ASSIGNMENT

Name S.AMEER ABBAS

Roll No. 520955311

Course MBA-Semester-3

SubjectResearch

Methodology

Subject Code MB0034-Set-1

1.What do you mean by research? Explain its significance in social

and business sciences?

Research simply means a search for facts –answers to questions and

solutions to problems. It is a purposive investigation. It is an organized

inquiry. It seeks to find explanations to unexplained phenomenon to clarify

the doubtful facts and to correct the misconceived facts.

The search for facts may be made through either:

Arbitrary (of unscientific) Method: It’s a method of seeking answers

to question consists of imagination, opinion, blind belief or

impression. E.g. it was believed that the shape of the earth was flat;

a big snake swallows sun or moon causing solar or lunar eclipse. It

is subjective; the finding will vary from person to person depending

on his impression or imagination. It is vague and inaccurate. Or

Scientific Method: this is a systematic rational approach to seeking

facts. It eliminates the drawbacks of the arbitrary method. It is

objectives, precise and arrives at conclusions on the basis of

verifiable evidences.

Characteristics of Research

It is a systematic and critical investigation into a phenomenon.

It is a purposive investigation aiming at describing, interpreting and

explain a phenomenon.

It adopts scientific method.

It is objective and logical, applying possible test to validate the

measuring tools and the conclusions reached.

Its is based upon observable experience or empirical evidence.

Research is directed towards finding answers to pertinent questions

and solutions to problems

It emphasized the development of generalization, principles of

theories.

The purpose of research is not only to arrive at an answer but also

to stand up the test of criticism.

Significance of Research

According to a famous Hudson Maxim, “All progress in born of inquiry Doubt

is often better than over confidence, for it leads to inquiry, and inquiry leads

to invention”. It brings out the significance of research, increased amounts

of which makes progress possible. Research encourages scientific and

inductive thinking , besides promoting the development of logical habits of

thinking and organization.

The role of research in applied economics in the context of an economy or

business is greatly increasing in modern times. The increasingly complex

nature government and business has raised the use of researching solving

operational problems. Research assumes significant role in provides the basis

for almost all government policies of an economic system. Government

budget formulation, for example, depends particularity on the analysis of

needs and desires of the people, and the availability of revenues, which

requires research. Research helps to formulate alternative policies, in

addition to examining the consequences of these alternatives. Thus, research

also facilitates the decision making of policy –makers, although in itself it is

not a part of research. In the process. Research also helps in the proper

allocation of country’s scare resources. Research is also necessary for

collecting information on the social and economic structure on an economy to

understand the process of change occurring in involves various research

problems. Therefore, large staff of research technicians or experts is engaged

by the government these days to undertake this work. Thus, research as a

tool of government economic policy formulation involves three distinct stages

of operation which are as follows:

Investigation of economic structure through continual compilation

of facts

Diagnoses of events the are taking place and the analysis of the

forces underlying them, and

The prognosis. i.e., the prediction of future developments.

Research also assumes a significant role in solving various operational and

planning problems associated with business and industry. In several ways,

operations research, market research, and motivational research are vital

and their results assist in taking business decisions. Market research is refers

to the investigation of the structure and development of a market for the

formulation of efficient policies relating to purchases, production and sales.

Operational research relates to the application of logical, mathematical, and

analytical techniques to find solution to business problems such as cost

minimization or profit maximization, or the optimization problems.

Motivational research helps to determine why people believe in the manner

they do with respect to market characteristics. More specifically, it is

concerned with the analyzing the motivations underlying consumer behavior.

All these researches are very useful for business and industry, which are

responsible for business decision making.

Research is equally important to social scientist for analyzing social

relationships and seeking explanations to various social problems. It gives

intellectual satisfaction of knowing things for the sake of knowledge. It also

possesses practical utility for the social scientist to gain knowledge so as to

be able to do something better or in a more efficient manner. This, research

in social sciences is concerned with both knowledge for its own sake, and

knowledge for what it can contribute to solve practical problems.

2. What is meant by research problem? What are the characteristics

of a good research problem?

Research really begins when the researcher experiences some

difficulty, i.e., a problem demanding a solution within the subject –are of his

discipline. Theis general area of interest, however, defines only the range of

subject matter within which the researcher whould see and pose a specific

problem for research. Personal values play an important role in the selection

of a topic for research. Social conditions do often shape the preference of

investigators in the subtle and imperceptible way.

