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CHAPTER 1 CONCEPT OF RESEARCH

1.0 INTRODUCTION Research can be called as a purposive investigation. The objectives of the research could be gaining familiarity with research objectives (exploratory research), to describe the characteristics of a market or many markets as well as of consumers, to decide the frequency with which some phenomenon (say stock-out situation for essential goods distributed as ration-card), to test hypothesis, etc. The significance / importance of the research can be understood from the fact that it provides the basis for all policies and strategies may be for any marketer or even to government. There are many types of research like descriptive, analytical, applied, basic, quantitative and qualitative, etc. 1.1 DEFINITIONS OF RESEARCH

(1) It is the activity which extends, corrects and verifies the knowledge.

(2) It is the activity of finding new ways of looking at known / familiar data in order to explore new ways to change it as expected / intended.

(3) Research is the process which involves the steps like defining the problem, identifying research objectives, formulating hypothesis, collecting and interpretation of data, deriving findings, conclusions and then identifying the action plan.

(4) Research is the well planned activity which is designed and implemented to provide the data for solving important genuine and recurrent problems.

(5) Research is the activity which involves manipulation of things, concepts or symbols for the purpose of generalizing to extend, correct or verify knowledge, whether that knowledge aids in construction of theory or in the practice of an art.

(6) Research can also be said as a movement from the known to unknown facts

(7) It is the systematized efforts to gain new knowledge.

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1.2 DEFINITIONS OF METHODOLOGY AND RESEARCH METHODOLOGY “Methodology is a term which should not be misused for “method” or “technique”. Methodology has got an important meaning. It becomes first an approach towards inquiry and or research then later evolves into particular methods or techniques. In the applied use it is concerned with selecting specific technical tools and techniques for collecting data and analyzing it. In the theoretical use, it is concerned with the philosophical fields of inquiry that can be used to conceptualize the problem under study. There are two different methodological stances. Discipline research is oriented towards enriching knowledge in a scientific discipline whereas policy research denotes to another methodology that is philosophically committed and serve as a guide to social action. Quite often methodology is used in the applied sense undermining its theoretical perspectives, though both are the two sides of a coin. Some of the definitions are as follows: (1) Methodology in the applied sense refers to various methods used

by the researcher right from data collection and various techniques used for the same for interpretation and inference. Methods and techniques are often used synonymously in research literature. Research methodology is what must be done, how it will be done, what data will be needed, what data gathering will be employed, how sources of data will be selected and how the data will be analyzed and conclusions reached. When we talk of research methodology we not only talk of the research methods but also consider the logic behind the methods we use in the context of our research study and explain why we are using a particular method or technique and why we are not using others so that research results are capable of being evaluated either by the researcher himself or by others.

(2) Research methodology is “a procedure designed to the extent to which it is planned and evaluated before conducting the inquiry and the extent to which the method for making decisions is evaluated”. The word methodology is used freely in different context.

(3) Methodology is not merely description of methods / set of methods or techniques. Techniques are aids to research like are aids to research like regression and correlation. Methodology provides

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arguments, perhaps relationships, which support various preferences entertained by the scientific community for certain rules of intellectual procedure, including those for forming concepts, building models, formulating hypotheses and testing theories. Methodology is neither a study of ‘good’ methods nor a study of ‘methods’ used but rather a study of reasons behind principles on the basis of which various types of propositions are accepted or rejected as part of the body of ordered knowledge in general or of any discipline. In short, we may define methodology as the science of procedure to build, verify or extend scientific knowledge.

A thorough understanding of a scientific methodology alone will contribute an appreciable research piece. Scientific piece of investigation will provide an argument which is as true for each individual mind as of the researcher’s own mind. Therefore, the most important step in a research design is the selection of an appropriate methodology. The significance of the use of the term ‘methodology’ is that it requires an argument to connect the choice and practice of particular methods to the way that the problem is conceived and the utility and limitations of the outcome. It is in this sense of the term, as requiring a critical justification for the adoption and practice of particular research methods that we claim that our concern is with ‘methodology’ rather than with methods alone. .. Only rarely do books on research methods discuss situations in which particular methods should not be used, or situations within which the methods chosen may cause distortion or precipitate changes are the not captured by the methods themselves.

(4) Methodologically designed research can be considered as a piece of scientific work though approximation to fool proof methodology is a continuous and never ending price. Thorough knowledge about the latest development in the concerned branch is absolutely necessary for designing a scientific methodology. Research is a fact finding process through systematic and in-depth study through the various research process including collection compilation, presentation and interpretation of derails or data. The only way to

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find truth and gain knowledge is the scientific method of investigation.

The researcher must be able to give clear scientific explanation and the logic behind them for a host of the following questions:

1. Why the particular research study is undertaken?

2. How the research problem is stated?

3. What are approaches towards the inquiry?

4. What are the tools and techniques that will be used for data collection? Why this method is adopted? Is the sampling design appropriate?

5. How the hypotheses have been framed?

6. How the hypotheses will be tested?

7. How the various statistical tools and techniques are selected. What is the method of data processing? How it will be calculated?

8. Which techniques are used to evaluate the accuracy of results?

The framing of a good research methodology is compared to that of an architect who designs a building, i.e., “he has to evaluate why and on what basis he selects particular size, number and location of doors, windows and ventilators, uses particular materials and others and the like”. Literature review and interactions with experts will help one to sharpen the methodology. The external examiners, who evaluate the thesis, approve one which is conceptually, methodologically and factually correct and to the best of his knowledge it is free from errors and plagiarism (copying) and sufficiently of good standard. The research quality that equates to international excellence or national excellence in all areas or in majority of the areas of a thesis to a great extent depends on the formulation of a good and scientific research methodology. Finally, the methodology adopted should be open to pubic so that others can know how one reached the conclusions about a study. The means of method of enquiry is opened for public evaluation and criticism. 1.3 OBJECTIVES OF THE RESEARCH

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The basic purpose of the research is to identify the action plan to answer to questions through the application of scientific procedures. The key objectives of the research could be as follows: (1) To become familiar with a certain mechanism or phenomenon or to

gain new insights into it. This type of research is normally called as exploratory research design. For example, before parliamentary elections all political parties conduct research on priorities of the voters so as to prepare election promotion plan.

(2) To describe accurately the characteristics of a particular consumer-group / segment, market situation or an industry. For example in Indian Tourism marketing per capita spending power of domestic tourists in Rest. 3500 per annum whereas spending power of inbound and outbound tourist is Rest. 58000 p.a. As regards to market situation is concerned, we can describe Indian Software industry revolving around only financial solutions for banking sector. We can also describe Indian home Appliances Industry as a market worth Rs. 48000 crores, growth rates 7.10% and having intense completion, but main dominant players are MNCs like LG, Samsung and home grown Videocon international.

(3) To identify the frequency with which some phenomenon, say stock-out, occurs or the causes associated with the particular phenomenon. For example in a thick populated area, there could be frequent stock-out of most essential goods like LPG-cylinders, kerosene, food grains, etc. State Govt. may be wanted to know frequency of stock out and causes like distribution bottlenecks, etc. The leading retailers of India like Food world, Shoppers Stop, etc. conduct stock taking and or implement latest SCM software to avoid the situations of stock out. Similarly many industries in India and in globe, conduct research to study business-recession cycle frequency. For example the global recession during 1991 and 1997 was well predicted before occurrence. In similarly way the recession to Indian Telecom Industry during 2000-2002 was also predicted. Current predictions about some of the industries are management education, especially MBA and MCA is booming and

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will appreciate for another 10 years. There is great demand for MBAs having engineering background.

(4) To formulate and test a hypothesis may be to establish the relationship between say sales and market share or sales and customer satisfaction, level, etc. For example, Gujarat Cooperative Milk Marketing Federation (GCMMF) was under impression that it has 20% market share for liquid milk (Amul-Taza) at Nagpur during 2004. It conducted research for which hypothesis was formulated and tested. The reality was, Amul’s market share formulated and tested. The reality was Amul’s market share was only 11.5% at Nagpur during 2004. Exactly reverse happened incase of Cadbury’s Dairy milk chocolates. It thought that its market share during 2004 is 70% whereas it was 72% across four metros and nine mini metros. After 1999, Videocon’s market share in CTV market was on decline. It wanted to know the cause. The research indicated that Videocon needed rectification in brand-image. Videocon immediately responded and appointed Shahrukh Khan as a brand-ambassador.

1.4 MOTIVATIONS IN RESEARCH Why to conduct the research? The motivations could be as follows: (1) Aspiration to derive the consequential benefits due to the

research . For example, till 2004, the brand image of Lux soap was ‘The soap of the stars’. This positioning worked for 50 years. However during 2005, the brand lost the magic. The research was done by HLL, which indicated that (a) positioning should be changed to glamour and luxury and (b) it should come out from the image product for female. HLL repositioned Lux like Luxurious product that make her feel beautiful and special’ with punch line, ‘Brings out stars in you’ HLL also changed celebrity from female actress to male actor Shahrukh Khan. Both things are now working to bring out Lux from red to black and white. Lifebuoy soap was also suffering from image, like ‘Tandurasti Ki Raksha Karta hai Lifebuoy’. The research said that repositioning and brand extension is must. HLL repositioned Lifebuoy from

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Tough Soap and Tandurasti to Germicidal perfume and extended to lifebuoy Gold and Lifebuoy Plus. The obvious benefit of rising sales is the contribution of the research. During 2000 AD, HLL’s all tea brands were suffering from competition due to which growth in sales became stagnant. India’s tea market size is Rs. 6000 crores, in which branded tea market occupies Rs. 2500 cr and unbranded or loose tea occupies Rs. 3500 cr. Research indicated that growth would be only in loose tea market. HLL successfully launched ‘A1’ brand tea to snatch the customers from loose tea market, with punch line ‘strong cup of tea’ and market segments focused are housewife and journalists. The philosophy used, ‘due to strong cup of tea, ordinary man like housewife and or a journalist get courage to face difficult situations in the life.

(2) Intention to face the challenges in overcoming the competition. During 2005, P&G reduced the prices of the detergent Tide considerably. For example its price was Rs. 40 for 500 gm. HLL quickly responded to the change and launched the brand extension of Surf ‘Surf Excel Blue’ for price of Rs. 50 for 750 gm. To sustain the competition from band ambassador Shekhar Suman, HLL opted comparably unknown faces with the statements and counter statements like Jayga-------- Nahi Jayyega. The well thought research by HLL for surf Excel Blue is clear winner. Tide is in big problem.

(3) Intention to apply research for successful crea tivity. During 21st century, washing machine almost became house-hold item. However, due to heavy traffics, habits towards consuming fast food at road side restaurants became very common. The result was the “dust” and “blackened cloths”. Marketers identified the priority of the consumer -------- she wanted to get rid of ‘daag’. HLL P&G successful developed the ad-campaigns, ‘Dundate Raha Jayoge’, Kuch Pane ke Liye Kuch Dhona Padata hai’, ‘Daag Acche Hai’, etc.

(4) Intention to integrate societal marketing (soci al welfare) with main strategic marketing.

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Consumer perception about the company and company products is very crucial to understand from marketing point of view. If social welfare is integrated with marketing strategy then it could have cutting edge. History proved that due to social welfare, the image of the company could be changed. For example, Tisco says manufacturing steel is our second priority. Our first; priority is society welfare. Hence Tisco say we also manufacture steel. The result ------------- in last 90 years there are no strikes and no production losses in spite of the location of factory on the border of Bihar and Orissa where labour and labour union problems are dominant. P&G also designed ‘Project Dhrusti’ for ‘Whisper’ and ‘Mr. Gold’ for all products under umbrella brand. HLL ‘Shakti Amma’, ‘Mobile hospital’ are winner society welfare projects, which helped to project favourable image.

(5) Intention to get respectability within the coun try and out of the country. India’s most successful IT trio is Infosys, Wipro and TCS. India and Indians are respected in globe due to the powerful brain of Narayan Moorthy, Azim Premjee and Mr. S. Ramadorai, CEO, TCS. The respectability is the result of lot of hard work to deliver the value to the stakeholders. For shareholders the respect is due to very attractive dividend and stock price. For country, the respect is due to forex earning ability and employment generation. For global countries the respect is due to its intelligence, price, service-delivery and out sourcing ability. I always admire the skill of the trio and hence would like to describe, ‘Real Gold of Indian IT’. (Per employee profit after tax for Infosys during 04-05 was Rs. 5,00,000 for TCS Rs. 4,40,000 and for Wipro Rs. 3,70,000). Please read article 1.35 India’s most Admired Companies.

1.5 IMPORTANCE OF THE RESEARCH

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Significance of research and research leads to invention. Following facts highlight the importance of the research

(1) Research facilitates logical or scientific thinking process which leads towards flow less strategy formulation.

(2) It facilitates identification of ‘trends’ which ultimately responsible in marketing opportunities.

(3) Decision making becomes easier for well researched phenomenon.

(4) Research is important in solving various

(5) operational and planning problems of business and industry. (6) It helps understanding perception of the society about the

marketer and accordingly designs the marketing strategy.

1.6 TYPES OF RESEARCH There are many types of the research like descriptive, analytical, basic, applied, qualitative,quantitative, conceptual, etc. Let us discuss some of the research. I (a) Descriptive Research: Researcher has to report what has happened or happening like say frequency of shopping, preference for specific brands, etc. In this research, normally market data is collected through observations, mail method and or personal interviews. For example buying habits can be studied through observations. Consider that consume prefer to shop for small pack sizes of 15 gm tube, say per week / fortnight rather than bigger pack sizes like 200 gm / 250 gm, etc. Similarly Indian housewife prefers to shop daily or alternate day for vegetables to ensure freshness. Brand preferences also can be studied through observations.

1.35: India’s most admired companies – Methodology Issues covered: Most admired corporation – Ranked Attributes

• Management Quality • Financial Performance • Returns to shareholders • Growth Prospects • Company Ethics

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Data collection Method: Quantitative, personal interviews with 200 respondents using a structure questionnaire. Target Respondent: Officer / Executive:

- with 2-5 years work experience, ‘or’ - with 5+ years with work experience, ‘or’ - with 5+ years of experience and is a CEO/MD/VP

Sampling: Purposive sampling – 100 face-to-face interviews done at home / office. Overall Index – Calculation

• The respondents were asked to divide 100 points, amongst these attributes according to the importance they attach to each of the attributes

• The average importance for that attribute was then calculated. This average importance was then used as a weight to arrive at the overall ranking of each company

• The top 5 ranks were taken. For each attribute 5 marks were given to the company which was ranked as 1 on that attribute. Similarly, 4 marks were given for rank 2, 3 marks for rank 3 and so on. 1 mark was given for rank 5.

• Average marks for each company were calculated and weights were then attached to them at the company level.

• Thus the overall composite index for the companies was arrived at.

Company Weights Weights used to arrive at overall index for companies * Management Quality 32.0 * Financial Performance 24.1 * Growth Prospects 16.6 * Returns to Shareholders 15.6 * Ethics 11.7 100.0

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0

20

40

60

80

100

120

Ethics 13

Growth Prospects 17

Returns to shareholders 17

Financial performance 25

Management quality 29

1

0

5

10

15

20

25

30

Series1 9 10 10 12 13 13 15 20 26 30

Hero

HondaHDFC Maruti TCS

Bharti

Telecom

Hindust

an

Lever

Tata

Motors

Relianc

e

Indsutrie

Wipro

Infosys

Technol

ogies

(b) Analytical Research: The researcher has to use secondary data

(information which is already available) and analyze it to make a critical evaluation of the situation. It may even aim testing hypothesis, specifying and interpreting relationships. In this research correlation technique is used to analyze the data.

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Example: (i) after union Budget is presented in Lokasabha during February of each year, analytical research is conducted by leading news channels to enlighten the viewer’s impact of budget on various commodities, industries and stock markets. (ii) During month of May every year i.e. after financial year ending, Economic Times conducts analytical research on performance of various industry sectors in total exports from India. One major finding is Agri Export’s share in total exports is declining every year.

II (a) Basic or Pure Research : The research which is done for knowledge enhancement, the research which does not have immediate commercial potential, the research which is done for human welfare, animal welfare and plant kingdom welfare is the basic or pure research. Government of India, through Census, does research on population count to identify total population of India, no. of male, female, no. of families, no. of voters, etc. One of the major findings of census is, some rural areas, proportion of female is 10% less as compared to male. In some metros and mini metros, female count is marginally less than male. This situation might create problems in future. Govt. responded quickly to this trend and have implemented ad-campaign having punch line ‘a world without women’. Discovery TV channel highlights the basic research done by Australia and US Governments towards animal welfare and plant kingdom welfare. Some of the documentaries on animal rescue operations are quite remarkable. (b) Applied Research : The research which has immediate commercial potential is called applied research. Applied research can further be classified as problem oriented and problem solving research. Problem Oriented Research – This type of research is done by Industry Apex Body for sorting out problems faced by all the companies. For example NASSCOM regularly conducts problem oriented research for the benefit of all software companies. Similarly CII does the research for all types of companies. At global level, WTO does problem oriented research for developing countries. In India, APEDA (Agriculture and

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Processed Food Export Development Authority) conducts regular research for the benefit of agri industry. Problem solving Research – This type of research is done by an individual company for the problem faced by it. For example if Videocon International conducts research to study customer satisfaction level, it will be problem solving research. The findings of problem solving research are unique and only true for that company which does the research and cannot be generalized. Whereas findings of the problem oriented research could be generalized. Market Research and Marketing Research are the applied research. III (a) Quantitative Research : It aims to measure the quantity or amount and compares it with past records and tries to project for future period. For example, total sales of soap industry in terms of rupee crores and or quantity in terms of lacs tones for a particular year, say 2005 could be researched, compared with past 5 years and then projections for 2006 could be made. (b) Qualitative Research : It is mostly useful in consumer behavioural science. The research undertaken to collect qualitative data (subjective data) in form of (i) word association (ii) sentence completion (iii) story completion (iv) picture (v) Thematic apperception test (TAT) is called qualitative research.

(i) Word Association – Words are presented, one at a time for a particular product category or service category and the respondents are asked to mention the first word that comes to their mind.

For example (a) which first word comes to your mind when you hear (Sahara) Air line services?

(i) Timely departure and arrival of flights (ii) Safety (iii) consumer friendly

(b) Which first brand comes to your mid when you think of anteceptic liquid (i) Dettol (ii) Savlon (c) Which first brand comes to your mind when you think of luxury of smoking (i) Gold Flake (ii) Wills (iii) Four-square

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(ii) Sentence completion : Respondents are asked to complete an incomplete sentence.

For example (a) when I choose flying by air, the most important consideration in my decision is ________________________ (b) When any graduate student thinks of post graduate management education, the most important parameters which influence the decision are _____________

(iii) Story completion: Respondents are asked to complete the incomplete story. Incomplete story could be as follows: ‘Three years back, when I used to fly with Indian Airlines, I never used to reach the destination in time due to the flights not taking off on time. Moreover the interiors of the plane were shabby and food quality was also not good. I never used to get the baggage in time. These facts compelled me thinking on _____________’. Now complete the story. (iv) Picture: A picture of two characters (say two airline passengers) is presented, with one character making a statement. Respondents are asked to complete the reaction of other passenger / character. Indian (Air Line) flight No. 4200 Indian Waiting Room

☺ ☺ ☺ ☺

Passenger 1 Passenger 2 Passenger 2 Passenger 1

(v) Thematic Apperception Test (TAT ) – A picture is presented and respondents are asked to make a story about what they think is happening or may happen.

