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Meaning and Definition of Research:
Research in simplified terms means searching for the facts searching for the replies to the various queries and also for the solutions to the various problems. Research is an inquiry or an investigation with a specific purpose to fulfill, it helps in clearing the various doubtful concepts and tries to solve or explain the various unexplained procedures or phenomenons.

The features that a good research procedure must possess are –
1. Should be systematic in nature.
2. Should be logical.
3. Should be empirical and replicable in nature.
4. Should be according to plans.
5. Should be according to the rules and the assumptions should not be based on the false bases or judgments.
6. Should be relevant to what is required.
7. Procedure should be reproducible in nature.
8. Controlled movement of the research procedure.

Qualities of a good researcher can be summarized as –
1. Method of approach – The researcher should adopt correct procedure for identifying a problem and then for working on it, to find a solution for that problem.

2. Knowledge – The researcher should be well aware and should have complete knowledge and information of the field of investigation so that he can go in for correct planning and then implementation of the correct and effective methods for selection of the problem and then for solving it.

3. Qualification – The researcher should have a good back ground of study, which will enable the researcher to have a better knowledge and understanding of the subject.

4. Attitude – The researcher must have a vision of his own, an aim with some objectives to achieve something.

5. Should have an open thinking.

6. Should be stable, having consistent thinking.

7. Should be honest, sincere, brave and ambitious.

Characteristics of research –
1. Research is based on the scientific method.
2. Helps in answering various pertinent questions.
3. It is an organized, planned and patient investigation or a critical enquiry.
4. It has logical roots, helping to establish facts or principles.

Limitations of research –
1. Problems of collection of data and conceptualization may occur.
2. Repetition problems.
3. Outdated and insufficient information system may cause problems.
4. Sometimes lack of resources becomes an obstacle.
5. Nonavailability of trained researchers.
6. Absence of code of conduct.

Research methodlogy

The process used to collect information and data for the purpose of making business decisions. The methodology may include publication research, interviews, surveys and other research techniques, and could include both present and historical information.

Areas/Scope of Business Research

  • Accounting: research interests include accounting information and capital markets, managerial accounting for decision making, international accounting, accounting theory, auditing, accounting ethics, and the behavioral implications of accounting. Faculty research is supported by the professional accounting bodies and other agencies. 
  • Finance: Research interests and accomplishments within the group range from the highly theoretical to the practical, covering such issues as corporate financing and investment, valuation methods, the functioning of capital markets, individual and institutional investing, controlling risk with options and futures contracts, hedge funds and private equity, and real estate finance.
  • International Business:In today’s global environment all business is international business. The IB Area focuses its research activities on understanding how managers and firms can thrive in an increasingly complex and dynamic global business environment.
  • Management Information Systems: Faculty members in Management Information Systems (MIS) examine information technology and information systems within an organizational context. Members of the group have established research programs that span a wide range of topics­—from technical issues, such as the use of artificial intelligence to valuate strategic alternatives and the impact of different graphical representations in systems analysis and design,  A diverse set of research methodologies are employed to investigate these topics, including computer simulations, laboratory experiments, large-scale surveys, in-depth interviews, and participant observation
  • Management and Organizational Studies: Over the past two years, MOS faculty published research articles in prestigious journals, presented research findings at several academic conferences, been keynote speakers at academic conferences and industry gatherings, received hundreds of thousands of dollars in research grants, and wrote books on emerging topics. This research covers diverse territory, including applied ethics, corporate social responsibility, employment relations, human resource management, organizational behaviour, organization development, and organization theory.
  • Marketing: Research and publication is a high priority of all tenure-track faculty. Members of the marketing area are currently investigating such topics as trademark infringement, counterfeiting, the application of econometrics to marketing, demand analysis for industrial marketing, political marketing techniques, corporate social responsibility, retailing and distribution, exporting for high-tech firms, cross-cultural negotiations, and a wide variety of topics in the related areas of international marketing, marketing and society, and marketing and public policy.
  • Strategy: The Strategy area enables students to pursue the study of broad strategic issues related to business. The area encourages students to study strategy, both from the perspective of corporate managers making strategic decisions and organizing their resources to implement them, and from the perspective of those responsible for government regulatory policy or management of state-owned enterprises. Strategy is the most interdisciplinary of the areas within the Business Faculty. The area includes members trained in strategy, economics, sociology, social psychology, philosophy, and law. 
  • Technology and Operations Management: TOM Area includes faculty members who are active researchers and educators on topics such as sustainability, operations management, entrepreneurship, new ventures, innovation and R&D management, game theory, auction analysis, decision making and collaboration networks. With this range of expertise, TOM faculty are committed to generating, synthesizing, and disseminating knowledge on the formation and management of sustainable firms, processes and technologies.

Nature and purpose of business research:

No hard and fast rules can be described in order to express the purpose of a business research. The reason is simple; each business research depends on the situation and the person or organization that is carrying out the research. However, only one thing is sure that the general purpose of any business research is to get success in the future.

It is an undeniable fact that every individual or organization enters the market hoping to gain massive profits. Thus, the primary objective of any business research can be explained as an attempt to improve the sales and income ratio of the company. In addition, it helps the government agencies in obtaining a sort of financial support    Therefore, conducting a business research will let you understand the trend of the market and what are the needs of the markets and if you would be able to respond to those needs in an efficient manner. You will have to sell only such products or services that the market requires; otherwise, you will have no sale at all. As it is the first rule of entering any market along with your business that never enter a market without knowing each and everything about it. Hence, you should make sure that everything is planned out for even worse of the scenarios. However, if you have done a thorough research, then you can have massive gain in profit in near future.

The Nature & Importance of Business Research

Research on aspects related your business, such as your target customer, marketplace trends, production processes, and financial practices, can help you predict trends, project sales, spot opportunities, and avoid potential problems. Understanding the nature of different types of business research will help you use data to maximize your sales and profit.

Reasons for Business Research

Business research can help you determine what potential customers want, which can guide you toward development of better products and services. It can keep you abreast of what your competition is doing and help you spot marketplace and industry trends. Research lets you analyze how your departments are performing, and then compare their performance against projections to determine if you need to make adjustments.

Scientific Methods in Research Methodology

Scientific method can be defined as the “method which consists of the systematic observation, classification and the interpretation of the data the main difference between our day to day generalization and the conclusions usually recognized as a scientific method lie in the degree of the formality, rigorousness, verifiability and the general validity of the later.”

Scientific method is a method, which is very systematic in nature and plays a very critical role in the field of investigation, evaluation, experimentation, interpretation and theorizing.

This type of method is also very effective in the cases of physical sciences as the various physical phenomenon can be easily verified and also evaluated but in case of the managerial factors (e.g. behavioral factors of an organization) cannot be absolutely verified and evaluated physically.

All this affects the scope of the scientific methods, so it can be said that the scientific methods are not able to verify and evaluate all the management related problems empirically. Also the scientific method affects the working schedule as it very greatly increases the demand for the time, resources, exposure and also the man – powers.

Characteristics of scientific method
1. Is a very systematic method, offering convenient working.
2. Helps in obtaining very accurate classification of facts.
3. This method is marked by the observation of heavy co relation and sequence.
4. Helps in the discovery of the scientific laws.
5. Depends and aims at achieving actual facts and not the desired ones.
6. Relies on the evidence.
7. Has a definite problem for solving, as every inquiry has a specific sense.
8. Results drawn from the scientific method are capable of being observed and then measured.
9. It links and tries to establish very general propositions.
10. Scientific results can be estimated with sufficient accuracy.
11. Scientific conclusions are very true in nature and working.
12. Observer’s own views find no place during the observation as the observation is made in a very true form.