Choosing the Problem:

The selection of a problem is the first step in research. The term problem

means a question or issue to be examined. The selection of problem for

research is not an easy task; it self is a problem. It is least amenable to

formal methodological treatment. Vision, an imaginative insight, plays an

important role in this process. One with a critical, curious and imaginative

mind and is sensitive to practical problems could easily identify problems for

study.

The sources from which one may be able to identify research problem or

develop problems awareness are:

Review of literature

Academic experience

Daily experience

Exposure to field situations

Consultations

Brain storming

Research

Intuition

Characteristics of a good research problem:

Horton and Hunt have given following characteristics of scientific research:

1. Verifiable evidence: That is factual observations which other observers

can see and check.

2. Accuracy: That is describing what really exists. It means truth or

correctness of a statement or describing things exactly as they are and

avoiding jumping to unwarranted conclusions either by exaggeration or

fantasizing.

3. Precision: That is making it as exact as necessary, or giving exact

number or measurement. This avoids colourful literature and vague

meanings.

4. Systematization: That is attempting to find all the relevant data, or

collecting data in a systematic and organized way so that the conclusions

drawn are reliable. Data based on casual recollections are generally

incomplete and give unreliable judgments and conclusions.

5. Objectivity: That is free being from all biases and vested interests. It

means observation is unaffected by the observer’s values, beliefs and

preferences to the extent possible and he is able to see and accept facts as

they are, not as he might wish them to be.

6. Recording: That is jotting down complete details as quickly as possible.

Since human memory is fallible, all data collected are recorded.

7. Controlling conditions: That is controlling all variables except one and

then attempting to examine what happens when that variable is varied. This

is the basic technique in all scientific experimentation – allowing one variable

to vary while holding all other variables constant.

8. Training investigators: That is imparting necessary knowledge to

investigators to make them understand what to look for, how to interpret in

and avoid inaccurate data collection.

3.What is hypothesis? Explain the procedures for testing hypothesis?

A hypothesis is an assumption about relations between variables. It is

a tentative explanation of the research problem or a guess about the

research outcome.

Before starting the research, the researcher has a rather general,

diffused, even confused notion of the problem. It may take long time for the

researcher to say what questions be had been seeking answers to.

Hence, an adequate statement about the research problem is very

important. What is a good problem statement? It is an interrogative

statement that asks: what relationship exists between two or more variables?

It then further asks questions like: Is A related to B or not? How are A and B

related to C? Is A related to B under conditions X and Y? Proposing a

statement pertaining to relationship between A and B is called a hypothesis.

According to Theodorson and Theodorson, “ a hypothesis is a tentative

statement asserting a relationship between certain facts. Kerlinger

describes it as “a conjectural statement of the relationship between

two or more variables”. Black and Champion have described it as “a

tentative statement about something, the validity of which is usually

unknown”. This statement is intended to be tested empirically and is

either verified or rejected. It the statement is not sufficiently

established, it is not considered a scientific law.

In other works, a hypothesis carries clear implications for

testing the stated relationship, i.e., it contains variables that are

measurable and specifying how they are related. A statement that

lacks variables or that does not explain how the variables are related

to each other is no hypothesis in scientific sense.

Procedures for testing hypothesis:

To test a hypothesis means to tell (on the basis of the data researcher has

collected) whether or not the hypothesis seems to be valid.

In hypothesis testing the main question is: whether the null hypothesis or not

to accept the null hypothesis? Procedure for hypothesis testing refers to all

those steps that we undertake for making a choice between the two actions

i.e., rejection and acceptance of a null hypothesis. The various steps involved

in hypothesis testing are stated below:

1 Making a Formal Statement

The step consists in making a formal statement of the null hypothesis (Ho)

and also of the alternative hypothesis (Ha). This means that hypothesis

should clearly state, considering the nature of the research problem. For

instance, Mr. Mohan of the Civil Engineering Department wants to test the

load bearing capacity of an old bridge which must be more than 10 tons, in

that case he can state his hypothesis as under:

Null hypothesis HO: μ =10 tons

Alternative hypothesis Ha: μ >10 tons

Take another example. The average score in an aptitude test administered at

the national level is 80. To evaluate a state’s education system, the average

score of 100 of the state’s students selected on the random basis was 75.

The state wants to know if there is a significance difference between the local

scores and the national scores. In such a situation the hypothesis may be

state as under:

Null hypothesis HO: μ =80

Alternative hypothesis Ha: μ ≠ 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:

1 .The magnitude of the difference between sample ;

2. The size of the sample;

3. The variability of measurements within samples;

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

hypothesis is one which predicts the direction of the difference between, 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 choice generally

remains between distribution and the t distribution. The rules for selecting

the correct distribution are similar to those which we have stated earlier in

the context of estimation.

