Research Methodology

22MBA23

As per 2022vtu MB A Syllabus

Introduction to Research Methodology

Module –1

TOPICS TO BE COVERED

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• Business Research: Meaning.
• Types of Research
• Process of research
• Management problem
• Defining the research problem
• Formulating the research Hypothesis
• Developing the research proposals
• Research design formulation
• Sampling design
• Planning and collecting the data for research
• Data analysis and interpretation.
• Research Application in business decisions
• Features of good research study.

INVENTION: ICE CREAM CONE

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• INVENTOR: Charles E. Menches, U.S., and an anonymous lady friend; Abe Doumar, U.S.
• YEAR: 1904
• HOW INVENTED: Menches was an ice cream salesman at the Louisiana Purchase Exposition, the great fair in St. Louis, Mo.
• Whenever Menches visited a certain lady friend, he brought a bouquet of flowers. On one occasion, for a super date, he brought flowers and an ice cream sandwich. Because his lady friend lacked a vase for the flowers, she took one of the sandwich layers and curled it into the form of a vase. Then she rolled the other layer to contain the ice cream itself--and the ice cream cone was born.

Research problem

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I observed families riding on two-wheelers — the father driving the scooter, his young kid standing in front of him, his wife seated behind him holding a little baby. It led me to wonder whether one could conceive of a safe, affordable, all-weather form of transport for such a family," Tata said.

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In the end What is the outcome?

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• Four years efforts to realise this goal.
• Today, we indeed have a people's car, which is affordable , safe, fuel efficient
• We hope it brings the joy, pride and utility of owning a car to many families who need personal mobility
• ." This small car — Nano is 20 per cent shorter in length than the Maruti 800 but Tata claims it has 21 per cent more space

— is powered by a 623 cc rear-mounted engine and will travel 20 km per litre.

• The car will cost Rs 1 lakh at the dealer-end but attract Value- Added Tax and transportation cost.
• Nano will also come in two deluxe models with air conditioning.

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Few World’s largest research companies

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• AC Nielson Corporation- Provides MR to Consumer products
• IMS Health- Pharmaceutical & Health care industries
• Taylor Nelson Sofres ( TNS)- Customer research, Omnibus studies, - 14 offices in India
• On line Research- Cross tab

Meaning

• Research is an art of scientific investigation. It is also a systematic design, collection, analysis and reporting the findings and solutions for the marketing problems of a company.

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• A scientific undertaking which by means of logical and systematic techniques, aims to:
• Discover new facts or verify and test old facts.
• Analyze their sequences, interrelationships and causal explanations.
• Develop new scientific tools, concepts and theories, which would facilitate valid and reliable study of human behavior.

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Requirement of Research

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Requirement of Research

To find alternate strategies

To identify and find solutions to the problems

To develop new concepts

To help making decisions

Requirement of Research

• Research is required for the following reasons :
• To identify and find solutions to the problems.
• To help making decisions.
• To find alternate strategies.
• To develop new concepts

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Types of Research

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Types of Research

Exploratory Research

Action Research

Historical Research

Causal Research

Conceptual Research

Pure/ Fundamental or Basic Research

Applied Research

Descriptive Research

Types of Research

Exploratory Research

• Provides insights into and comprehension of an issue or situation.
• Often relies on secondary research such as reviewing available literature and/or data, or qualitative approaches such as informal discussions with consumers, employees, management or competitors and more formal approaches through in-depth interviews, focus groups, case studies or pilot studies.
• Carried out at the very beginning when the problem is not clear or is vague.

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• Example : Exploring the reasons for sales decline of a product in a company.

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Descriptive Research

• Describes data and characteristics about the population or phenomenon being studied.
• Answers the questions who, what, where, when and how.
• Describes the characteristics of the respondent in relation to a particular product. Deals with demographic characteristics of the consumer.
• Fact finding investigation
• Ex-post-facto: Reporting only what happened
• Purpose: Describe state of affairs as it exists

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• Example : Degree of viewing TV Channels, its variation with age, income level, profession of respondent as well as time of viewing. Customer preference, Satisfaction and Perception surveys

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Applied Research

• Undertaken to find solutions for real-life problem requiring an action or policy decision (For society, business, industry)
• Deals with practical problems in the current scenario
• It may incidentally contribute to the development of theoretical knowledge.
• Contributions:
• Can contribute new facts about existing theories.
• Puts theories to test.
• May aid in conceptual clarification.
• Integrate previously existing theories.
• W.R.T to scientific theories

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• Example : To develop a new market for the product.

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Pure/Fundamental or Basic Research

• Undertaken for the sake of knowledge without any intention to apply or practice.
• Not directly involved with practical problems.
• Does not have any commercial potential.
• Lays foundation for applied Research

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• Contributions:
• By developing principle, pure research offers solutions to many practical problems.
• Helps to find critical factors in a practical problem.
• Develops many alternative solutions.

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• Example : Theory of Relativity (by Einstein)

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Conceptual Research

• Generally used by philosophers.
• Related to some abstract idea or theory.
• Researcher should collect the data to prove or disapprove his hypothesis.

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• Example : Various ideologies
• Hoffsted theory of culture in International HRM
• Theory X and Y
• Law of Motion, Gravitation, Energy

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Causal Research

• Conducted to determine the cause and effect relationship between the two variables.
• Way of seeing how actions now will affect a business in the future.

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• Example: A company selling health drink powder want to introduce new flavor to the market. causal research can measure the impact of the company changing the product flavor. The company bosses will be able to decide whether changing the flavor would be profitable.

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Historical Research

• Historical study is a study of past records and data in order to understand the future trends and development of the organization or market.
• There is no direct observation.
• Research has to depend on the conclusions or inferences drawn in the past.
• Main objective is to derive explanation and generalization from the past trends in order to understand the present and anticipate the future.

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• Example : Investors in the share market study the past records or prices of shares which he/she intends to buy. Studying the share prices of a particular company enables the investor to take decision whether to invest in the shares of a company.

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Shortcomings of Historical Research

• Reliability and adequacy information is subjective and open to question.
• Accuracy of measurement of events is doubtful.
• Verification of records are difficult.

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Action Research

• Research is undertaken by direct action.
• Action Research is conducted to solve a problem.
• Example : Test Marketing a product is an example of action research. Initially, the geographical location is identified. A target sample is selected from among the population. Samples are distributed to selected samples and feedback is obtained from the respondent.

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Objectives of Research

• To gain familiarity with a phenomenon or to achieve new insight into it (exploratory, save soil, global warming, OTT industry)
• To portray accurately the characteristics of an individual/group/situation (Descriptive, Gen Y Preference on social sites)
• To determine the frequency with which something occurs or aassociated with something else( Diagnostic, cause and effect; Price and demand etc)

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Contnd….

• To test the Facts (Clinical trails in R&D during New Drug Development, Present Covid Vaccinations testing, Brand Perception Surveys)
• To test hypothesis of a causal relationship between variables (training and productivity or technology and productivity, raise in income and shopping habits of gold)
• To discover New Facts or theory or an Molecule (Penicillin viz., Pure or Fundamental Research)

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Contd…

• To develop new scientific tool or to develop new concept (internet lead to e-commerce which lead to ……. Many new business concepts)
• To find solution to scientific, non scientific or social problem (Plastic, Waste Management, sanitary issues etc., )
• To overcome problems occurring on daily basis. (Charger in umbrella, tablet sachet groves on a water bottle)

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Research Process

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Define and Formulate the Problem

Develop a Working Hypothesis

Research Proposal

Data Collection

Data Analysis

Hypothesis Testing

Data Interpretation & Generalisations

Reporting of Results

RESEARCH DESIGN

• Sampling Design
• Data Collection Design
• Instrument Design

Stop

Rejected

Draw Sample

Pilot Testing

Instrument Revision

• Formulating the Problem – Problem formulation is the key to research process. For a researcher, the problem formulation means converting the management problem to a research problem.

Example : Management Problem: Want to increase the sale of product A.

Research Problem: What is the current standing of the product A?

There is a famous saying that “ problem well-defined is half-solved”. This statement is strikingly true in marketing research, because if the problem is not stated properly, the objectives will not be clear. If the objective is not clearly defined, the data collection becomes meaningless.

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Management Problem Vs Research Problem

• Management Decision Problem – The problem confronting the decision maker. It asks what the decision maker needs to do.
• Marketing Research Problem – A problem that entails determining what information is needed and how it can be obtained in the most feasible way.

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Differences between Management Decision Problem and Marketing Research Problem

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MANAGEMENT DECISION PROBLEM

MARKETING RESEARCH PROBLEM

Asks what the decision maker needs to do

Asks what information is needed and how it should be obtained

Action oriented

Information oriented

Focuses on symptoms

Focuses on underlying causes

Examples of Distinction between Management Decision problem and Marketing Research Problem

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Management Decision Problem

Marketing Research Problem

Should a new product be introduced ?

To determine consumer preferences and purchase intentions for the proposed new product?

Should the advertising campaign be changed?

To determine the effectiveness of the current advertising campaign?

Should the price of the brand be increased?

To determine the price elasticity of demand and the impact on sales and profits of various levels of price changes.

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Develop the package for a new product?

Evaluate the effectiveness of alternative package design.

To select a media for product advertising.

We should conduct an investigation to determine suitable media. Evaluate the impact of the media in terms of reach.

Increase the amount of repurchase behavior of the customer.

Assess current amount of repeat purchase behavior.

Introduce new product.

Design a test market through which the likely acceptance of new product can be gauged.

• Developing a Working Hypothesis –A hypothesis is a tentative assumption regarding the solution to the problem under study. The kind of data to be collected, the tools of analysis are influenced by the hypothesis. The hypothesis is a predictive statement which is made in the light of the available facts relating to the problem under study. E.g. The following hypotheses were formulated in relation to the research question on store loyalty:
• H1 : Customers who are store loyal are less knowledgeable about the shopping environment.
• H2 : Store loyal customers are more risk averse than are non – loyal customers.

These hypotheses guided the research by ensuring that variables measuring knowledge of the shopping environment and propensity to take risks were included in the research design.

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• Research Proposal – A research proposal is a brief summary outlining the research objectives, the proposed methodology of research, benefits of study along with a detailed bibliography. The research proposal is like a road map to the researcher showing where he has to start and where to go and how to get there.

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Sample – Research Proposal

A leading private sector bank has recently issued a platinum card to its card holders, who have membership with the bank for more than 15 years and have sound credit history. In addition to the facilities available on the existing credit card, platinum card provides additional facilities such as free health check up, free membership to golf club, cash back of 20% of any purchase, 20% discount in any 5 star hotel and host of other facilities.

Now company wants to understand the customer behavior and usage pattern of this card. M/S XYZ is appointed as a research agent to conduct the research. M/S XYZ submits the research proposal.

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Research Proposal

A study on customer usage pattern of platinum card

1. Purpose: It is proposed to collect information regarding attitude, behavior and other demographic information of platinum card holders. Management of the bank has a host of questions regarding the popularity of the card and usage pattern. Towards this management wants answers to the following questions:
1. Is the card being used by the existing card holder to whom it has been issued?
2. How do the card holders view the facilities provided under this card?
3. How often the card holders use this card?
4. For what privileges the card has been used?
5. What other benefits do the card holders expect from this card?

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2. Type of study: The study to be done will be in two stages:

1. Exploratory
2. Descriptive

It will be exploratory with respect to

1. Shortcomings of the card
2. Annual fee to be charged
3. Privileges provided under this card

It will be descriptive with respect to

1. Usage pattern
2. Awareness of privileges
3. How often it is used

3. Target population: Current platinum card holders of the bank.

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4. Sample size: To be chosen based on the level of accuracy required.

5. Sample design: Probability sampling will be adopted for the study. Respondents will be drawn from among the platinum card holders, based on random sampling technique.

6. Data collection: A structured, non-disguised questionnaire to be administered by skilled field force. A gift is proposed to encourage participation by the respondent.

7. Research instrument/techniques: Scales will be used to measure attitude and behavioral components of respondents. Suitable statistical techniques to be used to analyze the data.

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8. Benefits:

1. Profile of platinum card holders will be known.
2. Knowing the profile, customers can be segmented and appropriate promotion strategy can be developed for each segment.
3. Usage pattern can be tracked.
4. Any new privileges to be introduced will be known.

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9. Cost estimation of the project:

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10. Profile of the research firm: Research will be conducted by M/S XYZ. The company is located in Mumbai. The company has experience in conducting MR studies for many multinational and public sector

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enterprises. Mr. X the chief of marketing research coordinator is an engineer from IIT with PG from IIM-C. He has over 20 years of experience and has conducted several studies in various sectors like retailing, banking, insurance, consumer goods.

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• Research Design –
• Should the research be exploratory or conclusive?

Exploratory Research: Example, “ Causes for the decline in sales of a specific company’s product in a specific territory under a specific salesman”. The researcher may explore possible reasons as to why sales are falling.

• Faulty product planning
• Higher price
• Less discount
• Less availability
• Inefficient advertising/salesmanship
• Poor quality of salesmanship
• Less awareness

Not all the factors are responsible for decline in sales.

Conclusive Research: Narrow down the option. Only one or two factors are responsible for decline in sales.

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Sampling Design – Include a decision on the sampling unit, the sample size and the sampling method.

Sampling unit is the most elementary unit which would be a part of the study e.g. in a survey on newspaper readership pattern, a single household comprising of all the members of the household can be regarded as a sampling unit.

The size of the sample depends on factors like the availability of time and funds to the researcher, the ability of the researcher, the size of the population and the nature of the population.