Flight time Time now

� �

Well, here is the food

It’s Flight time. We are still in waiting room

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(IV) (a) Conceptual Research : It relates itself to abstract ideas, concepts or theory. It results in development of new concepts or re-interpretation of existing one. Example : Idea or concept about new brand launching could be, ‘Consumers will never buy new brand unless it offers more benefits with reasonable quality’. P&G while launching new shampoo brand Rejoice worked on above concept. While maintaining reasonable quality, it offered price benefit (Rejoice 100 ml bottle cost Rs. 38 as against Rs. 50 for clinic all clear and Rs. 55 for Sun Silk). P&G also identified that if one common theme is used for brand promotion, consumer respond favourably. For launching and thereafter promotion, P&G used the platform of R.D. Burman’s Bollywood songs like Ek Ladki Ko Dekha to Aisa Lag, Rim Zim Rim Zim, O Haseena Zulpho Wali Jane Jaha. Example : Abstract idea or myth could be, ‘consumer do not buy product but buy brand’. It is seen that this fact is half true. For low value products, consumer may buy product, whereas for high value things may not work sometimes. For example, US based marketer Metal, when launched Barbie Doll in Japan, suffered huge set back only because the doll did not look like Japanese Girl / Women. (b) Empirical Research : It relies on experience, observation. It develops hypothesis and proves that the hypothesis may be through observation or experiment. Example : After organized retailing accelerated Indian trail industry, the marketers believed that it is the manufacturers’ brands which mot6ivate trail marketing. Hypothesis was formulated with assumption that manufacturers’ brands dominate Indian retailing. Research disproved, revealing penetration of private brands to the extent of 65%. The

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hypothesis was disproved through observations as well as through experiments. V (a) One Time Research : The research for the product/brand is done only once in a year or once in few years because the market is steady and consumer tastes, preferences do not change so rapidly. For example, engineering products, industrial goods like boilers, dg sets, compressors, etc. do not require frequent changes. In such cases, it is better to buy the research from specialized research organization. (b) Longitudinal Research : If research needs to be undertaken several times in a year (2 to 3 times in a year) because of the volatility of the market, it is called longitudinal research. For consumer electronics goods and non-durables, the tastes and preferences of the consumer go on changing very soon and hence the frequent research is compulsory. In such cases, the marketer should have his own research department rather than outsourcing the research. VI (a) Field Research : The experiment done at the market place is called field research. Example : Hutch launched ‘chhota re-charge’ to increase its market share in pre-paid cards. Most of the sales promotion schemes aim at temporary acceleration of sales are the classic examples of field research. (b) Laboratory Research : The research done in-house or in the laboratory is known as laboratory research. For example, recall and recognition tests for the advertisements are done in-house. Similarly, pre-testing of the questionnaire is done in-house. VII. Clinical Research or Diagnostic Research : It is similar to descriptive research but with the difference that more emphasis is on what is happening, why it is happening, what course of action can be designed to prevent/augment the phenomenon etc. It aims at identifying cause of the problem and the possible solution. It tries to seek association between two or more variables. The technique used for data collection and analysis is Case Study, In-depth Interviews to discover causal relationship.

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Case Study: Mr.B.C.Sharma was business development management with Oberoi Group of Hotels to mange package tour programmes for domestic high net worth tourists and in-bound tourist. The Company was unable to meet tourist’s expectations on the attributes like food products, preferred hotel reservations, preferred week’s utilization, etc. Mr.B.C.Sharma left the job and conducted in-depth interviews with target customer and then launched a concept ‘Apartment on wheel’ i.e. moving apartment having cabin and beds for cook, driver, cabin for w.c., bath and kitchen and big cabin for living room containing 6 beds with A.C., mini-bar, mini-library, etc. Mr.Sharma constructed these cabins on truck chassis Tata LP42/1210. The concept succeeded for holiday locations at 8-10 hours journey. 1.7 SOCIAL RESEARCH (1) Social research can be defined as ‘a scientific undertaking, which

by using logical and system techniques, which aims at (i) discovering new facts and or verifying old facts, (ii) analyzing their sequences, inter relationships and casual explanations which could be derived within the appropriate theoretical frame of reference (iii) developing new scientific tools, concepts and theories which would facilitate reliable and valid study of human behaviour.

(2) Social research is a systematic method of exploring, analyzing and conceptualizing human life in order to extend, correct or verify knowledge of human behaviour and social life. In other words, social research seeks to find explanations to unexplained social phenomenon, to clarify the doubt and correct the misconceived facts of social life.

1.71 Objectives of social research

(i) To study human behaviour and its interaction with the environment and the social institutions.

(ii) To identify casual connection between human activities and natural laws governing them.

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(iii) To develop new scientific tools, concepts and theories which would facilitate reliable and valid study of human behaviour and social life.

1.72 Scope of social research Every group of social phenomenon, every phase of human life and every stage of past and present developments are the materials for the social scientists. Broadly it covers the areas like biological, psychological, socio-cultural, temporal and environmental factors associated with behaviour of human-beings. 1.73 Functions of social research

(i) Contribute to human understanding of social reality

(ii) Diagnosis of social problems and their analysis like poverty and crime, unemployment and poverty, economic imbalance, social tensions (e.g. security of call centres for girls working in night shift), low productivity, technological backwardness, etc.

(iii) To equip human beings with first hand knowledge about the organizing and working of the society and its institutions.

(iv) To identify the causes of social evils and problems and then formulate social welfare action plan.

1.74 Limitations of Social Research

(i) The social research is done by the scientist, who also is a human beings and part of human society and hence may have bias in research study.

(ii) Social science or human behaviour is too complex, varied and every changing. Hence experimentation cannot be standardized for longer durations.

(iii) Human behaviour can be studied by other human beings only and not by robotics. This always distorts fundamental facts

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being studied so that there can be no objective procedure for achieving the truth.

(iv) Common problems faced by researchers are refusal of sample, improper understanding of questions, loss of memory of the samples, etc.

(v) The quality of research findings and conclusions depends upon the soundness of decisions made by the social investigators on research process as correct definition of research problem, correct sample selection, appropriate statistical techniques for data processing. Any mistake in any of these decision areas will challenge the validity of research findings.

1.8 SCIENTIFIC METHOD The Scientific method is theorizing based on experimentation and is thus very close to Empiricism It also needs quite a bit of logical deduction and so appears closer to the top of the graph. The essential tenets of the scientific method are (1) direct observation of phenomena; (2) clearly defined variables, methods, and procedures; (3) empirically testable hypotheses; (4) the ability to rule out rival hypotheses; (5) statistical rather than linguistic alternative justification of conclusions; and (6) the self-correcting process.' This is the most commonly used style of thinking in research methodology. In this style of thinking hypotheses are proposed based on some proven theories and then they are practically tested. They require a considerable amount of logical deduction, but not as much as in Postulational theories. Nevertheless, the use of logical deduction is considerable. We shall discuss logical' deduction further in the following section. The Thought Process Reasoning forms the basis of the scientific inquiry. The thought process of a scientist may be based on deduction, induction or a combination of both. Let us understand in detail each of these processes of thinking needed for conducting and drawing conclusions from marketing research.

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Deduction Deduction is a form of inference where conclusion necessarily follows from the given premises, i.e. neither can the conclusion contradict the premises nor can it assume new premises. A deduction is correct if it is both true and valid. A deduction is true if the premises on which it is based are true, i.e., they agree with the real world. For example, premise like "world is flat" is a false premise. Deduction based on such premise will also be false. Deduction is valid if it is impossible for the conclusions to be false, and if the premises it is based on is true. That means the method of drawing conclusion should be logical and valid. Truth and validity of a deduction together mean that conclusion is not logically justified (even if true) if either one or more of the premises is false or if the method of deduction is incorrect. The conclusion may still be correct due to some other premises not considered. Let us look at an example. Premise 1: Pavan is a good boy. Premise 2: Prathap and Pavan are friends. Conclusion: Pratap is a good boy. Here both the premises are true, but the argument that led to the conclusion is not valid and so the deduction is not valid. The conclusion may be due to some other reasons but not as a result of the given premises. Let us look at an example where the deduction is not true. Premise 1: Reading too much dulls one's mind Premise 2: Prathap reads too much Conclusion: Prathap must be dull-headed. Here the conclusion logically follows the premises, but premise 1 is a dubious statement. If premise 1 is false then the conclusion is also false. Let us look at an example in which there is a logical flaw. Premise 1: Dogs do not hate water Premise 2: Rabid dogs hate water Conclusion: Rabid dogs are not dogs.

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Here premise 1 is correct and true in general. But, the rabid dogs are also dogs. If we consider this, then the first premise is not correct. One has to correct the first statement, based on the second. This leads to the invalid conclusion. Premise I: Rain is a probability if the sky is cloudy. Premise 2: The sky is cloudy today. Conclusion: Rain is a probability today. This deduction is true and valid. Such deductions are made every moment by one and all and look obvious. Induction Induction is the conclusion drawn from one or more facts, bat not necessarily from facts alone. The conclusion explains the facts, but the facts just given are not sufficient to lead to the conclusion. There is a need for additional facts from the previously learnt knowledge. To illustrate, suppose Kiran approaches his boss Pavan with a routine problem, but, to his shock, receives rude treatment for no mistake of his. Then Kiran can based on his previous experience conclude any of the following:

• Another colleague of his might have just annoyed Pavan. • May be, just then, his boss, Sudba, gave him a piece of her

mind. • May be a personal problem is bothering Pavan • May be he was annoyed by a traffic jam on his way to office.

Any of these conclusions can explain the fact that Pavan treated Kiran rudely. However, at the same time, the given fact cannot lead directly to any of these conclusions. These conclusions are based on some previous experience, i.e. on some other facts also. And conclusions are in reality only hypotheses and need further verification to ascertain the correctness. Combination Of Induction And Deduction

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To explain an observed phenomena a researcher formulates some hypotheses that needs to be verified by the use of induction. The researchers then use deduction to check whether each of the hypotheses can explain the given facts completely by itself. Once this is done, it is necessary to perform empirical tests with all these hypotheses and then select a hypothesis that passes these tests. This method is described as the double movement of reflective thinking by Dewey and is adapted by Cooper and Schindler. The process the researcher should follow is as outlined below. The researcher

• Encounters a curiosity, doubt, barrier, suspicion, or obstacle, generally termed as a problem.

• Struggles to state the problem: asks questions, contemplates the existing knowledge, gathers facts, and moves from an emotional to an intellectual confrontation with the problem

• Proposes hypotheses to explain the facts that are believed to be logically related to the problem

• Deduces outcomes or consequences of the hypotheses: attempts to discover what happens if the results are in the opposite direction of that predicted, or if the results support the expectations.

• Formulates several rival hypotheses

• Devises and conducts a crucial empirical test with various possible outcomes, each of which selectively excludes one or more hypotheses.

• Draws a conclusion, an inductive inference, based on acceptance or rejection of the hypotheses

• Feeds information buck into the original problem modifying it according to the strength of the evidence.

These steps are interdependent. They are also not sequentially fixed. Based on the nature of the study some of the above steps may be eliminated or new steps may be added. Scientific Method And Its Major Characteristics

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Two major characteristics of the scientific method are validity and reliability. Validity is a measure of the match between what the research claims to measure and what it actually measures. In other words it measures the effectiveness of measurement in research. For example, a people meter on a TV set is supposed to be measuring the viewership of a particular program, while in reality it only measures the number of occasions the TV was tuned to that particular channel when the program was relayed. The Television may be on but there may be no one watching it. Moreover even if there are viewers, the people-meter cannot count how many. Thus the assumption that the people-meter measures viewership validity is wrong. Hence a research which assumes it is measuring the viewership with people meter is not valid. Validity seems easily achievable, but as in the above example, there may be minor deviations that can easily go unnoticed. Hence, to ensure validity one should carefully and purposefully probe into every detail of the research. Reliability is a measure of repeatability of the research. It is also a measure of the investigator's independence of the research. In other words, if a research is reliable, then any other investigator repeating it will obtain the same results. This characteristic is also known as objectivity. Achieving this is very difficult even in the hard sciences. Apart from validity and reliability, the scientific method has many other characteristics. Some of the important ones are:

• Logical: Logic is necessary in designing and following up a research process, and arriving at conclusions.

• Systematic: The process of research is marked by thoroughness and regularity, and so it is considered to be systematic.

• Empiricism: Research is done through observations that are based on direct sense experience. Thus it is empirical in nature.

Research should be carried out in a scientific manner to reduce the uncertainty in a situation and to ensure accuracy of the results the research yields. This needs the use of scientific method.

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Box 1.7 :Steps that make up the scientific method Observation: A good scientist is observant and notices thing in the world around him / her. (S)he sees, hears, or in some other way notices what is gaining on in the world, becomes curious about what’s happenings and raises a questions about it. Hypothesis: This is a tentative answer to the question: an explanation for what was observed. The scientist tries to explain what caused what was observed (huy7po=under, beneath; thesis = an arranging)

• Hypotheses are possible causes. A generalization based on inductive reasoning is to a hypothesis. A hypothesis not an observation, rather, a tentative explanation for the observation.

• Hypotheses reflect past experience with similar questions (“educated propositions” about cause)

• Multiple hypotheses should be proposed whenever possible. One should think of alternative causes that could the observations (the correct one may not even be one that was though of)

• Hypotheses should be testable by experimentation and deductive reasoning.

• Hypotheses can be proven wrong / incorrect, but can never be proven or confirmed with absolute certainty.

• Someone in the future with more knowledge may find a case where the hypothesis is not true.

Prediction: Next, the experimenter uses deductive reasoning to test the hypothesis.

• Inductive reasoning goes from a set of specific observations to general conclusions: I observed cells in x, y and organisms, therefore all animals have cells.

• Deductive reasoning flows from general to specific. From general premises, a scientist would extrapolate to results: if all organisms have cells and humans are organisms, then humans should have cells. This about a specific case based on the general premises.

• Generally in the scientific method, if a particular hypothesis / premise are true, then one should expect (prediction) a result. This involves the use of if then logic.

Testing: Then, the scientist performs the experiment to see if the predicted results are obtained. If the expected results are, that supports the hypothesis.

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Scientific Method-In Marketing As Compare Dot Physi cal Sciences Scientific method, as the name suggest, is more applicable to the sciences than to the arts. I Marketing Research, the decision-maker applies the methods of science to the art of marketing. Thus the reliability and validity of the method are lower when it applied to marketing research. The following are the major differences between the physical sciences and marketing research that affect the reliability and validity of research process. Research conditions In physical sciences, an experiment is conducted under a controlled environment. For example, in a chemical experiment, temperature, pressure, etc. are controlled to the required extent. In marketing research, it is almost impossible to achieve such perfect control of all the variables. Unlike physical sciences, marketing research does not involve inanimate, controllable factors, but involves people their behaviour, their perceptions and their attitudes, which change with time, place, presence of others at that instance, etc. These factors, being complex, adversely affect the reliability of research in marketing. Figure 1.7 : Five Point scale to measure the likeli hood of purchase

Measuring Instruments

Very low Low Very high High Probable

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Measuring instruments in physical sciences provide very high accuracy; For instance, physicists can measure up to a 10-15 of a meter, which is a millionth of a billionth of a meter. However, in marketing it is difficult to arrive at such accuracy. For example many questionnaires use a five-point scale to measure the likelihood of purchase. The scale is shown in Figure 1.7. Such a scale gives only a crude measure: moreover the meaning of the words in the given scale may mean different things to different people. This affects both the validity and reliability of the research. Personal Interests Ideally, the personal interests of the researcher should not affect research results. But it happens both in physical and social sciences. The extent to which the results affect the researcher is more in marketing compared to the physical sciences. In physical sciences, research results affect only the fame of the researcher whereas in marketing research, they affect their work and thus their life in marketing research, it often happens that strong willed marketing managers may need research to support their decision, while researchers themselves may be anxious to see their organization, and thus their careers, prosper. This forces the researchers to push their research so that it is acceptable to their clients. If the researcher is associated in the decision-making process as well, the personal interests of the researcher in the result increase further. Influence Of Measurement Sometimes the process of measurement may itself affect the result, i.e. the process researchers undertake for making the measurement may result in a change in the outcome. In science, the affect of measurement on the result is not very pronounced except in fields like quantum mechanics. But in marketing research the influence of measurement on the result is very appreciable. For example, when a family has a "people meter" on its television set, it may modify their viewing habits because they know that all their viewing is recorded. Similarly, people participating in focus groups know that they are being observed and so they may come up with socially acceptable answers.