4. Selecting A Random Sample & Computing An Appropriate Value

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. Calculation of the Probability

One has then to calculate the probability that the sample result would

diverge as widely as it has from expectations, if the null hypothesis were

in fact true.

6 .Comparing the Probability

Yet another step consists in comparing the probability thus calculated with

the specified value for α, the significance level. If the calculated probability is

equal to smaller than α value in case of one tailed test (and α/2 in case of

two-tailed test), then reject the null hypothesis (i.e. accept the alternative

hypothesis), but if the probability is greater then accept the null hypothesis.

Selecting A Random Sample & Computing An Appropriate

In case we reject H0 we run a risk of (at most level of significance)

committing an error of type I, but if we accept H0, then we run some risk of

committing error type II.

4.Write an essay on the need for research design and explain the

principles of experimental designs:

Hypothesis-testing research studies (generally known as experimental

studies) are those where the researcher tests the hypothesis of causal

relationships between variables. Such studies require procedures that will not

only reduce bias and increase reliability, but will permit drawing inferences

about causality. Usually, experiments meet these requirements. Hence, when

we talk of research design in such studies, we often mean the design of

experiments.

Experimental design refers to the framework or structure of an experiment

and as such there are several experimental designs. We can classify

experimental designs into two broad categories, viz., informal experimental

designs and formal experimental designs. Informal experimental designs are

those designs that normally use a less sophisticated form of analysis based

on differences in magnitudes, where as formal experimental designs offer

relatively more control and use precise statistical procedures for analysis.

Informal experimental designs:

• Before and after without control design: In such a design, single test

group or area is selected and the dependent variable is measured before the

introduction of the treatment. The treatment is then introduced and the

dependent variable is measured again after the treatment has been

introduced. The effect of the treatment would be equal to the level of the

phenomenon after the treatment minus the level of the phenomenon before

the treatment.

• After only with control design: In this design, two groups or areas (test

and control area) are selected and the treatment is introduced into the test

area only. The dependent variable is then measured in both the areas at the

same time. Treatment impact is assessed by subtracting the value of the

dependent variable in the control area from its value in the test area.

• Before and after with control design: In this design two areas are

selected and the dependent variable is measured in both the areas for an

identical time-period before the treatment. The treatment is then introduced

into the test area only, and the dependent variable is measured in both for an

identical time-period after the introduction of the treatment. The treatment

effect is determined by subtracting the change in the dependent variable in

the control area from the change in the dependent variable in test area.

Formal Experimental Designs

1. Completely randomized design (CR design): It involves only two

principle viz., the principle of replication and randomization. It is generally

used when experimental areas happen to be homogenous. Technically, when

all the variations due to uncontrolled extraneous factors are included under

the heading of chance variation, we refer to the design of experiment as C R

Design.

2. Randomized block design (RB design): It is an improvement over the

C Research design. In the RB design the principle of local control can be

applied along with the other two principles.

3. Latin square design (LS design): It is used in agricultural research. The

treatments in a LS design are so allocated among the plots that no treatment

occurs more than once in any row or column.

4. Factorial design: It is used in experiments where the effects of varying

more than one factor are to be determined. They are especially important in

several economic and social phenomena where usually a large number of

factors affect a particular problem

5.Distinguish between primary and secondary data collection.

Explain the features, uses , advantages and limitations of secondary

data. Which is the best way of collecting the data for research

“primary or secondary”. Support your answer.

Primary Sources of Data

Primary sources are original sources form 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 methods such as observation, interviewing, mailing etc.

The search for answers to research questions is called collection of data. Data

are facts, and other relevant materials, past and present, serving as bases for

study and analyses. The data needed for a social science research may be

broadly classified into (a) Data pertaining to human beings, (b) Data relating

to organization and (c) Data pertaining to territorial areas.

Secondary Sources of Data

These are sources containing data which have been collected and compiled

for another purpose. The secondary sources consists of readily compendia

and already compiled 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 statements 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

unpublished records. The latter category includes various records and

registers maintained by the firms and organizations, e.g., accounting and

financial records, personnel records, register of members, minutes of

meetings, inventory records etc.

Features of Secondary Sources

Though secondary sources are diverse and consist of all sorts of materials,

they have certain common characteristics.

First, they are readymade and readily available, and do not require the

trouble of constructing tools and administering them.

Second, they consist of data which a researcher has no original control

over collection and classification. Both the form and the content of secondary

sources are shaped by others. Clearly, this is a feature which can limit the

research value of secondary sources.

