Final RM Notes

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Unit –  I

10Mark questions

1. 

What you mean by research methodology, explain its importance and its different

types of research?

Research Methodology :

Research Methodology is a way to find out the result of a given problem on a

specific matter or problem that is also referred as research problem. In Methodology,

researcher uses different criteria for solving/searching the given research problem.

Different sources use different type of methods for solving the problem. If we think

about the word “Methodology”,  it is the way of searching or solving the research

 problem. (Industrial Research Institute, 2010).

Importance of Research Methodology:

“All progress is born of inquiry. Doubt is often better than overconfidence, for

it leads to inquiry, and inquiry leads to invention”  is a famous Hudson Maxim in

context of which the significance of research can well be understood. Increased

amounts of research make progress possible. Research inculcates scientific and

inductive thinking and it promotes the development of logical habits of thinking and

organisation.

The role of research in several fields of applied economics, whether related to

 business or to the economy as a whole, has greatly increased in modern times. The

increasingly complex nature of business and government has focused attention on the

use of research in solving operational problems. Research, as an aid to economic

 policy, has gained added importance, both for government and business.

Research provides the basis for nearly all government policies in our economic

system. For instance, government‟s budgets rest in part on an analysis of the needs

and desires of the people and on the availability of revenues to meet these needs. The

cost of needs has to be equated to probable revenues and this is a field where research

is most needed. Through research we can devise alternative policies and can as well

examine the consequences of each of these alternatives.

Decision-making may not be a part of research, but research certainly facilitates the

decisions of the policy maker. Government has also to chalk out programmes for

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dealing with all facets of the country‟s existence and most of these will be related

directly or indirectly to economic conditions. The plight of cultivators, the problems

of big and small business and industry, working conditions, trade union activities, the

 problems of distribution, even the size and nature of defence services are matters

requiring research. Thus, research is considered necessary with regard to the

allocation of nation‟s resources. Another area in government, where research is

necessary, is collecting information on the economic and social structure of the

nation. Such information indicates what is happening in the economy and what

changes are taking place. Collecting such statistical information is by no means a

routine task, but it involves a variety of research problems. These day nearly all

governments maintain large staff of research technicians or experts to carry on this

work. Thus, in the context of government, research as a tool to economic policy has

three distinct phases of operation, viz., (i) investigation of economic structure through

continual compilation of facts; (ii) diagnosis of events that are taking place and the

analysis of the forces underlying them; and (iii) the prognosis, i.e., the prediction of

future developments.

Research has its special significance in solving various operational and planning

 problems of business and industry. Operations research and market research, along

with motivational research, are considered crucial and their results assist, in more than

one way, in taking business decisions. Market research is the investigation of the

structure and development of a market for the purpose of formulating efficient

 policies for purchasing, production and sales. Operations research refers to the

application of mathematical, logical and analytical techniques to the solution of

 business problems of cost minimisation or of profit maximisation or what can be

termed as optimisation problems. Motivational research of determining why people

 behave as they do is mainly concerned with market characteristics. In other words, it

is concerned with the determination of motivations underlying the consumer (market)

 behaviour. All these are of great help to people in business and industry who are

responsible for taking business decisions. Research with regard to demand and market

factors has great utility in business. Given knowledge of future demand, it is generally

not difficult for a firm, or for an industry to adjust its supply schedule within the

limits of its projected capacity. Market analysis has become an integral tool of

 business policy these days. Business budgeting, which ultimately results in a

 projected profit and loss account, is based mainly on sales estimates which in turn

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depends on business research. Once sales forecasting is done, efficient production and

investment programmes can be set up around which are grouped the purchasing and

financing plans. Research, thus, replaces intuitive business decisions by more logical

and scientific decisions.

Research is equally important for social scientists in studying social relationships and

in seeking answers to various social problems. It provides the intellectual satisfaction

of knowing a few things just for the sake of knowledge and also has practical utility

for the social scientist to know for the sake of being able to do something better or in

a more efficient manner. Research in social sciences is concerned both with

knowledge for its own sake and with knowledge for what it can contribute to practical

concerns. “This  double emphasis is perhaps especially appropriate in the case of

social science. On the one hand, its responsibility as a science is to develop a body of

 principles that make possible the understanding and prediction of the whole range of

human interactions. On the other hand, because of its social orientation, it is

increasingly being looked to for practical guidance in solving immediate problems of

human relations.” 

Types of research methodology:

It is imperative that a marketer has to have a broad understanding of the

various types of research, in general. There are eleven types of research depending on

whether it is primarily “fundamental” or “applied” in nature. They are as follows:

1. Applied research, also known as decisional research, use existing knowledge as an

aid to the solution of some given problem or set of problems.

2. Fundamental research, frequently called basic or pure research, seeks to extend

the boundaries of knowledge in a given area with no necessary immediate applicationto existing problems.

3. Futuristic research: Futures research is the systematic study of possible future

conditions. It includes analysis of how those conditions might change as a result of

the implementation of policies and actions, and the consequences of these policies and

actions.

4. Descriptive research  includes surveys and fact-finding enquiries of different

kinds. It tries to discover answers to the questions who, what, when and sometimes

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how. Here the researcher attempts to describe or define a subject, often by creating a

 profile of a group of problems,

 people, or events. The major purpose of descriptive research is description of the state

of affairs as it exists at present

5. Explanatory research:  Explanatory research goes beyond description and

attempts to explain the reasons for the phenomenon that the descriptive research only

observed. The research would use theories or at least hypothesis to account for the

forces that caused a certain phenomenon to occur.

6. Predictive research: If we can provide a plausible explanation for an event after it

has occurred, it is desirable to be able to predict when and in what situations the event

will occur. This research is just as rooted in theory as explanation. This research calls

for a high order of inference making. In business research, prediction is found in

studies conducted to evaluate specific courses of action or to forecast current and

future values.

7. Analytical research: The researcher has to use facts or information already

available, and analyse these to make a critical evaluation of the material.

8. Quantitative research:  Quantitative research is based on the measurement of

quantity or amount. It is applicable to phenomena that can be expressed in terms of

quantity.

