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ASSIGNMENT-02

Name : SAMPATH RAJRegistration No. : 521101999Learning Center : ManipalLearning Center Code : 952Course : MBASubject : Research MethodologySemister : ThirdModule No. :Date of submission : 20-12-2012

Marks awarded :

Directorate of Distance EducationSikkim Manipal UniversityII Floor, Syndicate House

Manipal 576 104

-------------------------------- ------------------------- -----------------------------

Signature of Coordinator Signature of Center Signature of Evaluator

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Master of Business Administration- MBA Semester 3

MB0050 Research Methodology - 4 Credits

(Book ID: B1206)

Assignment Set - 2 (60 Marks)

Q.1. Differentiate between nominal, ordinal, interval and ratio scales, with an example of each.

Answer:

1. Nominal measurement

This level of measurement consists in assigning numerals or symbols to different categories

of a variable.

The example of male and female applicants to an MBA program mentioned earlier is an

example of nominal measurement. The numerals or symbols are just labels and have noquantitative value. The number of cases under each category are counted.

Nominal measurement is therefore the simplest level of measurement. It does not have

characteristics such as order, distance or arithmetic origin.

2. Ordinal measurement

In this level of measurement, persons or objects are assigned numerals which indicate ranks

with respect to one or more properties, either in ascending or descending order.

Example

Individuals may be ranked according to their socio-economic class, which is measured by acombination of income, education, occupation and wealth.

The individual with the highest score might be assigned rank 1, the next highest rank 2, and

so on, or vice versa.

The numbers in this level of measurement indicate only rank order and not equal distance or

absolute quantities. This means that the distance between ranks 1 and 2 is not necessarily

equal to the distance between ranks 2 and 3.

Ordinal scales may be constructed using rank order, rating and paired comparisons. Variables

that lend themselves to ordinal measurement include preferences, ratings of organizations and

economic status.

Statistical techniques that are commonly used to analyze ordinal scale data are

the median and rank order correlation coefficients.

3. Interval measurement

This level of measurement is more powerful than the nominal and ordinal levels of

measurement, since it has one additional characteristic equality of distance. However, it

does not have an origin or a true zero. This implies that it is not possible to multiply or divide

the numbers on an interval scale.

Example

http://www.smusolutions.com/2011/11/q1-differentiate-between-nominal.htmlhttp://www.smusolutions.com/2011/11/q1-differentiate-between-nominal.html

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The Centigrade or Fahrenheit temperature gauge is an example of the interval level

of measurement. A temperature of 50 degrees is exactly 10 degrees hotter than 40 degrees and

10 degrees cooler than 60 degrees.

Since interval scales are more powerful than nominal or ordinal scales, they also lend

themselves to more powerful statistical techniques, such as standard

deviation, product moment correlation and t tests and F tests of significance.

4. Ratio measurement

This is the highest level of measurement and is appropriate when measuring characteristics

which have an absolute zero point. This level of measurement has all the three characteristics

order, distance and origin.

Examples

Height, weight, distance and area. Since there is a natural zero, it is possible to multiply and

divide the numbers on a ratio scale. Apart from being able to use all the statistical techniques

that are used with the nominal, ordinal and interval scales,

techniques like the geometric mean and coefficient of variation may also be used.

The main limitation of ratio measurement is that it cannot be used for characteristics such as

leadership quality, happiness, satisfaction and other properties which do not have natural zero

points.

The different levels of measurement and their characteristics may be summed up.

In the table below Levels of measurement Characteristics

Nominal No order, distance or origin

Ordinal Order, but no distance or origin

Interval Both order and distance, but no origin

Ratio Order, distance and origin

Q.2. What are the types of Hypothesis? Explain the procedure for testing Hypothesis.

Answer:

Types of Hypothesis

There are many kinds of hypothesis the researcher has to be working with. One type ofhypothesis asserts that something is the case in a given instance; that a particular object,

situation has particular characteristics.

1. Null Hypothesis and Alternative Hypothesis

In the context of statistical analysis, we often talk null and alternative hypothesis. If we

are to compare method A with method B about its superiority and if we proceed on the

assumptions that both methods are equally good, then this assumption is termed as null

hypothesis. As against this, we may think that the method A is superior, it is alternative

hypothesis.