Finally, secondary sources are not limited in time and space. That is,

the researcher using them need not have been present when and where they

were gathered.

Advantages of Secondary Data :

Secondary data, if available can be secured quickly and cheaply. Once

their source of documents and reports are located, collection of data is just

matter of desk work. Even the tediousness of copying the data from the

source can now be avoided, thanks to Xeroxing facilities.

Wider geographical area and longer reference period may be covered without

much cost. Thus, the use of secondary data extends the researcher’s space

and time reach.

The use of secondary data broadens the data base from which

scientific generalizations can be made.

Environmental and cultural settings are required for the study.

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 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.

The available data may not be as accurate as desired. To assess their

accuracy we need to know how the data were collected.

The secondary data are not up-to-date and become obsolete when

they appear in print, because of time lag in producing them. For example,

population census data are published tow or three years later after

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

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 example, 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.

The best way of collecting data is “SECONDARY” this is because the

secondary sources consists of readily compendia and already complied

statistical statements and reports. Finally secondary sources are not limited

in time and space, that is, the researched using them need not have been

present when and where they were gathered. Secondary data, if available

can be secured quickly and cheaply. Wider geographical area and longer

reference period may be covered without much cost. Thus, the use of

secondary data extends the researcher’s space and time reach. The use of

secondary data broadens the data base from which scientific generalizations

can be made.

6.Describe the interview method of collecting data. State the

conditions under which it is considered most suitable. You have been

assigned to conduct a survey on the reading habits of the house

wives in the middle class family. Design a suitable questionnaire

consisting of 20 questions you propose to use in the survey.

Interview method of collecting data:

Interviewing is one of the prominent methods of data collection. It may

be defined as a two way systematic conversation between an investigator

and an informant, initiated for obtaining information relevant to a specific

study. It involves not only conversation, but also learning from the

respondent’s gesture, facial expressions and pauses, and his environment.

Interviewing requires face to face contact or contact over telephone and

calls for interviewing skills. It is done by using a structured schedule or an

unstructured guide.

Interviewing may be used either as a main method or as a

supplementary one in studies of persons. Interviewing is the only suitable

method for gathering information from illiterate roles educated respondents.

It is useful for collecting a wide range of data from factual demographic data

to highly personal and intimate information relating to a person’s opinions,

attitudes, values, beliefs past experience and future intentions. When

qualitative information is required or probing is necessary to draw out fully,

and then interviewing is required. Where the area covered for the survey is a

compact, or when a sufficient number of qualified interviewers are available,

personal interview is feasible.

Interview is often superior to other data –gathering methods. People

are usually more willing to talk than to write. Once report is established, even

confidential information may be obtained. It permits probing into the context

and reasons for answers to questions.

Interview can add flesh to statistical information. It enables the

investigator to grasp the behavioral context of the data furnished by the

respondents.

Qualities of Interviews

The requirements or conditions necessary for a successful interview are:

Data availability: the needed information should be available with the

respondent. He should be able to conceptualize it in terms to the study, and

be capable or communicating it.

Role perception: the respondent should understand his role and know what is

required of him. He should know what is a relevant and how complete it

should be he can learn much of this from the interviewer’s introduction,

explanations and questioning procedure.

The interviewer should also know his role: he should establish a permissive

atmosphere and encourage frank and free conversation, he should not affect

the interview situation through subjective attitude and argumentation.

Respondent’s motivation : the respondent should be willing to respond and

give accurate answer. This depends partly on the interviewer’s approach and

skill. The interview has interest in it for the purpose of his research, but the

respondent has no personal interest in it. Therefore, the interviewer should

establish a friendly relationship with the respondent, and create in him an

interest in the subject –matter of the study. The interviewer should try to

reduce the effect of de-motivating factors like desire to get on with other

activities, embarrassment at ignorance, dislike of the interview content ,

suspicious about the interviewer, and fear of consequence, he should also try

to build up the effect of motivation actors like curiosity, loneliness, politeness,

sense of duty, respect of the research agency and liking for the interviewer.

The above requirement reminds that the interview is an interaction process.

The investigator should keep this in mind and take care to see that his

appearance and behavior do not distort the interview situation.