9. Qualitative research: It is concerned with qualitative phenomenon (i.e.)

 phenomena relating to or involving quality or kind. This type of research aims at

discovering the underlying motives and desires, using in depth interviews for the

 purpose. Other techniques of such research are word association test, sentence

completion test, story completion tests and similar other projective techniques.

Attitude or opinion research i.e., research designed to find out how people feel or

what the think about a particular subject or institution is also qualitative research.

10. Conceptual research: Conceptual research is that related to some abstract idea(s)

or theory. It is generally used by philosophers and thinkers to develop new concepts

or to reinterpret existing ones.

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11. Empirical research: It is appropriate when proof is sought that certain variables

affect other variables in some way. Evidence gathered through experiments or

empirical studies is today considered to be the most powerful support possible for a

give hypothesis.

2. Explain the steps in research process with the help of flow chart with the

research process?

Several authors have attempted to enumerate the steps involved in the research

 process, however, inconclusive. Nevertheless, the research process broadly

consists of the following steps and predominantly follows a sequential order as

depicted

1. Problem formulation

2. Development of an approach to the problem

3. Research Design

4. Selection of Data collection techniques

5. Sampling techniques

6. Fieldwork or Data Collection

7. Analysis and interpretation

8. Report preparation and presentation

The above mentioned steps may be placed in three groups as follows:

First there is initiating or planning of a study, which comprises the initial four steps in

our model: determining (1) problem formulation, (2) development of an approach to

the problem (3) Research design (4) selection of data collection techniques (5)

sampling techniques.

Second, there is (6) fieldwork or data collection

Third, there is (7) analysis and interpretation of the data and (8) report preparation and

 presentation.

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4Mark Questions

1.  What are the objectives of research?

Research findings should be factual, data-based and free from bias. The

conclusion drawn should be based on the facts of the findings derived form the

actual data and not on the basis of subjective or emotional values. Business

organizations will suffer a greater extent of damage if non-data-based or

misleading conclusions drawn from the research is implemented. Scientific

approach ensures objectivity of research.

2. 

Criteria of good research?

The following criteria can be kept in the minds of researcher in selecting the

research problem.

Subjects on which the research is carried on amply should not be normally chosen

as there will not be new dimension to reveal

Too narrow or too vague problems should be avoided

The researcher should be familiar with the subject chosen for research. The

researcher should have enough knowledge, qualification and training in the selected

 problem area. The resources needed to solve the problem in terms of time, money,

efforts, manpower requirement should be taken into account before embarking on a

 problem.

The subject of research should be familiar and feasible so that related research

material or sources of research can be obtained easily.

The selection of a problem must be preceded by a preliminary study.

Research problems trigger the research process. Defining the research problem is

a critical activity. A thorough understanding of research problem is a must for

achieving success in the research endeavor. Defining the research problem begins

with identifying the basic dilemma that prompts the research. It can be further

developed by progressively breaking down the original dilemma into more

specific and focus oriented objectives. Five steps could be envisaged (1)

Identifying the broad problem area(2) Literature review (3) Identifying the

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research question (4) Refining the research question (5) Developing investigative

questions.

Unit –  II

10Mark Questions

1. explain the difference between questionnaire and schedule ? describe the

contents in construction of questionnaire?

Difference between questionnaire and interview schedule

Questionnaire and interview schedule are both used for data collection and

they resemble each other. However, the important points of difference are

highlighted below:

i. The questionnaire can be sent thought mail with covering letter and the

same does not require further assistance. The schedule is filed out by

the researcher who interprets the question whenever needed.

ii. Collecting the questionnaire requires less expense as it is filled by the

respondent himself. In the case of schedules, enumerators should be

appointed. This involves additional expenses in terms of payments

made to them and training provided.

iii. The rate of non-response is usually higher in case of mailed

questionnaire. In case of schedules the non-response rate is lesser as

the enumerator himself fills the schedules and is personally present.

However, the danger of bias and cheating prevails.

iv. The identity of the respondent is not clear in the case of the

questionnaire, but in case of the schedules the identity is known.

v. The questionnaire method of data collection involves time as it

requires several reminders inspite of which it may not be returned. In

case of schedules direct personal contact is established and responses

are elicited soon.

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vi. Questionnaire method can be used only in case of educated or literate

respondents but the interview schedules can be administered even in

case of illiterate persons

vii. Wider and more representative population is possible in the

questionnaire method of data collection, but it remains as a difficulty

in case of schedules particularly when the respondents are distributed

over a wide geographical area.

viii. Risk of collecting incomplete and wrong information is more in

case of questionnaire method, but in case of schedules, the

enumerators are present to see that the questions are properly filled in.

As a result the information collected through the schedules are more

accurate than those obtained through the questionnaire.

ix. The success of the questionnaire method depends to a greater extent on

the quality of the questionnaire, but in case of the interview schedules

it depends on the honesty, sincerity and perseverance of the

enumerators.

x. The physical appearance of the questionnaire is very important to

attract and retain the respondents attention, however, the level of

importance is not the same in case of the interview schedule.

xi. Additional data can be obtained by the enumerator apart from what is

asked in the schedules by personal observation. This is not possible in

case of the mailed questionnaire.

STEPS IN QUESTIONNAIRE CONSTRUCTION

A Questionnaire is often the heart of a survey operation. If the heart is not properly set

up then the whole operation is bound to fail. Thus studying the main objective of the

questionnaire is important. There are two main objectives in designing a

questionnaire:

1. To maximize the proportion of subjects answering our questionnaire that is, the

response rate: To maximize our response rate, we have to consider carefully how we

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1. Is the Question Necessary/Useful?

Examine each question to see if there is a need to ask it at all and if you need to ask it

at the level of detail you currently have.

2. Do Respondents Have the Needed Information?

Look at each question to see whether the respondent is likely to have the necessary

information to be able to answer the question.

3. Does the Question Need to be More Specific?

Sometimes the questions are too general and the information we obtain is more

difficult to interpret.