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Method of drawing a sample – Probability method of sampling involves giving every member a known and unbiased chance of being a part of the sample.

In non-probability method, an element of bias is involved. The probability of a unit being a part of the sample is not known.

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Data collection Design – The method of gathering the data is planned in data collection design. Few data collection techniques are Questionnaire, Interview, Survey and Observation.

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Instrument Design – The researcher while designing the questionnaire must think in terms of :

Type of data

Communication Approach

Question Structure

Question Wording

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• Data Collection – The search for answers to research questions is called data collection. The sources of data may be classified as a) primary data b) secondary data.

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• Data Analysis –

Editing – The data collected should be scanned to make sure that it is complete and that all the instructions are followed. This process is called editing.

Coding – Coding means assigning numbers to each of the answers, so that they can be tabulated and analyzed easily.

Tabulation – The process of orderly arrangement of data in a tabular form.

Statistical Analysis – In the last stage, the tabulated data is analyzed using various statistical techniques like averages, percentages, trend analysis, co-relation and regression techniques etc.

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• Hypothesis Testing – Hypothesis testing will help a researcher in establishing the validity of his results. It would help him to state clearly that whether the difference, if any, between the two values is due to chance and hence can be attributed as insignificant or if the difference is actual and significant.

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• Data Interpretation and Generalisation- For studies in which no hypothesis has been tested, data interpretation is done with the intention of seeking explanation for the research results on the basis of existing theories. In case of hypothesis testing research studies, after the data has been analysed and tested repeatedly for arriving at conclusive results, generalizations are made to build a new theory.

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• Reporting of Results – The last step is concerned with bringing in public the results of the research so that the findings can be put to some use.

iii.

1. The preliminary section containing the title of the report, table of contents, list of tables, graphs, preface and an executive summary.
2. A main text section which contains the problem background, research objectives, a note on research methodology used, the importance of conducting the research and the conclusion arrived thereof.

The end section which includes appendices

supporting the research with items like questionnaires and schedules used, glossary of terms and any other matter which although not a part of the main research but required in order to support the research can be included.

Research Application in business decisions

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• Businesses conduct research for many reasons, such as gathering crucial information about consumers and business customers.
• The key function of management is take decisions and without help of the research and analysis of present situation and future forecasting, decisions may not be effective. So research helps to take right decisions.
• Based on research, management can make intelligent and well informed decisions.
• Businesses use research to ascertain the success of their advertising. For instance, a dairy manufacturer may want to find out what percentage of the people saw its latest TV commercial.
• A business can make well-informed decisions due to research. In the research process, the business will be able to acquire details about key business areas, analyze it, create a strategy and distribute business information. Reports, presented to the top management, often contain details on consumer and employee preferences and all the available channels for sales, marketing, finance and production. Management makes use of these details to determine the best strategy.

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Contd…

• Regarding the staff, a correctly carried out research can uncover important details on their satisfaction quotient, the difficulties experienced by them and how the Dept of MBA, SJBIT Page 21
• problems related to relationships at the workplace could be handled. An analysis into the results would allow the management to bring about changes for the all round effective functioning of the organization and its employees.
• The workers can be trained and coached in line with the needs. This would help personal as well as professional development improving overall organizational performance.
• Research is important for managerial decision making. All strategic business areas are analyzed and evaluated; then techniques for more efficient procedures are created. Through proper research, the organization will be able to pick the most effective, productive and profitable one.
• Research could possibly be applied to marketing, production, finance, IT and Human resources.

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Contd…

• Research can answer questions for various problems, from getting a grip on industry trends, identifying new products to produce and deliver to the market, or deciding on which site to locate an outlet, to better understanding what it needs to fulfill customer demands. Research can also help evaluate if a product is accepted in the market. Research aids expansion into new markets.
• Research helps in testing the potential success of new products. Businesses have to understand what kinds of products consumers would like before they market them.
• New Knowledge: The fascination and desire for new knowledge, new facts for business cycles, environment analysis and technological up gradation are the primary reasons of research.
• Research assists the company to find the right supplier at the right price and at the right time. An appropriate supplier choice makes it possible for the company to obtain or acquire top quality raw materials which result into production of good quality items which are consumed by the end user.

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FEATURES OF GOOD RESEARCH STUDY

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• Purpose clearly defined – The purpose of the business research-the problem involved or the decision to be made-should be clearly defined and sharply delineated in terms as unambiguous as possible. The statement of the decision problem should include its scope, its limitations, and the precise meanings of all words and terms significant to the research.

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• Research process detailed – The research procedures used should be described in sufficient detail to permit another researcher to repeat the research. Except when secrecy is imposed, research reports should reveal the sources of data and the means by which they were obtained.

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• Research design thoroughly planned – The procedural design of the research should be carefully planned to yield results that are as objective as possible. A survey of opinions or recollections ought not to be used when more reliable evidence is available from documentary sources or by direct observation.

• Originality – The first thing that a researcher should keep in mind is that a good research problem is original and is not a duplication of existing work. A researcher may work on existing studies but with the objective of re-interpreting the known findings.

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• Clarity and Unambiguity – A second feature should be that the problem is clearly stated and expresses relationship between variables. Eg. ‘Studying the relationship between family conditions and performance of students’ is a poorly stated problem. In this problem the term ‘family conditions’ is very vague and does not specify the meaning of family conditions.

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• High ethical standards applied – Ethical issues in research reflect important moral concerns about the practice of responsible behavior in society. A research design that includes safeguards against causing mental or physical harm to participants and makes data integrity a first priority should be highly valued.

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• Limitations frankly revealed – The researcher should report, with complete frankness, flaws in procedural design and estimate their effect on the findings.

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• Analysis adequate for decision maker’s needs- Analysis of the data should be extensive enough to reveal its significance, what managers call “insights”. The methods of analysis used should be appropriate.

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• Findings presented unambiguously – Language that is restrained, clear, and precise; assertions that are carefully drawn and hedged with appropriate

reservations; and an apparent effort to

achieve maximum objectivity tend to leave a favorable

impression of the researcher with the decision maker.

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• Conclusions justified – Conclusions should be limited to those for which the data provide an adequate basis. Good researchers always specify the conditions under which their conclusions seem to be valid.

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• Role of investigators – Organizations are the clients of researchers. Sometimes, the investigator tries to fit in results which are readily acceptable to clients. This is possible when the investigator manipulates the data or does not conduct an exhaustive study.

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• Inaccuracy of measuring instruments – Accuracy of measurement separates scientific and unscientific methods. Since human beings are the participants,

subjectivity information

invariably obtained

creeps from

in. Most the

of the respondent is

qualitative in nature.

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• Influence of Measurement – In physical sciences, the researcher can repeat an experiment any time to get the same results. This is not the case with marketing research. As human reaction changes quickly, reliability and validity of research will suffer a great deal.

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• Pressures of Time-frame – Marketing research must be conducted and completed within a given time- span. If more time is consumed in conducting the research, competitors might enter and capture the market.

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• Testing of Hypothesis – Any hypothesis formed in MR must be tested. Thus, experimentation has to be restored to. In Marketing Research, it is almost impractical to carry out experiment due to many factors which come in the way.

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• Complexity of the Subject – The subject becomes very complex, due to the fact that it is human beings, who interact with the researcher. Human reaction varies from time to time. Different individuals react differently for a given stimuli. Added to this are the environmental factors and peer influence adding to the complexity.

Research Design

Module – 2

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Topics to be covered

• Business Research Design: Meaning, types and significance of research design, errors affecting research design
• Business Research Design : Exploratory, Descriptive & Causal Research
• Exploratory Research: Meaning, suitability, collection, hypothesis formulation
• Descriptive Research: Meaning, Types of Descriptive studies, data collection methods
• Causal Research: Meaning, various types of experimental designs

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Business Research Design

A research design is a framework or blueprint for conducting the marketing research project. It details the procedures necessary for obtaining the information needed to structure and/or solve marketing research problems. A research design lays the foundation for conducting the project. A good research design will ensure that the marketing research project is conducted effectively and efficiently.

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A research design involves the following components:

• Define the information needed.
• Design the exploratory, descriptive and/or causal phases of the researches.
• Specify the measurement and scaling procedures.
• Construct and pretest a questionnaire (interviewing form) or an appropriate form for data collection.
• Specify the sampling process and sampling size.
• Develop a plan of data analysis.

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Classification of Marketing Research Designs

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Research Design

Exploratory Research Design

Conclusive Research Design

Descriptive Research

Causal Research

Cross-Sectional Design

Longitudinal Design

Comparison of Basic Research Designs

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Parameters

Exploratory

Descriptive

Causal

Objective

Discover ideas and insights.

Describe market characteristics or functions.

Determine cause and effect relationships.

Characteristics

Flexible, Versatile

Often the front end of total research design

Manipulation of one or more independent variables.

Control of other mediating variables.

Methods

Uses non-probability sampling. No preplanned analysis techniques. Uses method of expert survey, Secondary data, Case Studies, Qualitative Research

Marked by the prior formulation of specific hypotheses. Preplanned and structured design.

Probability sampling design. Data collected through Surveys, Panels and Secondary data

Well-defined sampling plan and data is collected through experiments under controlled or natural conditions.

Exploratory Research

The major emphasis in exploratory research is on converting broad, vague problem statements into small, precise sub-problem statements, which is done in order to formulate specific hypothesis.

Characteristics of Exploratory Research:

• Exploratory research is flexible and very versatile.
• For data collection structured forms are not used.
• Experimentation is not a requirement.
• Cost incurred to conduct study is low.
• This type of research allows very wide exploration of views.
• Research is interactive in nature and also it is open ended.

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Purpose of Exploratory Research

• Formulate a problem or define a problem more precisely.
• Identify alternative courses of action.
• Develop hypotheses.
• Isolate key variables and relationships for further examination.
• Gain insights for developing an approach to the problem.
• Establish priorities for further research.
• Appropriate to any problem about which very little is known. This research is the foundation for any future study.

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Exploratory Research Methods

• Literature Search – This refers to “referring to a literature to develop a new hypothesis.” The literature referred are: trade journals, professional journals, market research finding publications, statistical publications, etc.

Example: Suppose a problem is “Why are sales down?” This can quickly be analysed with the help of published data which should indicate “whether the problem is an industry problem or a firm problem”. Three possibilities exist to formulate the hypothesis.

1. The company’s market share has declined but industry’s figures are normal.
2. The industry is declining and hence the company’s market share is also declining.
3. The industry’s share is going up but the company’s share is declining.

If we accept the situation that our company’s sales are down despite the market showing an upward trend, then we need to analyse the marketing mix variables.

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• Expert Surveys – In expert surveys, it is desirable to talk to persons who are well informed in the area being investigated. These people may be company executives or persons outside the organisation. Here, no questionnaire is required. The approach adopted in an experience survey should be highly unstructured, so that the respondent can give divergent views.

Example :1. A group of housewives may be approached for their choice for a “Ready to Eat” product.

2. A publisher might want to find out the reason for poor circulation of newspaper introduced recently. He might meet a) Newspaper sellers b) Public reading room c) General Public d) Business community etc.

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• Focus Group – Another widely used technique in exploratory research is the focus group. In a focus group, a small number of individuals are brought together to study and talk about some topic of interest. The discussion is coordinated by a moderator. The group usually is of 8-12 persons. While selecting these persons, care has to be taken to see that they should have a common background and have similar experiences in buying. This is required because there should not be a conflict among the group members on the common issues that are being discussed.

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• Secondary Data Analysis – Secondary data refers to literature, published or unpublished, available as a result of studies made by others for their own purposes. A researcher may review the literature with the purpose of framing a hypothesis, or he may review hypothesis already developed for further study by others to see their applicability. The sources of data can be bibliographies available on the topic, journals, magazines, newspapers, reports and books, special catalogues, subject guides, online on the internet, digital libraries, e-databases maintained by the organization, guides, directories, indexes, Statistical data, Census data and other government publications.

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• Case- Study Analysis – Analysing a selected case sometimes gives an insight into the problem which is being researched. Case histories of companies which have undergone a similar situation may be available. These case are well suited to carry out exploratory research. For eg, Case Study on Mumbai Dabbawalas gives an insight into the problem of Supply Chain. A company implementing the practice of Supply Chain can take take useful inputs from the case.

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• A Case in Point

A company manufacturing electric shavers, known for its brand, wanted to introduce the product in Japan. Before the launch, the company made sure that all the 4Ps are acceptable to customers. When the product was launched, it met with failure.

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The company wondered what went wrong. Later investigations revealed that Japanese palms were very small and hence the product was not convenient for use.

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All possible causes were not listed and examined. This shows the importance of listing all factors during an exploratory research.

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Qualitative & Quantitative Research

Qualitative Research – An unstructured, exploratory research methodology based on small samples that provides insights and understanding of the problem setting.

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Quantitative Research – A research methodology that seeks to quantify the data and typically applies some form of statistical analysis.

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Qualitative Vs Quantitative Research

• Quantitative Research

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Objective

Parameters

Qualitative Research

To gain a qualitative understanding of the underlying reasons and motivations

To quantify the data and generalize the results from the sample to the population of interest.

Sample

Small number of nonrepresentative cases

Large number of representative cases

Data collection

Unstructured

Structured

Data analysis

Nonstatistical

Statistical

Outcome

Develop an initial understanding

Recommend a final course of action

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Qualitative Research Methods

Depth Interview

Delphi Technique

Focus Group

Projective Technique

Word Association Test

Completion Technique

Thematic Apperception Test

Expressive Technique

• Depth Interview – An unstructured, direct, personal interview in which a single respondent is probed by a highly skilled interviewer to uncover underlying motivations, beliefs, attitudes and feelings on a topic.