ASSIGNMENT

Name S.AMEER ABBAS

Roll No. 520955311

Course MBA-Semester-3

SubjectResearch

Methodology

Subject Code MB0034-Set-2

1.Write short notes on the following”a. Null hypothesisb. What is explanatory research?c. What is random sampling?d. Rank order co-relation

a. A null hypothesis is a hypothesis (within the frequents context of

statistical hypothesis testing) that might be falsified using a test of observed

data. Such a test works by formulating a null hypothesis, collecting data, and

calculating a measure of how probable that data was assuming the null

hypothesis were true. If the data appears very improbable (usually defined as

a type of data that should be observed less than 5% of the time) then the

experimenter concludes that the null hypothesis is false. If the data looks

reasonable under the null hypothesis, then no conclusion is made. In this

case, the null hypothesis could be true, or it could still be false; the data

gives insufficient evidence to make any conclusion. The null hypothesis

typically proposes a general or default position, such as that there is no

relationship between two quantities, or that there is no difference between a

treatment and the control. The term was originally coined by English

geneticist and statistician Ronald Fisher.

In some versions of statistical hypothesis testing (such as developed by Jerzy

Neyman and Egon Pearson), the null hypothesis is tested against an

alternative hypothesis. This alternative may or may not be the logical

negation of the null hypothesis. The use of alternative hypotheses was not

part of Ronald Fisher's formulation of statistical hypothesis testing, though

alternative hypotheses are standardly used today.

For instance, one might want to test the claim that a certain drug reduces the

chance of having a heart attack. One would choose the null hypothesis "this

drug does not reduce the chances of having a heart attack" (or perhaps "this

drug has no effect on the chances of having a heart attack"). One should then

collect data by observing people both taking the drug and not taking the drug

in some sort of controlled experiment. If the data is very unlikely under the

null hypothesis one would reject the null hypothesis, and conclude that its

negation is true. That is, one would conclude that the drug does reduce the

chances of having a heart attack. Here "unlikely data" would mean data

where the percentage of people taking the drug who had heart attack was

much less then the percentage of people not taking the drug who had heart

attacks. Of course one should use a known statistical test to decide how

unlikely the data was and hence whether or not to reject the null hypothesis.

b. Exploratory research provides insights into and comprehension of an

issue or situation. It should draw definitive conclusions only with extreme

caution. Exploratory research is a type of research conducted because a

problem has not been clearly defined. Exploratory research helps determine

the best research design, data collection method and selection of subjects.

Given its fundamental nature, exploratory research often concludes that a

perceived problem does not actually exist.

Exploratory research often relies on secondary research such as reviewing

available literature and/or data, or qualitative approaches such as informal

discussions with consumers, employees, management or competitors, and

more formal approaches through in-depth interviews, focus groups, projective

methods, case studies or pilot studies. The Internet allows for research

methods that are more interactive in nature: E.g., RSS feeds efficiently supply

researchers with up-to-date information; major search engine search results

may be sent by email to researchers by services such as Google Alerts;

comprehensive search results are tracked over lengthy periods of time by

services such as Google Trends; and Web sites may be created to attract

worldwide feedback on any subject.

The results of exploratory research are not usually useful for decision-making

by themselves, but they can provide significant insight into a given situation.

Although the results of qualitative research can give some indication as to

the "why", "how" and "when" something occurs, it cannot tell us "how often"

or "how many."

Exploratory research is not typically generalizable to the population at large..

c. Random Sampling is that part of statistical practice concerned with

the selection of an unbiased or random subset of individual observations

within a population of individuals intended to yield some knowledge about

the population of concern, especially for the purposes of making predictions

based on statistical inference. Sampling is an important aspect of data

collection.

Researchers rarely survey the entire population for two reasons (Adèr,

Mellenbergh, & Hand, 2008): the cost is too high, and the population is

dynamic in that the individuals making up the population may change over

time. The three main advantages of sampling are that the cost is lower, data

collection is faster, and since the data set is smaller is possible to ensure

homogeneity and to improve the accuracy and quality of the data.

Each observation measures one or more properties (such as weight,

location, color) of observable bodies distinguished as independent objects or

individuals. In survey sampling, survey weights can be applied to the data to

adjust for the sample design. Results from probability theory and statistical

theory are employed to guide practice. In business and medical research,

sampling is widely used for gathering information about a population.

d. Rank-order correlation - the most commonly used method of

computing a correlation coefficient between the ranks of scores on two

variables. In statistics, Spearman's rank correlation coefficient or

Spearman's rho, named after Charles Spearman and often denoted by the

Greek letter ρ (rho) or as rs, is a non-parametric measure of statistical

dependence between two variables. It assesses how well the relationship

between two variables can be described using a monotonic function. If there

are no repeated data values, a perfect Spearman correlation of +1 or −1

occurs when each of the variables is a perfect monotone function of the

other.