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Re-questioning a group of respondents may also affect the results. If a group of respondents are questioned a second or third time, they may give different answers from what they would have given if they were questioned for the first time. For example, if a company has quizzed a group of respondents about its brand before an advertisement campaign, then after this the group will start noticing the advertisements with more interest than it would have done without us quizzing. This would change their responses in the questioning from what the responses would have been had they not been questioned before. The person who administers questionnaires or conducts an interview can also influence the result by his communication skills and his knowledge of the project. When the respondents approach him for clarifications the knowledge or lack of it will affect his reply. This, in turn will affect the result. Some times the mere presence of an interviewer may affect the result. This is more clearly explained in the chapter "Instruments of Respondent Communication". Respondents themselves often change opinions with time. These factors affect the validity and reliability of marketing research. Time Pressure In managerial decisions, timeliness becomes more important than the aptness of the decisions, per se. In any given situation, there is no perfectly right decision, but only the most appropriate decision that can be taken in the available time. This exerts time pressure on marketing research and may reduce validity and reliability of the research results. Short-Term Goals Marketing research generally aims at reaching short-term goals, i.e. helping in solving an immediate managerial problem Management does not aim at preserving and propagating the acquired knowledge. In comparison, science aims at the accumulation of a knowledge pool and uses this knowledge pool to arrive at some general theories. Once established, these theories remove the need for reinventing the wheel and allow the researchers to concentrate on more advanced areas of research. But in marketing research, the knowledge once learnt is neither preserved, nor propagated. Even if the results are preserved by a

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company or a marketing research firm, the results are not shared with other firms due to competition. This internal data may not be sufficient to give rise to stand-alone theories. Moreover, the firm would not spare resources to go into such 'theoretical' research. This results in unnecessary repetition of the research. But increasingly, researchers are recording what was known in a project and are using it as a base the future projects. They are using more efficient methods like knowledge management for organizing and reusing internal knowledge. This is leading to a gradual understanding of the theoretical behavior of many issues, like consumer behavior, with respect to particular products. Difficulty In Experimentation In the physical sciences, cause and effect relationships can be easily identified with the help of experimentation. But experimentation with complete control of all the factors is impossible in marketing research. For example, to test the effect of a new design on the sale of umbrellas, it is not possible to hold factors like weather constant. Similarly, when one is testing the effect of a new design on the sale of jeans, one cannot control factors like changing fashions. Thus experimentation, to its fullest extent is not possible in marketing research. Terminology In The Scientific Method Scientific method, since it is founded in science, derives its terminology from science. The basic terminology required for understanding the scientific method is given below. Facts And Observations Facts are phenomena that we believe are true. These facts do not change with the person who reports them. Original documents and fact-gathering agencies are important sources of facts in marketing research. Observation is the process by which we recognize or note facts. These are experiential in nature (they are the expressions of our perception of reality) and tend to change from person to person. For example, during a sales promotion, it may be a fact that the sales volume has not changed,

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but the sales staff at an outlet may give higher sales estimates based on their perception. The perception of increased sales may be due to the increased work pressure on the reduced staff. Or it may be due to an actual increase in the number of non-buying customers visiting the outlet. Thus observations are the perceptions of the individuals based on 'their experience of reality,' and hence may vary from the facts. Variables And Definitions A variable is a physical or non-physical quantity that can take anyone of a predefined set of values, numerical or otherwise. It can be defined as a formal representation of a property of entities. An entity is something that exists as or perceived as separate object. For a table, chair, a human etc. are entities. Every entity has a multitude of properties. For example, a table has legs, wood type, feel, height, width, length, etc. Similarly we can consider two properties of human beings 'blood group and 'height.' It is usually represented by a symbol. The variables can be classified either based on their measurability or on their relationship with each other. On the basis of measurability the variables are of two types. Continuous: The variable that takes an infinite number of continuous values is called as continuous variable. For example, if satisfaction is represented as a variable, it can actually take any value from zero to infinity. Mathematically a continuous variable is such that, if we take any two values of a continuous variable we can find at least one more value between these values. Discrete: This type of variable takes only a fixed number of values. For example, the variable ‘occurrence of sale' can take any of the two values' 1 for sale and '0' for no sale, and so can be called a dichotomous variable. Similarly, 'degree of liking' is referred to as a polytomy because it takes multiple values. It can take the value '-1' for dislike, '0' for neither like nor dislike, and '1' for' like.' Dependence: The researcher tries to establish a relationship between two variables in his research. For sample, when he is conducting an experiment, the researcher manipulates a variable and measures the effects on some other variable. The variable manipulated is called the independent variable (IV), and the variable measured is called the dependent variable (DV). To illustrate further, suppose a researcher is

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trying to find the relationship between the length of an advertisement and the recall. The recall percentage is the dependent variable and the length of the advertisement is the independent variable. Table 1.7 lists some terms that are used as synonyms for the dependent and independent variables. The above two types of variables are different from each other in terms of their relationship. The other types of variables, based on their relationship with other variables are the following. Moderating variable (MY): The moderating variable is the second independent variable included in the study since it is believed to have a significant effect on the relationship between the main independent and dependent variables. For instance, let us state a hypothesis - the introduction of a dating allowance (IV) will lead to higher productivity (DV), especially among younger (age is MY) workers. Here the younger workers have a moderating effect on the original relationship. Extraneous variables : These are variables outside the immediate relationship between independent variables and Dependent variable. There are many extraneous variables that have some impact on the original relationship between the IV and DV, but the effects are either not significant or so random that they are not measurable. Intervening variable : Sometimes one finds that the IV-DV relationship stated is not direct and that the independent variable actually affects some other variable (the intervening variable or IIV), which in turn affects the dependent variable. In the hypothesis stated above, we can see that the dating allowance (IV) does not directly affect the productivity (II V), but affects the satisfaction in the personal life (IIV), which in turn affects the productivity (DV). Definition : Definitions are of two major types, constitutive and operational definitions. In constitutive definitions concepts are defined with the help of other concepts and constructs. In other words, they are theoretical definitions. Operational definitions are those which define concepts in terms of the process of measurement or manipulation. Definitions are required in research to provide an understanding and measure of concepts.

Table 1.8 : Synonyms for dependent and independent variables

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Independent variables Dependent Variables

Cause Effect Stimulus Response Predicted from ………. Predicted to …….. Antecedent Consequence Manipulated Measured outcome

Concepts and constructs Concepts are abstract ideas generalized from particular facts. They are characteristics associated with certain events, objects, conditions, situations and the like. They are used to classify, explain and communicate a particular set of observations. Concepts are developed out of personal or group experiences over time. The concepts developed are shared between the users and thus they form the basis for the development of new concepts. Concepts are also borrowed across fields. For example, the concept of distance is borrowed from physical is used in attitude measurement to refer to the degree of difference between the attitudes of two people. Further, we keep adding new meanings to the existing concepts, that is we broaden them as we acquire more knowledge about it. But people teed to differ in the meanings they attribute to a concept, and this may cause problems in communication. For example, concepts like personality, leadership, motivation, social class, etc. have a variety of meaning and so people may to perfectly understand each other when they use these words. Constructs are highly abstract concepts. These are not directly tied with reality but are derived on the basis of other concepts. These are normally ideas or images specifically invented for a specific research or theory building purpose. The difference between concepts ad constructs can be best explained through an example. A magazine wants to check the quality of the news reports it receives on various parameters. The job has been given to Vivek, the quality consultant. Vivek finds that various attributes like news coverage, grimmer, lucidity are important. These concepts are qualitatively measurable. Now he finds that these concepts can be classified under some related groups. These groups can be labeled and

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they represent some idea or image to describe the qualitative requirements of a news report. Problems There are two major types of problems in marketing research managerial problems and research problems. Managerial problems are defined as questions raised in a business setting. Every managerial problem may not require research. Where there is a need for research, one needs to define research problem. Research problems are the restatements of managerial problems so that the researcher understands the problem the decision-maker is facing. The objective of marketing research thus becomes solving the managerial problems by finding a solution to the respective research problems. DrinkIt, a soft drink company with a strong trendy image, now intends to target the older generation in order to expand its market. They have several alternatives before them. They can either shift the brand image and project it as a soft drink for all ages or they can introduce promotional campaigns with the message that the older generation can project themselves younger by consuming DrinkIt. Or they can introduce a new product for the older generation, with different packaging and a different brand name. This is the managerial problem. Now the research problem can be stated as, "Will the older generation like to project themselves younger? How will the younger generation react to each of these alternatives? Will too many brands create confusion? " The research problems are questions about the interaction between two or three variables or concepts. To further analyze these problems, a hypothesis is prepared. Hypothesis Hypotheses are conjectural statements of the relationship between two or more variables that carry clear implications for testing the stated

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relations. They further classify research problems into statements which can be tested. These can be considered as probable answers to the research problem. In the above example, the hypothesis statements can be as follows:

• The older generation feels that projecting themselves as young means accepting that they are old.

• The older generation feels that they should be accepted as they are.

• The younger generation will lose interest in a drink that is meant for all ages.

• People who consider themselves as neither young nor old may get confused with two brands.

Types of Hypotheses Descriptive Hypotheses: These are propositions that describe the state of a variable. For instance, one can hypothesize that the market share of Maruti is more than 50%. Relational Hypotheses: Relational hypotheses describe a relationship between two variables with respect to each other. The dating allowance example used earlier is of this type. Similarly, an increase in market share (DV) due to improved features (IV) is an example of a relational hypothesis. There are two types of relational hypotheses, Correlational and Explanatory hypotheses. Correlational Relationships state that two variables occur together without implying that one is the cause for the other. In such cases, the two variables occur together, but we do not know of any other variables that might cause both of them. Also, we have not developed enough evidence to make a strong claim. For example, we have a hypothesis stating that monsoon and demand for coffee are directly proportional to each other. These two occur together, but we do not have any evidence to prove that one of them causes the other. The explanatory or causal hypothesis have two variables interrelated to each other such that one implies the other. Laws

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Once a hypothesis is verified by numerous researchers, in different situations, the relationship between the variables may be considered a law. A law can be defined as a well-verified statement of relationship about an invariable association among variables. In business, we do not have many well-established laws, as the relationships are not fully invariable. We only have tentative laws that are the only to some extent. This is because of the presence of a number of IIVs and MVs in real situations. Theories And Models A theory is a set of statements that explains or predicts a phenomenon of interest. These statements may be facts, concepts, constructs, hypothesis or laws. Theories are always grounded in reality. An imaginative statement can at the most be called a hypothesis, but not a theory. These theoretical statements guide marketing researchers in conducting future research, and they also guide managers in decision-making. Theories narrow down the range of facts that researchers need to study. They also suggest methods for tackling a problem that are likely to yield the most accurate results. Such theories may also provide or suggest a system in which researchers can fit in the data, classify and analyze it. Theories are also useful for predicting facts that need to be found. If a system is represented in terms of symbols or physical analysis with the purpose of simplifying the understanding, testing and analysis of it, it is known as a model. They represent phenomena through the use of analogy and can thus explain theory better. There are three major functions of a model description, explication and simulation. Descriptive models seek to describe the behaviour of elements in a system where the theory is inadequate or non-existent. Explicative models are used to extend the application of well-developed theories or improve our understanding of their key concepts. Simulation models go beyond the goal of clarifying the structural relations of concepts and attempt to reveal the process relations among them.

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1.9 STEPS IN THE RESEARCH PROCESS The steps in marketing research process are given in Figure1.81. These steps are interdependent and simultaneous, though they are treated here as if they were sequential. For example, data collection methods are often dependent on the choice of sample and the analytical approach to be used. At the same time, the analytical approach itself is dependent on the data collection method. Thus the interdependency of these two steps requires them to be simultaneous. Thus the steps given here are only indicative of the possible major steps that can be taken in a marketing research project and do not represent the exact sequence of the steps that are taken. This sequence varies for one problem to another.

Fig. 1,81: The steps involved in the Research Proce ss

Defining the Research Problem and identifying research

objectives

Cost/Value Analysis of the Information, Formulation and testing of hypothesis

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Step 1: Defining The Research Problem and identifying resea rch objectives Problem identification in a research project is like choosing the destination a journey. A research project without identifying the right problem is as meaningless as a journey without a destination. A problem as presented to the researcher is only a tentative problem, and it is just a statement of the problem as perceived by the decision-maker. It may not be the real problem in most cases, and it is the duty of the researcher to identify it correctly. But the researcher cannot identify the problem on his own because he does not have all the information that the decision-maker has, and hence he needs the active participation of the decision-maker in problem identification. But in most cases the decision-makers may not be willing to give the complete details of the problem to researchers, either because they do not see the need to do so or because they would like to maintain

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secrecy. Further, some of the decision-makers perceive problem identification as the duty of the researchers and do not see any role for themselves in it. The reluctance of the management to discuss the problem and the lack of initiative on the part of a researcher often leads to either incorrect or partial problem identification. Obviously, if the research process is continued with such a defective problem identification and statement the results will not be of any use. This financial human and temporal resources used in that research would have been wasted. To avoid such wastage, the researcher should identify the problem in the first instance itself. Thus one can consider that in marketing research, problem identification is one of the most important steps. We can even say that right problem identification is equal to research half done. To identify the right problem and understand all its dimensions, the researcher should ideally know the following.

• The complete situation faced by the decision- maker • The alternatives he can choose from • The expected outcome of the alternatives • The objectives of the decision-maker.

But as we have already seen, the researcher is not provided with all this information. So, it is up to the researcher to use appropriate techniques to collected the needed information For example, a researcher can analyze the problem statement given by the client word by word. This will bring out the real objectives of the client.

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P = Perifery of circle as market data r1, r2 = radius of circle as research objectives F = Focus of circle as research problem The researcher needs to understand not only the problem but also the objectives of the management. Then alone the researchers can align the research objectives with that of the managerial objectives. Towards this end the most important requirement in research process is the communication between the researcher ad the decision-maker. Better the communication between them, closer the problem statement will be to the actual problem. The problem will be further clarified, if the researcher develops a situation model. A situation model is a description of variables and their relationships to the outcomes. Specification of information requirements Information requirements can be derived once the research objectives are clearly established. Even at this stage, the management and the researcher need a good amount of communication between them so as to avoid collecting irritant data or missing out the requirement data.

F

r1 r2

P

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Researcher should consider the data availability, data collection technique, and ability of sample to yield the required data and the techniques of analysis to be used to decide on the requirement of the data. A common temptation is to collect “all” the possible data. In aiming to collect all the possible data there are chances of missing some important data elements and including some irrelevant data elements. Moreover, the data elements collected with such an unclear objective as all the possible data will not be focused and so will not serve any purpose. The researchers can ensure that the data collected is indeed relevant by asking questions concerning the possible findings, due to each of the data elements. They should then trace the implications of each of these findings on the decision. If the findings do to affect the decision then the data element that lead to the finding, should be dropped. Step 2: Cost / Value Analysis of the information f ormulation and testing of hypothesis The major cost constraints in marketing research are time and money. These costs are justified only by the value of information which is the result of the research. The decision-maker depends on research for some additional information that can reduce the uncertainty about the situation. But the additional data does not have any value if it is not supplied in time. So the time factor is important in research. Thus time can be considered major resources for marketing research. At this stage of research, only a rough estimate can be made of financial d time costs. Larger the sample to be taken, the larger the costs in observational of experimental studies, because per day costs are going to remain the same. However, if one tries to reduce the time period in questionnaires and interviews, the costs will increase, often exponentially. Also, the quality of the output may come down exponentially again. There are two methods to estimate the value of research. Intuitive method, the first of the two, relies entirely on the judgment of the decision-maker. The second approach, known as the expected value approach, uses Bayesian statistics to qualify the judgment probabilities.

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In both these methods, certain considerations should be taken into account to estimate the probable value of the research. The following are the essential considerations for every problem. Apart from these there may be some considerations specific to the problem. The possible outcomes and their pay off: When a problem is being considered, if the payoffs in each of the possible outcomes are not very different from each other, then it does not matter which one is chosen. In such cases, the value of the research is very low. The higher the difference in pay off of various outcomes, the more valuable the information becomes. The degree of uncertainty in the situation: Research is done basically to reduce the uncertainty connected with a decision. If the decision-maker does not perceive much uncertainty in a situation, then the research does not have much importance. Thus the research is more valuable in a situation where the uncertainty is greater. The ability of research to reduce the uncertainty: From the above discussion it is cleared that higher the ability of research to reduce the uncertainty, the greater is its value. The risk presences of the decision-maker: Different firms and different individuals have different risk preferences. If an organizational culture is such that it prefers more risk, it will not value the research greatly. Individuals who are more risk averse value the research more than individuals who are risk takers. Thereafter appropriate hypothesis may be framed and tested.

SIGNS OF POOR PROBLEM DEFINTION

Poor problem formulation will come to light during the marketing research exercise. Three main signals indicate poor performance in this task. Extensive Iteration One early manifestation of inadequate problem definition in a research exercise is extensive iteration and reviewing of study proposals. With a growing uncertainty and lack of confidence in progress, mangers retreat to the exploratory research and to repetitive desk research. They begin to reread the study proposal in an attempt to regain the conviction that their particular line of inquiry is correct. Difficulties in drafting research Instruments Another induction that all is not clear problem statement terms is when considerable difficulties are experienced in drafting suitable questionnaires or other research instruments. The right questions are not asked and this is not noticed until after the survey. Lack of purpose and focus causes time-wasting and frustration.

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Step 3: Selection of the data collection method A researcher can use two types of data: primary data, data exclusively collected for the current problem; secondary data, data collected for some other purpose and which is useful in the present research. In an exploratory survey, secondary data is used more regularly because of its cost and time advantages, whereas in conclusive research the usage depends upon the case. The data can also be classified into survey data and experimental data depending on the method of collection used to collect it. These methods, i.e. the survey method and the experimental method, can be sources of primary and secondary data.

Table 1.9 : Major Data Collection methods

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I. Secondary Research Utilization of data that were developed for some purposes other than for solving the problem at hand.

a) Internal secondary data Data generated within the organization itself, such as sales person call reports, sales invoices and accounting records

b) External secondary data Data generated by sources outside the organization, such as government reports, trade association data and data collected by syndicated services

II. Survey Research Systematic collection of information directly respondents

a) Telephone interviews Collection of information from respondents via telephone

b) Mail interviews Collection of information from respondents via mail or similar techniques

c) Personal interviews

• Home interviews • Intercept

interviews

Collection of information in a face-to-face situation. Personal interviews in the respondent’s home or office Personal interviews in a central location, generally a shopping mall

d) Computer interviews Respondents enter data directly into a computer in response to questions presented on the monitor

Projective techniques and depth interview

Designed to gather information that respondents are either unable or unwilling to provide in response to direct questioning.

Experimental Research Researcher manipulation of independent variables in such a way that its effect on one or more other variable can be measured.

Laboratory experiments Manipulation of the independent variable (s) in an artificial situation.

Field experiments Manipulation of the independent variable(s) in a natural situation.

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Step 4: Selection of the sample Marketing Research, as discussed in the chapter “Introduction to marketing research” has come into existence basically because of the vast size of the market. Due to this size, it has become impossible to collect information from the entire population of a target market. Sampling is used to overcome this problem. The other reasons for the use of sampling are given in the chapter “Sampling”. There are various decisions a manager should take to arrive at a sampling plan. They are as follows: Population – determines what forms the population that provides the information. Sampling Unit – determines the individual sampling unit. (Persons, households, companies and city blocks, etc.). This is treated as an individual unit in the process of sampling. Sampling method – determines the method of sampling. Probability – sampling units are selected at random and there is a known probability of each unit being selected. Non probability – sampling units are selected on the basis of convenience or judgment, or by some other means, and so one cannot allocate to a unit a particular probability of being selected. Sample size – determines the size of the sample to be used. This is based o the time, cost and necessary precision. Step 5: Selection of the Method of Analysis A given method of analysis requires a particular data element in a particular format. Since each analytical method request different data elements, in different forms, it is necessary to determine the analytical method before venturing into data collection. Again, the method of analysis depends on the nature of the sampling process and the data collection method. So, decisions about the data collection method and the method of analysis should be taken simultaneously.

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Once the analytical methods have been selected and proposal approved, the researcher should design the response instruments and generate dummy data. Dummy data is the hypothetical data generated imaginatively by the researcher to check whether the analysis techniques are working as they should. This imaginative data has all the characteristics of original data. The dummy data should then be fed into the analysis tables and checked for completeness. The analysis will also expose any redundant data-elements in the original plan, and it will also reveal any missing essential data-elements. Step 6: Estimate the Resources Needed The resources needed for research are time, finance and personnel. Time and financial requirements are inversely dependent on each other i.e. if one likes to reduce financial expenses then the research may take a longer time period, and if one tries to conduct the same research in lesser time period the financial expenses may increase. Researchers need to strike a balance between the use of these two resources. Financial costs include direct and indirect costs like manpower, materials, overheads, etc. Many organizations have a rule of thumb for estimating the cost. For example, they can have Rs. X as fixed cost and Rs. Y as variable cost for each interview. Such formulae help in faster estimation of the resources needed.

Box 1.82 : ELEMENTS OF THE R ESEARCH PROPOSAL Executive Summary – a brief statement of the major points from each of the other sections. The objective is to allow an executive to develop a basic understanding the proposal without reading the entire proposal. Background – A statement of the management problem and the factors that influence it. Objectives – a description of the types of data the research project will generate and how these data are relevant to the management problem. A statement of the value of the information should generally be included in this section. Research Approach – a non-technical description of the data-collection method, measurement instrument, sample ad analytical techniques. Time and cost requirements – an explanation of the time and costs required by the planned methodology accompanied by a PERT chart. Technical Appendixes – any statistical or detailed information in which only one or a few of the potential readers may be interested.