4. Is Question Biased or Loaded?

One danger in question writing is that your own biases and blind spots may affect the

wording.

5. Will Respondents Answer Truthfully?

For each question see whether the respondent will have any difficulty answering the

question truthfully. If there is some reason why they may not, consider rewording the

question.

2. Question phrasing:

The way questions are phrased is important and there are some general rules for

constructing good questions in a questionnaire.

Use short and simple sentences

Short, simple sentences are generally less confusing and ambiguous than long,

complex ones. As a rule of thumb, most sentences should contain one or two clauses.

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Ask for only one piece of information at a time

For example, "Please rate the lecture in terms of its content and presentation" asks for

two pieces of information at the same time.

It should be divided into two parts: "Please rate the lecture in terms of

(a) its content,

(b) its presentation."

Avoid negatives if possible

 Negatives should be used only sparingly.

For example, instead of asking students whether they agree with the statement, "Small

group teaching should not be abolished," the statement should be rephrased as, "Small

group teaching should continue." Double negatives should always be avoided.

Ask precise questions

Questions may be ambiguous because a word or term may have a different meaning.

3. Question sequencing:  In order to make the questionnaire effective and to ensure

quality to the replies received, a researcher must pay attention to the question-

sequence in preparing the questionnaire.

•  A proper question sequence reduces the chances of the questions being

misunderstood

• The question sequence must be clear and smooth- moving, with questions that are

easiest to answer being put in the beginning.

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•  The first few questions are particularly important because they are likely to

influence the attitude of the respondent and in seeking his desired cooperation.

• Following the opening questions are the question that are rally vital to the research

 problem and a connecting thread should run through successive questions.

• Relatively difficult questions must be relegated towards the end so that even if the

respondent decides not to answer such questions, considerable information would

have been obtained.

• The order of the questions is also important. Some general rules are:

-Go from general to particular.

-Go from easy to difficult.

-Go from factual to abstract.

-Start with closed format questions.

-Start with questions relevant to the main subject.

-Do not start with demographic and personal questions.

4.Question layout:

• Questions should form a logical part of a well thought out tabulation plan.

• Questions should basically meet the following standards

-Should be easily understood

-Should be simple

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-Should be concrete and should conform as much as possible to the respondent‟s way

of thinking.

•  Items on a questionnaire should be grouped into logically coherent sections.

Grouping questions that are similar will make the questionnaire easier to complete,

and the respondent will feel more comfortable. Questions that use the same response

formats, or those that cover a specific topic should appear together.

•  Each question should follow comfortably from the previous question. Writing a

questionnaire is similar to writing anything else. Transitions between questions should

 be smooth.

Questionnaires that jump from one unrelated topic to another feel disjointed and are

not likely to produce high response rates.

2. Define Scale, Different types of Scaling techniques ?

Scale: Based on the characteristics of the mapping rules i.e., classification,

order, distance and origin, four classifications of measurement scales could be arrived

at: nominal, ordinal, interval and ratio scale.

Types of Scale :

1. Nominal scale

A nominal scale allows the researcher to assign subjects to certain categories

or groups. For e.g., the respondents can be grouped as male and female. The

two groups can be assigned numbers for the purpose of coding and further

analysis as 1 and 2. These numbers are simple and convenient labels and have

no intrinsic values. It only assigns subjects into either of the two mutually

exclusive categories. In other words, nominal scale allows the researcher to

collect information on a variable that naturally or by design can be grouped

into two or more categories that are mutually exclusive and are collectivelyexhaustive.

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The nominal scale provides only the basic, categorical, gross information.

Counting of members in each group and calculation of frequency or

 percentage is possible when nominal scale is employed. The researcher is

restricted to the use of mode as the measure of central tendency. One can

conclude which category has more members. Chi-square test can be used to

measure the statistical significance and for measures of association, phi,

lambda or other measures may also be appropriate.

 Nominal scales are weak but they are still useful to classify the data. It is

valuable in exploratory work where the objective is to uncover relationships

rather that to secure precise measurements. Nominal data type is also widely

used in survey and ex post facto research when data is classified by majorsubgroups of the population.

2. Ordinal scale

Ordinal scale indicates the order. It includes the characteristics of nominal

scale also. Thus an ordinal scale not only categorizes the variables but also

rank-orders categories in some meaningful way. The use of ordinal scale

implies a statement of „greater  than‟ or „less than‟ or „equal to„ without stating

how much greater or less. Other descriptors may also be used viz., „superior

to‟, „happier  than‟, „poorer  than‟, „above‟. It is also possible to rank more than

one property at a time. For e.g., researcher can ask the respondent to rank

various air lines on the basis of certain properties.

In ordinal scaling the differences in the ranking of objects, persons or events

investigated are clearly known. However, the ordinal data does not give any

indication of the magnitude of the differences among the ranks

3. Interval scale

Interval data has the power of the nominal and ordinal data and in addition it

incorporates the concept of equality of interval. The interval scale allows to

measure the distance between any two points on the scale. It not only enables

to group the individuals according to certain categories and taps the order of

the groups; it also measures the magnitude of differences in the preferences

among the individuals. The interval scale is more powerful than the nominal

and the ordinal scales. The measure of central tendency the arithmetic mean, is

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applicable. Its measures of dispersion are the range, the standard deviation and

the variance.

4. Ratio scale

Ratio data has the power of the nominal, ordinal and interval scale in addition

it also has the provision for absolute zero or origin. It covers the disadvantage

of the arbitrary origin point of the interval scale, i.e., it has an absoultue zero

 point. The ratio scale not only measures the magnitude of the differences

 between points on scale but also the proportion in the differences.

Multiplication or division would preserve the ratios. It is the most powerful of

the four scales because it has a unique zero origin and subsumes all the

 properties of the other three scales.

The measure of central tendency of the ratio scale could be either the

arithmetic or the geometric mean and the measure of dispersion could be

either the standard deviation or variance or the coefficient of variation. Some

examples of ratio scales are those pertaining to actual age, income and work

experience in organizations.

