sample of student communit.

7b8 Problem of element of chance

7c8 It cannot rule out bias of respondents.

"2# 9&OTA SAMPLING

Luota sampling is (uite fre(uentl used in mareting research. It in!ol!es the fi&ation ofcertain (uotas% "hich are to be fulfilled b the inter!ie"ers

Suppose in a certain territor "e "ant to conduct a sur!e of households Their total

number is #%22%222 It is re(uired that a sample of 1 per cent% i.e. #222 households are to

be co!ered@e ma fi& certain controls "hich can be either independent or inter$related

These controls are sho"n in the follo"ing tables, sample of #222 households has been chosen% sub-ect to the condition that 1#22 of these

should be from rural areas and 622 from the urban areas of the territor Jie"ise% of the

#222 households% the rich households should number 1/2% the middle class ones 3/2 andthe remaining 1#22 should be from the poor class These are independent (uota controls

The second table sho"s the inter$related (uota controls ,s can be seen% inter$related(uota controls allo" less freedom of selection of the units than that a!ailable in the caseof independent controls

Independent 'ontrols

4ural 1#22 4ich 1/2

Nrban 622 Middle class 3/2

Poor 1#22

Total #222 Total #222

Inter$related 'ontrols&ural 'rban otal

4ich 122 /2 1/2

Middle class 22 #/2 3/2Poor 522 /22 1#22

Total 1#22 622 #222

#2


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There are certain ad!antages in both the schemes Independent controls are much simpler%

especiall from the !ie"point of inter!ie"ers The are also liel to be cheaper as

inter!ie"ers ma co!er their (uotas "ithin a small geographical area In !ie" of this%independent controls ma affect the representati!eness of the (uota sampling Interrelated

(uota controls are more representati!e though such controls ma in!ol!e more time and

effort on the part of inter!ie"ers ,lso% the ma be costlier than independent (uotacontrols

In !ie" of the non$random element of (uota sampling% it has been se!erel critici


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a "a a -udgment sample "here the actual selection of units "ithin the earlier fi&ed (uota

depends on the inter!ie"er

It ma be noted that "hen a small sample of a fe" units is to be selected% a -udgment

sample ma be more suitable as the errors of -udgment are liel to be less than the

random errors of a probabilit sample 13 Ho"e!er% "hen a large sample is to be selected%the element of bias in the selection could be (uite large m the case of a -udgment sample

9urther% it ma be costlier than the random sampling

"4# MASTER SAMPLES

, master sample is one from "hich repeated sub$samples can be taen as and "hen

re(uired from the same area or population This "as first used in the Nnited States "hen

the NS Master sample of agriculture "as taen In this sampling% the rural area of o!er+222 NS counties "as di!ided into segments of about four farms each E,fter selecting a

sstematic sample of 1F6 of the segments% the materials "ere duplicated and made

a!ailable% "ith instruction% at lo" costE The crucial point to note in respect of mastersamples is that Ethe actual sample for each ne" sur!e is not selected directl from the

entire population but from a frame of segments and d"ellings that "as selected earlier

from the entire population E

The utilit of the samples is limited to a relati!el short period for there ma be changes

in the population "hich "ould distort the representati!e character of the master samplesIn !ie" of this% master samples should be relati!el permanent% sa% d"ellings rather than

indi!iduals or household "hich fre(uentl undergo changes on account of births% deaths

and migration The main ad!antage of master samples is that the can be e&peditiousl

selected on account of their simplicit ,nother ad!antage is that the are economical%because the same master frame is used for dra"ing samples for se!eral sur!es% as a

result of "hich the cost incurred on the preparation of the master frame is spread o!er

these sur!es. 9urther% on account of this econom in each sur!e% one can initiall spendmore to create a good master frame. Thus% econom ma lead to impro!ed (ualit in the

listing.

"5# PANEL SAMPLES

Panel samples are fre(uentl used in mareting research. In panel samples% the same units

or elements are measured on subse(uent occasions. To gi!e an e&ample Suppose that

one is interested in no"ing the change in the consumption pattern of households. ,sample of households is dra"n. These households are contacted to gather information on

the pattern of consumption% subse(uentl% sa after a period of si& months% the same

households are approached once again and the necessar information on their

consumption is obtained. , comparison of the results of the t"o sets of data "ouldindicate "hether there has been an change% and% if so% to "hat e&tent. In fact% the

information is collected on a more or less continuous basis "ith the help of panelsamples.