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Step 7: Prepare the Research Proposal A written research proposal puts down on paper the management problem, the research objectives, the research methodology and the resource requirements. This will help the researcher and the decision-maker to be in perfect agreement with each other to the extent that they derive the meaning from the same words. This will ensure that the research is on the right track. Box 1.82 gives a complete description of the various elements of the research proposal. Step 8 : Data Collection Data collection requires trained people for ensuring the validity of the research. If a firm’s requirement does not warrant a permanent team, it can outsource personnel from data collector suppliers. When the firm hires data collectors, it should take care that these hires are well trained. Moreover, data collectors, whether external or internal should be given a complete picture of the research before they are assigned a task. This will reduce errors as the data collector’s interpretation will be in tune with that of the researcher. Research training and evaluation of field workers can also help standardize data collection methods and reduce errors. Step 9 : Data Analysis. Before the data is collected is analyzed, it needs to be edited, coded and tabulated. Once it is tabulated the data is analyzed using statistical analysis. The results obtained from analysis are interpreted to some extent by the researcher. The rest of the interpretation is done by the decision-maker himself, as he understands the problem situation more clearly than the researcher. The reliability of the analysis is estimated by error estimation methods. Once the analysis is done and the interpretation is made, the researcher needs to report the research to the decision-maker.

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Step 10: Reporting Reporting is the culmination of a research effort. Since it involves communication, one should take care of the factors affecting communication. This means that the report should contain both technical detail and managerial implications. It should consist of an executive summary that mentions the managerial implications. This should be then supplemented by the technical details, so that the decision-maker can refer to them as and when needed. The report should also cover the methodology used in the research. However, one should remember that a written report might not really invite action unless the management is very interested in it. Such an interest can be generated only if the manager is involved in the research from the beginning of the project. Also, many managers do not respond to the written word. Some managers may respond, but may misunderstand the written material. Hence the written report must be supplemented with an oral report. This oral report, depending upon the situation, can range from a briefing to a full-fledged audio-visual presentation to an executive body. 1.10 REPORT WRITING Steps in writing report

(a) Logical analysis of the research objectives (b) Preparation of the final outline on findings (c) Preparation of rough draft (d) Preparation of final / corrected draft

1.101 Layout of Research Report (a) Preliminary pages Title Acknowledgement Preface / Foreword Content List of tables, figures Abbreviations used Company (b) Main Text Introduction Product / Service

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Statement of findings, conclusions and recommendations

Summary / Synopsis

(c) End Matter Index Bibliography Sample questionnaire List of sample 1.102

Type of report Emphasis on (a) Technical Report

(i) Methods and research (ii) Assumptions (iii) Analysis of findings

(b) Popular Report Objectives, findings and recommendations (mathematical part is avoided)

1.103 Essential qualities of research report

(1) It should have adequate length to cover the research subject.

(2) It should maintain interest of the reader. For this big paras as part of discussions to be avoided.

(3) Abbreviations to be avoided.

(4) Readers are interested in quick knowledge. Hence in the beginning of report, the findings should be highlighted in executive summary

(5) Layout must be as per research objectives.

(6) No grammatical mistakes

(7) All figures must be named, analysis must be in structured manner

(8) It must show originality.

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(9) Implications of the findings to be discussed

(10) Report must be attractive i.e. clean, neat in appearance

Above format is used mostly by western corporates. The type of the format Indian Corporate use is given in last chapter. CHAPTER 2

TYPES OF RESEARCH-DESIGNS 2.0 INTRODUCTION In the previous chapter we examined the various steps in the marketing research process. We also discussed the benefits of the research design. In this chapter we will analyse two types of research designs viz. exploratory and conclusive. Marketing research projects can be classified into two major categories. Exploratory research and conclusive research. Exploratory research helps in discovering new relationships while conclusive research helps such situations. Decision-makers use exploratory research for a preliminary investigation of the situation. Thus the purpose of an exploratory study is to provide new insights into a confusing issue in choosing the best from various possible courses of action. This chapter makes a detailed study of the above mentioned research techniques. 2.1 USE OF EXPLORATORY RESEARCH Exploratory research emphasizes the discovery of new ideas. Through exploration researches develop concepts more clearly, establish priorities develop operational definitions and improve the final research design. Quite often managers face situations that are vague in nature. They may not be able to understand whether a situation present an opportunity or poses a problem for them. Exploratory research is ideal in dealing with such situations. Decision-makers use exploratory research for a preliminary investigation of the situation. Thus the purpose of an exploratory study is to provide new insights into a confusing issue.

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Usually the researcher studies the situation and identifies the main factors contributing to that particular situation. As the number of the factors affecting the bottomline of an organization may be large, the researcher then converts these factors into specific hypotheses relative to possible actions. These hypotheses are then tested by conclusive research .For instance, a television company may notice a change in the sales figures. But they may not be able to pinpoint the factors that affect the sales. These factors may be technological changes poor marketing efforts, changing consumer preferences etc. In this case, the researcher may identify two factors and then convert them into a hypotheses that is subsequently tested by conclusive research .The process can be illustrated through the following figure 2.11 Design Of Exploratory Studies Exploratory studies are characterized by their flexibility and ingenuity. Researcher use their imaginative skill in exploratory research to develop new ideas. Their research will be based on certain hypothesis. But as they proceed ,they may redefine their approach or they may proceed with a new set of hypothesis. So the researcher’s expertise is of paramount importance in exploratory studies The researcher can take three approaches to arrive at a meaningful hypothesis.

1 Study of secondary sources 2 Survey of individual who are expert in the subject 3 Case analysis

Vague problem

Exploratory Research

Hypothesis

Conclusive Research

Decision

New Idea

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Fig. 2.1 Source: Marketing Research, Harper W. Boyd, Jr., Ralph Westfall and Stanley F. Stasch.

2.12 Study of Secondary Data Researcher can utilize the data compiled by other organizations to formulate the hypothesis. Research reports prepared by consultancies and marketing research organizations, sales data brought out by trade associates, survey reports of governmental and nongovernmental organizations are generally used for this purpose .Secondary data has proved to be quickest and most economical source for researcher. The information technology boom has made the search for data very easy .The internet is one of the largest repositions of secondary data, available at minimal or at no cost at all. Several Information Technology techniques such as datamining help researcher collect the right data and also aid them in establishing connections. At times researcher may be confused by the information glut. In such cases, the researcher should be prudent enough to select the relevant data and discard the rest. A detailed discussion regarding secondary data is given in the chapter Secondary Data.

2.13 Survey of Individuals with Ideas Researchers usually interview individuals who have a general idea about the subject and are also imaginative .They provide the necessary direction for researcher to focus on .Sales managers, sales representative and dealers can be interviewed for eliciting information. Even the consumers are approached directly by different organizations for new ideas. For instance, companies like GM has used this technique in the past. While approaching consumers care should be taken to interview a heterogeneous group.

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It is absolutely important for the researcher to find new ideas. Hence research is usually conducted by interviewing people who are cooperative and have an interest in the subject being researched. Respondents should be given the freedom to express their ideas however radical they may be. Qualitative research technique are used to collect exploratory data from individuals.These involve interviews with individuals and groups .Depth interviews or projectives techniques are used to elicit information from individuals, while focus group interviews are employed to elicit information from groups.

2.14 Depth Interviews Depth interviews are conducted to elicit information (from consumers) that is difficult to obtain through direct interviews. Factors such as consumer attitudes and motivation are understood mainly through depth interviews. In such interviews the researcher approaches the consumer with only an outline in mind. Formal questionnaires are not used in this technique. The interviewer may probe deeply to prompt the respondent to elaborate on new ideas. This is necessary because a direct question regarding the motivating factors behind a purchase may fail to elicit the appropriate answer. For instance, a consumer may avail of five star hotel services, because he perceives such behavior as a statement of his social status .But he may not admit the factor that actually mot6ivates him. The researcher has to probe deep into the consumer’s mind by asking several indirect questions. Procter & Gamble conducts depth interviews to identify ambiguity in consumers’ answers and to understand the true meaning of their responses. If a consumer says she uses a certain brand of shampoo because it does best job of getting her hair clean, researchers ask her, what does she mean by “clean”? Does “clean” mean the way it feels or the way it looks? Does “clean” mean free of dirt or nongreasy or free of dandruff or a less itchy scalp? Is “clean” hair squeaky, slippery, lively, bouncy, fluffy, shiny, easy-to-comb, or manageable? Only researchers with extensive experience and training will be successful in eliciting the information needed without any bias.

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One major disadvantage of depth interviews is that their results cannot be compared as interviewers have different interview styles. Another major disadvantage is the difficulty in analysing the data. The data available is highly subjective and varies from one analyst to another. 2.15 Projective Techniques Projective techniques are used to provide extremely useful data regarding the attitudes and values of the respondent. These techniques are based on the theory that the interpretation of any vague object or picture will reflect the individual’s background, attitudes and values. There are four categories of projective techniques. They are association, completion, construction and expression. Association Techniques In this technique the respondent is required to respond to the presentation of a stimulus with the first thing that comes into his mind. The respondents should give the first word that comes to their mind in a free word association technique while they should give a series of words or thoughts in a successive word association technique. This technique is normally used for testing potential brand names. It is also used for measuring customer attitude about products or product attributes. Completion Techniques In this technique, the respondent is required to complete an incomplete stimulus. Researchers use two types of completion techniques : sentence completion and story completion. This has proved to be an effective technique to understand more about a respondent’s attitudes and values. Normally, if a question is posed to respondents, they come up with an answer. But if they are asked to complete a sentence or a story, they will express a more revealing answer. For instance, an incomplete sentence like, “People who like white rum are .........” will induce the respondents to come up with an answer that they genuinely feel is true.

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The story completion method is similar to the sentence completion technique. Here the respondents are asked to complete a story told to him. Usually, a specific situation, like a couple visiting a shopping mall and having a disagreement over the purchase of a product, is presented to the respondent for completion. It has been found that the respondents will build the story using their own experience and attitudes. Construction Techniques Construction techniques are quite similar to completion techniques. These techniques require the respondent to construct a story, dialogue or description. In the cartoon technique the respondent may be asked to fill in the dialogues in a cartoon. In the picture response technique, a picture will be shown to the respondents and they will be asked to interpret it. The picture will be vague so that the respondent has to use his imagination to interpret the picture. 2.16 Focus group interviews Researchers conduct focus group interviews by bringing together consumers who have a common interest. The group will be interviewed by a researcher (who acts as the moderator). A large number of organizations now employ focus group interviews to gain more insights into customer preferences and expectations. Focus group interviews provide qualitative inputs and normally do not measure the subject qualitatively. For instance, Procter & Gamble uses this technique “to develop hypotheses for further exploration” or “to help design” a quantitative study. It uses a lot of focus groups to gain insights - such as to explore whether using any fabric softener has any perceived connection with being a good mom or what kinds of reactions consumers might have to a new way of demonstrating Bounty paper towel’s absorbency. But, it doesn’t make road generalizations and major decisions based on three or four focus groups*. Of late, research organizations have started recording the proceedings in order to do a detailed analysis later. In a focus group interview, the moderator normally briefs the group about the topic to be discussed. Then the moderator throws out some

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questions to the group. These questions will usually be simple, often aimed at breaking the ice. For instance, they may ask questions like, ‘what do you think about the product?’ Such questions are easy to answer. This will slowly help generate a discussion among the group. Once the atmosphere is relaxed, the moderator may bring up more specific issues and carefully watch the proceedings so as to check whether the group is coming up with new ideas. Towards the end of the discussion the moderator may give the group a task. The moderator then leaves the room and watches the proceedings through a television (or a one way window) to see if the discussion has caused the client to think of any more questions to ask. Normally the researcher will ask around nine to twelve questions. The moderator also informs the group that the proceedings are being watched by another group (clients/researchers). Usually, the discussion is watched by the organization’s staff through a one way window. Special care has to be taken to see that the moderator blends with the group. If the moderator is of the same age group and sex, the group members will express themselves freely. Normally a group consists of 6 to 12 people. However, groups can range from one to a dozen. This depends on the size of study being conducted. Members for the group are selected on the basis of their familiarity with the product. And if they are also articulate they will contribute more effectively to study. The sample should be dominated by the segment important for the project. It has been found out that different forms of group for different segments yield good results. 1 Synergism : The combined effect of the group will produce a wider

range of information, insight, and ideas than will the cumulation of the responses of a number of individuals when these replies re secured privately.

2 Snowballing : A bandwagon effect often operates in a group

interview situation in that a comment by one individual often triggers chain of responses from the other participants.

3 Stimulation : Usually after a brief introductory period the

respondents get “turned on” in that they want to express their ideas and expose their feelings as the general level or excitement over

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the topic increases in the group. 4 Security : The participants can usually find comfort in the group in

that their feelings are not greatly different from other participants and they are more willing to express their ideas and feelings.

5 Spontaneity : Since individuals aren’t required to answer any

given question in a group interview, their responses can be more spontaneous and less conventional, and should provide a more accurate picture of their position on some issues.

6 Serendipity : It is more often the case in a group rather than

individual interview that some idea will “drop out of the blue”. 7 Specialisation : The group interview allows the use of more highly

trained, but more expensive, interviewer since a number of individuals are being “interviewed” simultaneously.

8 Scientific scrutiny : The group interview allows closer scrutiny of

the data collection process in that several observers can witness the session and it can be recorded for later playback and analysis.

9 Structure : The group interview affords more flexibility than the

individual interview with regard to the topics covered and the depth with which they are treated.

10 Speed : Since a number of individuals are being interviewed at the

same time, the group 11 interviews speeds up the data collection and analysis process. 2.17 Case Analysis The case method involves examining a single or multiple situations when an organization is addressing a problem. The situation may involve factors that are interrelated. The organization may find the case method to be of absolute value sine it involves an in-depth examination of the

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problem. For instance, an organization which has noticed some variation in the quality of the product manufactured may feel that this variation is primarily due to several factors that may include proper design, testing, manufacturing processes, labor constraints, etc. In order to deal with the problem, they may take up a case that is similar to the situation at hand. The situation is analyzed thoroughly, thus helping them to arrive t a hypothesis. 2.171 Case Method Design Researchers use analogy as a method of analysis in cases. Through cases, researchers attempt to find � Common features to all cases in a general group � Features which are not common to all groups, but common to

some subgroups � Features unique to a specific case

Features that are common and those that are uncommon are analysed thoroughly to formulate hypothesis. Researchers should be careful in selecting the cases for analysis. Advantages of Case Method Advantages

• Cases are studied comprehensively, taking into consideration all aspects.

• Unlike statistical studies which involve abstracts from real situation,

case study describes a real-time situation.

Box 2.16: KEYS TO SUCCESSFUL FOCUS GROUPS Focus groups can be an effective marketing research tool. But like tools they need to be used properly in order to provide meaningful results. The most successful focus groups include the following characteristics. Appropriate research objectives : Robert Bohle, President of Focus on Issues, at a St. Louis-based marketing, consulting and research company, says the primary purpose of focus groups is to test and develop hypothesis. “Focus groups help define various customer population segments. They help companies make better judgements”, says Bohle.

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William Newbold ,supervisor of marketing research at Detroit Edison adds, “Focus groups are ideal for concept testing ,copy testing and preliminary advertising testing. ”The focus group format allows the moderator to change things on the fly and retest it. " When you need a real fast turnaround and fast input, "focus groups are appropriate", Newbold says. Bohle points out, however, that focus groups have a major limitation - they only provide directional information. The central figure in focus groups is the moderator, who guides and leads the discussion. This role is crucial to the overall success of the groups. However, a good moderator must walk a tightrope between asking questions and eliciting feedback from all the respondents. "The moderator has to be able to manage without leading (the respondents) and has to be able to control strong personalities in the group", Newbold says. "A moderator is 70 percent of what you get from a focus group. The moderator has to make everyone feel important, so they will talk." "You need a good moderator who knows the issues but isn't defensive - a moderator can't be too close to the issues. It should be a third party", says Robert Sitkauskas, director of communications technology for Detroit Edison's VRU system. Bohle agrees. "The discussion guide and the moderator are key. The most important part is the ability of the moderator to listen and probe without passing judgement." Good recruiting: Another key to good focus groups is proper recruiting. Good representation is crucial for achieving meaningful results. "The recruiting should be really representative of he customer base", says Newbold. Representative and balanced focus groups were one reason Detroit Edison's VRU groups were so successful. Sitauskas says focus groups are ideal for eliciting customer response from a variety of demographic groups relatively quickly and easily. Well planned discussions guide : While Detroit Edison's focus groups had a clear agenda, Detroit Edison was careful to build flexibility and fluidity into the groups. "You should have an genda, Sitauskas explains, "but not a rule -based agenda." Bohle adds that a discussion guider should be just that - a guide. Part of the success will depend upon the ability of the moderator and the respondents to go beyond the original guide and delve into the important underlying issues. Indeed, the accessibility issue was never a part of the original focus groups moderator’s guide. The utility thought power outages were the problem. However, the moderator uncovered inaccessibility as an underlying problem. Proper environment : Creating the proper environment is another key to the overall success of focus groups. To be truly effective, the research sponsors must establish the proper setting. "The setting has to provide the kind of environment where you can communicate not what you want to hear." To be truly effective, the research sponsors must establish the proper groups. " The setting hs to provide the kind of environment where you can communicate not what you want to hear, but what you ought to hear," Bohle says. Interpretation : Focus groups are meaningless if the findings are not interpreted correctly. You need someone insightful to draw the conclusion from the groups. Newbold says "People can jump to conclusions based on focus groups and can be misled by one strong personality. We advocate holding multiple groups." Since the researchers will be in association with the respondent for a longer period, they will develop an informal relationship. This relationship will help in collecting more data. Moreover, the data available will be accurate.

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Some of the disadvantages of case study are : Case analysis is very subjective. It is difficult to have a formal research method. Researchers tend to generalize the situations, though the case may not call for such generalizations. 2.2 CONCLUSIVE RESEARCH (DESCRIPTIVE RESEARCH OR

EXPERIMENTAL RESEARCH) Conclusive research provides information that helps the manager to evaluate and select a course of action. The decision-maker will have to choose one course of action from different alternatives. Conclusive research provides the relevant information to help the manager arrive at a decision. Conclusive research design is characterized by formal research procedure. Research Objectives are clearly stated and information needs are explicitly stated in this type of research. A formal study procedure achieves a variety of research objectives : description of phenomena or characteristics associated with a subject population, estimation of the proportion of a population that have these characteristics, discovery of association among different variables and discovery and measurement of cause-and-effect relationship among variables. Conclusive research ca be classified as either descriptive or experimental. We will start the discussion with descriptive studies. 2.21 Descriptive Research Unlike exploratory studies, descriptive studies are characterized by a formal design and an accurate description of the problem. This helps in identifying the information required and ensures that it covers all the areas required. It is imperative that the design of descriptive studies be such that it specifies the source of information and the data to be

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collected from those sources. This is done mainly to ensure the accuracy and the appropriateness of the information collected. It is equally necessary to prevent the collection of any unnecessary data associated with such research. There are basically two types of descriptive studies. 1. Case method 2. Statistical method Case Method This method is not often used in descriptive research. It is more widely used in exploratory research. The procedure is the same as in exploratory research. The only difference between the two lies in the fact that while exploratory research offers flexibility, descriptive research is more structured, clearly defining the research problem and the points to be investigated. Statistical Method This is the most widely used method in Marketing Research. It makes use of techniques that vary from simple means and percentages to very sophisticated techniques. In this method, a limited number of factors from a large number of cases are studied in-depth. Use of statistical method Statistical tools are used by most marketing research professionals to understand the dynamics of the market. Data is usually collected through observation or through interviewing. Surveys to find the consumption patterns of children ad generate profiles of the people using the internet to make purchases are examples of the statistical method. Let us see how Forrester research, the leading global marketing research agency, has studied online purchases. They have discovered that the average online buyer reports purchases in two product categories : books and software.

