Marketing Research Unit-2

7/27/2019 Marketing Research Unit-2

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UNIT-II

SAMPLE AND SAMPLING

DESIGN


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SAMPLING

A part of population is known as sample.The method consisting of the selecting for

study, a portion of the Universe with aview to draw conclusions about the

Universe and/or Population is known as

sampling.Sampling helps in time and cost saving. Italso helps in checking their accuracy.


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BASIC TERMS IN SAMPLING

POPULATION- the aggregate of all similar unitsunder consideration is called population.

SAMPLE- it may be defined as apart of the wholeunder consideration. Sample is a part of thepopulation.

SAMPLE SIZE- the total number of units/observations in a sample

SAMPLING METHOD TECHNIQUE OF SAMPLING


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Advantage of Sampling Survey The Size of the Population. - If the population

to be studied is quite large, sampling is warranted.However, the size is a relative matter. Whether apopulation is large or small depends upon the natureof the study, the purpose for which it is undertaken,and the time and other resources available for it.

Amount of funds budgeted for the study. -Sampling is opted when the amount of money budgeted issmaller than the anticipated cost of census survey.

Facilities. -The extent of facilities available- staff,access to computer facility and accessibility to

population elements. When the availability of thesefacilities is limited, sampling is preferable. Time. - The time limit within the study should be

completed in another important factor to beconsidered in deciding the question of sample survey.


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Disadvantages of sampling Sample selected, always is not a true representative of

the entire population. Bias is inherent feature of non- probability sampling

method.

Often all the characteristics of the entire population tobe studied are not included in the sample. Chances of occurrence of sampling error can not be

completely removed, however it can be minimized.

In order to achieve near to actual results, researchersoften choose large sample size, without considering thecost/budget and at times its vice-versa.


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Sampling process (contd.)

Information about Population : How much informationis available about the population to be studied? Where no list of population and no information about its nature are available, it isdifficult to apply a probability sampling method. Then exploratory

study with non-probability sampling may be made to gain abetter idea of population. The nature of the Population: In terms of the variables

to be studied, is the population homogenous or heterogeneous?In the case of a homogenous population, even a simple randomsampling will give a representative sample. If the population isheterogeneous, stratified random sampling is appropriate .


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TYPES OF SAMPLING DESIGNS

PROBABILTY SAMPLING METHOD

Also known as chance sampling . Every item inthe universe has an equal chance of inclusion inthe sample. it is blind chance only thatdetermines whether one item or other isselected. NON- PROBABILTY METHOD

It does not afford any basis for estimating theprobability that each item in the population has

of being included in the sample. The items forthe sample are selected deliberately by theresearcher; his choice concerning the itemsremains supreme.


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PROBABILITY SAMPLING1. Probability or Random Sampling.- It is a method

wherein each sampling unit in the population has aknown chance of being selected for answering asurvey. It is of two types

a) Simple random sampling the method is scientific,

unbiased and easy to employ. A draw of lots is atypical example. For example if a marketer wants toreward his loyal customers , he puts all theirpurchase order in a bag and extract one order out of it.

a) Systematic random sampling if an interviewerwants to takea sample of ten students from a class of 60 , he can

get a list of all students and then he select every 6 th student out

of this.


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PROBABILITY SAMPLINGMETHODS/TECHNIQUES (Contd.)Stratified random sampling the population is

first divided into various strata , usuallyrepresented by different demographicparameters such as age , gender and income.

this is usually done when the strata is notequally represented in the population and studyof each strata is important for marketer.

i) Proportionate stratified sampling it involvesthe draw of an equal ratio of the sampling units

as the ratio among the strata size of thepopulation.Ii) Disproportionate stratified sampling it is

the selection of sampling units in a ratio that isnot equal to the ratio among the strata size in

the population.


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PROBABILITY SAMPLINGMETHODS/TECHNIQUES (Contd.) CLUSTER SAMPLING in this the population

is divided into clusters . The division of clustersis usually done on the basis of geographic areas.