Research is essential to collect facts and statistics about a company’s customers, employees and competitors. On the basis of these numbers, companies are able to make better managerial decisions. The collected statistics are organized into reports and the management team uses them to take action. A good research mechanism is essential, irrespective of the size of the company and its client base. Research is imperative for staying competitive in the market.


A business is able to make knowledgeable decisions because of research. In the research process, the company is able to obtain information about key business areas, analyze it, develop a strategy and distribute business information. Reports, provided to the top management, often include information on consumer and employee preferences and all the available routes for sales, marketing, finance and production. Management uses this information to decide the best strategy. Research is a prerequisite at all stages and phases of business operations. Initial research is required to gauge whether getting into the given type of business would be profitable and whether there is demand for the proposed product.


By conducting business research, the organization ascertains what its customers want and then takes steps to prepare a product meeting those desires. Research also helps determine whether a product is accepted in the market. Research aids expansion into new markets.

Methods of Data Collection

There are two ways organizations typically collect data.

One is primary data collection from your immediate consumers, who provide feedback on your products. You can also invite customers to offer opinions on future products. To gain this information, an interviewer asks the customer to provide views on how the company can modify the existing product to satisfy his needs better. The interviewer uses surveys and questionnaires to collect and record data. This method is helpful for gaining insight about a company’s particular merchandise.

The second method is secondary data collection, which uses data that has already been printed over the Internet and in magazines and journals. This is predominantly useful in gauging the broad market scenarios.

Considerating a Consultant

Conducting research involves cost and time. The organization must weigh the pros and cons before hiring consultants to conduct research. Consultants must be made fully aware of what the organization is looking for from the research.


The primary benefit to business research is that the organization is able to learn more about consumer choices and preferences. Research provides information on the product features that lure customers and flaws in the product or marketing that contribute to slow sales. Research helps the organization fix problems and cash in on the strengths. Research also contributes to a company’s ability to clearly identify the customer demographics and target demographic, including age, gender, monthly income of the household and educational levels. Research mitigates business risks and can help increase demand and sales.


A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question.

Defining a research problem is the fuel that drives the scientific process, and is the foundation of any research method and experimental design, from true experiment to case study.

It is one of the first statements made in any research paper and, as well as defining the research area, should include a quick synopsis of how the hypothesis was arrived at.

Operationalization is then used to give some indication of the exact definitions of the variables, and the type of scientific measurements used.

This will lead to the proposal of a viable hypothesis. As an aside, when scientists are putting forward proposals for research funds, the quality of their research problem often makes the difference between success and failure.

5 Ways to Formulate the Research Problem

1. Specify the Research Objectives

A clear statement of objectives will help you develop effective research.

It will help the decision makers evaluate your project. It’s critical that you have manageable objectives. (Two or three clear goals will help to keep your research project focused and relevant.)

2. Review the Environment or Context of the Research Problem

As a marketing researcher, you must work closely with your team. This will help you determine whether the findings of your project will produce enough information to be worth the cost.

In order to do this, you have to identify the environmental variables that will affect the research project.

3. Explore the Nature of the Problem

Research problemsrange from simple to complex, depending on the number of variables and the nature of their relationship.

If you understand the nature of the problem as a researcher, you will be able to better develop a solution for the problem.

To help you understand all dimensions, you might want to consider focus groups of consumers, sales people, managers, or professionals to provide what is sometimes much needed insight.

4. Define the Variable Relationships

Marketing plans often focus on creating a sequence of behaviors that occur over time, as in the adoption of a new package design, or the introduction of a new product.

Such programs create a commitment to follow some behavioral pattern in the future.

Studying such a process involves:

  • Determining which variables affect the solution to the problem.
  • Determining the degree to which each variable can be controlled.
  • Determining the functional relationships between the variables and which variables are critical to the solution of the problem.

5. The Consequences of Alternative Courses of Action

There are always consequences to any course of action. Anticipating and communicating the possible outcomes of various courses of action is a primary responsibility in the research process.

5 Important Factors to Consider When Choosing Your Research Topics

Keeping Career Path in Focus

You should always focus on your career path which you would like to adopt in future after your graduate schooling. Since, you may have to spend many months on a chosen topic due to extensive research, you must try your best to make sure that the dissertation can help you get a great job or a promotion. Hence, the topic must be chosen keeping career path in focus.

Topic of Your Interest

When you start working on your dissertation project, it requires you to get yourself involved in it for months or sometimes even a full year and if you work on a subject in which you are not really interested, this will become quite difficult to work consistently with passion as you won’t be able to enjoy.

Knowledge on a Topic

If you prefer to choose a new topic in which you don’t have good knowledge, that will be a brave move from your side but on the other hand, you will have to be ready to invest too much effort in it. It might take much more time than your expectations because you will be required to invest lots of your time and energy in research work. Therefore, if you want to finish your dissertation in less time with better quality, the best option for you is to choose an area of your expertise.

Filed of Your Expertise

In case you are majoring in Art but chosen a subject on scientific matter, this does not really make any sense at all and this will only make you confused. It is also important to keep in mind that when it is about the topic of your interest, it does not mean something which is not related to your field or specialty of discipline, instead, it reflects your basic education and learning.

An Informative Topic

Rather than focusing on discovering something of lower interest, you better work out on something which can captivate entire world and here you need to think about your limitations and capabilities. Remember that a well written and well researched dissertation that gives readers good insights will prove to be better than a fragmented and sloppy one that does not give a solution or an alternative.

Qualitative research

Qualitative researchis a method of inquiry employed in many different academic disciplines, including in the social sciences and natural sciences, but also in non-academic contexts including market research, business, and service demonstrations by non-profits.

Qualitative researchgathers information that is not in numerical form. For example, diary accounts, open-ended questionnaires, unstructured interviews and unstructured observations. Qualitative data is typically descriptive data and as such is harder to analyze than quantitative data.


various types of research

1. Pure research

a. Also called as the fundamental or the theoretical research.

b. Is basic and original.

c. Can lead to the discovery of a new theory.

d. Can result in the development or refinement of a theory that already exists.

e. Helps in getting knowledge without thinking formally of implementing it in practice based on the honesty, love and integrity of the researcher for discovering the truth.

2. Applied research

a. Based on the concept of the pure research.

b. Is problem oriented.

c. Helps in finding results or solutions for real life problems.

d. Provides evidence of usefulness to society.

e. Helps in testing empirical content of a theory.

f. Utilizes and helps in developing the techniques that can be used for basic research.

g. Helps in testing the validity of a theory but under some conditions.

h. Provides data that can lead to the acceleration of the process of generalization.

3. Exploratory research

a. Involves exploring a general aspect.

b. Includes studying of a problem, about which nothing or a very little is known.

c. Follows a very formal approach of research.

d. Helps in exploring new ideas.

e. Helps in gathering information to study a specific problem very minutely.

f. Helps in knowing the feasibility in attempting a study.

4. Descriptive research

a. Simplest form of research.

b. More specific in nature and working than exploratory research.

c. It involves a mutual effort.

d. Helps in identifying various features of a problem.

e. Restricted to the problems that are describable and not arguable and the problems in which valid standards can be developed for standards.

f. Existing theories can be easily put under test by empirical observations.

g. Underlines factors that may lead to experimental research.

h. It consumes a lot of time.

i. It is not directed by hypothesis.

5. Diagnostic study

a. Quite similar to the descriptive research.

b. Identifies the causes of the problems and then solutions for these problems.

c. Related to causal relations.

d. It is directed by hypothesis.

e. Can be done only where knowledge is advanced.

6. Evaluation study

a. Form of applied research.

b. Studies the development project.

c. Gives access to social or economical programmes.

d. Studies the quality and also the quantity of an activity.