3. Different Methods of Collecting Data, Merits and de merits of it?

By primary data we mean the data that have been collected originally

for the first time. In other words, primary data may be the outcome of an

original statistical enquiry, measurement of facts or a count that is undertaken

for the first time. For instance data of population census is primary. Primary

data being fresh from the fields of investigation is very often referred to as raw

data. In the collection of primary data, a good deal of time, money and energy

are required.

QUESTIONNAIRE

A questionnaire is defined as a formalised schedule for collecting data from

respondents. It may be called as a schedule, interview form or measuring instrument.

Measurement error is a serious problem in questionnaire construction. The broad

objective of a questionnaire include one without measurement errors. Specifically, theobjectives of a questionnaire are as follows:

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a) It must translate the information needed into a set of specific questions that the

respondents can and will answer.

 b) The questions should measure what they are supposed to measure.

c) It must stimulate the respondents to participate in the data collection process. The

respondents should adequately motivated by the virtual construct of the questionnaire.

d) It should not carry an ambiguous statements that confuses the respondents.

Questionnaire Components

A questionnaire consists typically of five sections. They are: a) Identification data

 b) Request for cooperation

c) Instruction

d) Information sought e) Classification of data

a) Identification data occupation is the first section of a questionnaire where the

researcher intends to collect data pertaining to the respondents  name, address and

 phone number.

 b) Request for cooperation refers to gaining respondents cooperation regarding the

data collection process.

c) Instruction refers to the comments to the respondent regarding how to use the

questionnaire.

d) The information sought form the major portion of the questionnaire. This refers to

the items relating to the actual area of the study.

e) Classification data are concerned with the characteristics of the respondent.

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4Mark Questions

1. 

Write short notes on process of designing a questionnaire?

A good questionnaire accomplishes the research objectives. The logical sequences of

the steps involved in the development of a good questionnaire are discussed below:

I. Deciding the information to be collected

II. Formulate the questions needed to obtain the information

III. Decide on the wordings of the questions and layout of the questionnaire

IV. Pretesting the questionnaire and correcting the problem

I . Deciding the in formation to be coll ected

The researcher should have a clear idea of exactly what information is to be collected

from each respondent. Lack of clarity will lead to collection of irrelevant and

incomplete information which does not contribute towards the research purpose. Thesituation will diminish the value of the study.

I I . Formulating the questions

Before formulating the questions a decision has to be made by the researcher

regarding the degree of freedom to be given to the respondents in answering the

questions. The various types of the question that can be included in a questionnaire

are discussed below:

1. 

Open-ended versus closed questions

2.  Dichotomous questions

3. 

Multiple choice questions

4.  Checklist questions

5.  Ranking questions

6.  Positively and negatively worded questions

7. 

Double-barrelled questions

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8.  Ambiguous question

9. 

Memory related questions

10. Leading / Loaded questions

11. 

Bad questions

I I I . Decide on the wordings of the questions and layout of the questionnair e

iv. Pretesting the questionnair e

Unit –  III

10Mark Questions

1. Define sample. explain the various types of sample design with example?

research does not exist without sampling. Every research study requires the

selection of some kind of sample. It is the life blood of research.

Any research study aims to obtain information about the characteristics or parameters

of a population. A population is the aggregate of all the elements that share some

common set of characteristics and that comprise the universe for the purpose of the

research problem. In other words, population is defined as the totality of all cases that

conform to some designated specifications. The specification helps the researcher to

define the elements that ought to be included and to be excluded. Sometimes, groups

that are of, interest to the researcher may be significantly smaller allowing the

researcher to collect data from all the elements of population. Collection of data from

the entire population is referred to as census study. A census involves a complete

enumeration of the elements of a population.

Collecting data from the aggregate of all the elements (population) in case of, the

number of elements being larger, would sometimes render the researcher incur huge

costs and time. It may sometimes be a remote possibility. An alternative way would

 be to collect information from a portion of the population, by taking a sample of

elements from the population and the on the basis of information collected from the

sample elements, the characteristics of the population is inferred. Hence, Sampling isthe process of selecting units (e.g., people, organizations) from a

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 population of interest so that by studying the sample we may fairly generalize our

results back to the population from which they were chosen.

While deciding on the sampling, the researcher should clearly define the target

 population without allowing any kind of ambiguity and inconsistency on the boundary

of the aggregate set of respondents. To do so, the researcher may have to use his

wisdom, logic and judgment to define the boundary of the population keeping with

the objectives of the study.

TYPES OF SAMPLING PLANS

Sampling techniques are classified into two broad categories of probability samples or

non- probability samples.

Probability Sampling Techniques

Probability samples are characterised by the fact that, the sampling units are selected

 by chance. In such case, each member of the population has a known, non-zero

 probability of being selected. However, it may not be true that all sample would have

the same probability of selection, but it is possible to say the probability of selecting

any particular sample of a given size. It is possible that one can calculate the

 probability that any given population element would be included in the sample. This

requires a precise definition of the target population as well as the sampling frame.

Probability sampling techniques differ in terms of sampling efficiency which is a

concept that refers to trade off between sampling cost and precision. Precision refers

to the level of uncertainty about the characteristics being measured. Precision is

inversely related to sampling errors but directly related to cost. The greater the

 precision, the greater the cost and there should

 be a tradeoff between sampling cost and precision. The researcher is required to

design the most efficient sampling design in order to increase the efficiency of the

sampling.

Probability sampling techniques are broadly classified as simple random sampling,

systematic sampling, and stratified sampling.

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Simple Random Sampling

This is the most important and widely used probability sampling technique. They gain

much significance because of their characteristic of being used to frame the concepts

and arguments in statistics. Another important feature is that it allows each element in

the population to have a known and equal probability of selection. This means that

every element is selected independently of every other element. This method

resembles lottery method where a in a system names are placed in a box, the box is

shuffled, and the names of the winners are then drawn out in an unbiased manner.