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• The interview may be conducted in a casual and informal manner in which the flow of the conversation determines what questions are to be asked and the order in which they should be asked.

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• Delphi Technique – This is a process where a group of experts in the field gather together. They may have to reach a consensus on forecasts. In the Delphi approach, the group members are asked to make individual judgments about a particular subject, say ‘sales forecast’.

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• These judgments are compiled and returned to the group members, so that they can compare their previous judgment with those of others. Then they are given an opportunity to revise their judgments, especially if it differs from the others. After 5 to 6 rounds of interaction, the group members reach conclusion.

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• Projective Techniques – These are indirect method of gathering information/ indirect interview and are unstructured and involve indirect form of questioning.

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1. Word Association Test – This test consists of presenting a series of stimulus words to the respondent, who is asked to answer quickly with the first word that comes to his mind. The respondent, by answering quickly, gives the word that he or she associates most closely with the stimulus word.

Eg. What brand of detergent comes to your mind first, when I mention washing of an expensive cloth?

Surf, Tide, Key, Ariel.

​

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2. Completion Techniques – Sentence completion – Here the respondents have to finish a set of incomplete sentences. Eg. For providing a basis for developing advertising appeal for a brand of cooking oil, the following sentence may be used:

People use cooking oil ……………..

Costliest cooking oil ………..

Most of the new cooking oil …………

Story Completion – A situation is described to a respondent who is asked to complete the story based on his opinion and attitude.

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3. Thematic Apperception Test – It is used to measure the attitude and perception of the individual. Some picture cards are shown to respondents. The respondent is required to tell the story by looking at the picture. When the subjects start telling the story, the researcher notices the respondent’s expression, pauses and emotions to draw the inference.

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4. Expressive Technique – Respondents are presented with a verbal or visual situation and asked to relate the feelings and attitudes of other people to the situation. The techniques are role playing

(Respondents are asked to play the role of someone else.) and Third-Person (Respondent is presented with a verbal or visual situation and asked to relate the beliefs and attitudes of a third person.)

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Descriptive Research

Descriptive Research is to describe something – usually market characteristics or functions.

• It can describe the characteristics of a group such as, customers, organisations, markets etc. It provides association between two variables, like income and place of shopping, age and preferences.
• Inform us about the proportions of high and low income customers in a particular territory
• Requires a clear specification of “Who, what, when, where, why and how of the research.

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Hypothesis Study at the Descriptive Research Stage

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Reasons for conducting Descriptive Research:

• To describe the characteristics of relevant groups, such as consumers, salespeople, organizations or market areas.
• To estimate the percentage of units in a specified population exhibiting a certain behavior.
• To determine the perceptions of product characteristics.
• To determine the degree to which marketing variables are associated.
• To make specific predictions.

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Descriptive research is marked by a clear statement of the problem, specific hypotheses and detailed information needs.

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Examples of descriptive research are:

• Market studies, which describe the size of the market, buying power of the consumers, availability of distributors and consumer profiles.
• Market share studies, which determine the proportion of total sales received by a company and its competitors.
• Sales analysis studies, which describe sales by geographic region, product line, type and size of the account.

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​

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• Image studies, which determine consumer perceptions of the firm and its products.
• Product usage studies, which describe consumption patterns.
• Distribution studies, which determine traffic flow patterns and the number and location of distributors.
• Pricing studies, which describe the range and frequency of price changes and probable consumer response to proposed price changes.
• Advertising studies, which describe media consumption habits and audience profiles for specific television programs and magazines.

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Types of descriptive Studies

• Cross-Sectional Designs – A type of research design involving the collection of information from any given sample of population elements only once.

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• Longitudinal Designs – A type of research design involving a fixed sample of population elements that is measured repeatedly. The sample remains the same over time, thus providing a series of pictures which, when viewed together, portray a vivid illustration of the situation and the changes that are taking place over time.

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For eg., “ How did the Indian people rate the character of ministers immediately after CWG games?” would be addressed using a cross-sectional design. However, a longitudinal design would be used to address the question, “ How did the Indian people change their view of minister’s character during CWG Games?”

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Methods of Data Collection

There are mainly two methods of data collection In Descriptive Research:

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• Survey Method – A structured questionnaire given to respondents and designed to elicit specific information.

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• Observation Method – The recording of behavioral patterns of people, objects and events in a systematic manner to obtain information about the phenomenon of interest.

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TYPES OF SURVEY

• True Survey – This involves repeat measurement of the same variables. Eg. Perception towards frozen peas . Each member of the panel is examined at a different time to arrive at a conclusion on the above subject.

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• Omnibus Survey – A sample of elements is being selected and maintained, but the information collected from the member varies. At a certain point of time, the attitude of panel members “towards an advertisement” may be measured. At some other point of time the same panel member may be questioned about the “product performance”.

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Classification of Survey Methods

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Survey Methods

Telephone Interviewing

Personal Interviewing

Mail Interviewing

Electronic Interviewing

Traditional

Computer-Assisted

In-Home

Mall Intercept

Computer-Assisted

Mail

Mail Panel

E-mail

Internet

Survey Methods

• Traditional Telephone Interviews – Phoning a sample of respondents and asking them a series of questions.
• Computer-Assisted Telephonic Interviewing – Uses a computerized questionnaire administered to respondents over the telephone. The interviewer sits in front of a computer terminal and wears a miniheadset. When contact is made, the interviewer reads questions posed on the computer screen and records the respondent’s answers directly into the computer memory bank.

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• Personal In-Home Interviews – Respondents are interviewed face-to-face in their home.
• Mall Intercept Personal Interviews – Respondents are intercepted while they are shopping in malls and brought to test facilities in the malls.
• Computer-Assisted Personal Interviewing – The respondent sits in front of a computer terminal and answers a questionnaire on the computer screen by using the keyboard or a mouse.

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• Mail Interviews – Questionnaires are mailed to preselected potential respondents. A typical mail interview package consists of the outgoing envelope, cover letter, questionnaire, return envelope and possibly an incentive. The respondents complete and return the questionnaires.
• Mail Panels – A mail panel consists of a large, nationally representative sample of households that have agreed to participate in periodic mail questionnaires and product tests.

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• E-mail Interviews –To conduct an e-mail survey, a list of e-mail addresses is obtained. The survey is written within the body of the e-mail message. The e-mails are sent out over the internet. Respondents type the answers to either closed-ended or open-ended questions at designated places, and click on reply.
• Internet Interviews – Internet or Web surveys use hypertext markup language and are posted on a Website. Respondents are asked to go to a particular Web location to complete the survey.

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Classification of Observation Methods

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Observation Methods

Personal Observation

Mechanical Observation

Audit

Content Analysis

Trace Analysis

Observation Methods

• Personal Observation – A researcher observes actual behavior as it occurs. The observer does not attempt to control or manipulate the phenomenon being observed.
• Mechanical Observation – Mechanical devices, rather than human observers, record the phenomenon being observed. Devices are:

Audimeter – Attached to a television set to continually record what channel the set is tuned to.

People Meters – Attempt to measure not only the channels to which a set is tuned but also who is watching.

Turnstiles – Record the number of people entering or leaving a building.

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On-site cameras – Used by retailers to assess package designs, counter space, floor displays and traffic flow patterns.

UPC – Allows for mechanized information collection regarding consumer purchases by product category, brand, store type, price and quantity.

Eye-tracking monitors – Records the gaze movements of the eye. Used to determine how a respondent reads an advertisement and views a TV commercial.

Pupilometers – Measures changes in the diameter of the pupils of the respondent’s eye.

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​

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Psychogalvanometer – Measures changes in the electrical resistance of the skin.

Voice pitch analysis – Measures emotional reactions through changes in the respondent’s voice.

Response latency – Time a respondent takes before answering a question.

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• Audit – The researcher collects data by examining physical records or performing inventory analysis of inventory of brands, quantities and package sizes in a consumer’s home or at a retail store.

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• Content Analysis – Objective, systematic and quantitative description of the manifest content of a communication. The unit of analysis may be words, characters, themes, space and time measures or topics.

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• Trace Analysis – Data collection is based on physical traces or evidence of past behavior. Eg. No. of different fingerprints on a page was used to find out the readership of various advertisements in a magzine.

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Causal Research

Causal research is used to obtain evidence of cause and effect relationships. It is appropriate for the following purposes:

1. To understand which variables are the cause (independent variables) and which variables are the effect ( dependent variables) of a phenomenon.
2. To determine the nature of the relationship between the causal variables and the effect to be predicted.

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For eg. In the context of department store project, a researcher wishes to determine whether the presence and helpfulness of salespeople(causal variable) will influence the sales of housewares (effect variable).

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A causal design could be formulated in which two groups of otherwise comparable housewares departments of a particular chain are selected.

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For four weeks, trained salespeople are stationed in one group of housewares departments but not in the other. Sales are monitored for both groups, while controlling for other variables.

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A comparison of sales for the two groups will reveal the effect of salespeople on housewares sales in department stores.

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Classification of Experimental Designs

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Experimental Designs

Pre-experimental

True Experimental

Quasi-Experimental

Statistical

One- Shot Case Study

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One Group Pretest-Posttest

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Static Group

Pretest-Posttest Control Group

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Posttest-Only Control Group

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Time Series

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Multiple Time Series

Latin Square

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Factorial

DEFINITION OF SYMBOLS

X = the exposure of a group to an independent variable, treatment or event, the effects of which are to be determined.

O = the process of observation or measurement of the dependent variable on the test units or group of units.

R = the random assignment of test units or groups to separate treatments.

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Types of Experimentation:

• Pre-experimental designs do not employ randomization procedures to control for extraneous factors.
• One-Shot Case Study – Also known as the after-only design, it is represented as

X O1

A single group of test units is exposed to a treatment X, and then a single measurement on the dependent variable is taken (O1). There is no random assignment of test units.

For eg. An advertisement of Pears Soap is being shown to the respondent (X) and then they were asked whether they recall the ad or not (O1).

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2. One-Group Pretest-Posttest Design – Symbolized as

O1 X O2

In this design, a group of test units is measured twice. There is no control group. First, a pretreatment measure is taken(O1), then the group is exposed to the treatment(X). Finally, a posttreatment measure is taken(O2). The treatment effect is computed as O2 – O1.

For eg. Respondents are first interviewed to know their opinion towards consuming alcohol (O1). An advertisement depicting harmful effects of alcohol is being shown to the respondent (X). After watching ad, the respondents are again being interviewed to test their opinion towards consuming alcohol now (O2). The effectiveness of ad is measured as O2 – O1.

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3. Static Group design – It is a two-group experimental design. One group, called the experimental group(EG), is exposed to the treatment, and the other, called the control group (CG), is not. Measurements on both groups are made only after the treatment.Symbolically as:

EG : X O1

CG : O2

For eg. HUL was trying to find out the impact of free samples of shampoo on the sales of shampoo. To the experimental group, they offered both the free samples and redemption coupon, and to Control Group only the redemption coupon was being offered. Coupons were coded and the number of coupons redeemed by the respondents were calculated. Difference between the coupons redeemed by the EG and CG will give the impact of free samples on sales of the shampoo.

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• True Experimental design – Researcher randomly assigns test units to experimental groups and treatments to experimental groups.

4. Pretest-Posttest Control Group Design – Test units are randomly assigned to either the experimental or the control group, and a pretreatment measure is taken on each group. The design is symbolized as

EG: R O1 X O2

CG: R O3 O4

The Treatment Effect is measured as

(O2 - O1) – (O4 – O3)

E.g.. In order to measure the impact of tuition on the performance of students, first a sample of respondents would be selected at random. Half of these would be randomly assigned to the experimental group and the other half would form the control group. Respondents in both groups were given a test to check their performance. Only the respondents in the experimental group were given a tuition and then both groups were given test and their performance was checked.

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5. Posttest-Only Control Group Design – Experimental group is exposed to the treatment but the control group is not and no pretest measure is taken. It is symbolized as:

EG : R X O1

CG : R O2

The treatment effect is obtained by

TE = O1 - O2

Eg. To measure the effectiveness of ad, a sample of respondents is selected at random. Half of them would be taken as Experimental Group and the other half would be Control Group. An advertisement on harmful effects of alcohol would be shown to only the experimental group and not to the control group. Then the opinion of both the groups on alcoholism would be recorded. Difference in their opinion will tell us the impact of advertisement.

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• Quasi-Experimental Designs – Researcher can control when measurements are taken and on whom they are taken but is unable to expose test units to the treatments randomly.

5. Time series design – Involves a series of periodic measurements on the dependent variable for a group of test units. The treatment is then administered by the researcher or occurs naturally. After the treatment, periodic measurements are continued to determine the treatment effect. It may be symbolized as:

O1 O2 O3 O4 X O5 O6 O7 O8

Eg. There is a trend being followed in share market investment. After watching a business news channel the investment pattern changes.

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6. Multiple Time Series Design – A time series design that includes another group of test units to serve as a control group. Symbolically, it may be described as

EG : O1 O2 O3 O4 X O5 O6 O7 O8

CG : O1 O2 O3 O4 O5 O6 O7 O8

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• Statistical designs – Allows for statistical control and analysis of external variables.

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7. Latin Square Design – Allows the researcher to statistically control two noninteracting external variables as well as to manipulate the independent variable.

For eg. In order to understand the impact of showing advertisement A ( humor), B ( No Humor) and C(High Humor) to sales in the store. Two control variables are being taken as Store Patronage and Interest in the store.