7. Action research

a. Type of evaluation study.

b. Is a concurrent evaluation study.

Hypothesis, Meaning & Definition

Hypothesis is considered as a principal instrument in research. It is a preposition or assumption for research work to be tested. Its main function is to suggest new experiments and observations. It is a statement which is extended for proper guidance of research activity.

Meaning of Hypothesis

There are two concepts about the meaning of hypothesis according to first concept, it is the combination of two Greek words, hypo and “thesis”. Hypo means “under” and thesis means “refer to place”. So, it is anything under consideration. Second concept is that, hypo means less than while thesis means generally held view. Thus collectively it means less than generally held view. It means less than generalization.

Alternative Hypothesis

An alternative hypothesis states that there is statistical significance between two variables.

The alternative hypothesis states that the population parameter is different than the value of the population parameter in the null hypothesis. The alternative hypothesis is what you might believe to be true or hope to prove true.

Types of Hypothesis, Null, Empirical, Complex & Logical Hypothesis

1. Simple Hypothesis

2. Complex Hypothesis

3. Empirical Hypothesis

4. Null Hypothesis

5. Alternative Hypothesis

6. Logical Hypothesis

7. Statistical Hypothesis

Simple Hypothesis

Simple hypothesis is that one in which there exists relationship between two variables one is called independent variable or cause and other is dependent variable or effect. For example

1. Smoking leads to Cancer

2. The higher ratio of unemployment leads to crimes.

Complex Hypothesis

Complex hypothesis is that one in which as relationship among variables exists. I recommend you should read characteristics of a good research hypothesis. In this type dependent as well as independent variables are more than two. For example

2. The higher ration of unemployment poverty, illiteracy leads to crimes like dacoit, Robbery,

Empirical Hypothesis

Working hypothesis is that one which is applied to a field. During the formulation it is an assumption only but when it is pat to a test become an empirical or working hypothesis.

Null Hypothesis

Null hypothesis is contrary to the positive statement of a working hypothesis. According to null hypothesis there is no relationship between dependent and independent variable. It is denoted by ‘HO”.

Alternative Hypothesis

Firstly many hypotheses are selected then among them select one which is more workable and most efficient. That hypothesis is introduced latter on due to changes in the old formulated hypothesis. It is denote by “HI”.

Logical Hypothesis

It is that type in which hypothesis is verified logically. J.S. Mill has given four cannons of these hypothesis e.g. agreement, disagreement, difference and residue.

Statistical Hypothesis

A hypothesis which can be verified statistically called statistical hypothesis. The statement would be logical or illogical but if statistic verifies it, it will be statistical hypothesis.

Importance of Hypothesis

1.To the Point Enquiry

Hypothesis makes a research activity to the point and destination,

. So, research is to the point enquiry of problem due to the guidance of hypothesis.

2.Development of Research Techniques

There are various types of social problems which are complex in nature . For this research is very difficult. We cannot cover it with a single technique but it requires many techniques. These techniques are due to hypothesis provided to a researcher.

3.Separating Relevant From Irrelevant Observation

A Researcher during study will take the observations and facts which are accordance to the condition and situation. While drop out the irrelevant facts from his study. This separation is due to hypothesis formulation which keeps away relevant observation from irrelevant.

4.Selecting Required Facts

During study a researcher come across many factors but he confined himself to the selection of required facts through formulation of hypothesis. Hypothesis helps him in selection of relevant facts regarding to the problematic situation .

5.Direction of Research

Hypothesis acts as a guide master in research. It gives new knowledge and direction to a researcher. It directs a scientist to know about the problematic situation and its causes.

6.Acts as a Guide

Hypothesis gives new ways and direction to a researcher. It acts as a guide and a leader in various organizations or society. It is like the investigator’s eye.

7.Prevents Blind Research

Hypothesis provides lighting to the darkness of research. It gives difference b/w scientific and unscientific, false and true research. It prevents blind research and give accuracy.

8.Accuracy & Precision

Hypothesis provides accuracy and precision to a research activity. Accuracy and precision is the feature of scientific investigation which is possible due to hypothesis.

9.Link between Theory & Investigation

Theory is a source of hypothesis which leads to its formulation . Hypothesis leads to scientific investigation. So, hypothesis acts as a bridge b/w theory and investigation.

10.Link between Assumption & Observation

During formulation hypothesis is in the stage of assumption. In the field it transformed into hypothesis in working form. This transformation is due to observation in the field. So, it creates a link between assumption & observation.

11.Provide answer for a Question

A hypothesis highlights the causes of a problematic situation. Further solution is also given by a hypothesis which provides answer to a question.

12.Save Time, Money & Energy

Hypothesis save time, money and energy of a researcher because it is a guide for him and help him in saving these basic things.

13.Proper Data Collection

Hypothesis provides the basis of proper Data Collection Relevant and correct information collected by a researcher is the main function of a good formulated hypothesis.

Differences between primary and secondary data:

1. The term primary data refers to the data originated by the researcher for the first time. Secondary data is the already existing data, collected by the investigator agencies and organisations earlier.

2. Primary data is a real-time data whereas secondary data is one which relates to the past.

3. Primary data is collected for addressing the problem at hand while secondary data is collected for purposes other than the problem at hand.

4. Primary data collection is a very involved process. On the other hand, secondary data collection process is rapid and easy.

5. Primary data collection sources include surveys, observations, experiments, questionnaire, personal interview, etc. On the contrary, secondary data collection sources are government publications, websites, books, journal articles, internal records etc.

6. Primary data collection requires a large amount of resources like time, cost and manpower. Conversely, secondary data is relatively inexpensive and quickly available.

7. Primary data is always specific to the researcher’s needs, and he controls the quality of research. In contrast, secondary data is neither specific to the researcher’s need, nor he has control over the data quality.

8. Primary data is available in the raw form whereas secondary data is the refined form of primary data. It can also be said that secondary data is obtained when statistical methods are applied to the primary data.

9. Data collected through primary sources are more reliable and accurate as compared to the secondary sources.



Secondary data is information that has been collected for a purpose other than your current research project but has some relevance and utility for your research.

Sources of Secondary Data

You can break the sources of secondary data into internal sources and external sources.

Internal sources include data that exists and is stored inside your organization. External data is data that is collected by other people or organizations from your organization’s external environment.

Examples of internal sources of data include, but are certainly not limited to, the following:

  • Profit and loss statements
  • Balance sheets
  • Sales figures
  • Inventory records
  • Previous marketing research studies
  • If the secondary data you have collected from internal sources will not be sufficient, you can turn to external sources of data .

Some external sources include:

  • Government sources, such as the U.S. Census Bureau
  • Corporate filings, such as annual reports to the U.S. Securities and Exchange Commission (SEC)
  • Trade, business and professional associations
  • Media, including broadcast, print and Internet
  • Universities
  • Foundations
  • Think tanks, such as the Rand Corporation or Brookings Institute

What are the Precautions you must take while using Secondary Data?

The investigator should take precautions before using the secondary data. In this connection, following precautions should be taken into account.

1. Suitable Purpose of Investigation:

The investigator must ensure that the data are suitable for the purpose of enquiry.

2. Inadequate Data:

Adequacy of the data is to be judged in the light of the requirements of the survey as well as the geographical area covered by the available data.

3. Definition of Units:

The investigator must ensure that the definitions of units which are used by him are the same as in the earlier investigation.

4. Degree of Accuracy:

The investigator should keep in mind the degree accuracy maintained by each investigator.

5. Time and Condition of Collection of Facts:

It should be ascertained before making use of available data to which period and conditions, the data was collected.

6. Comparison:

Investigator should keep in mind whether the secondary data’ reasonable, consistent and comparable.