Simple random sampling has a definite process, though not, so rigid. It involves

compilation of a sampling frame in which each element is assigned a unique

identification number. Random numbers are generated either using random number

table or a computer to determine which elements to include in the sample. For

example, a researcher is interested in investigating the behavioural pattern of

customers while making a decision on purchasing a computer. Accordingly, the

researcher is interested in taking 5 samples from a sampling frame containing 100

elements. The required sample may be chosen using simple random sampling

technique by arranging the 100 elements in an order and starting with row 1 and

column 1 of random table, and going down the column until 5 numbers between 1 and

100 are selected. Numbers outside this range are ignored. Random number tables are

found in every statistics book. It consists of a

randomly generated series of digits from 0  –   9. To enhance the readability of the

numbers, a

space between every 4th digit and between every 10th row is given. The researcher

may begin reading from anywhere in the random number table, however, once started

the researcher should continue to read across the row or down a column. The most

important feature of simple random sampling is that it facilitates representation of the

 population by the sample ensuring that the statistical conclusions are valid.

Systematic Sampling

This is also another widely used type of sampling technique. This is used because of

its ease and convenience. As in the case of simple random sampling, it is conducted

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choosing a random starting point and then picking every element in succession from

the sampling frame. The sample interval, i, is determined by dividing the population

size N by the sample size n and rounding to the nearest integer.

Consider a situation where the researcher intends to choose 10 elements from a

 population of 100. In order to choose these 10 elements, number the elements from

one to 100. Within 20 population elements and a sample of size 10, the number is

10/100 = 1/10, meaning that one element in 10 will be selected. The sample interval

will, therefore, be 10. This means that after a

random start from any point in the random table, the researcher has to choose every

10th element.

Systematic sampling is almost similar to simple random sampling in that each

 population element has a known and equal probability of selection. However, the

difference lies in that simple random sampling allows only the permissible samples of

size n drawn have a known and equal probability of selection. The remaining samples

of size n have a zero probability of being selected

Stratified sampling

Stratified sampling is a two-way process. It is distinguished from the simple random

sampling and systematic sampling, in that:

a) It requires division of the parent population into mutually exclusively and

exhaustive subsets;

 b) A simple random sample of elements is chosen independently from each group or

subset.

Therefore, it characterises that, every population element should be assigned to one

and only stratum and no population elements should be omitted. Next, elements are

selected from each stratum by simple random sampling technique. Stratified sampling

differs from quota sampling in that the sample elements are selected probabilistically

rather than based on convenience or on judgemental basis.

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Strata are created by a divider called the stratification variable. This variable divides

the population into strata based on homogeneity, heterogeneity, relatedness or cost.

Sometimes, more than one variable is used for stratification purpose. This type of

sampling is done in order to get homogenous elements within each strata and, the

elements between each strata should have a higher degree of heterogeneity. The

number of strata to be formed for the research is left to the discretion of the

researcher, though, researchers agree that the optimum number of strata may be 6.

The reasons for using stratified sampling are as follows:

a) it ensures representation of all important sub-populations in the sample;

 b) the cost per observation in the survey may be reduced;

c) it combines the use of simple random sampling with potential gains in precision;

d) estimates of the population parameters may be wanted for each sub-population and;

e) increased accuracy at given cost.

Non-probability Sampling Methods

 Non-probability sampling does not involve random selection. It involves personal

 judgement of the researcher rather than chance to select sample elements. Sometimes

this judgement is imposed by the researcher, while in other cases the selection of

 population elements to be includes is left to the individual field workers. The decision

maker may also contribute to including a particular individual in the sampling frame.

Evidently, non probability sampling does not include elements selected

 probabilistically and hence, leaves an degree of sampling error associated with the

sample.

Sampling error is the degree to which a sample might differ from the population.

Therefore, while inferring to the population, results could not be reported plus or

minus the sampling error. In non-probability sampling, the degree to which the

sample differs from the population remains unknown However, we cannot come to a

conclusion that sampling error is an inherent of non probability sample.

 Non-probability samples also yield good estimates of the population characteristics.

Since, inclusion of the elements in the sample are not determined in a probabilistic

way, the estimates obtained are not statistically projectable to the population.

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The most commonly used non-probability sampling methods are convenience

sampling, judgment sampling, quota sampling, and snowball sampling.

Convenience Sampling

Convenience samples are sometimes called accidental samples because the elements

included in the sample enter by accident. It is a sampling technique where samples

are obtained from convenient elements. This refers to happening of the element at the

right place at the right time, that is, where and when the information for the study is

 being collected. The selection of the respondents is left to the discretion of the

interviewer. The popular examples of convenience sampling include (a) respondents

who gather in a church (b) students in a class room (c) mall intercept interviews

without qualifying the respondents for the study (d) tear-out questionnaire included in

magazines and (e) people on the street. In the above examples, the people may not be

qualified respondents, however, form part of the sample by virtue of assembling in the

 place where the researcher is conveniently placed.

Convenience sampling is the least expensive and least time consuming of all sampling

techniques. The disadvantage with convenience sampling is that the researcher would

have no way of knowing if the sample chosen is representative of the target

 population.

Judgement Sampling This is a form of convenience sampling otherwise called as

 purposive sampling because the sample elements are chosen since it is expected that

they can serve the research purpose. The sample elements are chosen based on the

 judgement that prevails in the researchers mind about the prospective individual. The

researcher may use his wisdom to conclude that a particular individual may be a

representative of the population in which one is interested.

The distinguishing feature of judgment sampling is that the population elements are

 purposively selected. Again, the selection is not based on that they are representative,

 but rather because they

can offer the contributions sought. In judgement sampling, the researcher may be well

aware of the characteristics of the prospective respondents, in order that, he includes

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methods. The initial respondents are chosen by a random method and the subsequent

respondents are chosen by non-probability methods.

4Mark Questions

1.  Difference between one way and two way table.

One way Anova and Two way Anova differ in terms of their purpose and concept.

The purpose of one way Anova is to verify whether the data collected from different

sources converge on a common mean. In other words it can be said that the purpose of

one way Anova is find out whether the groups carried out the same procedures in

conducting research.