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The following are some of the observations made by Forrester on line purchases.

• Media and technology lead net shopping. More than 50% of online shoppers buy software and more than 40% buy books. Buyers spend an average of more than $100 over three- month period.

• More than 250,000 North Americans bought cars and computers

online.

• Three percent of online consumers report online securities trades. Advantages of Statistical Method Statistical study involves a large number of interviews or observation. Statistical techniques are used specifically for mass data. Two different researchers conducting statistical research will arrive at the same results, while two people using the case study method may not arrive at the same results. This is mainly because of the subjectivity of the case study method. Statistical study helps the researcher to make more accurate generalizations. If the sampling is properly done, the generalization will be universally true. Disadvantages

• Fails to prove cause-and-effect relationship • Direction of causal effect is not clear in statistical studies.

Causal Research Causal research is also a technique used to perform conclusive research. It attempts to specify the nature of the functional relationship between two or more variables in the problem model. Managers analyze the impact of advertising etc. through causal research. For instance, once they give an advertisement they study how the advertising has caused the sales to change. Usually conclusions are drawn from three types of evidence.

• Concomitant variations • Sequence of concurrence

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• Absence of other potential casual factors. Concomitant Variations Advertising expenditures for an organization vary across geographic regions. An organization may notice the variations of sales across the region. It may also notice that sales revenue is high in those regions where the advertising expenditures are high and is low in those regions where advertisement expenditures are low. So it may infer that sales revenue is directly proportional to advertisement expenditure. But this has only been inferred, not proved. Sequence of Occurrence This is another type of evidence that can be used to make inferences about causation. Some events that happen first, cause the next event to occur. In such cases, researchers can infer that the first event has caused the second event. Absence of Other Potential Factors If the causative factors are identified the researcher will be able to eliminate all the factors except the one that he believes is the real causative factor. Then he can establish the same as the real causative factor. Quite often it is difficult to eliminate all the possible causative factors.

2.22 Experimentation Experimentation is another method used for conclusive research. It can be used to find the cause and effect relationship between two or more variables. This is usually done by manipulating one or more variables (known as the dependent variable). Unlike observational studies, the researcher systematically alters the variables of interest and observes the changes that follow. There are two types of experimentation, laboratory and field experiments. Laboratory experiments are more controlled but are conducted in an artificial environment. The environment is totally man-

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made. Field experiments are done in a natural environment and so are less controlled, but yield more realistic results. Advantages The degree to which the certainty of the causal relationship between two variables can be established is highest in experimental studies. This is mainly because the experimenter can manipulate the independent variable and directly observe the effects of this manipulation on the dependent variable. At least in theory, the ability to manipulate is unlimited and the relationship can be established with complete certainty. The use of a control group is useful to assess the existence and potency of manipulation. In experimental designs, one can isolate the experimental variables and thus avoid the effect of the extraneous variables on the results. The presence of control group assists further, due to the possible comparisons and the exclusion of the effects of the extraneous variables. Experimentation is a highly convenient method compared to others since the experimenter can adjust variables so as to achieve the extremes which are not observable under routine conditions, i.e. the manipulative control on the independent variable is high. Moreover, the researcher need not wait for the fortuitous occurrence of a particular incident to measure its effect, but can manipulate the variables to achieve the same. The experiments if repeated with various groups and various conditions can give rise to generalized theories about the relationship between the given variables. Once a theory is established, the need for many similar experiments is reduced. Researchers can use field experiments, to minimize distortions due to the intervention of the researcher on the results. Such experimentation also generally needs less financial resources than other methods. Disadvantages In laboratory experiments, artificiality is the main disadvantage. There are many internal and external validity problems unique to each experimental design. These are discussed in the following sections.

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Experimentation is not possible of the past and predictions based on experimentation are not possible for the distant future. So, it is applicable only to current problems or problems of the near future. The most important disadvantage is that the marketing research concerns itself with people and their behaviour, and so does not yield itself for thorough manipulation and ethical considerations limit these further. Experimentation is a familiar technique used by one and all in their daily lives. But it has been structured, theorized and systematically practiced by the scientific community to develop scientific knowledge and use it for social benefits. In this technique, one identifies the change that takes place in a variable because of changes in other variables under controlled conditions. Though ancient physical scientists like Archimedes used scientific methods, the behavioural sciences did not take to experimentation in its systematic form until recently. This is due to the fact that it is not possible for behavioral scientists to control all the variables of the experimental environment to the extent it can be done in the physical sciences. However this technique has recently gained popularity particularly due to the success of the technique in theorizing the learning theories. In chemistry, for example, the scientist adds two chemicals, under controlled temperature, pressure and other variables, to find the effect of these on the reaction. In marketing research, the researcher observes the purchase behavior of a customer, while controlling the variables like the amount of exposure to the promotion, store layouts, etc. The difficulties in applying the experimentation in marketing research lies in the fact that experimentation needs control of variables, and it is not easy to control variables in markets. The difficulties in doing so have been explained in the chapter “Scientific Method”. In the chapter “Types of Research”, we read about the various advantages and disadvantages of experimentation in comparison with other techniques. This chapter deals with various types of experiments based on the settings namely, the laboratory and field settings. To make the discussion easier, it also defines the terminology and symbols used in the latter section. Further, the potential threats to the validity of an experiment are discussed and various designs of experiments are also discussed in detail with their advantages and disadvantages.

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2.3 TYPES OF EXPERIMENTS ON THE BASIS OF EXPERIMENTAL

SETTINGS Experimentation can be done under two settings – laboratory and field. There is a huge difference between these two settings and the effects of the two settings can be seen in their results. Laboratory Experiment Laboratory experiments are performed in an artificial environment. Here the respondents know that they are a part of an experiment. The investigator can control almost all the variables and thus avoid changes in any other variable other than the defined one. Thus, the internal validity is high in a laboratory setting whereas the external validity is relatively low due to the artificial setting of a laboratory experiment. Field Experiment Field experiments are conducted in natural settings and hence the results obtained are closer to the real values. But all the variables cannot be controlled and hence the experimenter should design experiments that filter the effects of the uncontrolled variables. The time involved here is higher than in the laboratory as the observers have to wait for a natural process to occur. Natural processes are often slow compared to a laboratory experimental situation. The cost of the experimentation could also be high as the number of variables, the time and the amount of control necessary are all equally high. The experimentation cost could also be high because the necessary number of variables, the time and the amount of control are equally high. 2.4 IMPORTANT TERMS The theory of experimentation uses a few technical terms. Hence it is important to know these terms to completely understand the experimentation completely. We will discuss here a few terms that are

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used in this chapter. To understand the te4rms more clearly let us discuss a hypothetical example. Let us assume that Soundrya Naturals manufactures a product – name “Newhair”, positioned as a natural hair care. They would like to evaluate a new promotional campaign with respect to the old one. For this, they plan to measure the change in average consumption of “Newhair” by a family during the campaign. The researchers have divided the families investigated into two groups. One group continues to be exposed to the old campaign while the other one is exposed to the new campaign. Test Unit : A test unit represents an individual respondent, a group of respondents, a shop, a store, a chain of stores or any other unit considered as a single unit for the purpose of study. In the “Newhair” example, a family forms a test unit. Experimental Treatment or Treatment : The experimental treatment, commonly referred to as treatment, is a manipulative instance of a set of independent variables to observe the results on the dependent variable. In the above example, the new promotion campaign is the treatment given to the group of families participating in the test. Experimental group : The experimental group is a group of test units on which treatment is performed to observe the result of the treatment and draw conclusions. In the “Newhair” example, the group exposed to the new campaign is called as the experimental group. Control group : The control group acts as a reference level for observation made on the experimental group. This group does not undergo any treatment. In the above example, the group in which the campaign is not changed is the control group. Treatment levels : Treatment levels are different levels arrived at based on a characteristic of independent variables. For instance, in the above research let us assume that there are more than one new advertisement campaign has been designed and that the best one has to be selected. Now the advertisement becomes the independent variable, and this variable can be divided into three different levels based on the characteristic known as length – long, medium and short.

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Blind and Double Blind - If the test units do not know that they are a part of an experiment, the experiment is said to be blind. If the experimenters (as differentiated from the researchers) also do not know that they are part of an experiment, then the experiment is known as double blind. Factor : Factor is a word used to denote an independent variable. A factor can be divided into various treatment levels. For example, a factor can have levels like large, medium and small. In the above experiment the advertisement becomes a factor. Factors are classified into two types – active factors and blocking factors – based on the ability of the experimenters to cause a subject to be in a particular treatment level. The experimenter cannot change a blocking factor; he can only identify and classify the units based on pre-existing levels. He can even select units belonging to some desired levels. For instance, he can divide the respondents in the above experiment on the basis of level of income, average age, size of the family, etc. whereas an experimenter can manipulate active factors, to cause a unit to be a part of one level or another. For example, treatment levels like untrained, brief trained and extensively trained can be manipulated by the use of training, i.e. by training the treatment level untrained can be changed to extensively trained. Symbolic Representation The process of an experiment can be represented using symbols. The symbols used are more or less standardized. Usage of these symbols will make communication faster, easier and clearer. The following are the symbols generally used with their most common meanings. R O X O Time O X O O X O O

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O : O represents an observational instance, i.e., one complete set of measurements made as a part of one observation. For example, pretest is an observational instance. X : X represents an instance of experimental treatment, i.e., changes introduced in one or more independent variables. O X O O X O O X R : R indicates that a few of respondents were assigned at random to a group. A diagram with X’s and O’s is read from left to right in a temporal order i.e., as we move from left to right in the diagram time moves forward. X’s and O’s vertical to each other indicate that these two instances occur simultaneously in different groups. Parallel rows separated by a dotted line indicate that the groups were not equalized by randomization process. Parallel rows not separated by a line indicate that the groups have been equalized by a randomized process. 2.5 FACTORS AFFECTING VALIDITY IN EXPERIMENTATION We have already noted in the chapter “Scientific Method and Scientific Thinking” that validity is a measure of the difference between what a research is O X O O X O O X Supposed to measure and what it actually measures. But validity has many other meanings to it depending on the context. In experimentation, we consider two types of validity: Internal validity measures the extent, to which the cause and effect relationship identified in an experiment is true; External validity measures the extent to which the cause and effect relationship found to be true in an experiment is true across all

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environments and time periods. Let us see some factors that affect each of these types of validity. History Uncontrollable events that occur during experiments interfere with the results. For example, “Penguin”, an air-conditioner manufacturing company, intends to introduce new features to its existing models of aircondit6ioners. Test marketing was launched and the results were not found to be encouraging. But the experiment did not take into account some external variables like changes in climate, aggressive sales promotion by the competition and negative publicity of the product by environmentalists that affected the sales of the product. Maturation Maturation is the distortion in the results due to gradual changes in the test unit over a time. For example, if we consider a day as the time period of an experiment, by the end of the day the respondent may get tired or bored. If we consider the time period as two to three weeks, the respondent may change his purchase behavior or change his patronage of the dealer etc. These changes gradually distort the results of the experiment. Selection Bias When there is more than one group involved in an experiment, it is possible that the groups are not equivalent. This may result in a bias. This inequality occurs when there is a self-selected (or volunteered) group. Such group of volunteers is obviously more interested in the research than those who did not volunteer. Thus, we have a group that is more enthusiastic about the experiment than randomly selected group, and this enthusiasm may distort the results. Random selection can remove the distortion to a large extent, but sometimes it may also be necessary to match members between groups, on various important characteristics, to equalize the groups. Statistical Regression

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This is an unusual bias that occurs if the groups were selected on the basis of their extreme characteristics. For instance, let us assume that an observation (O1) was made on the frequency of purchase of an item. A team was then selected consisting of the top 20% and the bottom 20% of the buyers. After some time a second observation (O2) was made. Irrespective of whatever happens between O1 and O2, it is found that the observations tend to each more average values from their initial extremes, i.e., the high values at O1 reduce by O2, while the low values at O1 increase by O2. This is because the units tend to move closer to their long-run averages in the second observation. To avoid this, a researcher should not choose a team based on such criteria of extremes. Other factors affecting validity Diffusion of imitation of treatment : If the experimental and the control groups communicate, the difference between the two groups may disappear as the control group learns more about the treatment. Compensatory rivalry : If the participants of a control group know that they are part of an experiment, competitive pressures may build up as the control group members may try hard to get into the experimental group. Compensatory equalization : Sometimes experimental treatment is desirable to the respondents because the treatment may be beneficial to the respondents. An extreme example of such an experiment is drug testing in medical research. Since it is desirable that all the patients irrespective of the group they belong receive the treatment, the administration may not be ready to deprive the control group of the treatment. In marketing research, in such instances, the administration may act to compensate the control group for being deprived of the treatment, it will result in distorted results. Reverted demoralization of the disadvantages : If the control group knows that it is being deprived of an experimental treatment that is desirable, it may become resentful and less co-operative. External validity

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The sample selected from a population may not be representative due to many reasons. In such a case, results obtained by using that group cannot be extrapolated to the population. Moreover, the artificial setting of an experiment may affect the results. 2.6 DESIGNS OF EXPERIMENTS There are many designs of experiments. The most widely accepted designs are :

a) Pre-experimental designs. b) True experiments. c) Extensions of true experimental designs. d) Field experiments.

Pre-experimental Designs Pre-experimental designs are designs that do not control the variables affecting the study. The treatment is given and observations re made, and there is o way of removing the effect of the extraneous variables. Even if there is a control group in one of the designs, the control group is not considered properly equalized with the test group. There are three pre-experimental designs. All the three designs are weak considering the fact that they cannot control internal validity adequately. One Shot Case Study The diagrammatic representation is X O Here a single treatment is given and the observation is made. But there are no previous observations to compare the results of these observations with. For example, if an advertisement is released and then the brand awareness is measured, one cannot gauge the effect of the campaign without first assessing the pre-campaign awareness levels. Though this is a weak design, it is needed in the case of new products where the ‘before’ observations are not possible. In such cases, the ’after’ measurement are compared with planned estimates.

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One Group Pretest-Post test Design This is a better design than the previous one. The diagram can be given as O1 X O2 An observation is made to start with and then a treatment is given. This is followed by another observation. In this design, history can affect the group considerably, but the design does not provide a method to reduce the effect of history. If the time period between the measurement is long enough, it X O2 O4 may result in maturation. The testing effect, instrumentation and mortality also affect the results in this design. Static Group Design The diagrammatic representation of this design is X O2 O4 In this design there are two groups – control and experimental. The experimental group is first subjected to a treatment. After the treatment, observations are made on both the groups. This design is useful in situations where prior observations are not possible due to the sudden and natural occurrence of the treatment. For instance, let us consider a treatment like a natural disaster, say, the cyclone in Orissa. It is possible to measure the psychological trauma of the people who underwent it. Now another group of people who did not undergo this can be used as the control group.

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This design is better than the previous designs, but it suffers from a major weakness : there is no way by which one can estimate whether both these groups are equivalent. If they are not equivalent, equalizing them is extremely difficult. True experimental Design In pre-experimental designs we did not have proper comparative groups. Thus, we cannot compare the results with a group which did not undergo treatment. If we do not have a comparison we will not be able to conclude the experimental observations. True experimental designs remove this deficiency from the experimentation. In these experimental designs we have randomly selected groups that can be compared to each other. Pretest-Posttest Control Group Design The diagram for this design is R O1 X O2 R O3 O4 In this design two randomly selected groups are pre-tested, and then the experimental group alone is administered the treatment. After that both groups are post tested. The history, maturation and regression, which occur in the experimental group, occur in the control group as well. One can account for these effects by using the difference in the control group observations, that is, O4-O3. The final result is expressed as (O2 – O1) – (O4 – O3) But problems like instrumentation, selection and mortality can affect the results. Moreover, testing interaction with external environment is high in this design. Other factors also can influence the internal validity. Also, there is no guard against external validity in this design. Posttest only Control Group Design

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The previous design is expensive due to the pre and posttest requirements. Alternatively, we can have a Posttest only design, i.e., the control and experimental groups are observed only after the treatment. The groups are selected at random and the experimental group is subjected to the treatment straight away. Then observations are made on both the groups. The diagrammatic representation is as follows. R X O1 R O2 The result can be represented mathematically as (O2 – O1). The design is very simple and less expensive. The history, maturity and statistical regression can occur only between the treatment and the observations. By reducing the time gap between these two events these errors can be reduced. Reducing the time period can even reduce mortality rates. The testing interaction and interaction with the external environment also decreases. Extensions of True Experimental Design Researchers do not use the true experimental designs as they re. They are too simplistic as such. They are often extended to further complex designs in order to

� Consider a number of external stimuli simultaneously; � Increase precision by using assignment procedures to a large

extent. Completely Randomized Design Randomized Design is used to test more than one alternative state of an independent variable. Let us take an example. A toothpaste company wishes to repackage its neem-based brand, ‘Nimba’. The company needs to know the ideal packaging out of the three varieties they have designed. To test this they have selected 27 outlets all over the country. They divided the 27 stores randomly into three groups of 9 stores each. They introduced different designs in each of the groups and different package in each of the groups. Now the design can be R O1 X1 O2

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R O3 X2 O4 R O5 X3 O6 Here O1, O3 and O5 represent observations before the introduction of new packages. X1, X2 and X3 represent the three different packages and O2, O4 and O6 represent the observations after the introduction of new packages. In this design it is assured that the randomization has equalized the groups of stores. If there is a reason to doubt the validity of this assumption, one must try out more advanced designs. Randomized Block Designs In the above example, if there is any reason to believe that the groups are not equal and factors such as the type of the city6 in which the store is located influence the results, then a block design, is needed. Let us assume that there are three types of cities, class A, class B and class C. On the basis of these classes, the cities can be divided into three blocks, say, A, B, C. Now each city in each block is assigned to a different group at random. Here the size of the city is known as the blocking factor. This design can measure both main effects ad interaction effects. The main effect is only the effect that due to the treatment it is not influenced by any other factors. In the above example, the main effect is the effect of various package designs on sales.

Active Factors

B l o c k I n g

Class A Class B Class C Design 1R X1 X1 X1 Design 2R X2 X2 X2 Design 3R X3 X3 X3

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This effect is measured by averaging the sales of each treatment over the three blocks. The interaction effect is the effect of one factor over others. Here the effect of the size of the city on the choice of the package design is the interaction effect. The interaction effect may or may not be significant. The design serves its purpose only if there is a significant difference across the blocks. Latin Square Design When there are two major factors whose influence should be measured, the Latin Square Design is used. Let us continue with the Nimba example. Let us consider two major factors in this example; the size of the city and the size of the store. Each of the factors is divided into various levels.. A matrix is built with these two blocking factors, with levels of one blocking factor forming the rows and the levels of the other forming the columns. Each cell represents a unique combination of these two factors. Each of these combinations is then assigned a separate treatment such that each treatment appears only once in a row and once in a column. This assignment method places a severe restriction on the method that the number of levels in each of the blocking factors should be equal to the number of treatment levels.