7. Test Checking:

The use of the secondary data must do test checking and see that totals and rates have been correctly calculated.

8. Homogeneous Conditions:

It is not safe to take published statistics at their face value without knowing their means, values and limitations.

Methods of Data Collection- Primary and Secondary Data

Collection of Primary Data

Primary data is collected in the course of doing experimental or descriptive research by doing experiments, performing surveys or by observation or direct communication with respondents. Several methods for collecting primary data are given below –

1.Observation Method

It is commonly used in studies relating to behavioural science. Under this method observation becomes a scientific tool and the method of data collection for the researcher, when it serves a formulated research purpose and is systematically planned and subjected to checks and controls.

(a) Structured (descriptive) and Unstructured (exploratory) observation – When a observation is characterized by careful definition of units to be observed, style of observer, conditions for observation and selection of pertinent data of observation it is a structured observation. When there characteristics are not thought of in advance or not present it is a unstructured observation.

(b) Participant, Non-participant and Disguised observation – When the observer observes by making himself more or less, the member of the group he is observing, it is participant observation but when the observer observes by detaching himself from the group under observation it is non participant observation. If the observer observes in such a manner that his presence is unknown to the people he is observing it is disguised observation.

(c) Controlled (laboratory) and Uncontrolled (exploratory) observation – If the observation takes place in the natural setting it is a uncontrolled observation but when observer takes place according to some pre-arranged plans, involving experimental procedure it is a controlled observation.

Advantages →

⦁ Subjective bias is eliminated

⦁ Data is not affected by past behaviour or future intentions

⦁ Natural behaviour of the group can be recorded


⦁ Expensive methodology

⦁ Information provided is limited

⦁ Unforeseen factors may interfere with the observational task

2.Interview Method

This method of collecting data involves presentation of oral verbal stimuli and reply in terms of oral – verbal responses. It can be achieved by two ways :-

(A) Personal Interview – It requires a person known as interviewer to ask questions generally in a face to face contact to the other person. It can be –

Direct personal investigation – The interviewer has to collect the information personally from the services concerned.

Indirect oral examination – The interviewer has to cross examine other persons who are suppose to have a knowledge about the problem.

Structured Interviews – Interviews involving the use of pre- determined questions and of highly standard techniques of recording.

Unstructured interviews – It does not follow a system of pre-determined questions and is characterized by flexibility of approach to questioning.

Focused interview – It is meant to focus attention on the given experience of the respondent and its effect. The interviewer may ask questions in any manner or sequence with the aim to explore reasons and motives of the respondent.

Clinical interviews – It is concerned with broad underlying feeling and motives or individual’s life experience which are used as method to ellict information under this method at the interviewer direction.

Non directive interview – The interviewer’s function is to encourage the respondent to talk about the given topic with a bare minimum of direct questioning.

Advantages –

⦁ More information and in depth can be obtained

⦁ Samples can be controlled

⦁ There is greater flexibility under this method

⦁ Personal information can as well be obtained

⦁ Mis-interpretation can be avoided by unstructured interview.

Limitations –

⦁ It is an expensive method

⦁ Possibility of bias interviewer or respondent

⦁ More time consuming

⦁ Possibility of imaginary info and less frank responses.

⦁ High skilled interviewer is required

(B) Telephonic Interviews – It requires the interviewer to collect information by contacting respondents on telephone and asking questions or opinions orally.

Advantages –

⦁ It is flexible, fast and cheaper than other methods

⦁ Recall is easy and there is a higher rate of response

⦁ No field staff is required.

Limitations –

⦁ Interview period exceed five minutes maximum which is less

⦁ Restricted to people with telephone facilities.

⦁ Questions have to be short and to the point

⦁ Less information can be collected.


In this method a questionnaire is sent (mailed) to the concerned respondents who are expected to read, understand and reply on their own and return the questionnaire. It consists of a number of questions printed on typed in a definite order on a form on set of forms.

It is advisable to conduct a `Pilot study’ which is the rehearsal of the main survey by experts for testing the questionnaire for weaknesses of the questions and techniques used.

Essentials of a good questionnaire –

-It should be short and simple

-Questions should proceed in a logical sequence

-Technical terms and vague expressions must be avoided.

-Control questions to check the reliability of the respondent must be present

-Adequate space for answers must be provided

-Brief directions with regard to filling up of questionnaire must be provided

-The physical appearances – quality of paper, colour etc must be good to attract the attention of the respondent

Advantages –

⦁ Free from bias of interviewer

⦁ Respondents have adequate time to give

⦁ Respondents have adequate time to give answers

⦁ Respondents are easily and conveniently approachable

⦁ Large samples can be used to be more reliable

Limitations –

⦁ Low rate of return of duly filled questionnaire

⦁ Control over questions is lost once it is sent

⦁ It is inflexible once sent

⦁ Possibility of ambiguous or omission of replies

⦁ Time taking and slow process


This method of data collection is similar to questionnaire method with the difference that schedules are being filled by the enumerations specially appointed for the purpose. Enumerations explain the aims and objects of the investigation and may remove any misunderstanding and help the respondents to record answer. Enumerations should be well trained to perform their job, he/she should be honest hard working and patient. This type of data is helpful in extensive enquiries however it is very expensive.

Collection of Secondary Data

A researcher can obtain secondary data from various sources. Secondary data may either be published data or unpublished data.

Published data are available in :

a. Publications of government

b. technical and trade journals

c. reports of various businesses, banks etc.

d. public records

e. statistical or historical documents.

Unpublished data may be found in letters, diaries, unpublished biographies or work.

Before using secondary data, it must be checked for the following characteristics –

1. Reliability of data – Who collected the data? From what source? Which methods? Time? Possibility of bias? Accuracy?

2.Suitability of data – The object, scope and nature of the original enquiry must be studies and then carefully scrutinize the data for suitability.

3.Adequacy – The data is considered inadequate if the level of accuracy achieved in data is found inadequate or if they are related to an area which may be either narrower or wider than the area of the present enquir

Research design and its components:

Research design is a broad framework that states the total pattern of conducting research project. It specifies objectives, data collection and analysis methods, time, costs, responsibility, probable outcomes, and actions.

A research design is a broad plan that states objectives of research project and provides the guidelines what is to be done to realize those objectives. It is, in other words, a master plan for executing a research project.

Contents of Research Design:

The most common aspects involved in research design include at least followings:

1. Statement of research objectives, i.e., why the research project is to be conducted

2. Type of data needed

3. Definition of population and sampling procedures to be followed

4. Time, costs, and responsibility specification

5. Methods, ways, and procedures used for collection of data

6. Data analysis – tools or methods used to analyze data

7. Probable output or research outcomes and possible actions to be taken based on those outcomes

Types of Research Designs:

The research design is a broad framework that describes how the entire research project is carried out. Basically, there can be three types of research designs – exploratory research design, descriptive research design, and experimental (or causal) research design. Use of particular research design depends upon type of problem under study.

Let’s have glimpse of each of them:

1. Exploratory Research Design:

This design is followed to discover ideas and insights to generate possible explanations. It helps in exploring the problem or situation. It is, particularly, emphasized to break a broad vague problem statement into smaller pieces or sub-problem statements that help forming specific hypothesis.

1. Clarifying concepts and defining problem

2. Formulating problem for more precise investigation

3. Increasing researcher’s familiarity with problem

4. Developing hypotheses

5. Establishing priorities for further investigation

Exploratory research design is characterized by flexibility to gain insights and develop hypotheses. It does not follow a planned questionnaire or sampling. It is based on literature survey, experimental survey, and analysis of selected cases. Unstructured interviews are used to offer respondents a great deal of freedom. No research project is purely and solely based on this design. It is used as complementary to descriptive design and causal design.