On the other hand the purpose of the two way Anova is to verify whether the data

collected from different sources coverage on a common mean based on two categories

of defining characteristics. On the contrary the one way Anova uses only one category

of defining characteristics to carry out its procedure.

The test for the presence of an item in a sample selected at random is the example for

one way Anova. The process of choosing a sample from different sources at random

gets repeated in the case of one way Anova. On the other hand let us take for example

a steel company that has two factories each making three models of a product made of

steel. It is now reasonable to ask whether the durability of the product varies from

factory to factory as well as from model to model.

The other way of distinguishing one way Anova from two way Anova is that the one

way Anova is used for a single factor between subject designs. In other words it can

 be said that it is meant for two or more treatment means.

On the other hand two way Anova is used in the comparison of treatment means. This

involves the introduction of randomized block design. The experiment conducted in

the case of two way Anova gets split normally into many mini experiments. In short it

can be said that the two way Anova is employed for a design with two or more

treatment means that can be called factorial designs.

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There can be any number of levels in the case of one way Anova. It deals only with

one factor such as treatment or group. On the other hand the treatment is called as

fixed effects in the case of two way Anova. In both the cases it is interesting to note

that the calculations are usually done by the computer. In order to find how the

calculations are done it is quite natural that long hand is also occasionally employed.

2. 

Write short notes on editing , coding and tabulation

Editing:

Editing of data is a process of examining the collected raw data to detect

errors and omission and to correct these when possible. As the matter offact, editing involves a careful scrutiny of the completed questionnaires

and schedules. Editing is done to ensure that the data are accurate,

consistent with other facts gathered, uniformly entered, as completed as

 possible and have been well arranged to facilities coding and tabulation.

Coding:

Coding refers to the process of assigning numerals or other symbols to

answer so that responses can be put into the limited number of categories

or class. Such classes should be appropriate to the research problem under

consideration.

Tabulation:

When a mass of data has been assembled , it becomes necessary for the

researcher to arrange the same in some kind of concise and logical order.

This procedure is referred to as tabulation.

3. 

Difference between probability and non-probability sampling

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Unit –  IV

10Mark Question

1. What is hypothesis ? explain the characteristics and procedures of testing

hypothesis?

Basic analysis of the data involves testing of hypothesis. Lot of confusion

 prevails in developing a hypothesis. In simple terms, hypothesis refers to

assumption of a relationship between two variables or difference between two

or more groups. Hypothesis also contains the direction of relationship between

the variables concerned.

Examples for hypothesis is given below:

(a) The purchasing power of the consumers is positively related to the availability of

surplus income.

(b) Customers belonging to the Northern states in India have a different taste

 preference than those from Northern States.

Hypotheses are of two types: (a) Null hypothesis and (b) Alternative hypothesis. A

simple rule may be followed to develop a hypothesis:

1. What we hope or expect to be able to conclude as a result of the test usually should

 be placed in alternative hypothesis.

2. The null hypothesis should contain a statement of equality (=) and an alternative

hypothesis contains a > or < than sign.

3. The null is the hypothesis that is tested.

4. The null and alternate hypothesis are complementary.

Charesterisitcs and procedures of testing hypothesis :

An appropriate statistical test for analysing a given set of data is selected on

the basis of: Scaling of the data: Is the measurement scale nominal, ordinal, intervalor ratio;

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Dependence, Independence of the measurements;

Types of samples: Independent or dependent samples;

 Number of samples (groups) studied and;

Specific requirements such as sample size, shape of population distribution, are also

used for considering the choice of a statistical test.

There are two types of samples: Independent and dependent samples. Two samples

are independent sample if the sample selected from one of the populations has no

effect or bearing on the sample selected from the other population. E.g., responses

collected from Tamilians, Keralites, Kannadigas etc. They are exclusive groups ofrespondents where a Tamilian is exclusive in nature in that he does not take part in the

other groups. Similarly, a Kannadiga is exclusive in nature in his membership in his

group in that he does not take part in any other groups.

Dependent samples, also called related or correlated or matched samples, are ones in

which the response of the nth subject in one sample is partly a function of the

response of the nth subject in an earlier sample. Examples of dependent samples

include before-during-after samples of the same people or matched response of

similar people.

The nature of the samples is also considered while deciding on the appropriateness of

the statistical test. The following are the conditions to be followed while choosing the

tests:

Does the test involve one sample, two samples or k samples

If 2 samples or k samples are involved, are the individual cases independent or

related.

The selection of an appropriate statistical test rests with two criteria:

(a) Type of scale used (Nominal, ordinal, interval or ratio)

(b) Type and the size of the samples. Type relates to whether the samples are

independent or dependent.

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The hypothesis of type two mentioned in the example above could be tested using two

types of statistical tests. They are:

(a) Parametric tests

(b) Non-parametric tests

A simple understanding of the characteristics of the tests reveal that the term

 parametric  is derived from the term parameter which is a descriptive measure

computed from or used to describe a population of data. Parametric tests are used to

test hypothesis with interval and ratio measurements and non parametric tests are used

to test hypothesis involving nominal and ordinal data. Parametric tests are more

 powerful than non –  parametric tests. Explanation of parametric and non parametric

tests in detail is beyond the scope of this study material.

There are few simple, easy to understand assumptions made while applying a

 parametric test. They are:

The observations must be independent –  that is, the selection of any one case should

not affect the chances for any other case to be included in the sample.

The observations should be drawn from normally distributed populations. These

 populations should have equal variances.

The measurement scales should be at least interval so that arithmetic operations can

 be used with them.

 Non-parametric tests do not have any assumptions of such kind. This is the advantage

of non- parametric tests over parametric tests.

Hypothesis of the type 1 may be tested using Correlation and regression. Correlation

is a test of association only between two variables. It uses only interval and ratio

scale. Such correlations are called as Karl Pearson bi – variate correlation. Correlation

of a special type employed on ordinal data is called Rank Correlation. This is

otherwise called as Spearman Rank correlation. However, correlation will never tell

the researcher about the independent  –   dependent relationship. Correlation analysis

will give a result r called the correlation coefficient. R value ranges from -1 to +1

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through a O. As r value approaches 1, the strength of the association increases and as

it approaches 0, it decreases. R value will be associated with a positive or negative

sign. Positive sign refers to positive correlation where the change in one variable

causes change in the other variable in the same direction whereas a negative sign

indicates inverse relationship.