Siz

e of

the

Sto

res

Size of the City Class A Class B Class C

Large X1 X2 X3 Medium X3 X1 X2 Small X2 X3 X1

Table 1 : Example illustrating the Latin Square Des ign

In the example the stores are divided into three levels on the basis of their size – Large, Medium and Small, and again into three levels on the basis of the class of the city – Class A, Class B and Class C. The number of levels of the blocking factors is equal to the number of treatments, that is the package designs – X1, X2 and X3. Now these treatments are assigned in such a way that there is no repetition of a

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design in a row or in a column. The best design for these combinations is then evaluated from the sales figures. But this design does not consider the interrelationship of the variables, the size of the store, the size of the city and design. To do so, we need a three-dimensional matrix with echo if the three-dimensional matrix with each of the three variables forming an axis. Thus the matrix will contain twenty-seven combinations. . Though this becomes a limitation, one can repeat the Latin Square Design to get these interrelations, but it becomes more time consuming and expensive. If one is not interested in these interrelationships, then the Latin Square will yield the required results. Factorial Design This method is used to manipulate more than one variable. Continuing with the example of the Nimba toothpaste, if the manufacturer also wants to find the effect of the price at the same time, then a factorial design can be used to obtain the results. Let us say that there are three levels of price – Rs.1.00 (Y1), Rs.2.00 (Y2) and Rs.3.00 (Y3). We also have three levels of package designs – X1, X2 ad X3. In this method, combinations of these levels are used to obtain various treatments.

Package Designs Price differences X1 X2 X3 Rs.1.00 (Y1) X1Y1 X1Y2 X1Y3 Rs.2.00 (Y2) X2Y1 X2Y2 X2Y3 Rs.3.00 (Y3) X3Y1 X3Y2 X3Y3

Table 2.2 : Various treatment levels in the example for Factorial

Design

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Once the treatments are obtained, any of the previous methods like completely randomized design, randomized block design or Latin Square analysis can be used for obtaining observations. Table 10.2 represents various treatments thus obtained. These treatments are then assigned randomly to various stores if we use a completely randomized design for further analysis. Covariance Analysis We can statistically block various extraneous variables by using analysis of covariance (ANCOVA). We will discuss this further in the chapter “Analysis of the experiments”. This technique is useful if we used a completely randomized design, but later found that there was some effect of by other factors. It is statistically possible to block these factors and the technique used is known as Analysis of Covariance. Also, if there is any difference in average knowledge levels in control and experimental groups before the experiment, with the help of this technique one can adjust the difference statistically. Field experiments : Semi or Quasi Experiment Semi or Quasi experiments are experimental studies conducted under natural conditions, with less control on the variables than in a true experiment. It is difficult to achieve a perfectly controlled environment and hence it is necessary sometimes for such quasi experiments. Though this design is inferior to the true experimental designs, it is superior to the pre-experimental designs. Nonequivalent Control Group Design This method is used quite widely and is different from the pretest-posttest control group design. In this design the groups re not randomly assigned. O1 X O2 O3 O4

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The design can be diagrammatically represented as follows. The design is of two types – intact equivalent design and self-selected experimental group design. In intact equivalent design the assignment to various groups is done naturally. For example, various members in similar clubs, citizens in similar towns or townships or students of classes in a school etc. can be assigned to the groups. In a self-selected experimental group design, volunteers are assigned to the experimental group and non-volunteers to the control group. This assignment makes it a weak design because the average interest in the experimental group in the experiment is higher than that in the control group and may affect the results. The internal validity for both can be verified by comparing the pretest observations. If the observations O1 and O3 are similar then possibly the internal validity is high, and vice-versa. Separate Sample Pretest-Posttest Design Here two different samples are involved. While pretest is done on one sample, posttest is done on the other. This is applicable when we know when and whom to measure, but do not know when and with whom to introduce treatment. It is also used in instances where there is no way to restrict the treatment to a particular group. It is even used in instances where the pretest is highly reactive and can influence the results. For example, if a company plans to have an intense campaign on time management for its employees, a pretest is likely to increase the interest of the first group and will result in more attention to the campaign. So the posttest is done on a different group. The test can be diagrammatically represented as follows. It has high external validity as it is a field experiment where the conditions are realistic. But it has a very high threat to its internal validity, particularly from history. R O1 (X) R X O2 Group Time Series Design

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A time series design is any of the above designs with repeated observations both before and after treatment. It is highly useful in cases where the treatment is by the environment and is not in the hands of the experimenter. In such cases neither the treatment nor its time is known before the treatment occurs. For example, the effect of the changes in government policies can be measured, if the data before and after the changes were collected. Moreover, when a particular study needs a long observation period, this type of repeated observations are essential. History can be a major internal validity hindrance. This can be overcome by keeping track of all the possible extraneous factors. Later one can attempt to adjust the results to reflect their influence. 2.7 SOME ADDITIONAL TYPES OF EXPERIMENTAL RESEARCH DESIGNS

A) After only design : This design consists of measuring dependent variable after exposing independent variable at Test units.

B) Input Test

Units Output

Explanatory Variables Independent variables like Product, Price, Place and Promotion Extraneous Variables Un-Controllable Variables Like competition

Samples and Territories

Dependent Variables These are Sales, ad- recall, attitude, market share etc.

Examples :

a) Marketer distributes discount coupon to the consumers to buy the brand. The study measures the extent of coupons redeemed by the sample covered. Britania’s campaign, ‘britania khao, world

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cup jao’, involved scratch coupon to be surrendered to the retailer for matching the number.

b) Reliance Industry Ltd.,, in the month of June-July every year

disdtributes dividend warrants, along with which it also sends to shareholders 20% discount coupons for buying Vimal Fabric. In the month of February it conducts research to study number of coupons redeemed by the shareholders.

c) Before-After Design : In this design the dependent variables are

measured across test units with specific independent variable, once before the independent variable is exposed to test units and again after exposure. The difference between the two measures is treated as the effect of experimental variable.

Example : Pepsi Co. measured the sales before launch of ‘Oya babli’ campaign and after the ad campaign. It noticed healthy growth in the sales due to classic picturisation and content of the ad. C) Before-After with Control Group : In this design the research study includes a control group in the experiment, but the control group is not subjected to the experimentation. Suppose the marketer wants to study the movement of market share with reference to price reduction, then the impact of experimentation is studied as follows :

Measurements

Experimental Group

Market Share

Control Group

Market Share Before Experiment After Experiment

E1

E2

C1

C2

Mathematically, impact of experimentation = {(E2 – E1) – (C2 – C1)} Example : Most of the telecom companies reduce the tariffs, initially only for say one circle, study the impact of reduction in tariff on sales and

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market share and then repeat same strategy at other circles too. In such cases, the circle where benefit is offered is called Experimental Group, whereas the consumer outside the circle is called Control Group. They do not participate in experiment, but hope to get the benefit at later stage and hence buy the pre-paid or post-paid of same company which offered benefit at other circle. 2.71 Experimental Research Design Case Study

Case Study : Experimental Research Design Sr. No.

Name of Company

Brand Details of Experiment

Time period of experimentation

Impact

1. AT&T Idea Life long recharge Rs.995, local calls Rs.1.99, STD calls Rs.2.99. In case of Post-paid all calls At Rs.0.99

1st Jan. 2006 to 31st Jan. 2006

Three lacs new consumers added

2. Reliance Reliance India Mobile

(1) Buy Reliance hand-set at Rs.2500 and get equivalent free talk time.

1st Dec. to31st Dec.2005

Ten lacs new consumers added. (Total Consumers 170 lacs as on 1.1.2006.

(2) Recharge forever @Rs.1,000, call any-Where in India. Re.1SMS within the state At Re.0.01

1st Jan. to31st Jan. 2006

Five lacs new consumers added

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3. BHARATI TELECOM

Airtel Life-time recharge Rs.999

1st Jan to31st Jan. 2006

Three lacs sixty thousand new consumers added

4. HLL Liril a) Change of ad-agency from Lowe India to Mc Cann Erickson. from freshness, youth & exuberance to a young couple in a naughty mood with a slow humming jingle ‘l-ee-ra-ee-ra’, exhibiting husband wanting to catch the wife nearby bathroom.

November2005 No change in brand sales

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CHAPTER 3 SOURCES OF SECONDARY DATA

3.0 INTRODUCTION Once the research process starts, the researcher charts out the research objectives. Then the researcher turns his attention to the research design and determines the sources of marketing data. At times, researchers make the mistake of conducting primary research for collecting data while data is available from secondary sources at a lower cost. Moreover, the secondary data sources provide information that may not be obtained by other external agencies. However, the researcher should evaluate the secondary data for its reliability and accuracy. The researcher should also check whether the available data will fulfill the requirements. In this chapter we will discuss the nature of secondary data and its advantages and disadvantages. We shall also survey the sources of secondary data. 3.1 THE NATURE OF SECONDARY DATA Secondary data is available from publications, in-house databases, research agencies etc. It constitutes readymade information that can be used for research purpose with minimal analysis. However, the researcher should bear in mind that secondary data is published for purposes other than the current research. Collecting primary data involves field work and further analysis on the data collected to arrive at a conclusion. For instance, a marketer who wants to launch a particular product may be interested in collecting data regarding the buying habits of consumers in that particular region. The marketer can conduct field surveys to collect the relevant data, which, in turn, can be analyzed to arrive at a proper conclusion. But at the same time, he can refer to any published material that has already done an analysis. While the first method is tedious, time consuming, and expensive, the second method, which is collecting secondary data, is fast and inexpensive. 3.2 ADVANTAGES OF SECONDARY DATA

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One of the main advantages of secondary data is that it is quite inexpensive. A small start-up company study the market to launch a product may not be able to afford to do primary research. By getting hold of good reports and articles, such small organizations will be able to do the study cost effectively. Secondary data helps researchers save time. While primary research takes a considerable amount of time in the form of collecting and analyzing the data, secondary data offers readymade solutions. If the demographics of a particular region have to be studied, the researcher has to collect the statistics of the population. It is impossible for any organization to conduct such a census study. Here too, secondary data published by a government organization will be of considerable use. Moreover, data collected and published by the government will be less biased. 3.3 DISADVANTAGES OF SECONDARY DATA The major disadvantages of secondary data are

• Relevance • Accuracy • Sufficiency • Availability

Relevance Relevance refers to the extent to which the data fit the information needs of the research problem. While secondary data is available from many sources, it may not be relevant to the current research. This is mainly because the secondary data is collected from sources that are not directly related to the problems at hand. For instance, data may be available about the lifestyle pattern of a particular society. The study may have been conducted with respect to different age groups with an interval of 20 years. But this data would not be relevant for a marketer who is interested in introducing a particular product for the age group 20-25.

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Another major problem is relevance with regard to time. While secondary data may be available, it need not be relevant to the current time period. The marketing environment is dynamic and needs up-to-date information. For instance, in India, census is conducted once in ten years. So, the data becomes less and less relevant over a period of time. Accuracy Accuracy poses a great challenge to the researcher who depends on secondary data. First, since the secondary data is prepared by the research team for the problem at hand, it may fail to give an accurate picture about the area in which research is being conducted. Second, since the research process involves different steps, errors may creep into the data at any of these stages. And third, which are published by private organizations, for purposes best known to them, may be biased. Under these conditions, it becomes imperative for the researcher to assess the accuracy of the data. This can be done by checking the reliability of the source, the purpose of publication and the evidence regarding quality. It is always advisable to collect data from an original source rather than an acquired source. While the original source may be publications of state departments, developmental agencies, etc. the acquired source, which procured data from an original source, may be a newspaper report on the original data. Published data has to be examined to check if it is promoting the case of any group or carrying out any sort of propaganda. For instance, political parties may publish data that is often incomplete to drive home their point of view. To do so, they may conceal many factors that are vital for arriving at the right conclusion. Evidence regarding quality should be assured. This can be done by evaluating the sampling plan, the data collection procedure and the data analysis procedure. If details of the research are not being supplied, the validity of the data becomes suspect. Sufficiency

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Even if the secondary data available is relevant and accurate it may not be sufficient to meet all the data requirements of the problem being researched. Though the data may provide relevant material for the research, it need not contain data regarding the problem being researched. Availability Secondary sources may not be able to provide all the data relevant for some marketing problems. Thus, the researcher should check whether the secondary data is available or not before a decision regarding the selection of appropriate data source is taken. 3.4 EVALUATING SECONDARY DATA Secondary data can be collected from various sources. But before using the data it must be evaluated properly. Let us discuss some parameters against which the data should be evaluated. Pertinence Units of measurement in the data and project t hand should be the same and should be relevant to the pe4riod of time. For instance, in India, all measurements are done in the SI (Systems International) system while in the UK and the US measurements are done in the Imperial system. A marketer may need information regarding the characteristics of people within a certain region, say, for instance, coastal Andhra Pradesh. However, demographic statistics may not be available for the regions specified by the marketer. Most probably, information will be available only for cities, states or countries. There is a measurement mismatch between the available information and the required information. So while collecting data from an external source, the researcher should take care to ensure that the source follows the same measurement systems. Publisher’s Credibility

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The publisher’s credibility should be evaluated before collecting the data. Who published the data? Why they chose to publish the data, are the clients using the data satisfied with the project? Etc. are the questions to be raised before using secondary data. For instance, political organizations may publish statistical data through their own mouthpieces. These data may be incomplete and may conceal some factors are necessary for drawing proper conclusion. On the other hand, an organization whose business is collecting, analyzing and selling data will provide accurate and unbiased data. Some organizations have the authority to collect and publish certain data. Data published by such organizations will certainly be more credible. For instance, the revenue department will be able to publish data regarding the income distribution in the country. The capabilities and motivation of the individual preparing the data should also be evaluated before sourcing the data. This is mainly because some sources cheat by providing false data. Also some researchers will try to drive home their point of view and thereby end up making biased interpretations. And finally, the purpose of publishing the data should be checked for its validity and reliability. Data Collection Methods The data collection methods of the source should be studied before sourcing the data. If an organization fails to provide the methodology, the researcher should think twice before using the data. This is because data collected using wrong methods or without adequate research will lead to false information. 3.5 TYPES OF SECONDARY DATA Internal and External Data The data available within the organization, which may be published for purposes other than the problem at hand, is called the internal data.

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Internal Data may be sales reports, accounting records, inventory reports, budgets, profit and loss statement, etc. External data is the data available outside the organization. This can be data made available to the organization by external research organizations. Syndicated sources publish and sell data periodically and library sources include information from a wide array of publications. Census Data In the US, the Bureau of Census publishes census data. Data published by the Bureau of Census is known for its quality and credibility. In India, the census report is published by Registrar General of India. The survey is usually done once in ten years. Census data contains demographic information. The report contains information on different aspects such as age, sex, education and occupation. But one of the main disadvantages of census data is that it has a time lag of three to four years. Yet this is the only demographic data recognized by users as authentic. Commercial Information Organizations periodically provide information. This is also referred to as syndicated research. This service is mainly aimed at meeting the requirements of their clients. Information may be provided on a daily/weekly/yearly basis. Usually organizations providing such information conduct primary research to collect the data. Some organizations collect data from a sample growth over a period of time. This is called panel research. It helps track changes in customer preferences over a period of time. Marketing Research organizations like ACNeilsen, Forrester Research, Gallup MBA, ORG, MARG, IMRB, etc. sell information for a price. They usually study the client’s needs and customize their reports to suit those needs. In India, quasi-governmental institutions like the National Council of Applied Economic Research (NCAER) and Indira Gandhi Institute of Development and Research (IGIDR) sells market information and is considered to be highly qualitative. Consumer Purchase Data

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Consumer purchase data is extremely useful for devising marketing strategies. It provides information which can be used for understanding the market share, market segments, competitors, effects of advertising, etc. Syndicated Research agencies provide such information on a continual basis. Retail and Wholesale Sales Data Agencies like ORG-MARG, Francis Kanoi and ACNeilsen conduct studies and provide information on sales data for a number of items. These studies are mainly on packaged foods, toiletries, cosmetics and over-the-counter drugs. Different agencies collect data at different intervals. They usually collect data from stores located in different towns. Advertising Data Organizations spend a huge amount of money on advertising every year. Obviously, they will be interested in knowing the readership of various newspapers and magazines. They will also be interested in understanding the television viewing patterns. The National Readership Survey (NRS) is conducted in India to assess the consumer profile of newspapers and magazines. NRS is conducted by the National Readership Studies Council. It is constituted of the Advertising Association of India, the Audit Bureau of Cir5cultion and the Indian Newspaper Society (INS). The actual fieldwork is carried out by ACNeilsen, IMRB, and Taylor Nelson Sofres Mode. In India, IMRB conducts studies and publishes data on television viewing patterns. All this information is used by or4ganisations for media planning. Test Marketing Some marketing research agencies provide test-marketing services. A new product, before it is launched in the market, is introduced in a test market and its availability is ensured. Then the sales data is collected on a periodical basis and reported to the client. 3.6 ADDITIONAL SOURCES OF SECONDARY DATA 3.61 Government Sources

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Name of the Source Information provided

1) Directorate General of Supplies & Disposal (DGS&D)

Installed manufacturing capacities & actual utilized capacities for all manufacturers 2) Directorate General of Trade &

Disposal 3) Reserve Bank of India (RBI)

Availability of foreign currencies.

4) Directorate General of Commercial Intelligence & Statistics

Import-Export statistics

5) Centre for monitoring Indian Economy (CMIE)

Economic Growth, GDP

6) Census Population, no. of families, no.of voters

7) Geographic Survey of India Regionwise production of agri-produce

8) Horticulture Board of India Value-added fruits, vegetables & flowers and markets

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Name of the Source Information provided

9) Directorate General of Foreign Trade (DGFT)

Import Export Regulations

10) Exim Bank Creditworthiness of importers and countries.

11) Export Credit Guarantee Corporation of India (ECGC)

Insurance covers and financial guarantees available to exporters.

12) Agriculture & Processed Food Export Development Authority(APEDA)

High Tech Agri Farming, technology tie-ups, seed capital, inspection, etc.

13) Central Statistical Organisation (CSO)

Industry Economics

14) National Sample Survey (NSS) Per Capita consumption & monthly per capita income, literacy per state, employment across male & female etc.

3.62 Non-Government Sources

1) Org Marg

TRP ratings, Retail Store Audit

2) INSDOC (private Library)

Any publication after 1970

3) Path Finder

Household disposable income & consumer behaviour.

4) University Public Relation Offices

Various courses, fees, duration and eligibility.

5) Yellow Pages & Ask Me Classified information 6) Internet Sites Classified information 7) Indian Association of Retailers

No.of Retailers, their classification, types, etc.

8) J.D.Power Asia Pacific Customer satisfaction

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CHAPTER 4 HYPOTHESIS

4.0 INTRODUCTION In the chapter on preparation and tabulation of data we discussed the appropriate procedures for collection and tabulation. Once we tabulate the data we need to analyze it, i.e. is we should verify the hypothesis stated in the problem. To do so we need to learn hypothesis-testing methods. If the manager of a shopping mall wants to find out if customer satisfaction is at least 90 percent, we can test the validity of this hypothetical parameter by the use of hypothesis testing. Hypotheses test, also known as tests of significance, enable us to decide on the basis of the sample results if the deviation between the observed sample statistic and the hypothetical parameter value (or) statistic is significant (or) might be attributed to chance (or) the fluctuations of sampling. 4.1 METHOD OF HYPOTHESIS TESTING Definitions of Hypothesis

(i) Hypothesis – It is a statement or assertion about the statistical distributor or parameter of statistical distribution. Alternatively hypothesis is a claim to be tested.

(ii) Null hypothesis – A hypothesis of ‘no difference’ is called null hypothesis

(iii) Alternative Hypothesis – It is a hypothesis to be accepted in case null hypothesis is rejected. In other words, a complementary hypothesis to null hypothesis is called alternative hypothesis.