2. Descriptive Research Design:

Descriptive research design is typically concerned with describing problem and its solution. It is more specific and purposive study. Before rigorous attempts are made for descriptive study, the well-defined problem must be on hand. Descriptive study rests on one or more hypotheses.

For example, “our brand is not much familiar,” “sales volume is stable,” etc. It is more precise and specific. Unlike exploratory research, it is not flexible. Descriptive research requires clear specification of who, why, what, when, where, and how of the research. Descriptive design is directed to answer these problems.

3. Causal or Experimental Research Design:

Causal research design deals with determining cause and effect relationship. It is typically in form of experiment. In causal research design, attempt is made to measure impact of manipulation on independent variables (like price, products, advertising and selling efforts or marketing strategies in general) on dependent variables (like sales volume, profits, and brand image and brand loyalty). It has more practical value in resolving marketing problems. We can set and test hypotheses by conducting experiments.

Test marketing is the most suitable example of experimental marketing in which the independent variable like price, product, promotional efforts, etc., are manipulated (changed) to measure its impact on the dependent variables, such as sales, profits, brand loyalty, competitive strengths product differentiation and so on.


Census, Sampling and Population

Census and sampling are two methods of collecting survey data about the population that are used by many countries. Censusrefers to the quantitative research method, in which all the members of the population are enumerated. On the other hand, the samplingis the widely used method, in statistical testing, wherein a data set is selected from the large population, which represents the entire group.

Definition of Census

A well-organised procedure of gathering, recording and analysing information regarding the members of the population is called a census. It is an official and complete count of the universe, wherein each and every unit of the universe is included in the collection of data. Here universe implies any region (city or country), a group of people, through which the data can be acquired.

Under this technique, the enumeration is conducted about the population by considering the entire population. Hence this method requires huge finance, time and labour for gathering information. This method is useful, to find out the ratio of male to female, the ratio of literate to illiterate people, the ratio of people living in urban areas to the people in rural areas.

Definition of Sampling

We define sampling as the process in which the fraction of the population, so selected to represent the characteristics of the larger group. This method is used for statistical testing, where it is not possible to consider all members or observations, as the population size is very large.

As statistical inferences are based on the sampling observations, the selection of the appropriate representative sample is of utmost importance. So, the sample selected should indicate the entire universe and not exhibit a particular section. On the basis of the data collected from the representative samples, the conclusion is drawn from the whole population. For instance: A company places an order for raw material by simply checking out the sample.

Definition of Population

In simple terms, population means the aggregate of all elements under study having one or more common characteristic, for example, all people living in India constitutes the population. The population is not confined to people only, but it may also include animals, events, objects, buildings, etc. It can be of any size, and the number of elements or members in a population is known as population size, i.e. if there are hundred million people in India, then the population size (N) is 100 million. The different types of population are discussed as under:

  1. Finite Population: When the number of elements of the population is fixed and thus making it possible to enumerate it in totality, the population is said to be finite.
  2. Infinite Population: When the number of units in a population are uncountable, and so it is impossible to observe all the items of the universe, then the population is considered as infinite.
  3. Existent Population: The population which comprises of objects that exist in reality is called existent population.
  4. Hypothetical Population: Hypothetical or imaginary population is the population which exists hypothetically.

Sampling Procedure/ process

1. Identify the population of interest.population is the group of people that you want to make assumptions about. For example, Brooke wants to know how much stress college students experience during finals. Her population is every college student in the world because that’s who she’s interested in. Of course, there’s no way that Brooke can feasibly study every college student in the world, so she moves on to the next step.

2. Specify a sampling frame.sampling frame is the group of people from which you will draw your sample. For example, Brooke might decide that her sampling frame is every student at the university where she works. Notice that a sampling frame is not as large as the population, but it’s still a pretty big group of people. Brooke still won’t be able to study every single student at her university, but that’s a good place from which to draw her sample.

3. Specify a sampling method. There are basically two ways to choose a sample from a sampling frame: randomly or non-randomly. There are benefits to both. Basically, if your sampling frame is approximately the same demographic makeup as your population, you probably want to randomly select your sample, perhaps by flipping a coin or drawing names out of a hat.

But what if your sampling frame does not really represent your population? For example, what if the school where Brooke works has a lot more men than women and a lot more whites than minority races? In the population of every college student in the world, there might be more of a balance, but Brooke’s sampling frame (her school) doesn’t really represent that well. In that case, she might want to non-randomly select her sample in order to get a demographic makeup that is closer to that of her population.

4. Determine the sample size. In general, larger samples are better, but they also require more time and effort to manage. If Brooke ends up having to go through 1,000 surveys, it will take her more time than if she only has to go through 10 surveys. But the results of her study will be stronger with 1,000 surveys, so she (like all researchers) has to make choices and find a balance between what will give her good data and what is practical.

5. Implement the plan. Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample.

Advantages of sampling

1.Very accurate.

2.Economical in nature.

3.Very reliable.

4.High suitability ratio towards the different surveys.

5.Takes less time.

6.In cases, when the universe is very large, then the sampling method is the only practical method for collecting the data.

Disadvantages of sampling

1.Inadequacy of the samples.

2.Chances for bias.

3.Problems of accuracy.

4.Difficulty of getting the representative sample.

5.Untrained manpower.

6.Absence of the informants.

7.Chances of committing the errors in sampling.

Stratified Random sampling

Stratified random sampling is a method of samplingthat involves the division of a population into smaller groups known as strata. In stratified random sampling, the strata are formed based on members’ shared attributes or characteristics. A random sample from each stratum is taken in a number proportional to the stratum’s size when compared to the population. These subsets of the strata are then pooled to form a random sample.

**Stratified random sampling breaks down the population into smaller groups of data with similar attributes so that each smaller group individual results can be accumulated into the final result.


The sampling distribution is a distribution of a sample statistic. It is a model of a distribution of scores, like the population distribution, except that the scores are not raw scores, but statistics. It is a thought experiment; “what would the world be like if a person repeatedly took samples of size N from the population distribution and computed a particular statistic each time?” The resulting distribution of statistics is called the sampling distribution of that statistic.

For example, suppose that a sample of size sixteen (N=16) is taken from some population. The mean of the sixteen numbers is computed. Next a new sample of sixteen is taken, and the mean is again computed. If this process were repeated an infinite number of times, the distribution of the now infinite number of sample means would be called the sampling distribution of the mean.

Every statistic has a sampling distribution. For example, suppose that instead of the mean, medians were computed for each sample. The infinite number of medians would be called the sampling distribution of the median.

Snowball Sampling

Definition: The Snowball Sampling is a non-random sampling technique wherein the initial informants are approached who through their social network nominate or refer the participants that meet the eligibility criteria of the research under study. Thus, this method is also called as the referral sampling method or chain sampling method.

The snowball sampling method is extensively used in the situations when the population is unknown and rare, and it is hard to select the subjects therefrom. First-of-all the initial informants (acquaintances) are contacted who further give the reference of other people whom they think will fit best for the research study, and then they are contacted to get the insights and knowledge about the research being conducted.

For example, the group of people suffering from AIDS is limited and often reluctant to disclose their disease. And in such case, if the interviewer wants to know how the life of these people have changed due to AIDS, might approach those acquaintances who can refer those individuals who can potentially contribute to the study.

In snowball sampling, the initial subject gives a link to other subject and likewise the chain of respondents gets created. Hence, the success of this method depends purely on the initial subject which gives further references.

Multistage Sampling

Definition:The Multistage Sampling is the probability sampling technique wherein the sampling is carried out in several stages such that the sample size gets reduced at each stage.