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Interest in the store

8. Factorial Design – Used to measure the effects of two or more independent variables at various levels and to allow for interactions between variables.

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For eg. A Departmental Store is planning to reduce the price of a product. For the same product, they are conducting three promotional campaigns, display at the entrance, display at POP and display anywhere in the store. They have got six combinations, P1X1, P2X1, P1X2, P2X2, P3X1, P3X2. Sales of each of the combinations is recorded in six different stores. Combination giving maximum will be taken as the best.

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Various Types of Errors

• Nonsampling Error

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Total Error

Random Sampling Error

Response Error

Nonresponse Error

Researcher Errors

Surrogate Information Error

Measurement Error

Population Definition Error

Sampling Frame Error

Data Analysis Error

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Interviewer Error

Respondent Selection Error

Questioning Error

Recording Error

Cheating Error

Respondent error

Inability Error

Unwillingness Error

• Total error is the variation between the true value in the population of the variable of interest and the observed value obtained in the research project.
• Random Sampling Error occurs because the particular sample selected is an imperfect representation of the population of interest.
• Nonsampling error can be attributed to sources other than sampling like error in problem definition, approach, scales, questionnaire design, interviewing methods etc.
• Nonresponse error arises when some of the respondents included in the sample do not respond.

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• Response Error arises when respondents give inaccurate answers or their answers are misrecorded or misanalyzed.
• Surrogate Information Error may be defined as the variation between the information needed for the marketing research problem and the information sought by the researcher.
• Measurement Error may be defined as the variation between the information sought and information generated by the measurement process employed by the researcher. Seeking to measure consumer preferences, the researcher employs a scale that measures perceptions rather than preferences.
• Population Definition Error may be defined as the variation between the actual population relevant to the problem at hand and the population as defined by the researcher.

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• Sampling Frame Error may be defined as the variation between the population defined by the researcher and the population as implied by the sampling frame used. For eg. The telephone directory used to generate a list of telephone numbers does not accurately represent the population due to disconnected and new numbers.
• Data analysis error encompasses errors that occur when raw data from questionnaires are transformed into research findings due improper statistical analysis.
• Respondent selection error occurs when interviewers select respondents other than those specified by the sampling design.

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• Questioning Error denotes errors made in asking questions of the respondents or in not probing when more information is needed.
• Recording error arises due to errors in hearing, interpreting and recording the answers given by the respondents.
• Cheating error arises when the interviewer himself fills the answers to a part or all of the interview.
• Inability Error results from the respondent’s inability to provide accurate answers due to unfamiliarity, fatigue or boredom.
• Unwillingness error arises from the respondent’s unwillingness to provide accurate information because of a desire to provide socially acceptable answers, avoid embarrassment or please the interviewer.

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Deduction and Induction

Deduction

The basic concept in deduction is from

‘Many to One’

or

‘Population to Sample’

In this type of logic, we are given information about a population, and we deduce the information about a sample or just one unit.

Deduction reasoning works from the ‘General to the Specific’. It may also be termed as ‘top-down’ approach. It is analogous to Brand Image wherein conclusions are drawn just by the name of the brand.

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Induction

The basic concept of induction is from:

‘One to Many’

or

Sample to Population

Induction could also refer to Generalisation, from what we observe or know. Induction involves reasoning about the future from the past, but in a broad sense, it involves reaching conclusions about unobserved things on the basis of what is actually observed.

Induction starts from Specific observations or set of observations to Generalised Theory or Law. It could be termed as ‘bottom-up’ approach.It can also be considered as divergent thinking. It is used when nothing or little is known, and we wish to expand our knowledge.

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Deduction Vs Induction

• Inductive research is a model in which theories are developed from specific observations. In deductive research, the specific expectations of a hypothesis are developed on the basis of general principle: we start from existing theory and find its proof.

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• Inductive reasoning is open-ended and exploratory especially in the beginning. Deductive reasoning is specific in nature and is concerned with testing or confirming hypothesis.

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For example, in Chennai, a social researcher observes that in a restaurant people from north India prefer to take tea over coffee. He extrapolates or uses inductive logic to conclude that all north Indians prefer tea over coffee.

In deductive logic, a researcher starts from the hypothesis that north Indians prefer tea over coffee, and then starts collecting observations to prove or disprove this hypothesis.

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Variables

A business research study involves study of characteristics of an individual/ item/ unit/ entity etc. These characteristics are represented by variables.

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A variable changes values for different individual/ item at the same time. Eg. Income of individuals for the year 2009-2010, prices of stocks on a day or for the same individual/ item at different time. Eg. Income of an individual, sales of a company.

​

The income of an individual is a quantitative variable, gender is a qualitative variable.

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Categories of Variables

• Independent Variable – Variable which influences or explains the variation in the other variables, under consideration, in the study. The value of this variable can be decided or controlled by the researcher.

​

• For eg. If a company increases the number of its branches, it will also increase the business as well as manpower in the company.The number of branches in this study is termed as independent variable.

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• Dependent Variable – A dependent variable is one which depends on an independent variable defined in a study.

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• For eg. Expenditure on R&D could be taken as independent variable , in pharmaceutical firms and sales could be termed as dependent variable.

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• The value of dependent variable is not manipulated or controlled in a research study, it changes due to the change in the independent variable.

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• Moderating Variable – In a study, involving an independent variable and a dependent variable, a relationship could be established through a variable.

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• However, we may come across a third variable, which is not an independent variable but forms strong contingent effect on the relationship of the independent and dependent variables.

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Training

(Independent Variable)

Performance

(Dependent Variable)

Willingness to learn

(Moderating Variable)

• Intervening Variables – In a study involving independent and dependent variables, there could be a variable/ factor which might affect the dependent variable, but it cannot be directly observed or measured.

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• For eg. The sales of a retail store might increase with increasing discounts and a relationship could be established.

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• Suppose, a scheme is introduced that monthly prizes will be given to randomly selected customers, this might increase the sales of a store but it cannot be measured, only its impact can be observed. Thus, monthly prizes scheme is an intervening factor.

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• Extraneous Variables – Extraneous variable is one that is outside or external to the situation under study, and its impact on dependent variable is beyond the scope of the study.

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• For eg the family income of students could be taken as extraneous variable while studying performance of MBA students. The value of extraneous variable may be controlled to neutralize its impact.

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Categories of Extraneous Variable

• History – Refers to specific events that are external to the experiment but occur at the same time as the experiment.eg. Downfall in sales can not only be due to poor advertisement but also due to economic downturn.
• Maturation – Refers to changes in the test units themselves like the people become older, experienced, tired or bored.
• Testing Effects – Caused by the process of experimentation.
• Instrumentation – Refers to changes in the measuring instrument(questionnaire), observer or the interviewer.

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• Control Variable – This variable is held constant in order to assess or clarify the relationship between two other variables.

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Questions

• What is an omni bus survey? 3 Marks
• What is a survey? 3 Marks
• Differentiate qualitative and quantitative research. 3 Marks
• Distinguish between cross sectional studies and longitudinal studies. 3 Marks
• What is a research design? What are its three types? 3 Marks
• What is a hypothesis? 3 Marks
• What is an extraneous variable? 3 Marks
• What do you mean by deduction and induction? 3 Marks
• What is before and after without control design? 3 Marks
• Explain the different projective techniques in qualitative research. 7 Marks
• Explain the various types of errors that affect the research design. 7 Marks
• What does experimentation involve? Explain after only design and Latin Square design. 7 Marks
• What is observation? Explain the different types of observation methods. 7 Marks
• Explain the different qualitative techniques for data collection. 10 Marks
• What do you mean by research design? Explain the different research designs with examples. 10 marks
• Distinguish the exploratory, descriptive and causal research designs. 10 Marks
• Detailed research plan for Company XYZ who wants to test the advertisement effectiveness of its recently released advertisement. 10 Marks

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RESEARCH METHODOLOGY��MODULE – 3��SAMPLING

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Topics to be covered

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• Sampling – Meaning
• Concepts
• Steps in Sampling process
• Sampling – Meaning, Steps in Sampling process
• Types of Sampling
• Probability and Non probability Sampling Techniques
• Errors in Sampling

​

SAMPLING - MEANING

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• A sample is a subgroup of the population selected for participation in the study. Sample characteristics, called statistics, are then used to make inferences about the population parameters.
• The process of selecting sample for the purpose of research study is called sampling.

Concepts of sampling

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• Population/ Universe(N)
• Sample(n)
• Sampling unit
• Sampling frame
• Sample size
• Sampling process

Distinction between Census and Sampling

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food served in a restaurant

​

• A census involves a complete enumeration of the elements of a population. A sample, on the other hand, is a subgroup of the population selected for participation in the study.

When is a Census Appropriate ?

• A census is appropriate if the size of population is small. For eg. A researcher may be interested in contacting firms in iron and steel industry. These industries are limited in number, so a census will be suitable.
• Sometimes, the researcher is interested in gathering information from every individual. Eg. Quality of

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When is Sample Appropriate?

1. When the size of population is large.
2. When time and cost are the main considerations in research.
3. If the population is homogeneous.
4. Circumstances when a census is not possible. Eg. Reactions to global advertising by a company.

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Characteristics of a good sample design

​

• True representative of the whole population.
• Appropriate sample size
• Sample should be chosen properly by appropriate sampling technique.
• A perfect mix of all the population elements.

Steps in Sampling Design Process

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Define the target population

Determine the sampling frame

Specify the sampling unit

Selection of sampling method

Determine the sample size

Execute the sampling process

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10

• Define the target population – The target population is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made. The target population should be defined in terms of elements, sampling units, extent and time.

An element is the object about which or from which the information is desired.

A sampling unit is an element or a unit containing the element, that is available for selection at some stage of the sampling process. Suppose that Revlon wanted to assess consumer response to new line of lipsticks and wanted to sample females over 18 years of age. Here the sampling unit would be households and all females over 18 in each selected house_Dh_{R M}o_Al_Md_ATHw_{A J,} o_DEu_PT l_Od_{F MB}b_A, e_SJBIp_T, opulation element.

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Extent refers to the geographical boundaries, and the time factor is the time period under consideration.

For eg., For a study of Departmental Store:

Elements : male or female head of the household responsible for most of the shopping at Departmental stores

Sampling units : households Extent : Bangalore City Time : 2011

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• Determine the Sampling Frame : A sampling frame is a representation of the elements of the target population. It consists of a list or set of directions for identifying the target population. Examples of a sampling frame include the telephone directory, an association directory listing the firms in an industry, a mailing list purchased from an organization, a city directory or a map. EG. You want to learn about scooter owners in a city. The RTO will be the frame which provides you names, addresses and the types of vehicles possessed.

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• Specify the Sampling Unit – Individuals who are to be contacted are the sampling units. If retailers are to be contacted in a locality, they are the sampling units.
• Selection of sampling method – The researcher must decide whether to use to use non-probability or probability sampling.

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• Determine the sample size – Sample size refers to the number of elements to be included in the study. The sample size depends upon the type of study that is being conducted. If it is an exploratory research, the sample size will be generally small and for descriptive research, the sample size will be large. The sample size also depends on the resources available with the company. It depends on the accuracy required in the study and the permissible errors allowed.

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• Execute the sampling process – A detailed specification of the sampling design decisions with respect to the population, sampling frame, sampling unit, sampling technique and sample size are to be implemented. If households are the sampling unit, an operational definition whether household includes husband or wife or both, and procedure should be specified if household is not available whether to call back or eliminate it from the sample.

TYPES OF SAMPLING TECHNIQUES

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• Probability sampling – Every unit in the population has equal chances for being selected as a sample unit. The sampling units are selected by chance and no biasness is involved,
• Non probability sampling – The units in the population have unequal or negligible chances for being selected as a sample unit. Sampling relies on the personal judgment of the researcher rather than chance to select sample elements. The researcher can arbitrarily or consciously decide what elements to include in the sample.

CLASSIFICATION OF SAMPLING TECHNIQUES

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Sampling Techniques

Probability

Non Probability

Systematic

Sampling

Stratified

Sampling

Cluster Sampling

Area Sampling

& Multistage Sampling

Simple Random Samplin g

Proportionate

Disproportionate

Convenience

Judgmental

Quota Sampling

Snowball Sampling

Panel Sampling

5

PROBABILITY SAMPLING TECHNIQUES

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• Simple Random Sampling – Each element in the population has a known and equal probability of selection. This implies that every element is selected independently of every other element. The sample is drawn by a random procedure from a sampling frame. This method is equivalent to a lottery system – Take a population containing four departmental stores: A,B, C & D. Suppose we need to pick a sample of two stores from the population. We write down all possible combinations AB, AC, AD, BC, BD, CD on pieces of papers and fold the pieces. Put them in a box. Mix them and pull one at random.

Random Number table

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• Systematic Sampling – Sample is chosen by selecting a random starting point and then picking every Kth element in succession from the sampling frame. There are three steps:
1. Sampling interval K is determined by the foll formula: K = No. of units in the population =N

No. of units desired in the sample=n

• One unit between the first and Kth unit in the population list is randomly

chosen.

• Add Kth unit to the randomly chosen number.

Example – Consider N-1000 households from which we want to select n-50 units.

Calculate K = 1000 = 20

50

To select the first unit, we randomly pick one number between 1 to 20, say 17. So our sample begins with 17, 37, 57 …… Only the first item is randomly selected and rest are systematically selected.