Each cluster is non over-lapping. A member of one cluster cannot be the member of othercluster.


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NON-PROBABILITY SAMPLINGMETHODS/TECHNIQUES CONVENIENCE SAMPLING - it involves

selecting the sampling units according to theconvenience of the researcher. A small interceptstudy is a typical example.

JUDGEMENT SAMPLING - here the judgmentof the researcher is used to identify thesampling unit . This is often done during thecourse of exploratory research wherein expert

opinions and their insights is considered. QUOTA SAMPLING quota s are fixed forvarious sub groups in the population . The subgroups generally represent various demographiccharacteristics of the population, such asgender, income and educational qualification.


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HYPOTHESISTESTING


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HYPOTHESIS

A Hypothesis is an assumption made by researcher that istested to find its logical or empirical consequence. It isstatement of relationship between two or morevariables. The need for hypothesis is as follows:

Hypothesis indicates the point of the investigation andgives direction on the study.

A Hypothesis specifies the source of data, that shall bestudied and in what context they shall be studied.

It determines data needs. It suggests the type of research that is likely to be most

appropriate. A Hypothesis contributes to the development of theory.


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CRITERIA FOR HYPOTHESISCONSTRUCTION

It should be empirically testable, whetherit is right or wrong.

It should be specific and precise. The statements in Hypothesis should notbe contradictory.

It should specify variables between whichthe relationship is to be established. It should describe one issue only.


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TYPES OF HYPOTHESIS

We generally consider two types of Hypothesis.

1 Null Hypothesis (Ho ) While comparing two differentmethods in terms of their superiority, wherein theassumption is that both the methods are equally good iscalled null hypothesis.

2 Alternative Hypothesis (Ha ) - While comparing twodifferent methods in terms of their superiority, wherein,stating a particular method to be good or bad ascompared to the another one is called alternatehypothesis.


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COMPARISION

Null Hypothesis is always specific, whilealternate hypothesis gives an approximate value.

The rejection of null hypothesis involves greatrisk, which is not in case of alternate hypothesis. Null hypothesis is more frequently used in

statistics then alternate hypothesis because it isspecific and it is not based on probabilities.


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HYPOTHESIS TESTING(FLOW DIAGRAM)


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IMPORTANT TERMS INHYPOTHESIS TESTING STANDARD ERROR- it gives an idea about the

unreliability of a sample. The greater the S.E.,greater will be the departure of actualdistribution from the expected one and hencegreater will be unreliability.

LEVEL OF SIGNIFICANCE (alpha)- a fixed %tageusually 5% which implies that a researcher isready to take risk of rejecting null hypothesiswhen it was true. it is usually determined inadvance before testing of hypothesis.


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CONFIDENCE INTERVAL- the max. and min.within which the parameters of the populationsare expected to lie, with the degree of confidence.

TWO TAILED TEST- A two tailed test rejects thenull hypothesis if, the sample mean is

significantly higher or lower than thehypothesized value of the mean of thepopulation.


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TWO TAILED TEST CONTINUED

FOR EXAMPLE:- IF we take population mean(hypothesiszed)=100, if our sample mean

deviates significantly from 100 in eitherdirection then we shall reject nullhypothesis and vice versa.

ONE TAILED TEST- This test is used whenthe population mean is either lower orhigher than some hypothesized value.


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TYPES OF ERRORS IN HYPOTHESIS TESTINGTYPE I error (alpha) - rejection of null hypothesis,

even when it is trueTYPE II error (beta) - acceptance of null

hypothesis even when it is false.It is always desirable to minimize both these

errors. The tests of significance are so designedso as to limit the probability of Type I error to aspecified value (usually 5% or 1%).