The Spearman correlation coefficient is often thought of as being the Pearson

correlation coefficient between the ranked variables. In practice, however, a

simpler procedure is normally used to calculate ρ. The n raw scores Xi, Yi are

converted to ranks xi, yi, and the differences di = xi − yi between the ranks of

each observation on the two variables are calculated.

If there are no tied ranks, then ρ is given by:

If tied ranks exist, Pearson's correlation coefficient between ranks should be

used for the calculation:

One has to assign the same rank to each of the equal values. It is an average

of their positions in the ascending order of the values.

2.Elaborate the format of a research report touching briefly on he mechanics of writing.

Research report is a means for communicating research experience to

others. A research report is formal statement of the research process and it

results. It narrates the problem studied, methods used for studying it and the

findings and conclusions of the study.

The format of a research report is given below:

1. Prefatory Item

Title page

Declaration

Certificates

Preface/ acknowledgment

Table of contents

List of tables

List of graphs/ figures/ charts

Abstracts or synopsis

2. Body of the Report

Introduction

Theoretical background of the topic

Statement of the problem

Review of literature

The Scope of the study

The objectives of the study

Hypothesis to be tested

Definition of the concepts

Models if any

Design of the study

Methodology

Method of data collection

Sources of data

Sampling Plan

Data collection instruments

Field work

Data processing and analysis plan

Overview of the report

Limitation of the study

Result: Findings and discussions

Summary, conclusions and recommendations

3. Reference Material

Bibliography

Appendix

Copies of data collection instruments

Technical details on sampling plan

Complex tables

Glossary of new terms used.

Mechanics of Writing:

A research report requires clear organization. Each chapter may be

divided into two or more sections with appropriate heading and in each

section margin headings and paragraph headings may be used to indicate

subject shifts. Physical presentation is another aspect of organization. A page

should not be fully filled in from top to bottom. Wider margins should be

provided on both sides and on top and bottom as well.

Centered section heading is provided in the center of the page and is usually

in solid font size. It is separated from other textual material by two or three

line space.

Marginal heading is used for a subdivision in each section. It starts from the

left side margin without leaving any space.

Paragraph heading is used to head an important aspect of the subject matter

discussed in a subdivision. There is some space between the margin and this

heading.

Presentation should be free form spelling and grammar errors. If the writer is

not strong in grammar, get the manuscript corrected by a language expert.

Use the rules of punctuations.

Use present tense for presenting the findings of the study and for stating

generalizations

Do not use masculine nouns and pronouns when the content refers to both

the genders. Do not abbreviate words in the text; spell out them in full.

Footnote citation is indicated by placing an index number, i.e., a superscript

or numeral, at the point of reference. Reference style should have a clear

format and used consistently.

3.Discuss the importance of case study method.

Case study is a method of exploring and analyzing the life of a social

unit or entity, be it a person, a family, an institution or a community. Case

study would depend upon wit, commonsense and imagination of the person

doing the case study. The investigator makes up his procedure as he goes

along. Efforts should be made to ascertain the reliability of life history data

through examining the internal consistency of the material.. A judicious

combination of techniques of data collection is a prerequisite for securing

data that are culturally meaningful and scientifically significant. 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. The case documents hardly fulfill the criteria of reliability,

adequacy and representativeness, but to exclude them form any scientific

study of human life will be blunder in as much as these documents are

necessary and significant both for theory building and practice. 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.

Let us discuss the criteria for evaluating the adequacy of the case

history or life history which is of central importance for case study.

John Dollard has proposed seven criteria for evaluating such adequacy as

follows:

i) The subject must be viewed as a specimen in a cultural series. That is, the

case drawn out from its total context for the purposes of study must be

considered a member of the particular cultural group or community. The

scrutiny of the life histories of persons must be done with a view to identify

the community values, standards and their shared way of life.

ii) The organic motto of action must be socially relevant. That is, the action of

the individual cases must be viewed as a series of reactions to social stimuli

or situation. In other words, the social meaning of behaviour must be taken

into consideration.

iii) The strategic role of the family group in transmitting the culture must be

recognized. That is, in case of an individual being the member of a family, the

role of family in shaping his behaviour must never be overlooked.

iv) The specific method of elaboration of organic material onto social

behaviour must be clearly shown. That is case histories that portray in detail

how basically a biological organism, the man, gradually blossoms forth into a

social person, are especially fruitful.

v) The continuous related character of experience for childhood through

adulthood must be stressed. In other words, the life history must be a

configuration depicting the inter-relationships between thee person’s various

experiences.

vi) Social situation must be carefully and continuously specified as a factor.