Null hypothesis= H0 and Alternative hypothesis=HaSuppose we want to test the hypothesis that the population mean is equal to the hypothesis

mean ( H0 ) = 100. Then we would say that the null hypotheses are that the population

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mean is equal to the hypothesized mean 100 and symbolical we can express as: H 0: = H0=100

If our sample results do not support these null hypotheses, we should conclude that

something else is true. What we conclude rejecting the null hypothesis is known as alternative

hypothesis.

Alternative Hypothesis To be read as follows

Ha: H0 (The alternative hypothesis is that the population

mean is not equal to 100 i.e., it may be more or

less 100)

Ha: > H0 (The alternative hypothesis is that the population

mean is greater than 100)

Ha: 10 tons

Take another ex: the average score in an aptitude test administered at the national level is 80.To evaluate a states education system, the average score of 100 of the states students

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selected on the random basis was 75. The state wants to know if there is a significance

different b/w the local scores and the national score. In such a situation the hypothesis may be

state as under:

Null hypothesis H0 : = 80

Alternative hypothesis Ha : 80

The formulation of hypothesis is an important step which must be accomplished with due

care in accordance with the object and nature of the problem under consideration. It also

indicates whether we should use a tailed test or a two tailed test. If Ha is of the type greater

than, we use alone tailed test, but when Ha is of the type whether greater or smaller then we

use a two tailed test.

2. Selecting a significant level

The hypothesis is tested on a pre determined level of significance and such the same should

have specified. Generally, in practice, either 5% level or 1% level is adopted for the purpose.

The factors that affect the level of significance are:

The magnitude of the difference b/w sample.

The size of the sample.

The variability of measurements within samples.

Whether the hypothesis is directional or non directional ( A directional hypothesis is

one which predicts the direction off the different b/w , say, means). In brief, the level

of significance must be adequate in the context of the purpose and nature of enquiry.

3. Deciding the distribution to use

After deciding the level of significance, the next step in hypothesis testing is to determine the

appropriate sampling distribution. The rules for selecting the correct distribution are similarto those which we have stated earlier in the context of estimation.

4. Selecting A Random Sample and Computing an Appropriate Valve

Another step is to select a random sample (S) and compute an appropriate value from the

sample data concerning the test statistic utilizing the relevant distribution. In other words

draw a sample to furnish empirical data.

5. Comparing the Probability

Yet another step consisting comparing the probability thus calculated with the specified value

for a, the significance level. If the calculated probability is equal to smaller than a value in

case of the tailed test (and /2 in case of two tailed test), then reject the null hypothesis

(i.e., accept the alternative hypothesis), but if the probability is greater than accept the null

hypothesis. In case we reject H0 we run a risk of (at most level of significance) committing anerror of type I, but if we accept H0 , then we run some risk of committing error type II.

Q.3.What are the advantages and disadvantages of Case study Method? How is Case

study method useful to Business Research?

Answer:

Advantages of Case study Method

Case study of particular value when a complex set of variables may be at work in

generating observed results and intensive study is needed to unravel the complexities. For ex,

an in-depth study of a firms top sales people and comparison with worst salespeople might

reveal characteristics common to stellar performers. Here again, the exploratory investigation

is best served by an active curiosity and willingness to deviate from the initial plan whenfindings suggest new courses of enquiry might prove more productive. It is easy to see how

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the exploratory research objectives of generating insights and hypothesis would be well

served by use of this technique.

Disadvantages of Case study Method

Blummer points out that independently, the case documents hardly fulfil the criteria of

reliability, adequacy and representiveness, but to exclude them from any scientific study of

human life will be blunder in as much as these documents are necessary and significant bothfor theory building and practice.

The Case study method useful to Business Research

In-depth analysis of selected cases is of particular value to business research when a

complex set of variables may be at work in generating observed results and intensive study is

needed to unravel the complexities. For instance, an in-depth study of a firms top sales

people and comparison with the worst sales people might reveal characteristics common to

stellar performers. The exploratory investigator is best served by the active curiosity and

willingness to deviate from the initial plan, when the finding suggest new courses of enquiry,

might prove more productive.

Q.4 What are the Primary and secondary sources of Data?

Answer:

Primary sources are original sources from which the researcher directly collects data that

have not been previously collected e.g., collection of data directly by the researcher on brand

awareness, brand preference, brand loyalty and other aspects of consumer behavior from a

sample of consumers by interviewing them. Primary data are first hand information collected

through various method such as observation, interviewing, mailing etc.