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A FORMAL STATEMENT STATING Ho AS WELL AS Ha

SPECIFY THE LEVEL OF SIGNIFICANCE

DECIDE THE CORRECT SAMPLING DISTRIBUTION

SELECTING A RANDOM SAMPLE AND WORKOUT AN APPROPRIATE VALUE(on basis of empirical data collected)

CALCULATE THE PROBABILITY THAT SAMPLE RESULT WOULD DIVERGEAS WIDELY AS IT HAS FROM EXPECTATIONS,IF Ho WERE TRUE

IS THIS PROBABILITY EQUAL TO OR SMALLER THAN alpha VALUE INCASE OF ONE-TAILED TEST AND alpha/2 IN CASE OF TWO-TAILED TEST

Reject H o Accept H o

YESNO


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DETERMINING THE SAMPLE SIZE

The main point to be kept in mind whiledetermining a sample size is that it should

be of optimum size i.e. it should giveconfidence interval of the desired widthand should be chosen through some

logical process.


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Following points should be kept inmind while determining the sample

size Nature of population Number of classes proposed

Nature of study- general/technical Type of sampling Standard of accuracy and acceptable confidence

level Availability of finance Other considerations


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SAMPLING DISTRIBUTION OFMEAN

Sampling distribution of mean refers tothe probability of distribution of all

possible means of random samples of agiven size that we take from a population.some sample means will be above be the

population mean, while some will bebelow the population mean. Therebymaking up the sampling distribution.


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Some important terms Sample mean- the mean or centre of the samplingdistribution should ideally be equal to population mean.

Mean of sampling distribution- There is a tendency for asample mean to be above or below the population mean

(even if the population is not normal), hence the meanof sampling distribution is an unbiased estimate of population mean, it will be correct on average in manysamples.

Standard deviation of sampling distribution- it measureshow much the sample static (mean) varies from sampleto sample


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If samples are taken from a normal population,

the sampling distribution of mean would also benormal. The standard deviation (sigma) iscalculated. And z-test is used to calculate thedegree of reliability.

In case of populations that are not normal, eventhan we assume that the sample mean shouldbe as near as possible to the population mean,thereby assuming it to be near normal howeverwe have to take a sample size more than30.(hence normal variate z is calculated) but if we are using sample size less than 30, t-test isused.


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Characteristics of attitudes

An attitude has to be favorable or unfavorable. Attitudes are firmly embedded in a complex

psychological structure of beliefs. They are different from Values. Attitude is a predisposition to respond to a

certain set of facts. Attitudes are always in form of evaluative

statements.


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Functions of attitude Utilitarian function

Value-expressive function

Ego-defensive function

Knowledge function


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Components of attitude

Cognitive component (knowledge, beliefs)

Affective component (emotions, feelings)

Conative component (behavioral aspect)


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Measurement of attitudes

Attitudes are very difficult to measurebecause of the nature of attitudes.

since they deal with the concept of feelings and beliefs therefore they cannotbe quantified as the other physical aspectssuch as height, weight etc.

Therefore we use scales to measuredifferent types of consumer attitudes.


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Difficulty of measurement process

We use different scales depending upon thetype of item to be measured. Eg.

~Length is a weight item and hence can be easily

measured with a RATIO SCALE~Preference is an attitude and therefore can notbe easily measured hence we need an intervalscale.

~Happiness on the other hand is a creativeperception/attitude and hence is most difficult tomeasure therefore we use a ordinal/nominalscale to measure it.


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TYPES OF SCALES USED INMEASURING ATTITUDES Scaling refers to the process of measuring of attitudes.

Choice of scale depends upon the type of attitudebeing measured.