One of the important criteria for the life history is that a person’s life must be

shown as unfolding itself in the context of and partly owing to specific social

situations.

vii) The life history material itself must be organised according to some

conceptual framework.

4. Give the importance of frequency tables and discuss the principles

of table construction, frequency distribution and class intervals

determination:

Principles of table construction:

1) Every tables should have a title. The tile should represent a

succinct description of the contents of the table. It should be clear

and concise. It should be place above the body of the table.

2) A number facilitating easy reference should identify every table.

The number can be centered above the title. The table number

should run in consecutive serial order. Alternative tables in chapter

1 be numbered as 1.1, 1.2,1…….., in chapter2 as 2.1, 2.2,

2.3…………and so on.

3) The caption (or column heading) should be clear and brief.

4) The units of measurement under each heading must always be

indicated.

5) Any explanatory footnotes concerning the table itself are placed

directly beneath the table and in order to obviate any possible

confusion with the textual footnoted such reference symbols as the

asterisk (*) Danger(+) and the like may be used.

6) If the data in a series of table has been obtained from different

sources, it is ordinarily advisable to indicate the specific source in a

place just below the tables.

7) Usually lines separated columns from one another. Lines are

always drawn at the top and bottom of the table and below the

captions .

8) The column may be numbered to facilitate reference.

9) All column figures should be properly aligned. Decimal points and

‘plus’ and ‘minus’ signs should be in perfect alignment.

10)Columns and rows that are to be compared with one another

should be brought closed together.

11) Totals of rows should be placed at the extreme right column and

totals of columns at the bottom.

12)IN order to emphasize the relative significance of certain

categories, different kind of type, spacing and identifications can be

used.

13)The arrangement of the categories in a table may be chronological,

geographical, alphabetical or according to magnitude. Numerical

categories are usually arranged in descending order of magnitude.

14)Miscellaneous and exceptions items are generally placed in the last

row of the table.

15)Usually the larger number of item is listed vertically. This means

that a table length is more than its width.

16)Abbreviations should be avoided whenever possible and ditto

marks should not be used in a table.

17)The table should be made as logical, clear, accurate and simple as

possible.

Principles of frequency distribution:

In statistics, a frequency distribution is a tabulation of the values that one

or more variables take in a sample. Managing and operating on frequency

tabulated data is much simpler than operation on raw data. There are simple

algorithms to calculate median, mean, standard deviation etc. from these

tables.

Statistical hypothesis testing is founded on the assessment of differences and

similarities between frequency distributions. This assessment involves

measures of central tendency or averages, such as the mean and median,

and measures of variability or statistical dispersion, such as the standard

deviation or variance.

A frequency distribution is said to be skewed when its mean and median are

different. The kurtosis of a frequency distribution is the concentration of

scores at the mean, or how peaked the distribution appears if depicted

graphically—for example, in a histogram. If the distribution is more peaked

than the normal distribution it is said to be leptokurtic; if less peaked it is said

to be platykurtic.

Letter frequency distributions are also used in frequency analysis to crack

codes and refer to the relative frequency of letters in different languages.

Principles of class interval determination:

In musical set theory, an interval class (often abbreviated: ic), also known

as unordered pitch-class interval, interval distance, undirected

interval, or (completely incorrectly) interval mod 6 (Rahn 1980, 29;

Whittall 2008, 273–74), is the shortest distance in pitch class space between

two unordered pitch classes. For example, the interval class between pitch

classes 4 and 9 is 5 because 9 − 4 = 5 is less than 4 − 9 = −5 ≡ 7 (mod 12).

See modular arithmetic for more on modulo 12. The largest interval class is 6

since any greater interval n may be reduced to 12 − n.

The concept of interval class was created to account for octave, enharmonic,

and inversion equivalency

5.Write short notes on the following:

a. Type I error and type II error

b.One tailed and two tailed test

c. Selecting the significance level

Ans.

a.Type I error and type II error

In statistics, the terms type I error (also, α error, false alarm rate (FAR) or

false positive) and type II error (β error, miss rate or a false negative)

are used to describe possible errors made in a statistical decision process. In

1928, Jerzy Neyman (1894-1981) and Egon Pearson (1895-1980), both

eminent statisticians, discussed the problems associated with "deciding

whether or not a particular sample may be judged as likely to have been

randomly drawn from a certain population" (1928/1967, p. 1), and identified

"two sources of error", namely:

Type I (α): reject the null hypothesis when the null hypothesis is true,

and

Type II (β): fail to reject the null hypothesis when the null hypothesis is

false

Type I error, also known as an "error of the first kind", an α error, or a

"false positive": the error of rejecting a null hypothesis when it is actually

true. Plainly speaking, it occurs when we are observing a difference when in

truth there is none, thus indicating a test of poor specificity. An example of

this would be if a test shows that a woman is pregnant when in reality she is

not. Type I error can be viewed as the error of excessive credulity.