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• Class strength- 54-N
• Sample size-n-10
• K=N/n=54/10= 5(difference)
• USN from 1 to 5 researcher can pick any number
• First no. 3, 3+5=8, 8+5=13, 13+5=18,

23,28,33,38,43,48.

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• Stratified Random Sampling – It is a two step process:
1. Population to be sampled is divided into groups or strata. The strata should be mutually exclusive and collectively exhaustive, i.e. every population element should be assigned to one and only one stratum and no population elements should be omitted. Strata are more or less equal on some characteristics.
2. Elements are selected from each stratum by a random procedure.

​

Stratified sampling are of two types:

1. Proportionate stratified Sampling – The number of sampling units drawn from each stratum is in proportion to the population size of that stratum.
2. Disproportionate stratified Sampling – The number of sampling units drawn from each stratum is based on analytical consideration, but not in proportion to the size of the population of that stratum.

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Sim25p-lJeunr-a21ndom sampling/ systematic

• Strata-meaning group
• Groups should be sharing some common characteristics(inside the group-Homogeneous, outside the group-different)
• Eg: MBA, MTech, BE
• 3 strata
• Stratum1-MBA
• S2-Mtech
• S3-BE S1-MBA N-250

n-100

Simple random sampling/ systematic sampling

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S2-Mtech N-100

n-30

Simple random sampling/ systematic sampling

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S3-BE N-800 n-300

​

​

Population-students of RIT

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MBA N-250

BE N-800

Mtech N-100

Strata 1

Strata 2

Strata 3

n-100

n-30

n-300

USING SIMPLE RANDOM OR SYSTEMATIC RANDOM SAMPLING DRAW SAMPLE(n)

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• Cluster(Group) Sampling – The following steps are followed:
1. The population is divided into mutually exclusive and collectively exhaustive clusters.
2. A simple random sample of few clusters is selected. For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically.
3. All the units in the selected cluster are studied.

If all the elements in each selected cluster are included in the sample, the procedure is called one-stage cluster sampling. If a sample of elements is drawn probabilistically from each selected cluster, the procedure is two-stage cluster sampling.

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N

n

Similarities

• Both methods are examples of probability sampling methods – every member in the population has an equal probability of being selected to be in the sample.
• Both methods divide a population into distinct groups (either clusters or stratums).
• Both methods tend to be quicker and more cost-effective ways of obtaining a sample from a population compared to a simple random sample.

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Differences

• Cluster sampling divides a population into groups, then includes all members of some randomly chosen groups.

​

• Stratified sampling divides a population into groups, then includes some members of all of the groups.

​

• In Cluster Sampling, the sampling is done on a population of clusters therefore, cluster/group is considered a sampling unit.

​

• In Stratified Sampling, elements within each stratum are sampled.

​

• In Cluster Sampling, only selected clusters are sampled.

​

• In Stratified Sampling, from each stratum, a random sample is selected.

​

• In Cluster Sampling, the aim is to reduce cost and increase the efficiency of sampling.

​

• In Stratified Sampling, the motive is to increase precision to reduce error.

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• Area Sampling – It is a form of cluster sampling, in which the clusters consist of geographic areas, such as counties, housing tracts or blocks.

​

• Area sampling is of two types : a) One-stage area sampling – If only one level of sampling takes place in selecting the basic elements (for eg. The researcher samples blocks and then all the households within the selected blocks are included in the sample), the design is called one-stage area sampling.

​

• If two or more levels of sampling take place before the basic elements are selected (the researcher samples blocks, and then samples households within selected blocks), the design is called two-stage area sampling.

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Area sampling involves the following procedure:

• Segmenting the total area (state or country) into sub areas.
• Select sub areas randomly.
• Study all the units in the sub area which has been selected. For eg. Retailers or wholesalers or households in a particular sub-area selected.

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• Multistage Sampling – Sampling is done in several stages.
• An Illustration:

The management of a newly-opened club solicits new membership. During the first rounds, all corporates were sent details so that those who are interested may enroll. The second round concentrates on how many are interested to enroll for various entertainment activities that club offers such as billiards, indoor sports, swimming and gym etc. After obtaining this information, you might stratify the interested respondents. This will also tell you the reaction of new members to various activities.

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Multistage Sampling Example

A research firm in the UK conducted a survey in which it divided the country into its counties and randomly selected some of these counties as a cluster sample (the first stage of sampling).

Each county was then divided into its towns, and areas were chosen at random

from each town (the second stage of sampling).

Finally, within each town, each town was divided into small areas and

households were selected at random from each area. These households formed

the sample population for the research study (third stage of sampling).

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Advantages of Probability Sampling

• It is unbiased.
• Quantification is possible in probability sampling.
• Less knowledge of universe is sufficient.

​

Disadvantages of Probability Sampling

• It takes time.
• It is costly.
• More resources are required to design and execute than in non-probability design.

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NON-PROBABILITY SAMPLING TECHNIQUES

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• Convenience Sampling – Attempts to obtain a sample of convenient elements. The selection of sampling units is left primarily to the interviewer. Often, respondents are selected because they happen to be in the right place at the right time.

​

• Examples of convenience sampling include: 1) use of students, church groups and members of social organizations, mall intercept interviews without qualifying the respondents,

2) department stores, Least expensive and least time-consuming of all sampling techniques.

Convenience sampling

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• Judgmental/Purposive Sampling –It is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher. The researcher exercising judgment or expertise, chooses the elements to be included in the sample, because he or she believes that they are representative of the population of interest or are otherwise appropriate.
• Common examples of judgmental sampling include
1. test markets selected to determine the potential of a new product, 2) purchase engineers selected in industrial marketing research because they are considered to be representative of the company, 3) expert witnesses used in court.

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• Quota Sampling – It involves the fixation of certain quotas, which are to be fulfilled by the interviewers. It involves two stages:
• The first stage consists of developing control categories, or quotas of population elements. To develop these quotas, the researcher lists relevant control characteristics and determines the distribution of these characteristics in the target population. The control characteristics include sex, age and race.
• In the second stage, Sample elements are selected based on convenience or judgment.

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Example of Quota Sampling

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• Suppose 200,000 students are appearing for a competitive examination. We need to select 1% of them based on quota sampling. The classification of quota may be as follows:

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• Snowball Sampling - An initial group of respondents is selected, usually at random. After being interviewed, these respondents are asked to identify others who belong to the target population of interest. Subsequent respondents are selected based on the opinion or referrals provided by the initial respondents. This process may be carried out in waves by obtaining referrals from referrals, thus leading to a snowballing effect.
• Examples include special census groups such as widowed males under 35 and members of a scattered minority population. College students bring in more students on the consumption of Pepsi.

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• Panel Sampling – A sampling technique where the same sample group is being contacted on a regular basis and the necessary information is gathered.

​

• For eg. Suppose that one is interested in knowing the change in the consumption pattern of households. A sample of households are drawn. These households are contacted to gather information on the pattern of consumption. Subsequently, say after a period of six months, the same households are approached once again and the necessary information on their consumption is collected.

ERRORS IN SAMPLING

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• Sampling Error – Error due to inappropriate selection of sample/ inappropriate sampling process, methods. It can be minimized by choosing the appropriate sample size.
• Non-sampling error –error due to other steps in the research process

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• Total error= sampling error + non sampling error
• N
• n
• Technique
• methods

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• Sampling Frame Error – Errors in the specific list of population units, from which the sample for a study is being chosen.

Eg. Assume that a bank wants to contact the people belonging to a particular profession over phone to market a home loan product. The sampling frame in this case is the telephone directory. This sampling frame may pose several problems:1) People might have migrated. 2) Numbers have changed. 3) Many numbers were not yet listed. Residents who are included in the directory likely to differ from those who are not included.

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• Non-response error – The two major non-response issues in sampling are improving response rates and adjusting for non-response. Non-response error arises when some of the potential respondents included in the sample do not respond.

The primary causes of low response rates are refusals and not-at-homes.

Refusals, which result from the unwillingness or inability of people included in the sample to participate, result in lower response rates.

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Attempts to lower refusal rates:

1. Prior notification – Potential respondents are sent a letter notifying them of the imminent mail, telephone, personal or internet survey.
2. Motivating the respondents – The interviewer starts with a small request such as ‘Will you please take five minutes to answer five questions?’ which is followed by a larger request (foot-in-the-door strategies). In the reverse strategy, the initial request is relatively large, followed by a smaller request (door-in-the-face strategy).
3. Incentives – Offering monetary as well as nonmonetary incentives to potential respondents.

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4. Questionnaire design and administration – A well designed questionnaire can decrease the overall refusal rate as well as refusals to specific questions.
5. Follow-up – Contacting the non respondents periodically after the initial contact or by sending a letter to remind non respondents to complete and return the questionnaire.
1. Data Error – This occurs during the data collection, analysis of data or interpretation. Respondents sometimes give distorted answers unintentionally for questions which are difficult, or if the question is exceptionally long and the respondent may not have answer.

BIBLIOGRAPHY

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• Research Methodology-Methods and techniques, C R Kothari, 2^nd edition, New

age International pulications

• Adam, F. and M. Healy (2000). A Practical Guide to Postgraduate Research in the Business Area. Dublin, Blackhall Publishing.
• Becker, H. S. (2008). Tricks of the rade: How to Think About Your Research While You're Doing It.
• Charmaz, K. (2004). Premises, Principles, and Practices in Qualitative Research: Revisiting the
• Jackson, S. L. (2009). T H I R D E D I T I O N Research Methods and Statistics A Critical Thinking Approach.

Questions

• What is non-probability sampling? 3 Marks
• Distinguish simple and stratified random sampling. 3 Marks
• Distinguish cluster and stratified random sampling. 3 Marks
• Name the two types of errors in sampling and briefly explain them. 3 Marks
• What are the different types of sampling errors? 3 Marks
• What is probability sampling? Is it the same as random sampling? 3 Marks
• What is panel data? What are its merits and demerits? 3 Marks
• Write a note on Convenience sampling. 3 Marks
• Distinguish between probability and non probability sampling techniques. 3 Marks
• What are the characteristics of a good sample design. 3 Marks
• Explain the various steps involved in sampling process. 7 marks
• Describe the sampling process. 7 Marks
• Explain the various non-sampling errors. 7 Marks
• Write a note on Total Error in Research Methodology. 7 Marks/10 Marks
• Discuss the advantages and disadvantages of Probability Sampling 7 Marks
• Explain the different non-probability sampling techniques. 10 Marks
• What are various steps involved in sampling plan? 10 Marks
• Explain the various probability sampling techniques. 10 Marks

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Data Collection�Questionnaire Designing�Measurement & Scaling

MODULE 4

Topics to be covered

• Data collection: Primary and Secondary data – Sources – advantages/disadvantages.
• Data collection methods – Observations, Survey, Interview and Questionnaire design, Qualitative techniques of data collection.
• Measurement & Scaling Techniques: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Criteria for good measurement, attitude measurement – Likert’s scale, Semantic Differential Scale, Thurstone-equal appearing interval scale
• MDS – Multi Dimensional Scaling.

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Primary Data and Secondary Data

• Primary data are originated by a researcher for the specific purpose of addressing the problem at hand.
• Obtaining primary data can be expensive and time consuming.
• Secondary data are data that have already been collected for purposes other than the problem at hand.
• These data can be located quickly and inexpensively.

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Differences between Primary and Secondary Data

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Primary Data

Secondary Data

Collection purpose

For the problem at hand

For other problems.

Collection process

Very involved

Rapid and easy

Collection cost

High

Relatively low

Collection time

Long

Short

Advantages and uses of Secondary Data

• Secondary data are easily accessible, relatively inexpensive and quickly obtained.
• Available on topics where it would not be feasible for a firm to collect primary data.

Secondary data can help you:

• Identify the problem.
• Better define the problem.
• Develop an approach to the problem.
• Formulate an appropriate research design.
• Answer certain research questions and test some hypotheses.
• Interpret primary data more insightfully.

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Disadvantages of secondary Data

• Because secondary data have been collected for purposes other than the problem at hand, their usefulness to the current problem may be limited in several important ways, including relevance and accuracy.
• The objectives, nature and methods used to collect the secondary data may not be appropriate to the present situation.
• Secondary data may be lacking in accuracy, or they may not be completely current or dependable.

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Classification of Secondary Data

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Secondary Data

Internal

External

Ready to Use

Requires Further Processing

Published Materials

Computerized Databases

Syndicated Services

Classification of Secondary data

• Internal data are those generated within the organization for which the research is being conducted.
• This information may be available in a ready-to-use format, such as information routinely supplied by the management decision support system.
• On the other hand, these data may exist within the organization but may require considerable processing before they are useful to the researcher. For eg. A variety of information can be found on sales invoices. Yet this information may not be easily accessible; further processing may be required to extract it.
• External data are those generated by sources outside the organization. These are mainly published materials, computerized databases and syndicated services.

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Sources of Secondary Data

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Published Secondary Data

General Business Sources

Government Sources

Guides

Directories

Indexes

Statistical Data

Census Data

Other Government Publications

Sources of Published Secondary Data

General Business Data

• Guides – Excellent source of standard or recurring information. A guide may help identify other important sources such as directories, trade associations and trade publications.
• Directories – Helpful for identifying individuals or organizations that collect specific data. Eg. Research Services Directory.
• Indexes – It is possible to locate information on a particular topic in several different publications by using an index. Indexes can, therefore, increase the efficiency of the search process. Eg. Business Periodical Index.
• Statistical Data – Published statistical data are of great interest to researchers. Graphic and statistical analyses can be performed on these data to draw important insights. Eg. A Guide to Consumer Markets.