4.11 Statistical Significance Every sample will vary from population to population to some extent. To accept that a population parameter to be equivalent to the sample statistic, we should be sure that the difference between these two is only due to random fluctuations. The difference has statistical significance only if there is good reason to believe that the difference does not

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n

represent random sampling fluctuations. A test of significance is to verify if the deviation of a statistic is statistically significant or not. Let us take an example. A retail store is concerned about the possible slowdown in the average purchase per purchase per week. The previous average purchase per person per week was Rs. 1500. With the sample size of one hundred, the average purchase was found to be Rs. 1470 per week. The sample standard deviation was found to be Rs. 150. Let us assume that the true mean is Rs. 1500 i.e. we assume that there is no drop the average purchase rate. This assumption is considered a hypothesis. Given this population mean and standard deviation, is it reasonable to observe this sample mean? If so, what is the probability of observing such a mean? If it is too low, then we have to reject the hypothesis that the population mean is Rs.1500. Let us calculate the probability of obtaining this sample mean. Standard Error Now we find that the number of standard errors in the sample mean differs from the hypothesized population mean. We find this to be two standard errors. σ 150 σ x = ------ = ---- = 15 √ 10 x – u 1470 – 1500 z = -------- = -------------- = 2 σ x 15 From normal distribution tables, we find that the probability of the sample differing from the populating mean by 2 standard errors is 4.5%. This is too low a chance for the sample to be from a population of the given mean. We conclude that the hypothesis that the population mean is Rs. 1500 is wrong. Thus we prove that there is a drop in average purchases per customer per week from Rs. 1500. 4.12 Steps In Formulating And Testing

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Testing for statistical significance follows a well-defined pattern. Though one may not be able to understand all the terms in these steps at this stage, we are mentioning them here. They will be discussed in subsequent chapters. The steps are as follows: State the null hypothesis: The null hypothesis must be stated. Choose the statistical test: The choice of the statistical test is dependent on the power and efficiency of the test, the nature of the population, the method of drawing the sample and the type of measurement scale. Select the desired level of significance: The exact level of choice depends on how much Alpha risk one is willing to take in comparison with beta risk (Alpha risk and Beta risk are explained later in this chapter). Compute the calculated difference value: After the data is collected, the formula for the appropriate significance test should be used to obtain the calculated value. Obtain critical test value: The critical value for the calculated value should be looked up in the appropriate tables. The critical value is the criterion that defines the region of rejection from the region of acceptance of the null hypothesis. Make the decision: For most tests, if the calculated value is larger than the critical value, we reject the null hypothesis rejected and it is conclude that the alternate hypothesis is accepted. If the critical value is larger, we conclude we have failed to reject the null.

2.5% of area

Rejection region

95% of area

Acceptance region

2.5% of area

Rejection region

95

4.13 Formulating A Hypothesis The first in hypothesis testing is stating the hypothesis itself. A hypothesis to a problem can be basically stated in two ways – Null hypothesis and Alternative Hypothesis. Null Hypothesis: In tests of hypothesis we always begin with the assumption (or) hypothesis called Null Hypothesis. The Null hypothesis asserts that there is no significant difference between the statistics and the population parameters; and whatever observed difference is there is merely due to population. It is denoted by the symbol H0. The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data bring out the contradiction. In the above example, the null hypothesis is that the average purchase has not changed from Rs. 1500. it is represented by

H0 : µ (mu) = Rs. 1500 Alternative Hypothesis: Alternative hypothesis is complementary to the Null hypothesis and is denoted by the symbol H1. In the above example, the alternative hypothesis is that there has been a change in the average purchases per week from Rs. 1500. We can have three different alternative hypotheses about this change. These are indicated below as:

HA : µ (mu) ≠ Rs 1500 HA: µ (mu) > Rs 1500 HA : µ (mu)< Rs 1500

The hypothesis can be tested with a two-tailed test. The regions of rejection for null hypothesis are divided between the two tails. The

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second hypothesis uses the right tail for rejecting the null hypothesis whereas the third uses the left tail for rejecting it. A hypothesis is never accepted; it is only rejected or failed to be rejected. This statistical testing is not sufficient proof for disproving a hypothesis. But instead of a clumsily saying that we have failed to reject the hypothesis, we say that we accept the hypothesis. Rejecting a null hypothesis is equivalent to accepting the alternative hypothesis and rejecting an alternative hypothesis is equivalent to accepting the null hypothesis. 4.14 Errors In Testing The decision to accept or reject the null hypothesis H0 is made on the basis of the information supplied by the observed sample observations. The conclusion drawn on the basis of a particular sample may not always be true with respect to the population. For instance, in the above mentioned example we have a 5.0% chance of rejecting a true hypothesis in the above mentioned example. In table 4.14, four cases are presented. When the alternative hypothesis is true, it means that the null hypothesis is false. Using this concept we can deduce that the cases are accepting a true null hypothesis and rejecting a false null hypothesis from the table it is clear that in any testing problem we are liable to two types of errors. Type-I error: Rejecting a true null hypothesis is called a Type-I error. It is compared to convicting an innocent person. This is considered a serious error and researchers generally try to minimize its occurrence as much as possible. The probability of rejecting a true null hypothesis in the above example is 5%. This indicates the probability of a type I error. It is denoted by α. Here, α = 0.05, or 5% The region between the acceptance and rejection region is called the critical value. In the above problem the critical values are Rs. 1470 and Rs. 1530 at a given significance level of 5%. Alternatively, for a given

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significance level we can calculate the critical values above or below which a hypothesis can be rejected or accepted. Type-II error: Accepting a false null hypothesis is called a Type II error and is compared to acquitting a guilty person. It is difficult to detect such an error. It is denoted by β. And this error depends on (1) the true value of the parameter, (2) the α level we have selected, (3) the nature of the test used (one or two-tailed) to evaluate the hypothesis, (4) the sample standard deviation, and (5) the size of the example. Let us assume that the mean has actually moved from 1500 to 1470. Our null hypothesis is that the average purchase is 1500. This is false. The probability of not finding this out, which is nothing but assuming that the given hypothesis is correct, is (β) 95%. For a different population mean the value of β will be different. Ideally, a zero β indicates an error free test. This means that ideally 1- β must be equal to 1. The closer this value is to 1, the better is the test. 1- β is considered as the power if a hypothesis test for it is the probability of rejecting a false null hypothesis.

Accept H 0 Reject H 0 H0 is true Correct Wrong – Type-I

error HA is true Wrong – Type-II

error Correct

4.15 Selecting A Test Three questions should be raised when choosing between various tests.

• How many samples does the test involve? One, two or K? • If moor than one sample is involved, are they related or not? • What is the type of data? Nominal, ordinal, interval, or ratio?

Questions like the size of the sample, the quality of the sample size and weighted data can be raised. These questions will be answered in advanced statistics books and researchers should make use of them when required.

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Two samples are often used when there are two different products. Two samples, one for each product, are taken and tested to find out whether they belong to the same population. Table 4.1 lists the various statistical techniques appropriate for different measurement levels and test situations. ANOVA is discussed in the text, but in a separate chapter. Only the most commonly used tests are surveyed in the following sections. Non-parametric tests except chi square tests, call for an involved discussion and so are not discussed here. Refer to advanced spastics books for studying these methods in detail. Parametric Tests Parametric tests are called so because they measure the statistical significance between the parameter and a given static. There are two main parametric tests: Z and t tests. There is no difference between the two except that the t test is used for sample sizes of less than 30. However, the necessity of suing the t test is a contentious issue. Measurement level

One sample Two sample Related Independent Related Independe

nt

Nominal

• Binomial • χ2 one

sample

• McNemar

• Fisher exact test

• χ2 two

sample test

• Cocharan Q

• χ2 for K

samples

Ordinal

• kolmogorov-Smirnov

one-sample test • runs test

• Sign test

• Wilcoxon matched pairs

• Median test • Mann-

Whitney test

• Kolmogotov Smirnov

• Wald-Wolfowitz

• Friedman Two-way ANOVA

• Median Extension

• Kruskal-Wallis one-way ANOVA

Interv • t test • t test • t test • Repeate • One-

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n

al and ratio

• Z test for paired samples

• Z test d measures ANOVA

way ANOVA

• N-way ANOVA

(A) Single Sample Test Let us illustrate the Z test with the same retail store chain example. Suppose we have a sample of 121 accounts. It is found that the sample mean is Rs. 1470 and the sample standard deviation 165. Can you tell with 90% confidence that the sample mean has not changed from Rs. 1500? The formula for testing this is X - µ t/z = -------------- s ---- √

Where X = Sample Mean Μ = Hypothesized mean S= Sample Standard deviation N = Sample Size Let us follow the six-step Hypothesis : H0 = Rs. 1500 HA = < Rs 1500 Statistical test : Choose the z test because n is larger than 30. Significance Level : α = 0.1,with n = 121 Calculated Value : Standard error = 15 Z = 2 Degrees of Freedom = (n-1) (121-1) = 120

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Critical Value : From the tables for a significance of 10% we get a critical value of 1.289. Decision : Here the calculated value is greater than the critical value, and so we reject the null hypothesis and conclude that the average has changed. (B) Two independent sample test The procedure is the same as for single sample tests, except the formulae for finding z and t values. The required formula for the z test is ( X1 - X2 ) - (µ1- µ2)0 z = --------------------------------

S12 S2

2 ---- + ---- n1 n2

Where the symbols represent the same as in the single test. For samples of less than 30 the t test is used. The ( X1 - X2 ) - (µ1- µ2)0 z = --------------------------------

1 1 Sp

2 ---- + ---- n1 n2

Formula is as follows: Where

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n

Two related Samples If the observations are dependent on each other, we have to use a slightly different method. This problem (n1-1)S1

2 + (n2-1)S22

Sp2 = ----------------------------

n1-n2-2 is solved by finding the difference between each matched pair of observations, thereby reducing the two samples to the equivalent of one-sample. The formula for t statistic is as follows. D t = -----------

SD /√ Where ∑ D D = -------- n (∑ D2 ) ∑ D2 - ---------- n

SD = -------------------------- n – 1 4.2 CHI SQUARE (χχχχ2) ANALYSIS This is the most widely used non-parametric test, particularly for nominal data, but it can also be used for higher scales. It is used for actual values rather than percentages. It is used to find if difference between the

χ2 = ∑ observed distribution of data among categories and expected distribution is significant.

(Oi – Ei)2

Ei

k

i=1

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One sample Test In this test, we first note the expected (hypothesized) frequencies in each of the categories. Then the values of actual frequencies are compared with the hypothesized frequencies. The value χ2 is a measure that expresses these differences in the form of a mathematical value. The larger this difference, the larger this difference, the larger is the χ2 value. The formula for χ2 is given as Where Oi = Observed number of cases categorized in the ith category Ei = expected number of cases in the ith category K= The number of categories χ

2 is unique for each degree of freedom. The degrees of freedom involved in a category are equal to K-1. Care should be taken in using the chi square method in the following cases:

• When d.f. =1,each expected frequency should be at least 5 in size.

• If d.f.>1, then the χ2 test should not be used if more than 20 percent of the expected frequencies are smaller than 5, or when any expected frequency is less than 1.

Let us take an example. A survey was conducted in Delhi to measure the intent of purchasing a second car. A sample of 200 people was taken. We would like to analyze the data based on the profession of the respondents. Is the intent dependent on the profession or not? We assume that these categories have no effect on the income. Now we proceed with the procedure recommended earlier. Hypothesis : H0: O1 – Ei. The proportion of the population that intends to buy independent of their professional categories as given. Alternative hypothesis is

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HA:O1<> Ei Statistical test: The responses are divided into nominal categories and so we should use Chi square analysis Calculated value: Using the Table 4.2 we have calculated the value chi-square to be χ2= 12.68 Degrees of freedom are 4-1=3 Critical value: From the tables we get a critical value of 7.82 for a significance of 5%. Decision: here the calculated value is greater than the critical value and so we reject the null hypothesis conclude that the categories do have an effect on the intent to purchase a new car.

Table 4.2: The Data and Calculations for Chi-Square with Single Sample Problem

Profession Intendent

to buy O i Number

interviewed Percent

(No. Interviewed/

200)

Expected Frequencies (Percent x

60) Ei

(Oi-Ei)2

Self employed (like doctors, lawyers)

14 90 45 27 3.26

Front Line workers

17 40 20 12 2.08

Administrative 14 40 20 12 0.33 Academic 15 30 15 9 4.00 Total 60 200 100 60 12.68 Two Sample Test The basic methodology is same as in the one sample test but the formula involved is as follows:

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Here the data is categorized and so is placed in a two χ2 = ∑ ∑ Dimensional matrix. The subscript ij refers to ijth cell. The degree of freedom are given as (r-1)(c-1). 4.3 ONE AND TWO TAILED HYPOTHESIS There could be two types of situations, based on which hypothesis is classified as one sided or one tailed and tow sided or two tailed. When alternate hypothesis HA is defined as only more than or less than hypothesized mean (µ) i.e. HA> µ i.e. HA > µ or HA < µ is called one tailed hypothesis. On the other side when alternate hypothesis is stated as not equal to hypothesized mean (µ) i.e. HA ≠ µ, it means HA could be less than µ or more than µ. Hence this is called as two sided or two tailed hypothesis. 4.4 LEVEL OF SIGNIFICANCE AND CRITICAL VALUE OF Z

Level of ignorance ( α)

Critical value of Z One tailed test (zα)

Two tailed test (zα)

10% 1.28 1.64 5% 1.64 1.96 1% 2.33 2.58

4.5 ILLUSTRATIONS Case (i) Two tailed test Problem : Nicrome Metal works, a leading name in Packaging Industry, has designed automatic milk packing mache ‘Fill-Pack’ to fill plastic pouch with 1 litre of milk with a standard deviation of 0.01 litre. A sample of 100 pouches was examined and then the average volume / quantity of

i j Eij

(Oij - Eij)

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milk found was 0.98 litre. Can we say with 95% confidence that the machine is working property? Null Hypothesis = H0 = 1 lit. Alternate Hypothesis = HA ≠ 1 Lit X - µ Test Statistics = t/z = ----------- S / √ Data : x = 0.98 lit., µ = H0=1 lit, n = sample size = 100, standard deviation = s = 0.01 lit. 0.98 -1 - 0.02 Hence t/z = ---------------- = --------- = 20 0.01/ 100 0.01 For 95% confidence level, corresponding level of significance is 5%, and the value of z for two tailed test is 1.96. As such calculated value of z i.e. 20 is more than actual value of z i.e. 1.96. Hence null hypothesis is rejected. Conclusion – Packing machine is not working properly. Case (ii): One tailed test A sample of 1000 spherical roller bearing is found to have average weight of 50 grams. Sample population standard deviation is 5 gm. One bearing, randomly selected was found of 60 gm. What is the guarantee that balance bearing will be of correct weight? Null Hypothesis = H0=µ – 50 gm Alternate Hypothesis = H1: µ< 50 gm x - µ Test statistics = t/z = --------- S / √n Data: x=60 gm, µ = 50gm, S=5 gm, n = sample size =1000 60-50 10 10

n

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t/z = ----------- = --------- = ----------- = 63.29 5√1000 5 / 31.62 0.158 Assume level of significance 1% hence value of z for one tailed test is 2.33. Since calculated value of Z (63.29) is much more than actual value of z (2.33) null hypothesis is rejected. Conclusion: There is no guarantee that remaining bearings will be of correct weight of 50 gm.

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CHAPTER 5 SAMPLING

5.0 INTRODUCTION An important step in the data collection process is sampling. Sampling is the process of selecting a representative part of a population, studying it and thereby drawing conclusions about the population itself. The most commonplace examples of the sampling technique are tasting a small part of a dish to determine its taste, testing the temperature of water in a bathtub by dipping a finger, glancing through a book before buying it, etc. Sampling is a very important aspect of marketing research and due care has to be taken to arrive at the right sample to be studied. Often it is impossible or too expensive to study the entire population for the decision-maker to understand the market. In this chapter we will discuss the basic concepts of sampling, types of sample designs and calculation of sample size. 5.1 THE SAMPLING TERMINOLOGY The terminology of sampling has evolved over the period of its existence. Knowledge of these terms is necessary for understanding sampling. Let us examine these terms through the hypothetical case of Wild goose, a marketing research firm. This firm wants to find out the types of movies the owners of VCD players in India would like to watch. 5.11 Element An element is a unit of study, which is measured for the purposes of research. This can be an individual or an organization or even inanimate objects like soaps manufactured in a production line. In the example mentioned above, the families owning VCD players constitute the elements. 5.12 Population The total collection of elements under investigation is known as the population. In the study conducted by Wild Goose, all the families owning VCD players form the population of the study or (a) All members who buy branded baby products (b) all teenagers who watch MTV.

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5.13 Sample The subset of the elements of the population chosen for study is called the sample or the study sample. The characteristics of a good sample are discussed later in the chapter. Wild Goose may choose a few cities for sampling and within these cities it may further select a few families. The list of the families, thus selected, forms the sample used in the study. 5.14 Sampling Units Sampling units are non-overlapping elements from a population. A sampling unit can be an individual element or a set of elements based on the sampling process used. If Wild Goose uses simple random sampling, it considers a single family as a unit. If it uses cluster sampling, it views each cluster of families as its primary sampling unit, while the individual family becomes the secondary-sampling unit. 5.15 Sampling frame The sampling frame refers to a complete enumeration/list of the population as specified by the research problem. It is a list of all the sampling units. For example, a list of all the people in the country owning VCD players constitutes a sampling frame. One should be careful in designing and selecting the sampling frame. Wild Goose may obtain its sampling frame from all the manufacturers of branded VCD players, but this frame will not be exhaustive, as it will not include the people who made their purchase from the unorganized sector. Example (a) Telephone directory of any city (b) List of Income Tax Payers. 5.16 Parameters and Statistic A parameter is the summary descriptor of given variable in the population, while a statistic is the summary descriptor of a given variable in the sample. For example, Wild Goose might have found from its study that the average number of movies per week watched by a family in its sample is 3.45. This is a Statistic. But the average number of movies watched per week by a family in the entire population is 3.65. This is

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parameter. Statistics are used to estimate the corresponding Parameters. Table 5.13 Samples taken to measure the average siz e of the family.

X (Sample Mean) 4.3 4.05 4.23 4.31 3.9

S (Sample Deviation)

1.15 1.35 1.25 1.03 1.33

5.17 Sampling Errors There are two types of errors (i) imprecision inherent in using statistics to estimate parameters and, (ii) errors associated with applying a decided sampling procedure. If probability samples are used, sampling theory can estimate the degree of imprecision that may be associated with a sampling design. 5.18 Sampling Plan A sampling plan is a formal method for specifying the sampling process of a particular study. 5.2 THE NEED FOR SAMPLING If one measures each and every element of a population for some characteristics of interest, the study is referred to as a census. But if one selects a small subset of the population for the study and then generalizes the results to the entire population, then it referred to as sampling. Sampling is an attractive alternative to the census method as can be seen from the following discussion. 5.21 Resource constraints Decision-makers operate with many resource constraints. Of particular interest here are financial and temporal constraints. There is only a limited amount of time and money available to the decision-maker. Since it is not possible to conduct a census study with such resource constraints, sampling is the better alternative. It provides the required

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information, albeit with some uncertainty, within the given resource constraints. 5.22 Accuracy Management lend decision-makers do not need exact data in order to take decision. Obviously, they can tolerate some amount of error. Choosing the right sample and sampling method, the researcher can provide information within the given tolerable limits. This requires a lower utilization of the resources and lesser effort than census taking, but it gives an acceptably accurate answer. 5.23 Impossibility It is sometimes impossible to take a census, particularly is natural sciences. In the case of Wild Goose, it is clear that the will probably never be able to contact all the families that own VCDs. In such cases, sampling is a must. 5.24 Destructive measurement A measurement can often involve destruction of the element. For example, to measure the tensile strength of a steel rod, the rod itself may have to be destroyed. If we take a census in such cases, all the produced steel rods will be destroyed. 5.25 Quality Since samples are smaller than the population, it is possible to be more thorough in a sample study than in a study of the population. This results in higher quality of the information obtained. 5.3 CHARACTERISTICS OF A GOOD SAMPLE

• A good sample should be accurate. Accuracy is a measure of the absence of bias or the absence of systematic variance. Systematic variation is the variation of a measure in one

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direction due to some known factors. The following example illustrates this definition.