Panel samples are e&tremel con!enient and economical and the cost of dra"ing a secondsample is not incurred. But the main limitation of such samples is that it ma be difficult

to sustain the interest of indi!iduals included in the panel for a long period. Man

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respondents on the panel ma refuse to be inter!ie"ed t"ice or ma gi!e poor ans"ers.

In either case the (ualit of the sur!e "ill suffer. ,nother limiting factor in panel

samples is that there ma be bias on account of the continued participation in the panel. Itis felt that the indi!idual is conditioned to some e&tent b the fact that data on purchases

are reported. In such a case the purchase beha!ior of panel members ma become

different from others not co!ered b the panel. 9urthermore% panel samples ma turn outto be more e&pensi!e "hile locating the same sample of respondents after a lapse of% sa%

a ear% "hen some of them might ha!e migrated to other areas. This "ould in!ol!e tra!el

costs in addition to being difficult.

C0ARACTERISTICS OF A GOO) SAMPLE )ESIGN

Kish mentions that a good sample design re(uires the -udicious balancing of four broad

criteriaA goal orientation% measurabilit% practicalit and econom.

Goal orientation

This suggests that a sample design Eshould be oriented to the research ob-ecti!es% tailored

to the sur!e design% and fitted to the sur!e conditionsE If this is done% it shouldinfluence the choice of the population% the measurement as also the procedure of

choosing a sample

Measura%ility

, sample design should enable the computation of !alid estimates of its sampling

!ariabilit Dormall% this !ariabilit is e&pressed in the form of standard errors in sur!esHo"e!er% this is possible onl in the case of probabilit sampling In non$probabilit

samples% such as a (uota sample% it is not possible to no" the degree of precision of the

sur!e results

Pra!ti!ality

This implies that the sample design can be follo"ed properl in the sur!e% as en!isaged

earlier It is necessar that complete% correct% practical and clear instructions should begi!en to the inter!ie"er so that no mistaes are made in the selection of sampling units

and the final selection in the field is not different from the original sample design

Practicalit also refers to simplicit of the design% i.e. it should be capable of beingunderstood and follo"ed in actual operation of the field "or

E!onomy

9inall% econom implies that the ob-ecti!es of the sur!e should be achie!ed "ith

minimum cost and effort Sur!e ob-ecti!es are generall spelt out in terms of precision%

i.e. the in!erse of the !ariance of sur!e estimates 9or a gi!en degree of precision% the

sample design should gi!e the minimum cost ,lternati!el% for a gi!en per unit cost% thesample design should achie!e ma&imum precision 7minimum !ariance8

It ma be pointed out that these four criteria come into conflict "ith each other in most of

the cases% and the researcher should carefull balance the conflicting criteria so that he isable to select a reall good sample design ,s there is no uni(ue method or procedure b

"hich one can select a good sample% one has to compare se!eral sample designs that can

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be used in a sur!e This means that one has to "eigh the pros and cons% the strong and

"ea points of !arious sample designs in respect of these four criteria% before selecting

the best possible one

MET0O)S OF )ETERMINING SAMPLE SI>E

There are si& methods of determining sample si


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. 9rom abo!e step the optimal sample si


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respondents at some geographical location or to select households in specified streets.

The result is liel to be that certain inds of people or households or organi


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samples "ill be close to the real population !alue Ho"e!er% "e usuall tae onl one

sample% and e!en a sample that has used unbiased selection procedures "ill seldom be

e&actl representati!e of the population from "hich it"as dra"n. ach sample "ill% inshort% e&hibit a degree of error. Such error is often called )sampling error)% )hut it "ould he

clearer to thin of it as )random sampling error) to distinguish it from bias 7"hich some

statisticians and some te&tboos% confusingl% categori


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In nearl all research there "ill be missing cases% but in sur!e research there "ill al"as

be a degree of non$response because some people "ill refuse to he inter!ie"ed or to

complete a (uestionnaire% some "ill be ineligible because the turn out not to be part ofthe sur!e population% some "ill terminate the inter!ie" or refuse to ans"er some of the

(uestions% and some "ill be non$contactable% for e&ample% because the ha!e mo!ed

a"a% died% or are on holida at the time of the sur!e. !en "here a census is attempted%it "ill often remain incomplete. The e&tent of non$response "ill !ar considerabl

according to the tpe of research% the topic of the research% and% "here based on face$to$

face inter!ie"s% on the e&perience and training of the inter!ie"ers. 'alculating theamount of non$response can be confusing since some researchers "ill% for e&ample% tae

the proportion of refusals in the sample dra"n% others "ill tae refusals and non$contacts

as a proportion of those found eligible% and so on.