Type II error, also known as an "error of the second kind", a β error, or a

"false negative": the error of failing to reject a null hypothesis when it is in

fact not true. In other words, this is the error of failing to observe a difference

when in truth there is one, thus indicating a test of poor sensitivity. An

example of this would be if a test shows that a woman is not pregnant, when

in reality, she is. Type II error can be viewed as the error of excessive

skepticism.

b. One tailed and two tailed test

A one- or two-tailed t-test is determined by whether the total area of a

is placed in one tail or divided equally between the two tails. The one-tailed t-

test is performed if the results are interesting only if they turn out in a

particular direction. The two-tailed t-test is performed if the results would be

interesting in either direction. The choice of a one- or two-tailed t-test effects

the hypothesis testing procedure in a number of different ways.

TWO-TAILED t-TESTS

A two-tailed t-test divides a in half, placing half in the each tail. The null

hypothesis in this case is a particular value, and there are two alternative

hypotheses, one positive and one negative. The critical value of t, tcrit, is

written with both a plus and minus sign (± ). For example, the critical value of

t when there are ten degrees of freedom (df=10) and a is set to .05, is tcrit= ±

2.228. The sampling distribution model used in a two-tailed t-test is

illustrated below:

ONE-TAILED t-TESTS

There are really two different one-tailed t-tests, one for each tail. In a one-

tailed t-test, all the area associated with a is placed in either one tail or the

other. Selection of the tail depends upon which direction tobs would be (+ or -)

if the results of the experiment came out as expected. The selection of the

tail must be made before the experiment is conducted and analyzed.

A one-tailed t-test in the positive direction is illustrated below:

The value tcrit would be positive. For example when a is set to .05 with ten

degrees of freedom (df=10), tcrit would be equal to +1.812.

A one-tailed t-test in the negative direction is illustrated below:

The value tcrit would be negative. For example, when a is set to .05 with ten

degrees of freedom (df=10), tcrit would be equal to -1.812.

Comparison of One and Two-tailed t-tests

1. If tOBS = 3.37, then significance would be found in the two-tailed and the

positive one-tailed t-tests. The one-tailed t-test in the negative direction

would not be significant, because was placed in the wrong tail. This is the

danger of a one-tailed t-test.

2. If tOBS = -1.92, then significance would only be found in the negative one-

tailed t-test. If the correct direction is selected, it can be seen that one is

more likely to reject the null hypothesis. The significance test is said to have

greater power in this case.

The selection of a one or two-tailed t-test must be made before the

experiment is performed. It is not "cricket" to find a that tOBS = -1.92, and

then say "I really meant to do a one-tailed t-test." Because reviewers of

articles submitted for publication are sometimes suspicious when a one-tailed

t-test is done, the recommendation is that if there is any doubt, a two-tailed

test should be done.

c.Selecting the significance level

Significance is commonly designated as:

plain ol' "significance"

"statistical significance"

"probability" This word, "probability is the source of the letter tt

represents significance, the letter, "p"

The p value identifies the likelihood tt a particular outcome may have

occurred by chance.

6.Explain Karl pearson co-efficient of correlation. Calculate Karl

pearson co-efficient for the following data:

X(Ht)-cm 174 175 176 177 178 182 183 186 189 193

Y (Wt)-Kg 61 65 67 68 72 74 80 87 92 95

In statistics, the Pearson product-moment correlation coefficient

(sometimes referred to as the PMCC, and typically denoted by r) is a

measure of the correlation (linear dependence) between two variables X and

Y, giving a value between +1 and −1 inclusive. It is widely used in the

sciences as a measure of the strength of linear dependence between two

variables

Pearson's correlation coefficient between two variables is defined as the

covariance of the two variables divided by the product of their standard

deviations:

The above formula defines the population correlation coefficient, commonly

represented by the Greek letter ρ (rho). Substituting estimates of the

covariances and variances based on a sample gives the sample correlation

coefficient, commonly denoted r :

An equivalent expression gives the correlation coefficient as the mean of the

products of the standard scores. Based on a sample of paired data (Xi, Yi), the

sample Pearson correlation coefficient is

where

are the standard score, sample mean, and sample standard deviation.