The multistage sampling is a complex form of cluster sampling. The cluster sampling is yet another random sampling technique wherein the population is divided into subgroups called as clusters; then few clusters are chosen randomly for the survey.

While in the multistage sampling technique, the first level is similar to that of the cluster sampling, where the clusters are formed out of the population, but further, these clusters are sub-divided into smaller targeting groups, i.e. sub-clusters and then the subject from each sub-clusters are chosen randomly. Further, the stages can be added depending on the nature of research and the size of the population under study.


CODING The process of identifying and classifying each answer with a numerical score or other character symbol. The numerical score or symbol is called a code, and serves as a rule for interpreting, classifying, and recording data.  Identifying responses with codes is necessary if data is to be processed by computer.


Que; Which magazines do you read?

  1. Hindustan Times
  2. Economic times
  3. The Hindu
  4. The times of India


Tabulation is the process of summarizing raw data and displaying the same in compact form (i.e., in the form of statistical table) for further analysis When mass data has been assembled, it becomes necessary for the researcher to arrange the same in some kind of concise logical order, which may be called tabulation.

Advantages of Tabulation:

  1. It simplifies complex data. 2. It facilitates comparison. 3. It facilitates computation. 4. It presents facts in minimum possible space. 5. Tabulated data are good for references and they make it easier to present the information in the form of graphs and diagrams.

Precautions while interpreting data

The following points must be in mind while interpreting observations:
One should always remember that even if the data are properly collected and analysed, wrong interpretation would lead to inaccurate conclusions. It is, therefore, absolutely essential that the task of interpretation be accomplished with patience in an impartial manner and also in correct perspective.
• The researcher must remember that “ideally in the course of a research study, there should be constant interaction between initial hypothesis, empirical observation and theoretical conceptions. It is exactly in this area of interaction between theoretical orientation and empirical observation that opportunities for originality and creativity lie.” He must pay special attention to this aspect while engaged in the task of interpretation.
At the outset, researcher must invariably satisfy himself that the data are appropriate, trustworthy and adequate for drawing inferences, the data reflect good homogeneity; and that proper analysis has been done through statistical method

Advantages and disadvantages of Statistical Diagrams


What is statistical inference ?

Statistical inferenceis the process of deducing properties of an underlying distributionby analysis of data.[1]Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. The population is assumed to be larger than the observed data set; in other words, the observed data is assumed to be sampledfrom a larger population.

Statistical inference aims at learning characteristics of the population from a sample; the population characteristics are parameters and sample characteristics are statistics.

statistical model is a representation of a complex phenomena that generated the data.

  • It has mathematical formulations that describe relationships between random variables and parameters.
  • It makes assumptions about the random variables, and sometimes parameters.
  • A general form: data = model + residuals
  • Model should explain most of the variation in the data
  • Residuals are a representation of a lack-of-fit, that is of the portion of the data unexplained by the model.

Importance of Hypothesis

1. To the Point Enquiry

Hypothesis makes a research activity to the point and destination, Research without hypothesis is like a sailor in the sea without compass. So, research is to the point

enquiry of problem due to the guidance of hypothesis.

2. Development of Research Techniques

There are various types of social problems which are complex in nature. For this research is very difficult. We cannot cover it with a single technique but it requires many

techniques. These techniques are due to hypothesis provided to a researcher.

3. Separating Relevant From Irrelevant Observation

A Researcher during study will take the observations and facts which are accordance to the condition and situation. While drop out the irrelevant facts from his study. This

separation is due to hypothesis formulation which keeps away relevant observation from irrelevant.

4. Selecting Required Facts

During study a researcher come across many factors but he confined himself to the selection of required facts through formulation of hypothesis. Hypothesis helps him in

selection of relevant facts regarding to the problematic situation.

5. Direction of Research

Hypothesis acts as a guide master in research. It gives new knowledge and direction to a researcher. It directs a scientist to know about the problematic situation and its


6. Acts as a Guide

Hypothesis gives new ways and direction to a researcher. It acts as a guide and a leader in various organizations or society. It is like the investigator’s eye.

7. Prevents Blind Research

Hypothesis provides lighting to the darkness of research. It gives difference b/w scientific and unscientific, false and true research. It prevents blind research and give


8. Accuracy & Precision

Hypothesis provides accuracy and precision to a research activity. Accuracy and precision is the feature of scientific investigation which is possible due to hypothesis.

9. Link between Theory & Investigation

Theory is a source of hypothesis which leads to its formulation. Hypothesis leads to scientific investigation. So, hypothesis acts as a bridge b/w theory and investigation.

10. Link between Assumption & Observation

During formulation hypothesis is in the stage of assumption. In the field it transformed into hypothesis in working form. This transformation is due to observation in the field.

So, it creates a link between assumption & observation.

11. Provide answer for a Question

A hypothesis highlights the causes of a problematic situation. Further solution is also given by a hypothesis which provides answer to a question.

12. Save Time, Money & Energy

Hypothesis save time, money and energy of a researcher because it is a guide for him and help him in saving these basic things.

13. Proper Data Collection

Hypothesis provides the basis of proper Data Collection Relevant and correct information collected by a researcher is the main function of a good formulated hypothesis.

14. Proper Conclusion

A proper formulated hypothesis may lead to a good reasonable, utilized and proper conclusion. If the hypothesis is better than the conclusions drawn by a researcher would be

better for solution of a problem.

Types of test in Hypothesis testing

One-Tailed Test

A test of a statistical hypothesis, where the region of rejection is on only one side of the sampling distribution, is called a one-tailed test. For example, suppose the null hypothesis states that the mean is less than or equal to 10. The alternative hypothesis would be that the mean is greater than 10. The region of rejection would consist of a range of numbers located on the right side of sampling distribution; that is, a set of numbers greater than 10.

Two-Tailed Tests

A test of a statistical hypothesis, where the region of rejection is on both sides of the sampling distribution, is called a two-tailed test. For example, suppose the null hypothesis states that the mean is equal to 10. The alternative hypothesis would be that the mean is less than 10 or greater than 10. The region of rejection would consist of a range of numbers located on both sides of sampling distribution; that is, the region of rejection would consist partly of numbers that were less than 10 and partly of numbers that were greater than 10.

Test of Significance

Test of significance is a formal procedure for comparing observed data with a claim (also called a hypothesis) whose truth we want to assess. Test of significance is used to test a claim about an unknown population parameter. A significance test uses data to evaluate a hypothesis by comparing sample point estimates of parameters to values predicted by the hypothesis. We answer a question such as, “If the hypothesis were true, would it be unlikely to get data such as we obtained?

Hypothesis Testing Procedure

Step 1: Identifying the null hypothesis and alternative hypothesis to be tested.

Step 2: Identifying the test criterion to be used

Step 3: Calculating the test criterion based on the values obtained from the sample

Step 4: Finding the critical value with required level of significance and degrees of freedom

Step 5: Concluding whether to accept or reject the null hypothesis.

Parametric Test and Nonparametric Test

The parametric test is the hypothesis test which provides generalisations for making statements about the mean of the parent population. A t-test based on Student’s t-statistic, which is often used in this regard. The t-statistic rests on the underlying assumption that there is the normal distribution of variable and the mean in known or assumed to be known. The population variance is calculated for the sample. It is assumed that the variables of interest, in the population are measured on an interval scale.

The nonparametric test is defined as the hypothesis test which is not based on underlying assumptions, i.e. it does not require population’s distribution to be denoted by specific parameters. The test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. The test assumes that the variables are measured on a nominal or ordinal level. It is used when the independent variables are non-metric.