Regression is a powerful technique dealing with two or more than two number of

variables. Regression analysis will tell the researcher about the independent and

dependent relationship. It deals with one dependent variable and any number of

independent variables. Regression analysis involving only one independent variable,

is called simple regression and that involves more than one independent variables is

called multiple regression. Regression results in r2 value which explains the amount

of variance accounted for, by the independent variables on the dependent variable.

Standardized β  coefficient determines the strength and the direction of relationship

 between the independent and dependent variables.

4Mark Questions

1.  objectives of hypothesis

The purpose of hypothesis testing is to determine the accuracy of the

hypotheses framed due to the fact that the data is collected from

sample and not from the entire population. The accuracy of hypotheses

is evaluated by determining the statistical likelihood that the data

reveal true differences and not the random sampling error.

There are two approaches to hypothesis testing; classical or sampling

theory and the Bayesian approach. Classical approach is mostly used in

research application. This approach represents an objective view of

 probability and the decision making is made totally on an analysis of

available sampling data. A hypothesis is accepted or rejected based on

the sample data collected. The sample drawn may vary at least to a

smaller extent from the population and hence it is a must to know

whether the differences are statistically significant or insignificant. Adifference is statistically significant if there is a good reason to believe

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that the difference does not represent the random sampling fluctuations

only.

2.short notes on one way and two way ANOVA

One way anova:

one-way analysis of variance (abbreviated one-way ANOVA) is a

technique used to compare means of two or more samples (using the F

distribution). This technique can be used only for  numerical data.  the

one-way ANOVA is used to test for differences among at least three

groups, since the two-group case can be covered by a t-test . When

there are only two means to compare, the t-test and the F-test are

equivalent; the relation between ANOVA and t  is given by F  = t 2.

Two way anova:

The two-way analysis of variance (ANOVA) test is an extension of

the one-way ANOVA test that examines the influence of

different categorical independent variables on one dependent variable. 

While the one-way ANOVA measures the significant effect of

one independent variable,  the two-way ANOVA is used when there is

more than one independent variable and multiple observations for each

independent variable. The two-way ANOVA can not only determine

the main effect of contributions of each independent variable but also

identifies if there is a significant interaction effect between the

independent variables.

Unit –  V

10Mark Questions

1. discuss the guidelines measures and outline of a research report

Research formats may vary from researcher to researcher as well

depending on the need of the decision maker. However, any researcher could

not violate the fundamental contents a report should have. They should

include the following:

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x) Results comprise of the results presented not only at the aggregate level but also at

the subgroup level. The results, as mentioned earlier, should be presented in the most

simpler way, enabling the decision maker to understand in the right sense.

xi) Limitations and Caveats contain the limitations caused by the research design,

cost, time and other organizational constraints. However, a research should not

contain many limitations. The researcher should have controlled many of the

limitations during the research process.

xii) Conclusions and recommendations involve interpretation of the results in light of

the problem being addressed to arrive at major conclusions. The decision maker

makes decision based on the conclusion and recommendations of the researcher.

GUIDELINES FOR TABLES

Data analysed should be presented in the research report in a tabular form. The

guidelines for tables are as follows:

i) Title and number should be given for every table such as 1a. The title should be

very brief just explaining the description of the information provided in the table.

ii) Arrangement of data items indicate that the data should be arranged in some order

either pertaining to time or data etc.

iii) Leaders, ruling and spaces should be made in such a way that they lead the eye

horizontally, impart uniformity, and improve readability.

iv) Explanations and comments: explanations and comments clarifying the table may

 be provided in the form of captions, stubs and footnotes. Designations placed on thevertical columns are headings; those placed in the left-hand are called stubs.

Information that cannot be incorporated in the table should be explained by footnotes.

v) Sources of the data refer to citing the source of secondary data used in the research.

GUIDELINES FOR GRAPHS

The researcher may have used graphical interpretation of the results. Use of graphs

complements the textand the table adding clarity of communication and impact. The

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researcher may use any type of graphssuch as pie or round charts, line charts,

 pictographs, histograms and bar charts. While presenting the graphs, the researcher

should ensure that each section or line or bar of the charts should be represented in

different colours or shades.

2. difference between the factor analysis and multi variant analysis ?

Like principal component analysis, common factor analysis is a technique for

reducing the complexity of high-dimensional data. (For brevity, this chapter refers to

common factor analysis as simply "factor analysis.") However, the techniques differ

in how they construct a subspace of reduced dimensionality. Jackson (1981,1991)

 provides an excellent comparison of the two methods.

Principal component analysis chooses a coordinate system for the vector space

spanned by the variables. (Recall that the span of a set of vectors is the vector space

consisting of all linear combinations of the vectors.) The first principal component

 points in the direction of maximum variation in the data. Subsequent components

account for as much of the remaining variation as possible while being orthogonal to

all of the previous principal components. Each principal component is a linear

combination of the original variables. Dimensional reduction is achieved by ignoring

dimensions that do not explain much variation.

While principal component analysis explains variability, factor analysis explains

correlation. Suppose two variables, and , are correlated, but not collinear.

Factor analysis assumes the existence of an unobserved variable that is linearly

related to and , and explains the correlation between them. The goal offactor analysis is to estimate this unobserved variable from the structure of the

original variables. An estimate of the unobserved variable is called a common factor.

The geometry of the relationship between the original variables and the common

factor is illustrated in Figure 27.1. (The figure is based on a similar figure in Wickens

(1995), as is the following description of the geometry.) The correlated variables

and are shown schematically in the figure. Each vector is decomposed into a

linear combination of a common factor and a unique factor. That is,

, . The unique factors, and , are

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original variables, you should interpret the results with caution. The following list

describes special issues that can occur:

•  Some of the eigenvalues of the reduced correlation matrix might be negative.