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Government Sources

• Census Data – Provides detailed view of the human population, their income and education level. The quality of census data is high and the data are often extremely detailed. Important census data include Census of Housing, Census of Manufacturers, Census of Population, Census of Retail Trade, Census of Service Industries and Census of Wholesale Trade.
• Other Government Publications – n addition to the census, the government collects and publishes a great deal of statistical data. The more useful publications are Business Conditions Digest and Survey of Current Business.

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Computerized Databases

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Computerized Databases

Online

Internet

Offline

Bibliographic Databases

Numeric Databases

Full-Text Databases

Directory Databases

Special-Purpose Databases

• Online Databases- Databases, stored in computers, which require a telecommunications network to access.
• Internet Databases – Internet databases can be accessed, searched and analyzed on the internet. It is also possible to download data from the internet and store them in the computer or an auxiliary storage device.
• Offline Databases – Databases that are available on diskette or CD-ROM.

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• Bibliographic Databases –Databases composed of citations to articles in journals, magazines, newspapers, marketing research studies, technical reports, government documents and the like. They often provide summaries or abstracts of the material cited.
• Numeric Databases – Contain numerical and statistical information that may be important sources of secondary data.
• Full-Text Databases –Databases containing the complete text of secondary source documents comprising the database.
• Directory Databases –Provide information on individuals, organizations and services. Eg. National Electronic Yellow Pages.
• Special-purpose Databases – Contain information of a specific nature, e.g. data on a specialized industry.

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Syndicated Sources of Secondary Data

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Unit of measurement

Households/ Consumers

Institutions

Panels

Purchase

Media

Surveys

Electronic Scanner Services

Psychographic and Lifestyles

General

Advertising Evaluation

Volume Tracking Data

Scanner Diary Panels

Scanner Diary Panels with Cable TV

Retailers

Wholesalers

Industrial Firms

Audits

Direct Inquiries

Clipping services

Corporate Reports

Syndicated sources, also referred to as syndicated services, are companies that collect and sell common pools of data of known commercial value, designed to serve information needs shared by a number of clients. These data are not collected for the purpose of marketing research problems specific to individual clients, but the data and reports supplied to client companies can be personalized to fit particular needs.

Surveys – Involve interviews with a large number of respondents using a predesigned questionnaire.

• Psychographics and Lifestyles – Psychographics refer to the psychological profiles of individuals and to psychologically based measures of lifestyle. Lifestyles refer to the distinctive modes of living of a society or some of its segments.

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• General surveys – Surveys conducted for a variety of other purposes, including examination of purchase and consumption behavior.
• Advertising Evaluation – The purpose of advertising evaluation surveys is to assess the effectiveness of advertising using print and broadcast media.

Purchase and Media Panels – Panels are samples of respondents who provide specified information at regular intervals over an extended period of time. These respondents may be organizations, households or individuals, although household panels are most common. The distinguishing feature of purchase and media panels is that the respondents record specific behaviors in an electronic diary as they occur.

• Purchase Panels – Respondents record their purchases of a variety of different products.
• Media Panels – Electronic devices automatically record viewing behavior, thus supplementing a diary or an online panel.

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Electronic Scanner Services- Scanner data reflect some of the latest technological developments in the marketing research industry. Scanner data are collected by passing merchandise over a laser scanner, which optically reads the barcoded description printed on the merchandise.

• Volume Tracking Data – Provide information on purchases by brand, size, price and flavor or formulation, based on sales data collected from the checkout scanner tapes.
• Scanner Panels – Each household member is given an ID card that can be read by the electronic scanner at the cash register. The scanner panel members simply present the ID card at the checkout counter each time they shop.
• Scanner Panels with cable TV – Combines scanner panels with new technologies growing out of the cable TV industry. Households on these panels subscribe to one of the cable TV systems in their market.

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Syndicated Data from Institutions

Retailer and Wholesaler Audits- A data collection process derived from physical records or performing inventory analysis. Data are collected personally by the researcher or by representatives of the researcher, and the data are based upon counts usually of physical objects other than people. Retailers and wholesalers who participate in the audit receive basic reports and cash payments from the audit service.

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Industry Services – Provide syndicated data about industrial firms, businesses and other institutions. Financial, operating and employment data are also collected by these syndicated research services. These data are collected by making direct inquiries, from clipping services that monitor newspapers, the trade press and broadcasts; and from corporate reports.

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Questionnaire Design

A questionnaire, whether it is called a schedule, interview form, or measuring instrument, is a formalized set of questions for obtaining information from respondents. Typically, a questionnaire is only one element of a data-collection package that might also include fieldwork procedures, such as instructions for selecting, approaching and questioning respondents; some reward, gift or payment offered to respondents, and communication aids, such as maps, pictures, advertisements and products and return envelopes.

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Questionnaire Design Process

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Specify the information needed.

Specify the type of interviewing method.

Determine the content of individual questions.

Design the question to overcome the respondent’s inability and unwillingness to answer.

Decide on the question structure.

Determine the question wording.

Arrange the questions in proper order.

Identify the form and layout.

Reproduce the questionnaire.

Eliminate bugs by pretesting.

• Specify the information needed –It is helpful to review components of the problem and the approach, particularly the research questions, hypotheses and the information needed. It is also important to have a clear idea of the target population. The characteristics of the respondent group have a great influence on questionnaire design. Questions that are appropriate for college students may not be appropriate for housewives.

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• Type of Interviewing Method –An appreciation of how the type of interviewing method influences questionnaire design can be obtained by considering how the questionnaire is administered under each method. In personal interviews, respondents see the questionnaire and interact face to face with the interviewer. Thus lengthy, complex and varied questions can be asked. In telephone interviews, the respondents interact with the interviewer, but they do not see the questionnaire. This limits the type of questions that can be asked to short and simple ones. Mail questionnaires are self-administered, so the questions must be simple and detailed instructions must be provided. For eg. Ranking 10 stores is too complex a task to be administered over the telephone. Instead, the simpler rating task, where the stores are rated one at a time, is selected to measure preferences. Note the use of cards to facilitate the ranking task in the personal interview.

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• Individual Question Content – Is the Question Necessary? Every question in a questionnaire should contribute to the information needed or serve some specific purpose. If there is no satisfactory use for the data resulting from a question, that question should be eliminated.

Are several questions needed instead of one? Sometimes, several questions are needed to obtain the required information in an unambiguous manner. Eg. Do you think Coca-Cola is a tasty and refreshing soft drink? (Incorrect)

Do you think Coca-Cola is a tasty soft drink? and

Do you think Coca-Cola is a refreshing soft drink?(Correct)

Double barreled question – A single question that attempts to cover two issues. Such questions can be confusing to respondents and result in ambiguous responses.

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• Overcoming Inability to Answer – Certain factors limit the respondent’s ability to provide the desired information. Is the respondent informed? Respondents are often asked about topics on which they are not informed. A husband may not be informed about monthly expenses for groceries and department store purchases if it is the wife who makes these purchases, or vice versa.

Can the Respondent Remember? Evidence indicates that consumers are particularly poor at remembering quantities of products consumed. Thus, soft drink consumption may be better obtained by asking:

How often do you consume soft drinks in a typical week?

1. Less than once a week.
2. 1to 3 times per week.
3. 4 to 6 times per week.
4. 7 or more times per week.

The inability to remember leads to errors of omission ( inability to recall an event that actually took place.), telescoping ( when an individual telescopes or compresses time by remembering an event as occurring more recently than it actually occurred and creation ( when a respondent remembers an event that did not actually occur.

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Can the respondent articulate – Respondents may be unable to articulate certain types of responses. For eg. If asked to describe the atmosphere of the department store they would prefer to patronize, most respondents may be unable to phrase their answers. On the other hand, if the respondents are provided with alternative descriptions of store atmosphere, they will be able to indicate the one they like the best.

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• Overcoming Unwillingness to Answer – Effort required of the Respondents – Most respondents are unwilling to devote a lot of effort to provide information. Hence, the researcher should minimize the effort required of the respondents. For eg.

Please list all the departments from which you purchased merchandise on your most recent trip to a department store. ( Incorrect)

In the list that follows, please check all the departments from which you purchased merchandise on your most recent trip to a department store.

1. Women’s Apparel
2. Cosmetics
3. Men’s apparel ( Correct)

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Context – Some questions may seem appropriate in certain contexts but not in others. For eg. Questions about personal hygiene habits may be appropriate when asked in a survey sponsored by Medical association ,but not in a one sponsored by a fast food restra.

Sensitive Information – Respondents are unwilling to disclose information on sensitive topics like money, family life, political and religious beliefs. In order to avoid this, sensitive topics to be placed at the end of the questionnaire.

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• Choosing Question Structure –

Unstructured Questions – Open-ended questions that respondents answer in their own words.

Eg. What is your occupation?

They are good as first questions on atopic. They enable the respondents to express general attitudes and opinions that can help the researcher interpret their responses to structured questions.

Structured Questions – Specify the set of response alternatives and the response format.

1. Multiple-Choice Questions – Researcher provides a choice of answers and respondents are asked to select one or more of the alternatives given.
2. Dichotomous Questions – Has only two response alternatives: yes or no, agree or disagree, and so on.
3. Scales –Scale can be used as :

Do you intend to buy a new car within the next six months?

Definitely will Probably will Undecided Probably will buy

buy not buy

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• Choosing Question Wording – Translation of the desired question content and structure into words that respondents can clearly and easily understand.

Define the issue- A question should clearly define the issue being addressed. Define the issue in terms of who, what, when, where and why. Eg. Which brand or brands of shampoo have you personally used at home during the last month? In case of more than one brand, please list all the brands that apply.

Use Ordinary words- Ordinary words should be used in a questionnaire and they should match the vocabulary level of the respondents.

Eg. Do you think the distribution of soft drinks is adequate? (Incorrect)

Do you think soft drinks are readily available when you want to buy them? (Correct)

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Use Unambiguous Words – The words used in a questionnaire should have a single meaning that is known to the respondents. A number of words that appear to be unambiguous have different meanings to different people. These include ‘usually’, ‘normally’, frequently, often and sometimes.

• Determining the Order of Questions –

Opening Questions- The opening questions can be crucial in gaining the confidence and cooperation of respondents. The opening questions should be interesting, simple and nonthreatening.

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Type of Information – Basic information relates directly to the research problem. Classification information, consisting of socioeconomic and demographic characteristics, is used to classify the respondents and understand the results. Identification information includes name, address and telephone number. Basic information should be obtained first, followed by classification and, finally, identification information.

Difficult Questions– Difficult questions or questions that are sensitive, embarrassing, complex or dull should be placed late in the sequence. After rapport has been established and the respondents become involved, they are less likely to object to these questions.

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• Form and Layout – The format, spacing and positioning of questions can have a significant effect on the results and should be done properly.
• Reproduction of the Questionnaire - If the questionnaire is reproduced on poor quality paper or is otherwise shabby in appearance, the respondents will think the project is unimportant and the quality of response will be adversely affected. Therefore, the questionnaire should be reproduced on good-quality paper and have a professional appearance. A researcher should avoid splitting a question, including its response categories.The tendency to crowd questions together to make the questionnaire look shorter should be avoided.

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• Pretesting – Refers to the testing of the questionnaire, on a small sample of respondents to identify and eliminate potential problems. All aspects of the questionnaire should be tested, including question content, wording, sequence, form and layout, question difficulty and instructions. The respondents in the pretest should be similar to those who will be included in the actual survey in terms of background characteristics, familiarity with the topic and attitudes and behaviors of interest.

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Measurement and Scaling

Measurement means assigning numbers or other symbols to characteristics of objects according to certain prespecified rules.

Scaling involves creating a continuum upon which measured objects are located. For eg. Consider a scale from 1 to 100 for locating consumers according to the characteristic “attitude toward department stores.” Each respondent is assigned a number from 1 to 100 indicating the degree of favorableness with 1 = extremely unfavorable and 100 = extremely favorable. Measurement is the actual assignment of a number from 1 to 100 to each respondent. Scaling is the process of placing the respondents on a continuum with respect to their attitude toward department stores.

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Primary Scales of Measurement

• Nominal Scale – A nominal scale is a figurative labeling scheme in which the numbers serve only as labels or tags for identifying and classifying objects. For eg. The numbers assigned to the respondents in a study constitute a nominal scale.

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• University Registration Numbers assigned to students, Bus Route Numbers and Numbers on the jerseys of cricket players are examples of Nominal scale.

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• The numbers used in nominal scales serve only the purpose of counting and the idea is to make sure that no two persons or objects receive the same number.

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• Ordinal Scale (Ranking Scale)- An ordinal scale is a ranking scale in which numbers are assigned to objects to indicate the relative extent to which the objects possess some characteristic. Ordinal Scales are used to ascertain the consumer perceptions, preferences etc. For eg. The respondents may be given a list of brands which may be suitable and were asked to rank on the basis of ordinal scale of 1-5.
• Lux
• Liril
• Cinthol
• Dove
• Pears

In Market Research, we often ask the respondents to rank the items, like for eg., “ A soft drink, based upon flavor or color”. In such a case, the ordinal scale is used.

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• Interval Scale – In an interval scale, numerically equal distances on the scale represent equal values in the characteristic being measured. An interval scale contains all the information of an ordinal scale, but it also allows you to compare the differences between objects.

Eg. Suppose we want to measure the rating of a refrigerator using interval scale. It will appear as follows:

1. Brand name Poor_____Good
2. Price High_____ Low
3. Service after-sales Poor_____Good

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Temperature scale i.e., Centigrade and Fahrenheit are also interval scale, e.g the temperature of four cities are:

Shimla – 15degrees, Delhi – 30 degrees, Bangalore – 22 degrees, Jaipur – 37 degrees.