For managerial decision making, sometimes one has to carry out tests of significance. The analysis of variance is an effective tool for this purpose. The objective of the analysis of variance is to test the homogeneity of the means of different samples.


According to R.A. Fisher, “analysis of variance is the separation of variance ascribable to one group of causes from the variance ascribable to other groups”.

Assumptions of ANOVA

The technique of ANOVA is mainly used for the analysis and interpretation of data obtained from experiments. This technique is based on three important assumptions, namely

1.  The parent population is normal.

2.  The error component is distributed normally with zero mean and constant variance.

3.  The various effects are additive in nature.

The technique of ANOVA essentially consists of partitioning the total variation in an experiment into components of different sources of variation. These sources of variations are due to controlled factors and uncontrolled factors. Since the variation in the sample data is characterized by means of many components of variation, it can be symbolically represented in the mathematical form called a linear model for the sample data.

Classification of models of ANOVA

Linear models for the sample data may broadly be classified into three types as follows:

In any variance components model, the error component has always random effects, since it occurs purely in a random manner. All other components may be either mixed or random.

1. Random effect model

A model in which each of the factors has random effect (including error effect) is called a random effect model or simply a random model.

2. Fixed effect model

A model in which each of the factors has fixed effects, buy only the error effect is random is called a fixed effect model or simply a fixed model.

3. Mixed effect model

A model in which some of the factors have fixed effects and some others have random effects is called a mixed effect model or simply a mixed model.

The ANOVA technique is mainly based on the linear model which depends on the types of data used in the linear model. There are several types of data in ANOVA, depending on the number of sources of variation namely,

1. One-way

One fixed factor (levels set by investigator) which can have either an unequal (unbalanced) or equal (balanced) number of observations per treatment.

2. Balanced

Model may contain any number of fixed and random factors (levels are randomly selected), and crossed and nested factors, but requires a balanced design.

3. General linear model

Expands on Balanced ANOVAs by allowing unbalanced designs and covariates (continuous variables).

Randomized Block Design

With a randomized block design, the experimenter divides subjects into subgroups called blocks, such that the variability within blocks is less than the variability between blocks. Then, subjects within each block are randomly assigned to treatment conditions. Compared to a completely randomized design, this design reduces variability within treatment conditions and potential confounding, producing a better estimate of treatment effects.

Correlation Coefficient

A correlation coefficient is a statistical measure of the degree to which changes to the value of one variable predict change to the value of another. In positively correlated variables, the value increases or decreases in tandem. In negatively correlated variables, the value of one increases as the value of the other decreases.

Correlation coefficients are expressed as values between +1 and -1. A coefficient of +1 indicates a perfect positive correlation: A change in the value of one variable will predict a change in the same direction in the second variable. A coefficient of -1 indicates a perfect negative correlation: A change in the value of one variable predicts a change in the opposite direction in the second variable. Lesser degrees of correlation are expressed as non-zero decimals. A coefficient of zero indicates there is no discernable relationship between fluctuations of the variables.

The Spearman’s Rank Correlation Coefficient is the non-parametric statistical measure used to study the strength of association between the two ranked variables. This method is applied to the ordinal set of numbers, which can be arranged in order, i.e. one after the other so that ranks can be given to each.

In the rank correlation coefficient method, the ranks are given to each individual on the basis of its quality or quantity, such as ranking starts from position 1st and goes till Nth position for the one ranked last in the group.

Rank Correlation-I-final-1

Where, R = Rank coefficient of correlation

D = Difference of ranks

N = Number of Observations

The value of R lies between ±1 such as:

R =+1, there is a complete agreement in the order of ranks and move in the same direction.

R=-1, there is a complete agreement in the order of ranks, but are in opposite directions.

R =0, there is no association in the ranks.

Cluster analysis

Cluster analysis finds groups of similar respondents, where respondents are considered to be similar if there are relatively small differences between their average ratings. As an example, look at the plot below. It is a scatterplot showing data for 18 respondents on two numeric variables. You can hopefully see that the dots fall into two groups. If you used cluster analysis to analyze this data, provided you didn’t do something wrong, it would also identify the same two groups that you can see. These groups are then called clusters

Cluster analysis  is a Statistical classification technique in which cases, data, or objects (events, people, things, etc.) are sub-divided into groups (clusters) such that the items in a cluster are very similar (but not identical) to one another and very different from the items in other clusters. It is a discovery tool that reveals associations, patterns, relationships, and structures in masses of data.


A report is a written document on a particular topic, which conveys information and ideas and may also make recommendations. Reports often form the basis of crucial decision making. Inaccurate, incomplete and poorly written reports fail to achieve their purpose and reflect on the decision, which will ultimately be made. This will also be the case if the report is excessively long, jargonistic and/ or structureless. A good report can be written by keeping the following features in mind:

1.  All points in the report should be clear to the intended reader.

2.The report should be concise with information kept to a necessary minimum and arranged      logically under various headings and sub-headings.

3.  All information should be correct and supported by evidence.

4.  All relevant material should be included in a complete report.

Contents of  Research Report

The researcher must keep in mind that his research report must contain following aspects:

  1. Purpose of study
  2. Significance of his study or statement of the problem
  3. Review of literature
  4. Methodology
  5. Interpretation of data
  6. Conclusions and suggestions
  7. Bibliography
  8. Appendices

Layout of research report

There is scientific method for the layout of research report. The layout of research report means as to what the research report should contain. The contents of the research report are noted below:

  1. Preliminary Page
  2. Main Text
  3. End Matter

(1) Preliminary Pages:

These must be title of the research topic and data. There must be preface of foreword to the research work. It should be followed by table of contents. The list of tables, maps should be given.

(2) Main Text:

It provides the complete outline of research report along with all details. The title page is reported in the main text. Details of text are given continuously as divided in different chapters.

  • (a)    Introduction
  • (b)   Statement of the problem
  • (c)   The analysis of data
  • (d)   The implications drawn from the results
  • (e)   The summary

(a)    Introduction:

Its purpose is to introduce the research topic to readers. It must cover statement of the research problem, hypotheses, objectives of study, review of literature, and the methodology to cover primary and secondary data, limitations of study and chapter scheme. Some may give in brief in the first chapter the introduction of the research project highlighting the importance of study. This is followed by research methodology in separate chapter.

The methodology should point out the method of study, the research design and method of data collection.

(b)   Statement of the problem:

This is crux of his research. It highlights main theme of his study. It must be in nontechnical language. It should be in simple manner so ordinary reader may follow it. The social research must be made available to common man. The research in agricultural problems must be easy for farmers to read it.

(c)    Analysis of data:

Data so collected should be presented in systematic manner and with its help, conclusions can be drawn. This helps to test the hypothesis. Data analysis must be made to confirm the objectives of the study.

(d)   Implications of Data:

The results based on the analysis of data must be valid. This is the main body of research. It contains statistical summaries and analysis of data. There should be logical sequence in the analysis of data. The primary data may lead to establish the results. He must have separate chapter on conclusions and recommendations. The conclusions must be based on data analysis. The conclusions must be such which may lead to generalization and its applicability in similar circumstances. The conditions of research work limiting its scope for generalization must be made clear by the researcher.

(e)    Summary:

This is conclusive part of study. It makes the reader to understand by reading summary the knowledge of the research work. This is also a synopsis of study.

(3) End Matter:

It covers relevant appendices covering general information, the concepts and bibliography. The index may also be added to the report.

Two types of report formats are described below:

1. A Technical Report

A technical report mainly focuses on methods employed, assumptions made while conducting a study, detailed presentation of findings and drawing inferences and comparisons with earlier findings based on the type of data drawn from the empirical work.