Advantage of Primary Data

It is original sources of data.

It is possible to capture the changes occurring in the course of time. It is flexible to the advantage of researcher.

Extensive research study is based on primary data.

Disadvantage of Primary Data

Primary data is expensive to obtain.

It is time consuming.

It requires extensive research personnel who are skilled.

It is difficult to administer.

Secondary sources of Data

These are sources containing data which have been collected and complied for another

purpose. The secondary sources consists of readily compendia and already complied

statistical statements and reports whose data may be used by researchers for their studies e.g.,

census reports, annual reports and financial statements of companies, Statistical statement,

Reports of Government Departments, Annual reports of currency and finance published by

the Reserve Bank of India, Statistical statement relating to Co-operatives and Regional

Banks, published by the NABARD, Reports of the National sample survey Organization,

Reports of trade associations, publications of international organizations such as UNO, IMF,

World Bank, ILO, WHO, etc., Trade and Financial journals newspapers etc.

Secondary sources consist of not only published records and reports, but also unpublishedrecords. The latter category includes various records and registers maintained by the firms

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and organizations, e.g., accounting and financial records, personnel records, register of

members, minutes of meetings, inventory records etc.

Advantages of Secondary Data

Secondary sources have some advantages:

1. Secondary data, if available can be secured quickly and cheaply. Once their source ofdocuments and reports are located, collection of data is just matter of desk work. Even the

tediousness of copping the data from the source can now be avoided, thanks to Xeroxing

facilities.

2. Wider geographical area and longer reference period may be covered without much cost.

Thus, the use of secondary data extends the researchers space and time reach.

3. The use of secondary data broadens the data base from which scientific generalizations can

be made.

4. Environmental and cultural settings are required for the study.

5. The use of secondary data enables a researcher to verify the findings bases on primary

data. It readily meets the need for additional empirical support. The researcher need not wait

the time when additional primary data can be collected.

Disadvantages of Secondary Data

The use of a secondary data has its own limitations.

1. The most important limitation is the available data may not meet our specific needs. The

definitions adopted by those who collected those data may be different; units of measure may

not match; and time periods may also be different.

2. The available data may not be as accurate as desired. To assess their accuracy we need to

know how the data were collected.

3. The secondary data are not up-to-date and become obsolete when they appear in print,

because of time lag in producing them. Foe ex., population census data are published two or

three years later after compilation, and no new figures will be available for another ten years.

4. Finally, information about the whereabouts of sources may not be available to all social

scientists. Even if the location of the source is known, the accessibility depends primarily on

proximity. For ex., most of the unpublished official records and compilations are located in

the capital city, and they are not within the easy reach of researchers based in far off places.

Q.5. Differentiate between Schedules and Questionnaire. What are the alternative

modes of sending Questionnaires?

Answer:

DIFFERENCE BETWEEN QUESTIONNAIRE AND SCHEDULE

1. Questionnaire can be sent via mail but schedule is done only Personally2. Questionnaire is cheaper method than schedule (for schedule you have to move

everywhere

3. Questionnaire can be returned without answering all the questions but, in schedule,

enumerator ensures the filling all the questions.

4. Questionnaire can be filled by anyone but schedule is always filled by enumerator.

5. Respondent should be literate & co-operative in Questionnaire but schedule can be filled

by illiterate.

6. Risk of incomplete & wrong information is more in Questionnaire.

7. Physical appearance of Questionnaire has to be attractive but not such case is necessary

with schedule.

8.Success of Questionnaire depends on its design but in case of Schedule it depends onhonesty & competency of Enumerator.

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Alternative Modes of sending Questionnaires

There are some alternative methods of distributing questionnaires to the respondents.

They are(1) personal delivery, (2) attaching questionnaire to a product (3) advertising

questionnaire in a newspaper of magazine, and (4) news stand insets.

1. Personal Delivery

The researcher or his assistant may delivery the questionnaires to the potentialrespondents with a request to complete them at their convenience. After a day or two he can

collect the completed questionnaires from them. Often referred to as the self-administered

questionnaire method, it combines the advantages of the personal interview and the mail

survey. Alternatively, the questionnaires may be delivered in person and the completed

questionnaires may be retured by mail by the respondents.