It can be said that the difference in the temperature of Delhi and Shimla is the same as difference in the temperature of Jaipur and Bangalore. However we cannot say that Delhi is two times warmer than Shimla.

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• Ratio Scale – It is a special kind of internal scale that has a meaningful zero point. With this scale, length, weight or distance can be measured. In this scale, it is possible to say, how many times greater or smaller one object is being compared to the other.

Eg. Sales this year for product A are twice the sales of the same product last year.

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Scaling Techniques

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Scaling Techniques

Comparative Scales

Noncomparative Scales

Paired Comparison

Rank Order

Constant Sum

Q-Sort

Continuous Rating Scales

Itemized Rating Scales

Likert

Semantic Differential

Stapel

Comparative Scaling Techniques

• Comparative scales involve the direct comparison of stimulus objects. Comparative scale data must be interpreted in relative terms and have only ordinal or rank order properties.

Eg. In each of the following, which store do you think is better

1. Big Bazaar b) Food World c)Total d)Smart
1. In non-comparative scales, each object is scaled independently of the others in the stimulus set. The resulting data are generally assumed to be interval or ratio scaled. For eg. Respondents may be asked to evaluate Coke on a 1-to-6 preference scale (1=not at all preferred, 6=greatly preferred)

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Comparative Scaling Techniques

• Paired Comparison Scaling – In paired comparison scaling, a respondent is presented with two objects and asked to select one according to some criterion. The data obtained are ordinal in nature. For eg.

Instruction

We are going to present you with 6 pairs of Shampoo brands. For each pair, please indicate which one of the two brands of shampoo in the pair you would prefer for personal use.

Recording Form

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Head and Shoulders

Head and Shoulders

Clinic Plus

Pantene

Clinic Plus

Pantene

1

0

1

1

0

0

Number of times preferred

1

0

2

A 1 in a particular box means that the brand in that column was preferred over the brand in the corresponding row. A 0 means that the row brand was preferred over the column brand. The number of times a brand was preferred is obtained by summing the 1’s in each column.

• Rank Order Scaling – A comparative scaling technique in which respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion.
• For eg. Rank the various brands of toothpaste in order of preference. Begin by picking out the one brand that you like the most and assign it a number 1. The least preferred brand should be assigned a rank of 4.

Brand Rank Order

_______

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_______

_______

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_______

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Colgate __

Close Up

Pepsodent

Meswak

• Constant Sum Scaling – In constant sum scaling, respondents allocate a constant sum of units, such as points among a set of stimulus objects with respect to some criterion. Respondents may be asked to allocate 100 points to attributes of a toilet soap in a way that reflect the importance they attach to each attribute. If an attribute is unimportant, the respondent assigns it zero points. If an attribute is twice as important as some other attribute, it receives twice as many points. The sum of all the points is 100. Hence, the name of the scale.

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• Q-Sort – Q-sort scaling was developed to discriminate among a relatively large number of objects quickly. This technique uses a rank order procedure in which objects are sorted into piles based on similarity with respect to some criterion. For eg respondents are given 100 attitude statements on individual cards and asked to place them into 11 piles, ranging from “most highly agreed with” to “least highly agreed with”.

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Non Comparative Scaling Techniques

Respondents using a noncomparative scale employ whatever rating standard seems appropriate to them. They do not compare the object being rated either to another object or to some specified standard, such as “your ideal brand.”

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• Continuous Rating Scale – In a continuous rating scale, also referred to as a graphic rating scale, respondents rate the objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. Thus, the respondents are not restricted to selecting from marks previously set by the researcher.

For eg. How would you rate Big Bazaar as a department store?

Probably the worst ------------------- Probably the best

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Probably the worst ------------------- Probably the best

0 10 20 30 40 50

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Itemized Rating Scales – In an itemized rating scale, the respondents are provided with a scale that has a number or brief description associated with each category. The categories are ordered in terms of scale position; and the respondents are required to select the specified category that best describes the object being rated.

• Likert Scale – It is a widely used rating scale that requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus objects. Typically, each scale item has five response categories ranging from “strongly disagree” to “strongly agree”.

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Example of Likert Scale

Listed below are different opinions about Spar Hypermarket. Please indicate how strongly you agree or disagree with each by using the following scale:

1= Strongly disagree

2=Disagree

3=Neither agree nor disagree

4=Agree

5=Strongly agree

Strongly Diasgree Disagree Neither agree nor disgree Agree Strongly Agree

1. Spar sells high-quality merchandise.
2. Spar has poor in store-service.
3. I like to shop at Spar.
4. Sears charges fair prices.

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• Semantic Differential Scale – The semantic differential is a seven-point rating scale with end points associated with bipolar labels that have semantic meaning. In a typical application, respondents rate objects on a number of itemized, seven point rating scales bounded at each end by one of two bipolar adjectives, such as cold and warm. The respondents mark the blank that best indicates how they would describe the object being rated. For eg.

Instructions

This part of the study measures what certain department store means to you by having you judge them on a series of descriptive scales bounded at each end by one of two bipolar adjectives. Please mark (X) the blank that best indicates how accurately one or the other adjective describes what the store means to you.

Form

Big Bazaar is:

Powerful _:_:_:_:_:_:_: Weak

Unreliable _:_:_:_:_:_:_: Reliable

Modern _:_:_:_:_:_:_: Old-fashioned

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• Stapel Scale – It is a unipolar rating scale with 10 categories numbered from -5 to +5, without a neutral point (zero). This scale is usually presented vertically. Respondents are asked to indicate how accurately or inaccurately each term describes the object by selecting an appropriate numerical response category. The higher the number, the more accurately the term describes the object.
• For eg. Please evaluate how accurately each word or phrase describes each of the department stores.

Form : Big Bazaar

+5 +5

+4 +4

+3 +3

+2 +2

+1 +1

High Quality Poor Service

-1 -1

-2 -2

-3 -3

-4 -4

-5 -5

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Multi-Dimensional Scaling

• MDS is a statistical technique for taking the preference and perceptions of respondents and representing them on a visual grid, called perceptual maps.

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• Potential customers are asked to compare pairs of products and make judgments about their similarity. Whereas other techniques obtain underlying dimensions from responses to product attributes identified by the researcher, MDS obtains the underlying dimensions from respondents’ judgments about the similarity of products.

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• The underlying dimensions come from respondents’ judgments about pairs of products.

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For eg. Given a matrix of perceived similarities between various brands of deodorants, MDS plots the brands on a map such that those brands that are perceived to be very similar to each other are placed near each other on the map and those brands that are perceived to be very different from each other are placed far away from each other on the map.

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Low Price

High Price

Strong Fragrance

Mild Fragrance

Revlon

Dove

Eva

Yardley

Liril

• Thurstone Equal appearing Interval Scale –
• Collect statements on the topic from people holding a wide range of attitudes, from extremely favorable to extremely unfavorable. For this example, we will use attitude toward action movies.
• Action movies are thrilling.
• Action movies make the kids courageous.
• Action movies reduce the boredom of our life.
• Action movies provoke the kids to perform dangerous actions.

2. Duplicate and irrelevant statements are omitted. The rest are typed on 3/5 cards and given to a group of people who will serve as judges.

3. Originally, judges are asked to sort the statements into 11 stacks representing the entire range of attitudes from extremely unfavorable (1) to extremely unfavorable (11). The middle stack is for statements which are neither favorable nor unfavorable. Only the end points and the midpoint are labeled. The assumption is the intervening stacks will represent equal steps along the underlying attitude dimension.

Rate each of the following statements indicating the degree to which the statement is unfavorable or favorable to action movies.

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Unfavorable

Favorable

Neutral

Contd.

4. Each statement will have a numerical rating (1 to 11) from each judge, based on the stack in which it was placed. The number or weight assigned to the statement is the average of the ratings it received from the judges.

If the judges cannot rate the item on its favorability or show a high degree of variability in their judgments, the item is eliminated.

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Characteristics of good Measurement

• Reliability – Refers to the extent to which a scale produces consistent results if repeated measurements are made. Approaches for assessing reliability include:
• Test-Retest Reliability – Respondents are administered identical sets of scale items at two different times under as nearly equivalent conditions as possible. The time interval between tests or administrations is, typically two to four weeks.
• Alternative forms Reliability – Two equivalent forms of the scale are constructed. The same respondents are measured at two different times, usually two to four weeks apart, with a different scale form being administered each time.

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• Validity – Extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured. Researchers may assess:
• Content Validity – It is a subjective but systematic evaluation of how well the content of a scale represents the measurement task at hand.
• Criterion Validity – Reflects whether a scale performs as expected in relation to other variables selected as meaningful criteria. It may include demographic and psychographic characteristics, attitudinal or behavioral measures.
• Construct Validity – Addresses the question of what construct or characteristic the scale is, in fact, measuring.

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• Possibility – Addresses whether the research is possible or not. It includes:
• Time possibility – whether it is possible in terms of time period available.
• Money possibility – whether sufficient funds are available to conduct the research.

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Questions

• What is multi dimensional scaling? 3 Marks
• What are the advantages of secondary data? 3 Marks
• What is multi dimensional scaling? Give an example in what study is it useful. 3 Marks
• What is panel data? What are its merits and demerits? 3 Marks
• Why are scaling techniques used? 3 Marks
• Secondary data are by far the most popular sources of marketing information. Elaborate. 7 Marks
• Questionnaires are the back bones of most surveys and require careful planning and execution. Explain. 7 Marks
• Distinguish between Likert scale and Semantic Differential scale with illustrations for both. 7 Marks
• What are the issues involved in question phrasing? 7 Marks
• Which are the sources of secondary data? Explain. 7 Marks
• Discuss various methods of collecting data from primary sources. 7 Marks
• Explain the advantages and disadvantages of secondary data. 7 Marks
• Explain the various steps involved in designing a questionnaire. 7 Marks
• A consumer durable company manufacturing TV sets wants to know customers brand perception in the color TV segment. Prepare a questionnaire for consumers to conduct marketing research. 7 Marks
• Develop a seven item scale to measure the attitude of the consumers towards Café coffee Day. 10 Marks
• What is syndicated data? Explain each one of them in detail 10 Marks
• Describe nominal, ordinal, interval and ratio scales as used in business research giving suitable examples. 10 Marks
• Design a questionnaire to determine the penetration of various brands of washing detergent powder in rural markets. 10 marks
• A leading advertising agency in Bangalore wants to study the casual clothing preferences of today’s youth. Design a questionnaire to suit the purpose. 10 Marks
• A cosmetic manufacturing company wants to ascertain the perception of the customers towards the product. Construct a 7 item scale to measure the perception of the product using Likert and Semantic Differential Scale.

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Module 5

Research Methodology - Data Analysis & Report Writing

Topics to be covered

• Data Analysis : Editing, Coding, Classification, Tabulation, Analysis and Interpretation
• Statistical Analysis of Business Research: Bivariate Analysis (Chi-square)
• Multivariate Analysis – Factor Analysis, Discriminant Analysis, Cluster Analysis, Conjoint Analysis
• ANOVA – One-way & Two-way classification

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Difference between Data and Information

• Any raw facts or figures is known as data.
• When the data is processed by doing statistical analysis and some conclusion can be drawn from it, it is known as information.

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Steps in Processing of Data

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Questionnaire checking

Editing

Coding

Tabulation

Data Cleaning

Statistically adjusting the data

Selecting a Data Analysis Strategy

• Questionnaire checking – The initial step in questionnaire checking involves a check of all questionnaires for completeness and interviewing quality. A questionnaire returned from the field may be unacceptable for several reasons:
• Part of the questionnaire may be incomplete.
• The pattern of responses may indicate that the respondent did not understand or follow the instructions.
• The responses show little variance.
• The questionnaire is answered by someone who does not qualify for participation.
• The returned questionnaire is physically incomplete, one or more pages are missing.

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• Editing – Review of the questionnaires with the objective of increasing accuracy and precision. It consists of screening questionnaires to identify illegible, incomplete, inconsistent or ambiguous responses. This can be done in two stages:
• Field Editing – Objective of field editing is to make sure that proper procedure is followed in selecting the respondent, interview them and record their responses. The main problems faced in field editing are:
• Inappropriate Respondents – Instead of house owners, tenant is interviewed.
• Incomplete interviews, 3. Improper understanding, 4. Lack of consistency, 5. Legibility, 6, Fictitious interview – Questionnaires are filled by interviewer himself without conducting the interview.

b) Office Editing – It is more thorough than field editing. Problems of consistency, rapport with respondents are some of the issues which get highlighted during office editing.

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Example of Inconsistency:

A respondent indicated that he doesn’t drink coffee, but when questioned about his favorite brand, he replied ‘BRU’.

Treatment of Unsatisfactory Responses

Returning to the field – Questionnaires with unsatisfactory responses may be returned to the field, where the interviewers recontact the respondents.

Assigning missing value – Editor may assign missing values to unsatisfactory responses. This approach may be desirable if 1) the number of respondents with unsatisfactory responses is small, 2) the proportion of unsatisfactory responses for each of these respondents is small, or 3) the variables with unsatisfactory responses are not the key variables.

Discarding unsatisfactory respondents – This is possible only when proportion of unsatisfactory respondents is small or the sample size is large.