An outline of a Technical Report mostly consists of the following:

Title and nature of the study:

Brief title and the nature of work sometimes followed by subtitle indicate more appropriately either the method or tools used. Description of objectives of the study, research design, operational terms, working hypothesis, type of analysis and data required should be present.

Abstract of Findings:

A brief review of the main findings just can be made either in a paragraph or in one/two pages.

Review of current status:

A quick review of past observations and contradictions reported, applications observed and reported are reviewed based on the in-house resources or based on published observations.

Sampling and Methods employed

Specific methods used in the study and their limitations. In the case of experimental methods, the nature of subjects and control conditions are to be specified. In the case of sample studies, details of the sample design i.e., sample size, sample selection etc are given.

Data sources and experiment conducted

Sources of data, their characteristics and limitations should be specified. In the case of primary survey, the manner in which data has been collected should be described.

Analysis of data and tools used.

The analysis of data and presentation of findings of the study with supporting data in the form of tables and charts are to be narrated. This constitutes the major component of the research report.

Summary of findings

A detailed summary of findings of the study and major observations should be stated. Decision inputs if any, policy implications from the observations should be specified.


A brief list of studies conducted on similar lines, either preceding the present study or conducted under different experimental conditions is listed.

Technical appendices

These appendices include the design of experiments or questionnaires used in conducting the study, mathematical derivations, elaboration on particular techniques of analysis etc.

2. General Reports

General reports often relate popular policy issues mostly related to social issues. These reports are generally simple, less technical, good use of tables and charts. Most often they reflect the journalistic style. Example for this type of report is the “Best B-Schools Survey in Business Magazines”. The outline of these reports is as follows:

1.  Major Findings and their Implications

2.  Recommendations for Action

3.  Objectives of the Study

4.  Method Employed for Collecting Data

5.  Results

Writing Styles

There are atleast 3 distinct report writing styles that can be applied by students of Business Studies. They are called:

1.  Conservative

2.  Key points

3.  Holistic

1. Conservative Style

Essentially, the conservative approach takes the best structural elements from essay writing and integrates these with appropriate report writing tools. Thus, headings are used to deliberate upon different sections of the answer. In addition, the space is well utilized by ensuring that each paragraph is distinct (perhaps separated from other paragraphs by leaving two blank lines in between).

2. Key Point Style

This style utilizes all of the report writing tools and is thus more overtly ‘report-looking’. Use of headings, underlining, margins, diagrams and tables are common. Occasionally reporting might even use indentation and dot points. The important thing to remember is that the tools should be applied in a way that adds to the report. The question must be addressed and the tools applied should assist in doing that. An advantage of this style is the enormous amount of information that can be delivered relatively quickly.

3. Holistic Style

The most complex and unusual of the styles, holistic report writing aims to answer the question from a thematic and integrative perspective. This style of report writing requires the researcher to have a strong understanding of the course and is able to see which outcomes are being targeted by the question.

Essentials Of A Good Report:

Good research report should satisfy some of the following basic characteristics:


Reports should be easy to read and understand. The style of the writer should ensure that sentences are succinct and the language used is simple, to the point and avoiding excessive jargon.


A good layout enables the reader to follow the report’s intentions, and aids the communication process. Sections and paragraphs should be given headings and sub¬-headings. You may also consider a system of numbering or lettering to identify the relative importance of paragraphs and sub-paragraphs. Bullet points are an option for highlighting important points in your report.


Make sure everything you write is factually accurate. If you would mislead or misinform, you will be doing a disservice not only to yourself but also to the readers, and your credibility will be destroyed. Remember to refer to any information you have used to support your work.


Take a break from writing. When you would come back to it, you’ll have the degree of objectivity that you need. Use simple language to express your point of view.


Experts agree that the factors, which affect readability the most, are:

  • Attractive appearance
  • Non-technical subject matter
  • Clear and direct style
  • Short sentences
  • Short and familiar words


When first draft of the report is completed, it should be put to one side at least for 24 hours. The report should then be read as if with eyes of the intended reader. It should be checked for spelling and grammatical errors. Remember the spell and grammar check on your computer. Use it!


Reinforcement usually gets the message across. This old adage is well known and is used to good effect in all sorts of circumstances e.g., presentations – not just report writing.


Bivariate and multivariate analyses are statistical methods that help you investigate relationships between data samples. Bivariate analysis looks at two paired data sets, studying whether a relationship exists between them. Multivariate analysis uses two or more variables and analyzes which, if any, are correlated with a specific outcome. The goal in the latter case is to determine which variables influence or cause the outcome.

Bivariate analysis investigates the relationship between two paired data sets. The two data sets are paired because a pair of observations are taken from a single sample or individual, but each sample is independent. The data is analyzed, using tools such as t-tests and chi-squared tests, to see if the two groups of data correlate with each other and, if the variables are quantitative, they are usually graphed on a scatterplot. Bivariate analysis also examines the strength of any correlation.

One example of bivariate analysis is a research team recording the age of both husband and wife in a single marriage. This data is paired because both ages come from the same marriage, but independent because one person’s age doesn’t cause another person’s age. The data is plotted, showing a correlation in the data: the older husbands have older wives. A second example is recording measurements of grip strength and arm strength from individuals. The data is paired because both measurements come from a single person, but independent because different muscles are used. Data is plotted from many individuals, showing a correlation: people with higher grip strength have higher arm strength.

Multivariate analysis : analyzes several variables to see if one or more of them are predictive of a certain outcome. The predictive variables are considered independent variables, and the outcome is the dependent variable. The variables can be either continuous, meaning they can have a range of values, or they can be dichotomous, meaning they represent the answer to a yes or no question. Multiple regression analysis is the most common method used in multivariate analysis to find correlations between data sets, but many others, such as logistic regression and multivariate analysis of variance, are also used.

Multivariate analysis example: It was used in by researchers in a 2009 Journal of Pediatrics study to investigate whether negative life events, family environment, family violence, media violence and depression are predictors of youth aggression and bullying. Negative life events, family environment, family violence, media violence and depression were the independent predictor variables. Aggression and bullying were the dependent outcome variables. Over 600 subjects, with an average age of 12 years old, were given questionnaires that determined the predictor variables for each child. A survey was also given that determined the outcome variables for each child. Multiple regression equations and structural equation modeling was used to study the data set. Negative life events and depression were found to be the strongest predictors of youth aggression.

The law of statistical regularity

lays down that a large number of items taken at random from each group are almost sure on an average to possess characteristics of each group”.

 The above two quotations emphasize two points, namely :

          (i) That the sample size should be large. As the size of the sample increases it becomes more and more representative of the universe and exhibits its characteristics. In fact the larger the sample the better would be the result. However, in actual practice very large samples create their own problems, and are more expensive. Thus a balance has to be stuck between the sample size, the degree of accuracy desired and the financial and other resources available.

          (ii) The second point emphasized is that the sample on the basis of which inference is drawn about the universe must be selected at random. A random selection is one in which each and every unit of the population has the same chance of being included in the sample.

 If a sample has been selected taking into account the above two conditions, it can be expected that the inference drawn from the sample study would be by and large applicable to the universe as a whole.

What are type I and type II errors?

When you do a hypothesis test, two types of errors are possible: type I and type II. The risks of these two errors are inversely related and determined by the level of significance and the power for the test. Therefore, you should determine which error has more severe consequences for your situation before you define their risks.

No hypothesis test is 100% certain. Because the test is based on probabilities, there is always a chance of drawing an incorrect conclusion.

Type I error

When the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for α. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists.

Type II error

When the null hypothesis is false and you fail to reject it, you make a type II error. The probability of making a type II error is β, which depends on the power of the test. You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists.

The probability of rejecting the null hypothesis when it is false is equal to 1–β. This value is the power of the test.