2. Attaching Questionnaire to a Product

A firm test marketing a product may attach a questionnaire to a product and request the

buyer to complete it and mail it back to the firm. The respondent is usually rewarded by a gift

or a discount coupon.

3. Advertising the Questionnaires

The questionnaire with the instructions for completion may be advertised on a page ofmagazine or in section of newspapers. The potential respondent completes it tears it out and

mails it to the advertiser. For ex., the committee of Banks customer services used this

method. Management studies for collecting information from the customers of commercial

banks in India. This method may be useful for large-scale on topics of common interest.

4. News-Stand Inserts

This method involves inserting the covering letter, questionnaire and self addressed

reply-paid envelope into a random sample of news-stand copies of a newspaper or magazine.

5. Improving the Response Rate in Mail survey

The response rate in mail surveys is generally very low more so in developing countries

like India. Certain techniques have to be adopted to increase the response rate. They are:

1. Quality Printing: The questionnaire may be neatly printed in quality light coloured paper,

so as attract the attention of the respondent.

2. Covering Letter: The covering letter should be couched in a pleasant style so as to attract

and hold the interest of the respondent. It must anticipate objections and answer them briefly.

It is a desirable to address the respondent by name.

3. Advance Information: Advance information can be provided to potential respondents by a

telephone call or advance notice in the newsletter of the concerned organization or by a letter.

Such preliminary contact with potential respondents is more successful than follow up

efforts.

4. Incentives: Money, stamps for collection and other incentives are also used to induce

respondents to m complete and return mail questionnaire.5. Follow-up-contacts: In the case of respondents belonging to an organization, they may be

approached through some one in that organization known as the researcher.

6. Large sample size: A large sample may be drawn than the estimated sample size. For ex.,

if the required sample size is 1000, a sample of 1500 may be drawn. This may help the

researcher to secure an effective sample size closer to the required size.

Q.6. Explain the various steps in processing of Data.

Answer:

5 Steps To Data Processing

Data is an integral part of all business processes. It is the invisible backbone that

supports all the operations and activities within a business. Without access to relevant

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data, businesses would get completely paralyzed. This is because quality data helps

formulate effective business strategies and fruitful business decisions.

Therefore, the quality of data should be maintained in good condition in order to

facilitate smooth business proceedings. In order to enhance business proceedings, data

should be made available in all possible forms in order to increase the accessibility ofthe same.

Data processing refers to the process of converting data from one format to another. It

transforms plain data into valuable information and information into data. Clients can

supply data in a variety of forms, be it .xls sheets, audio devices, or plain printed

material. Data processing services take the raw data and process it accordingly to

produce sensible information. The various applications of data processing can convert

raw data into useful information that can be used further for business processes.

Companies and organizations across the world make use of data processing services in

order to facilitate their market research interests. Data consists of facts and figures,based on which important conclusions can be drawn. When companies and

organizations have access to useful information, they can utilize it for strategizing

powerful business moves that would eventually increase the company revenue and

decrease the costs, thus expanding the profit margins. Data processing ensures that the

data is presented in a clean and systematic manner and is easy to understand and be

used for further purposes.

Here are the 5 steps that are included in data processing:

Editing

There is a big difference between data and useful data. While there are huge volumes

of data available on the internet, useful data has to be extracted from the huge volumes

of the same. Extracting relevant data is one of the core procedures of data processing.

When data has been accumulated from various sources, it is edited in order to discard

the inappropriate data and retain relevant data.

CodingEven after the editing process, the available data is not in any specific order. To make it

more sensible and usable for further use, it needs to be aligned into a particular system.

The method of coding ensures just that and arranges data in a comprehendible format.

The process is also known as netting or bucketing.

Data Entry

After the data has been properly arranged and coded, it is entered into the software that

performs the eventual cross tabulation. Data entry professionals do the task efficiently.

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Validation

After the cleansing phase, comes the validation process. Data validation refers to the

process of thoroughly checking the collected data to ensure optimal quality levels. All

the accumulated data is double checked in order to ensure that it contains no

inconsistencies and is utterly relevant.

Tabulation

This is the final step in data processing. The final product i.e. the data is tabulated and

arranged in a systematic format so that it can be further analyzed.

All these processes make up the complete data processing activity which ensures the

said data is available for access.