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• Coding – Coding refers to those activities which helps in transforming edited questionnaires into a form that is ready for analysis. Coding speeds up the tabulation while editing eliminates errors. Coding involves assigning numbers or other symbols to answers so that the responses can be grouped into limited number of classes or categories. The code includes an indication of the column and data record it will occupy. For eg. Sex of respondents may be coded as 1for males and 2 for females.

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• Tabulation – Refers to counting the number of cases that fall into various categories. The results are summarized in the form of statistical tables. The raw data is divided into groups and sub-groups. The counting and placing of data in a particular group and sub-group are done. The tabulation involves:
• Sorting and counting.
• Summarising of data.

Tabulation may be of two types:

1. Simple tabulation – In simple tabulation, a single variable is counted.
2. Cross tabulation – Includes two or more variables, which are treated simultaneously.

Tabulation can be done entirely by hand, or by machine, or by both hand and machine.

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Sorting and counting of data: Sorting can be done as follows:

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Format of a Blank table

Table No.

TITLE – Number of children per family

Head Note – Unit of measurement

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Sub-Heading

Caption

Body

Foot note

Total

Sub heading indicates the row title or the row headings. Caption indicates what each column is meant for.

Body of the table gives full information of the frequency.

Kinds of Tabulation

1. Simple or one-way tabulation – The multiple choice questions which allow only one answer may use on-way tabulation or univariate. The questions are predetermined and consist of counting the number of responses falling into a particular category and calculate the percentage.

Example

Table 14.1: Study of number of children in a family

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2. Cross Tabulation or Two-way Tabulation – This is known as Bivariate Tabulation.The data may include two or more variables.

Eg. Popularity of a health drink among families having different incomes.

Table 14.3: Use of Health Drink

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• Data cleaning – Includes consistency checks and treatment of missing responses. Although preliminary consistency checks have been made during editing, the checks at this stage are more thorough and extensive, because they are made by computer.

Consistency checks – Identify data that are out of range, logically inconsistent or have extreme values. For eg. A respondent may indicate that she charges long distance calls to a calling card, although she does not have one.

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Treatment of missing responses – Missing responses represent values of a variable that are unknown, either because respondents provided ambiguous answers or their answers were not properly recorded.

1. Substitute a Neutral Value – A neutral value, typically the mean to the variable, is substituted for the missing responses.
2. Substitute an Imputed Response – The respondent’s pattern of responses to other questions are used to impute or calculate a suitable response to the missing questions.
3. Casewise Deletion – Cases or respondents with any missing responses are discarded from the analysis.
4. Pairwise deletion – Instead of discarding all cases with any missing values, the researcher uses only the cases or respondents with complete responses for each calculation. As a result, different calculations in an analysis may be based on different sample sizes.

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• Statistically Adjusting the Data – If any correction needs to be done for the statistical analysis, the data is adjusted accordingly.
• Selecting a Data Analysis Strategy – The selection of a data analysis strategy should be based on the earlier steps of the marketing research process, known characteristics of the data, properties of statistical techniques and the background and philosophy of the researcher.

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Univariate Techniques

Univariate techniques are appropriate when there is a single measurement of each element in the sample, or there are several measurements of each element but each variable is analyzed in isolation.

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Multivariate Techniques – Suitable for analyzing data when there are two or more measurements of each element and the variables are analyzed simultaneously. Concerned with the simultaneous relationships among two or more phenomena.

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Multivariate techniques differ from univariate techniques in that they shift the focus away from the levels (averages) and distributions (variances) of the phenomena, concentrating instead upon the degree of relationships among these phenomena.

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• Bivariate Technique – Chi-square
• Multivariate Techniques

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• Dependence Techniques are appropriate when one or more variables can be identified as dependent variables and the remaining as independent variables.
• In interdependence techniques, the variables are not classified as dependent or independent, rather the whole set of interdependent relationships is examined.

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Dependence Techniques

Interdependence Techniques

• Discriminant Analysis
• Conjoint Analysis

• Factor Analysis
• Cluster Analysis

Chi-square Test

• The chi-square test is represented by the symbol χ2 and owes its origin to greek letter chi. This test was first used by Karl Pearson and is one of the most widely used test today.
• Through the test, we are able to determine the extent of difference between the theory or expected value and the observed or the actual value.

χ2 = ∑ (O-E)2

E

Where, O = Observed frequencies

E = Expected frequencies

• It is particularly useful in tests involving nominal data. The chi distribution is positive but an asymmetrical distribution.
• It has only one parameter, namely degrees of freedom. Degrees of freedom refers to the number of classes to which a value can be assigned freely without exceeding the limitation placed.

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Applications of chi-square:

• Chi-square as a test of independence – We can establish if two or more attributes are associated or independent. Eg. A doctor may be interested in knowing if the new BCG vaccine is effective in controlling the target diseases or not. If there is any relationship between divorce rates and working wives.

We start with the null hypothesis that there is no association between the attributes specified .

Acceptance criteria – Calculated value is less than the table value, null hypothesis is accepted.

Rejection Criteria – Calculated value is greater than the table value, null hypothesis is rejected.

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• As a test of goodness of fit – It can be used to determine how well a theoretical distribution (poisson or normal) fits on the observed data or how appropriately a theoretical distribution fits empirical distribution.
• As a test of homogeneity – Used to find out if two or more randomly selected independent samples have been drawn from the same population or not.

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Conditions for chi-square Test

• The total number of items should be large enough to guarantee a modem of similarity between the theoretically correct distribution and the sampling distribution being studied.
• The number of items in each group should be reasonable.
• The observation used in the study should be collected on the random basis so that there is no element of bias.
• Data given should be in original and absolute form and not in relative form like proportions, percentages etc.

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Merits of chi-square test

• It can be used without making any assumption about the form of parent distribution or its parameters, since it is based on observed frequencies and not on parameters like mean and standard deviation.
• It is a distribution free test and hence can be used in any type of population distribution.
• Additive property of chi-square test that allows the researcher to add the results of independent but related samples.
• It is easy to calculate and interpret.

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Demerits of chi-square

• It has some limitations associated with it.
• It is used for testing hypothesis but is not useful for estimation.
• It is not as reliable as parametric tests.

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Discriminant Analysis

In this analysis two or more groups are compared. Discriminant Analysis is a technique for analyzing data when the dependent variable is categorical and the independent variables are interval in nature. For eg. The dependent variable may be the choice of a brand of personal computer and the independent variables may be ratings of attributes of PCs on a seven-point Likert scale.

Eg. Where discriminant analysis is used:

1. Those who buy our brand and those who buy competitor’s brand.
2. Heavy user, medium user and light user of the product.

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The value of dependent variable is calculated by using the data of independent variable.

Z = b1x1+b2x2+b3x3+…………

Where, Z = Discriminant score

b1 = Discriminant weight for variable

x1 = Independent variable

The objectives of discriminant analysis:

• Development of discriminant functions, or linear combinations of independent variables, which will best discriminate between the categories of the dependent variable.
• Examination of whether significant differences exist among the groups, in terms of independent variable.
• Determination of which independent variables contribute to most of the intergroup differences.
• Classification of cases to one of the groups based on the values of the independent variable.
• Evaluation of the accuracy of classification.

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Example of Discriminant Variable

A study of 294 consumers was undertaken to determine the correlates of rebate proneness, or the characteristics of consumers who respond favorably to rebate promotions.The independent variables were four factors related to household shopping attitudes and behaviors, and selected demographic characteristics (sex, age and income). The dependent variable was the respondents degree of rebate proneness, of which three levels were identified. Respondents who reported no rebate-triggered purchases during the past 12 months were classified as nonusers, those who reported one or two such purchases as light users and those with more than two purchases, frequent users of rebates.Two findngs emerged: Rebate sensitive customers associate less effort with fulfilling the requirements of the rebate purchase, And consumers who are aware of the regular prices of products are more likely to respond to rebate offers.

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• When the dependent variable has two categories, the technique is known as two-group discriminant analysis. When three or more categories are involved, the technique is referred to as multiple discriminant analysis.

Example of Discriminant analysis:

• In terms of demographic characteristics, how do consumers who exhibit store loyalty differ from those who do not?
• Do heavy, medium, and light users of soft drinks differ in terms of their consumption of frozen foods?

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Conjoint Analysis

Conjoint Analysis attempts to determine the relative importance consumers attach to salient attributes and the utilities they attach to the levels of attributes.

This information is derived from consumer’s evaluation of brands, or brand profiles composed of these attributes and their levels. The respondents are presented with stimuli that consist of combinations of attribute levels. They are asked to evaluate these attributes in terms of their desirability.

In a situation where the company would like to know the most desirable attributes or their combination for a new product or service, the use of conjoint analysis is most appropriate.

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Example

• An airline would like to know, which is the most desirable combination of attributes to a frequent traveller: a) Punctuality, b) Air fare, c)Quality of food served on the flight, and d) Hospitality and empathy shown.
• A comparison between the utility of a price level of Rs. 400 versus Rs 500, a delivery period of 1 week versus 2 weeks or an after-sales response of 24 hours versus 48 hours.
• Eg. Of Conjoint Analysis for a laptop:

Weight (3kg or 5 kg), Battery life (2 hrs or 4 hrs), Brand name ( Lenovo or Dell)

Rank order the combination of the characteristics, i.e. 3kg, 2hrs, Lenovo

5kg, 4 hrs, Dell

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Three steps in Conjoint Analysis

• Identification of relevant products or service attributes.
• Collection of data.
• Estimation of worth for the attribute chosen.

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Conjoint Analysis has been used in marketing for a variety of purposes:

• Determining the relative importance of attributes in the consumer choice process.
• Estimating market share of brands that differ n attribute levels.
• Determining the composition of the most preferred brand.
• Segmenting the market based on similarity of preferences for attribute levels.

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Factor Analysis

Factor Analysis is a general name denoting a class of procedures primarily used for data reduction and summarization.

In marketing research, there may be a large number of variables, most of which are correlated and which must be reduced to a manageable level.

Relationships among sets of many interrelated variables are examined and represented in terms of a few underlying factors.

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Example of Factor Analysis

Respondents in a survey were asked to rate the importance of 15 bank attributes. A five point scale ranging from not important to very important was employed. These data were analyzed via principal component analysis.

A four factor solution resulted, with the factors being labeled as traditional services, convenience, visibility and competence. Traditional services included interest rates on loans, easy to read monthly statements and obtainability of loans. Convenience was comprised of convenient branch location, convenient ATM locations, speed of service, and convenient banking hours. The visibility factor included recommendations from friends and relatives and attractiveness of the physical structure. Competence consisted of employee competence and availability of extra services.

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Two most commonly employed factor analysis procedures:

• Principal Component Analysis – When the objective is to summarise information from a large set of variables into fewer factors, principal component factor analysis is used.
• Common factor analysis – If the researcher wants to analyse the components of the main factor, common factor analysis is used.

Eg. Common Factor – Inconvenience inside a car.The components may be:

1. Leg Room
2. Seat arrangement
3. Entering the rear seat
4. Door locking mechanism.

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Principal Component Factor Analysis

Customer feedback about a two wheeler manufactured by a company.

Identified six variables:

1. Fuel efficiency
2. Durability of life
3. Comfort
4. Spare parts availability
5. Breakdown frequency
6. Price

Grouping of 1,2,4,5 into Factor 1(Technical Factor)

6 into Factor 2 ( Price Factor)

3 into Factor 3 (Personal Factor)

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Factor analysis is used in the following circumstances:

• To identify underlying dimensions or factors that explain the correlations among a set of variables.
• To identify a new, smaller set of uncorrelated variables to replace the original set of correlated variables.
• To identify a smaller set of salient variables from a larger set for use in subsequent multivariate analysis.

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Applications of Factor Analysis

• It can be used in market segmentation for identifying the underlying variables on which to group the customers.
• In product research, factor analysis can be employed to determine the brand attributes that influence consumer choice.
• In advertising studies, to understand the media consumption habits of the target market.
• In pricing studies, to identify the characteristics of price sensitive consumers.

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Cluster Analysis

Cluster Analysis is a class of techniques used to classify objects or cases into relatively homogeneous groups called clusters. Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters.

Cluster Analysis is used:

• To classify persons or objects into small number of clusters or group.
• To identify specific customer customer segment for the company’s brand.

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Applications of Cluster Analysis

• Segmenting the market: For eg. Consumers may be clustered on the basis of benefits sought from the purchase of a product.
• Understanding buyer behaviors
• Identifying new product opportunities – By clustering brands and products, competitive sets within the markets can be determined. A firm can examine its current offerings compared to those of its competitors to identify potential new product opportunities.
• Selecting test markets
• Reducing data- Data reduction tool to develop clusters or subgroups of data.

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Example of Cluster Analysis

In a study examining decision making pattern among international vacationers, 200 respondents were clustered into six clusters. Namely respondents preferring vacations for psychological purpose, educational purpose, social, relaxational , physiological and aesthetic purpose. The behavior of vacationers was studied and appropriate promotional strategies were designed for each of the clusters.

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Questions

• What is conjoint analysis? (3 Marks)
• What are editing and coding? 3 Marks
• Distinguish data from information. 3 Marks
• What are the measures of central tendency? 3 Marks
• What is cross tabulation? Give an example. 3 Marks
• Explain the problems during editing of data. 7 Marks
• What is editing? What is its importance? 7 Marks
• What is cluster analysis? What are the steps involved in it? Explain. 7 Marks
• What is tabulation? What are the types of tabulation? 7 Marks
• Explain the problems during editing of data. 7 Marks
• Write short notes on the following:

i) Conjoint Analysis, ii) Cluster Analysis, iii) Discriminant Analysis, iv) Chi-square Test. 10 Marks

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