“The classic example of a sample with systematic variance is the Literary Digest presidential poll, in which more than 2 million persons participated. The poll said Alfred London would defeat Franklin Roosevelt for the presidency of the United States. Even the large size of this sample did not counteract its systematic bias. Later evidence showed that the poll drew its sample from the middle and upper classes, while Roosevlet’s appeal was heavily among the much larger working class.” • A good sample should be precise, that is, it should have a low

standard error of its estimate.

• A good sample should be able to specify the accuracy and precision associated with it.

• It should enable researchers to specify the degree of confidence that can be placed in its parameter estimate.

5.4 SAMPLING DESIGNS Sampling Designs are of two major types: probability and non-probability methods. These two types are presented in Table 5.3 along with five important considerations. Each of these two types comprises a variety of methods of sampling. The characteristics given in the table are not accurate to describe each of the sub designs, but only give the overall characteristics of the probability and non-probability designs. Probability samples are considered to be more costly because they need a sampling frame of the entire population. Moreover, in probability sampling, the selected sample units may be located at inconveniently distant places, thus entailing higher expenses for covering them. This type of sampling also takes more time because it is systematic. But this method is very accurate due to its known probability distribution. Since the properties of the sample are well defined and predictable, they are generally accepted. Due to their strong theoretical base, these studies are replicable and their results are generalisable.

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Before we deal with complex sampling methods, we shall study basic sampling concepts.

Table 5.3 : Sampling Design Choice Considerations

Consideration Design Type Probability Non-Probability

Cost More Costly

Less Costly

Accuracy More Accurate Less Accurate Time More Time Less Time Acceptance of results Universal

acceptance Reasonable acceptance

Generalisability of results

Good Poor

5.5 SAMPLING CONCEPTS Let us analyse an example to understand sampling concepts. In a study on the number of members in a family in city A, the actual census gave the following figures:

Table 5.4 : Number of family members in a city

Size of Family No. of Families

1 2 3 4 5 6 7 8

Total

5,000 25,000 35,000 40,000 30,000 25,000 13,000 2,000

175,000

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Mean ξ = 4.15 Standard Deviation σ = 1.27 Now let us assume that we have taken five different samples and their results are as follows. The mean and standard deviation of the five samples do not match with each other or with the population mean and standard deviations. Then how do we determine the real mean, or at least the range in which it falls? Let us assume that we have taken a repeated number of samples and then plotted all the means of these samples. What will be distribution be? Is there any relationship between their distribution and the original population distribution? If we can answer these questions, we will probably be able to obtain a method for predicting the real values from the sample values. According to the central limit theory for sufficiently large sample (n>30, where n is the member of limit in a sample), the sample mean will be distributed around the population mean in a form of distribution referred to as normal distribution. This result does not depend on the shape or the size of the population distribution. The major characteristics of a Sample mean distribution are as follows (See Figure 5.5)

(a) Its mean coincides with that of the sample mean. (b) The normal distribution is well defined and so its properties are well

known. (c) The larger the size of the sample, the more tightly clustered the

population mean. The standard deviation of a sample mean distribution curve is known as standard error and is denoted by the symbol. The standard error is important because the proportion of the sample means lying between any two points on the graph depends only on the value of the standard error at those points. Further, the proportion of the sample means lying between the mean and a given point on the graph depends on the distance of the point from the mean in terms of the standard error.

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For example, 68.2% of the sample means lie within one standard error from the mean. 95.4% of the sample means lie within two standard errors from the mean. Similarly, one can calculate the proportion of the means within a given limit by using normal distr5ibution tables. Now let us assume 95.4% of all sample means lie within two standard errors from the sample distribution mean. So, if one takes a sample mean, there is a 95.4% probability of finding the sample distribution mean within the two standard errors of the given sample mean. But the sample distribution mean is the same as that of the population mean. This means that there is a 95.4% chance of finding the population mean within two standard errors from the sample mean. To put it in another way, we can say with 95.4% confidence that the population mean will be in the range of two standard errors from the sample mean. Symbolically, the interval is represented as ξ+2σ. Figure 5.5 : Relationship between distribution of s ample means and

population distribution

Using this concept and a given sample mean and a given standard error, w can calculate an interval and say with a certain degree of confidence that the population mean will lie within the given interval. This is known as the confidence interval estimate. But how does one calculate the standard error of a sample mean distribution? This can be done by a formula given as

σ σx = --------

Population Distribution Population & Sample

distribution mean

Population and Sample distribution

n

115

Where = standard error of the mean Standard deviation of the universe. Number of observations in the sample. But generally we do not know the population mean of a population while we are conducting the research project. So we substitute it by sample standard deviation to find the estimated standard error.

σ (N – n) σx = -------- ----------- N Though this is only an approximation, the accuracy obtained is sufficient for all practical purposes, particularly if the sample is large. Let us construct a confidence interval estimate. A random sample of families provided the following information about the number of members in a family. Sample mean – 4.23 Sample standard deviation – 0.21 Let us find the interval in which the population mean may lie with a confidence of 95.4%. Now the estimated standard error is given by the formula Using this formula the standard deviation is 0.03. σ Z = ------- σx Let us find a 95.4% confidence interval estimate of the population mean. The confidence level of 95.4% corresponds to two standard errors. So, the interval is within which the population mean will fall with 95.4% probability.

n

116

4.23 ± 2*0.03 = 4.23 ± 0.06 We can represent the distance from the mean by the number of standard deviations. This number is represented by Z and is given by Where σ = standard deviation of the sample

σx = standard error of the population 5.51 Calculation of sample size The sample a researcher selects has a particular size, depending on the precision of the estimate required. To decide on the sample size, a researcher requires the following information.

a) The precision defined in terms of • The size of the interval estimate. • The confidence desired in the estimate.

BOX 5.51: USING STATISTICAL POWER TO SET SAMPLE SIZ ES When we learnt about estimating sample sizes right after we learnt about confidence intervals, but before we learnt about hypothesis testing? The sample size calculations took into account the confidence level and the width of your confidence interval. We also learned that Type I error is the mistake of concluding that there is a difference when really there is not one, and “confidence” is the probability that we’re not making a Type I error. Our sample size calculations were designed to control Type I error. Type II error, on the other hand, is the mistake of saying that there is not a difference when there is one. The probability of not making a Type II error is called “power”. It turns out those standard formulas we learned for determining sample size tacitly assume only 50% power. What this means. Suppose we are interested in a pre/post-advertising study and that we want to detect a 10% difference in unaided awareness before and after. Using our standard sample size for confidence interval analysis, we’d compute a sample size of 193 pre-study and 193 post-study. This means that, even if there is a real 10% difference in the marketing place, our research design has only a 50% chance of detecting it! In other words, there’s a 50% chance of incorrectly concluding that our company’s advertising had no effect and is a poor use of our employer’s money. The table identities sample sizes required for specific levels of power and for specific magnitudes of differences between two independent proportions at 95% confidence. Power computations allow for other levels of confidence and for a wide variety of other statis5tical tests, but Figure 1 demonstrates the concept. The table allows us to determine the sample size that best meets our statistical testing needs, because it accounts for bot6h %Type I and Type II error. A rule of thumb is that we should aim for a least 70% power.

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b) Standard deviation of the population. This is generally unknowable

and so the Standard deviation of a sample has to be used. This is obtainable from a pilot Test, previous research or a rule of Thumb (one-sixth of the range).

c) Whether a finite population needed corrections. This is needed if

the sample size is larger than 5%, the following formula is used for the standard error calculation instead of the given equation

Table 5.6 : Sampling Methods or Types of Sampling d esigns

Note that a sample size of about 150 gives just a 40% chance of detecting a real 10% difference in, for example, unaided advertising awareness. Why spend money on research that has a better than even chance of calling successful advertising a failure? The table also helps us quantify the efficiency of different sample sizes. For instance, remember hearing that sample size is a diminishing returns sort of thing That’s because the width of a confidence interval is only halved when sample size is quadrupled. But power isn’t like that; often we can cut Type II error in half by less than doubling sample size. Recall the example of the pre and post ad test above. With 193 respondents pre and 193 post, we have a 50% chance of detecting real 10% change in unaided awareness. By increasing sample size to 348 pre and 3post (an increase in sample size that yields approximately 40% increase in cost) we cut our chance of missing a genuine 10% bump in unaided and awareness in half. Designing, selling or buying research without understanding the implications of sample size on statistical power is likely to lead you to spend an unnecessary amount of money, both directly (money spent on useless research) and indirectly (discovering false negatives, such as finding that a successful new ad campaign is not a potential success). Difference 40% 50% 60% 70% 75% 80% 90%

.01 14562 19209 24487 30877 34717 39201 52489

.05 582 769 980 1236 1389 1569 2100

.10 145 193 245 309 348 393 525

.15 54 86 109 138 155 175 234

.20 36 49 52 78 87 99 132 ------------------------------------------- www.intelliquest.com

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Type of Sampling

Brief Description

Advantages Disadvantages

Probability designs A. Simple random

Assign to each population member a unique number; select sample items by use of random numbers.

1. Requires minimum knowledge of population in advance.

2. Free of possible classification errors.

3. Easy to analyze data and compute errors

1. Does to make use of knowledge of population that researcher may have.

2. Larger errors for same sample size than in stratified sampling.

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Type of

Sampling Brief

Description Advantages Disadvantages

b. Systematic Use natural ordering or order population; select random starting point between1 and the nearest integer to the sampling ratio (N/n); select items at interval of nearest integer to sampling ration.

1. If population is ordered with respect to pertinent property, gives stratification effect and hence reduces variability compared to A.

2. Simplicity of drawing sample, easy to check

1. If sampling is related to periodic ordering of the population, increased variability may be introduced.

2. estimates of error likely to be high where there is stratification

C. Multistage random

Use a form of random sampling in each of the sampling stages where there are at least two stages

1. Sampling lists, identification and numbering required only for members of sampling units selected in sample.

2. If sampling units are geographically defined, cuts down field costs (i.e. travel)

1. Errors likely to be larger than in A or B for same sample size

2. Errors increase as number of sampling units selected decreases

With probability proportionate to size

Select sampling units with probability proportionate to their size

reduces variability Lack of knowledge of size of each sampling unit before selection increases variability

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D. Stratified 1. Proportionate

Select from every sampling unit at other than last stage, a random sample proportionate to size of sampling unit.

1. Assures representativeness with respect to property that forms basis of classifying units; therefore, yields less variability than A or C,

2. decreases chance of failing to include members of population because of classification process

3. characteristics of each stratum can be estimated and hence comparisons can be made.

1. Requires accurate information on proportion of population in each stratum; otherwise increases error.

2. If stratified lists are not available, may be costly to prepare them; possibility of faulty classification and hence increase in variability

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Type of

Sampling Brief

Description Advantages Disadvantages

2. Optimum allocation

Same as D1 except sample is proportionate to variability within strata as well as their size.

Less variability for same sample size than D1.

Requires knowledge of variability of pertinent characteristic within strata

3. Disproportionate

Same as D1 except that size of sample is not proportionate to size of sampling unit but is indicated by analytical considerations or convenience

More efficient than D1 for comparison of strata or where different errors are optimum for different strata.

Less efficient than D1 for determining population characteristics i.e. more variability for same sample size.

E. Cluster Select sampling units by some form of random sampling; ultimate units are groups; select these at random and take a complete count of each.

1. If clusters are geographically defined, yields lowest field costs.

2. Requires only listing of individuals in selected clusters.

3. Characteristics of clusters as well as those of population can be estimated.

4. Can be used

1. Larger errors for comparable size than other probability samples.

2. Requires ability to assign each member of population uniquely to a cluster, inability to do so may result in duplication or omission of individuals

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for subsequent samples, since clusters, not individuals, are selected and substitution of individuals may be permissible

F. Stratified cluster

Select clusters at random for every sampling unit.

Reduces variability of plain cluster sampling

1. Disadvantages of stratified sampling added to those of cluster sampling.

2. Since cluster properties may change, advantage of stratification may be reduced and make sample unusable for later research.

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Type of

Sampling Brief

Description Advantages Disadvantages

G. Repetitive: multiple or sequential (doubt)

Two or more samples of any of the above types are taken, using results from earlier samples to design later ones or determine if they are necessary.

Provides estimates of population characteristics that facilitate efficient planning of succeeding sample; therefore, reduces error or final estimate.

1. Complicates administration of field work.

2. More computation and analysis required than in non-repetitive sampling.

3. Sequential sampling can be used only where a very small sample can approximate representativeness and where the number of observations can be increased conveniently at any stage of the research.

Non-probability Designs Judgment

Select a subgroup of the population that, on the basis of available information, can be judged to be representative of the total population;

Reduces cost of preparing sample and fieldwork, since ultimate units can be selected so that they are close together.

1. Variability and bias of estimates cannot be measured or controlled.

2. Requires strong assumptions of considerable knowledge of population and subgroup

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take a complete count or sub sample of this group.

selected.

Quota Classify population by per income properties; determine desired proportion of sample from each class; fix quotas for each observer.

1. Same cost considerations as Judgment (Advantage)

2. Introduces some stratification effect.

Introduces bias of observers’ classification of subjects and non random selection within classes

1. Convenience

Select units of analysis in any convenient manner specified by the researcher

Quick and inexpensive Contains unknown amounts of both systematic and variable

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Type of

Sampling Brief

Description Advantages Disadvantages

2. Snowball Select units with rate characteristics; additional units are referred by initial respondents.

Only highly specific application.

Representativeness of rare characteristic may not be apparent in sample selected.

Let us take an example. Where S Sx = -------- N = the number of elements in the population. n = the number of elements in the sample. Example A team intends to find the size of the sample they require to estimate the number of cards owned on an average by the population of Delhi. They decided that they should have a confidence of 95% and that the size of the interval estimate should be 0.05 car. They also conducted a preliminary survey that resulted in a standard deviation of 0.42, Computation of the sample size is as follows. For 95% interval z = 1.96 Standard error is given by i.e. size of the sample must be at least of 261 car owners. 5.6 TYPES OF SAMPLE DESIGNS/SAMPLING METHODS

n

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Sample design can be basically of two types; probability and non-probability sampling. Each of these sampling methods contains a variety of sampling types (sub-designs). Simple Random sampling A random sample is chosen either by using random tables or by using computer software that σ 0.05 σx = ---- = ------- = 0.026 Z 1.96 S2 n = ------ ≈ 261 σx

2 generates random numbers. These random tables contain digits in random order. Whether one goes along horizontally, vertically or diagonally, there is no repeated sequence. However, care has been taken to ensure that whatever methodology is used, all the numbers in a given range have an equal chance of occurring. Let us assume that Wild Goose needs to choose a sample. First, it numbers its sample frame in a sequence. The research team then begins randomly in the table. Let us assume they agree to read the last four digits of the random numbers as their sample frame consists of 8876 elements. Let us also say they agreed to move horizontally. Now, suppose that they obtained numbers like 5576, 2396, 9367, 3434, 8244, … The numbers repeated or numbers larger than the sample frame are omitted. The remaining numbers are used to select the corresponding elements from the sample frame. Complex random sampling Simple random sampling is not practical in most cases since it requires the entire population list. It may also be expensive and time consuming. So one has to turn to mere sophisticated methods. Various complex

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sampling methods like systematic sampling, stratified sampling etc., are given in Table 5.6 along with their advantages and disadvantages. Non-probability sampling Due to practical considerations, one often uses non-probability sampling even though it is technically inferior to probability sampling. It is useful when researchers do not need to generalize the results. In exploratory research, for example, the researchers may be studying at only a limited number of objectives, like dramatic variations. To find these variations, the researchers may need to concentrate only on a particular group or groups of individuals. Since cost and time-saving are high in non-probability savings, it is a valuable tool in certain cases. This method is also useful when the entire population is not available for the study. In all the above cases, non-probability sampling is favored. 5.7 ANOTHER APPROACH TO SAMPLE SIZE CALCULATION (Z S) Sample size = n = e where n = sample size, Z = standard normal distribution for certain confidence level, S = population standard deviation and e = Tolerable error in estimating the variable. The value of S is calculated as follows Maximum Value – Minimum Value S = population standard deviation = 6 The denominator is 6 because 99.7% of the values of the variables would lie within ± 3 x standard deviation i.e. 3 σ Illustration Whirlpool conducted Customer Satisfaction Survey during December 2005 for Washing Machines. It intended to measure customer satisfaction on a scale of 1 to 10, where 1 means not at all satisfied and

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10 means completely satisfied. Assume level of significance 5% and tolerable error 0.5. Solution : First we compute S Max Value of Cust Satisfaction – Minimum Value of Cust Satisfaction Here S = 6 = 10-1 6 = 1.5 Value of Z for 5% significance level is 1.95 Hence Sample size = n = (ZS)2 = (1.95 x 1.5)2 = 35 E 0.5 5.8 SAMPLING TECHNIQUES

Probability Sampling Non-probability Sampling 1) Each sampl4e unit in sample frame has equal or know chance of being included as sample

1) The chance of each sample unit from sample frame being included as sample cannot be estimated.

2) Samples are selected at random from sample frame.

2) Samples are selected w.r.t. prior Experience or judgement of the researcher

3) Whenever large sample size is involved, this method is used.

3) For accessing small sample size this method is used.

4) When highly accurate decisions of known errors are intended regardless of cost, this method is useful.

4) Whenever time and cost constraints are inevitable (like exploratory Research), this method is used.

5) Normally used for consumer goods survey.

5) Normally used for industrial goods survey.

5.9 ILLUSTRATION Emami wants to launch ‘Madhuri’ and ‘Ishwarya’ range beauty creams, say in Pune. How should it do sample design. Solution:

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Sample Population : All women in Pune. Sample Frame : All women between age group 10-50 Sampling Method : Stratified. Sampling Plan “ Sample frame is divided into 4 groups as follows : Group 1 – School-going girls between 10-16 Group 2 – College –going girls between 17-23 Group 3 – Working ladies between 24 – 35 Group 4 – Housewives and working ladies between 36-50. Samples can be drawn from schools, colleges, offices, societies, etc. Justification : Beauty creams are costly and hence stratified sampling will ensure the income i.e. affordability. It is seen that at higher secondary school level, the girls are more cautious about looks. Hence, the age limit begins with 10. At the age 50, the ladies might value natural beauty. Four groups are formed to understand in depth the consumer profile and its preferences. Sample size : 1% from each group. (Sample frame for Pune contains 8 lacs ladies)