Processing errors can arise bac at the office% particularl at the stage of entering ans"ers

to (uestions onto a computeri


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controlling random sampling fluctuations% the more important in proportion become bias

and non$sampling error.

CONTROLLING NON/SAMPLING ERRORS

In practice% maret research agencies mae all reasonable attempts% "ithin the limits

imposed b cost and time constraints% to minimi


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there are too man "omen in the sample% or too fe" men aged #2$#% compared "ith

no"n population proportions. Man agencies "ill mae corrections to the data to ad-ust

for these biases b )"eighting) them.

In the real "orld of maret research agencies and their clients it is unfortunatel true that

man clients do not understand or lac interest in the basics of sampling. In conse(uenceman clients do not as for estimates of bias or calculations of random sampling error. ,t

the same time the agencies feel that to produce calculations% for e&ample of confidence

inter!als for a large number of !ariables "ill onl add confusion and perhaps distrust ofthe data. In conse(uence% sampling errors are often (uietl ignored% and the estimates

gi!en are taen to be the )truth). ,gencies "ill instead tr to assure their clients that the

occurrence and impact of non$sampling errors ha!e been minimi


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7#8 "ampling distribution of proportionJie sampling distribution of mean% "e can as

"ell ha!e a sampling distribution of proportion. This happens in case of statistics of

attributes. ,ssume that "e ha!e "ored out the proportion of defecti!e parts in largenumber of samples% each "ith sa 122 items% that ha!e been taen from an infinite

population and plot a probabilit distribution of the said proportions% "e obtain "hat

is no"n as the sampling distribution of proportion. Nsuall the statistics of attributescorrespond to the conditions of a binomial distribution that tends to become normal

distribution as n becomes larger and larger. If p represents the proportion of

defecti!es i.e.% of successes and +the proportion of non$defecti!es i.e.% of failures 7or+ 1 p)and ifpis treated as a random !ariable% then the sampling distribution of

proportion of successes has a mean p"ith standard de!iation p +

n

"here n is

the sample si


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( ) ( )1#

# 1 1

1 1i

s

ns

-=

- and ( ) ( )#

#

# # #

# 1i

s

ns

-=

-has an 9 distribution

"ith n1A 1 and n#A 1 degrees of freedom.Fratio is computed in a "a that thelarger !ariance is al"as in the numerator. Tables ha!e been prepared for F

distribution that gi!e !alue ofFfor !arious !alues of degrees of freedom for larger as

"ell as smaller !ariances. The calculated !alue ofFfrom the sample data is compared"ith the corresponding table !alue ofF and if the former e&ceeds the latter% then "e

infer that the null hpothesis of the !ariances being e(ual cannot be accepted.

/. 1hi-s+uare7#8 distribution'hi$s(uare distribution is encountered "hen "e deal "ith

collections of !alues that in!ol!e adding up s(uares. ariances of samples re(uire usto add a collection of s(uared (uantities and thus ha!e distributions that are related to

chi$s(uare distribution. If "e tae each one of a collection of sample !ariances% di!ide

them b the no"n population !ariance and multipl these (uotients b (n A 18%"here n means the number of items in the sample% "e shall obtain a chi$s(uare

distribution. Thus% ( ) ( )# # 1sp

nss

- "ould ha!e the same distribution as chi$s(uare

distribution "ith 7n $ 18 degrees of freedom. 'hi$s(uare distribution tat notsmmetrical and all the !alues arc positi!e. 0ne must no" the degrees of freedom for

using chi$s(uare distribution. This distribution ma also be used for -udging the

significance of difference bet"een obser!ed and e&pected fre(uencies and also as a

test of goodness of fit. The generali

Sampling research methodology

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