A reduced correlation matrix is the correlation matrix of the original variables,

except that the 1's on the diagonal are replaced by prior communality

estimates. These estimates are less than 1, and so the reduced correlation

matrix might not be positive definite. In this case, the factors corresponding to

the largest eigenvalues might account for more than 100% of the common

variance.

•  The communalities are the proportions of the variance of the original variables

that can be attributed to the common factors. As such, the communalities

should be in the interval . However, factor analyses that use iterative

fitting estimate the communality at each iteration. For some data, the estimate

might equal (or exceed) 1 before the analysis has converged to a solution. This

is known as a Heywood (or an ultra-Heywood) case, and it implies that one or

more unique factor has a nonpositive variance. When this occurs, the factor

analysis stops iterating and reports an error.

These and other issues are described in the section "Heywood Cases and Other

Anomalies about Communality Estimates" in the documentation for the FACTOR

 procedure.

You can use many different methods to perform a factor analysis. Two popular

methods are the principal factor method and the maximum likelihood method. The

 principal factor method is computationally efficient and has similarities to principal

component analysis. The maximum likelihood (ML) method is an iterative method

that is computationally more demanding and is prone to Heywood cases,

nonconvergence, and multiple optimal solutions. However, the ML method also

 provides statistics such as standard errors and confidence limits that help you to assess

how well the model fits the data, and to interpret factors. Consequently, the ML

method is often favored by statisticians.

In addition to these various methods of factor analysis, you can use Stat Studio to

compute various component analyses: principal component analysis, Harriscomponent analysis, and image component analysis.

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4Mark Questions

1. 

Importance of report writing

Report enables the management to monitor the operations undertaken at various levels

and control the same

The written report acts as a guideline for future course of action. It enables to plan and

organize things in an effective manner.

The feed back regarding the various aspects, controls and processes implemented in

the organization can be obtained through the reports.The information regarding specific problems or issues can be obtained by way o

report. This report may be narrowly focused and provide the desired information to

the management in a brief format.

Information provided in the reports enables decision making.

6 Report may also be prepared to convince the reader or to sell an idea. The report in

this case would be more detailed and convincing as to how the proposed idea could

add to the organizations value or the justification as to why it should be adopted.

Report may also be prepared to provide several alternative solutions or

recommendations so as to compare the pros and cons and select a best course o

action. A detailed discussion of methodology, criteria for comparison, data analysis

etc should be provided

All 2Mark Questions

1.  Define Research?

definition of research is given by Creswell who states that - "Research

is a process of steps used to collect and analyze information to increase

our understanding of a topic or issue". It consists of three steps: Pose a

question, collect data to answer the question, and present an answer to

the question.

2.  What is research design?

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3.  A research design is a systematic plan to study a scientific problem. The

design of a study defines the study type (descriptive, correlational, semi-

experimental, experimental, review, meta-analytic) and sub-type (e.g.,

descriptive-longitudinal case study), research question,  hypotheses, 

independent and dependent variables, experimental design, and, if applicable,

data collection methods and a statistical analysis plan.

4.  Define Scale?

A scale is a type of composite measure that is composed of several

items that have a logical or empirical structure among them. That is,

scales take advantage of differences in intensity among the indicators

of a variable. The most commonly used scale is the Likert scale, which

contains response categories such as "strongly agree," "agree,"

"disagree," and "strongly disagree." Other scales used in social science

research include the Thurstone scale, Guttman scale, Bogardus social

distance scale, and the semantic differential scale.

5. 

What is meant by scaling technique?

Scaling is the process of assigning numbers to various degrees of

attitudes, preferences, opinion, and other concepts. Scaling is defined

as a procedure for the assignment of numbers (or other symbols) to a

 property of objects in order to impart some of the characteristics of

numbers to the properties in question.

6. 

What is sample?

The group of people who are the subject of a piece of research is

known as the “population”

7.  What is meant by hypothesis?

A hypothesis (plural hypotheses) is a proposed explanation for a

 phenomenon.  For a hypothesis to be a scientific hypothesis, the

scientific method requires that one can test it. Scientists generally base

scientific hypotheses on previous observations that cannot

satisfactorily be explained with the available scientific theories. Even

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though the words "hypothesis" and "theory"  are often used

synonymously, a  scientific hypothesis  is not the same as a  scientific

theory.  A  scientific hypothesis  is a proposed explanation of a

 phenomenon which still has to be rigorously tested.

8.  Cluster analysis?

Cluster analysis or clustering is the task of grouping a set of objects in

such a way that objects in the same group (called a cluster) are more

similar (in some sense or another) to each other than to those in other

groups (clusters). It is a main task of exploratory data mining, and a

common technique for statistical data analysis,  used in many fields,

including machine learning,   pattern recognition,  image analysis, 

information retrieval, and bioinformatics. 

9.  What is meant by report writing?

A report or account is any informational work (usually of writing,

speech, television, or film) made with the specific intention of relaying

information or recounting certain events in a widely presentable form. 

10. 

Simple Random sampling method ?

A method of selecting a sample (random sample) from a statistical

 population in such a way that every possible sample that could be

selected has a predetermined probability of being selected.

11. 

Chi square test?

A chi-squared test, also referred to as chi-square test or test, is any

statistical hypothesis test in which the sampling distribution of the test

statistic is a chi-squared distribution when the null hypothesis is true.

Also considered a chi-squared test is a test in which this is

asymptotically true, meaning that the sampling distribution (if the null

hypothesis is true) can be made to approximate a chi-squared

distribution as closely as desired by making the sample size large

enough.

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12. 

ANOVA Test

Analysis of variance (ANOVA) is a collection of statistical models

used to analyze the differences between group means and their

associated procedures (such as "variation" among and between

groups), developed by R.A. Fisher. In the ANOVA setting, the

observed variance in a particular variable is partitioned into

components attributable to different sources of variation. In its

simplest form, ANOVA provides a statistical test of whether or not the

means of several groups are equal, and therefore generalizes the  t -test

to more than two groups.