NON COMPARITIVE SCALES1. NOMINAL SCALE- These scales are comprised of

numbers that are used to categorize objects or events. A nominally scaled number serves only as a label for aclass or category. It is the procedure of assigningfacts/choice to various alternative categories which asusually exhaustive as well as mutually exclusive.EXAMPLE:How do you keep the books at present in the library?( ) Subject wise ( ) Departmentwise( ) Alphabetically ( ) Author wise


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2. ORDINAL SCALE- These scales represent numbers,letters or other symbols used to rank items. It can beclassified not only on the basis of whether they sharesome characteristic with another item but also whetherthey have more or less of this characteristic than someother object. The ranking of certain attributes/benefitsas deemed important by the respondent is obtainedthrough this scale.EXAMPLERank the following attributes on a scale of 1-5according to their importance to a washing machine.a) Company imageb) Functionsc) Priced) Comforte) Design


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3. INTERVAL SCALE- This scale representnumbers used to rank items such thatnumerically equal distances on the scalerepresent equal distances in the property beingmeasured. It involves qualities description of number of respects of a product or traits of aperson. EXAMPLE:

A. Product designPoor 1 2 3 4 5 Good

B. Brand nameLesser known 1 2 3 4 5 Well known

C. PriceLow 1 2 3 4 5 High


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RATIO SCALE- This scale consists of

numbers that rank items such thatnumerically equal distances on the scalerepresent equal distances in the propertybeing measured and have a meaningfulzero. All descriptive measures andinferential techniques are applicable toratio scaled data. EXAMPLE:Same as previous but the scale used-3 -2 -1 0 1 2 3


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THE TURSTONE SCALE-This scale is also called themethod of equally appearing intervals . The researcher

first assembles a large number of statements, say 150 to200 statements, about the attitude under study. Several judges are then asked to classify these statementsindependently into 5 piles, ranging from extremelynegative to extremely positive. The sixth pile is to

represent the neutral point. A final selection of 20-25items is made based on those statements. Thesestatements are finally presented to respondents who areasked to check those statements they agree with.

Following may be few statements

I like the layout of book The book does not contain any examplesThe book is confusing .The language of book is simple.


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THE LIKERT SCALE- This scale is also

called the method of summated ratings isa 5- point scale ranging from strongagreement to strong disagreement . Inthis method, a judging group is notinvolved. The respondent is given astatement about a topic and he recordsthe degree of his agreement ordisagreement with the statement.


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THE SEMANTIC DIFFERENTIAL-This scaleassociates probes into the intensity and content of arespondent s attitude towards, say, 4 companiesoperating in a particular sector on parameters such as:

-3 -2 -1 0 +1 +2+3Brand name not known _ _ _ _ _ _ _ well knownNot reliable _ _ _ _ _ _ _ reliable

The evaluation is made using a scale of adjectives whichare polar opposites, ranging from one extreme positionto the opposite extreme, separated by seven equalintervals.


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To determine the perception of a consumer for 5


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To determine the perception of a consumer for 5insurance companies to be evaluated on 2attributes viz. convenient locality and courteous

personal service

Not courteouscourteous

convenient

InconvenientB

C

D

A

E


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Reliability Reliability means the extent to which themeasurement process is free from errors.

It deals with accuracy and consistency.The scale is said to be reliable, if it yieldsthe same results when repeatedmeasurements are made under constantconditions. Criteria used:

Test- retest- respondents are administeredscales at 2 different times under nearlyequivalent conditions


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Reliability (contd.)

Alternative form reliability- 2 equivalentforms of a scale are constructed, then

tested with the same respondents at 2different times. Internal- consistency reliability- split half

reliability.


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Validity Success of a scale lies in measuring whatwe intend to measure. Validity tries tomeasure the same and therefore has moreimportance than reliability. Criteria used:

Construct validity Content validity Predictive validity Criterion validity Convergent validity- positive correlation

with other measures of same construct

Discriminant validit - o osite to above


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Uses of scales in MarketingResearch Helps in collecting reliable quantifiable

data for otherwise uncountable attributes

such as attitudes. Help in understanding not only thepresence or absence of a particular

attribute but also helps in knowing thedegree of the same. Scaled data is easy to analyze & interpret.

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Marketing Research Unit-2

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