Research methodology

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MARKETING RESEARCH : 

MARKETING RESEARCH UNIT-I

Redefining Marketing Research : 

Redefining Marketing Research The American Marketing Association(AMA) Redefined Marketing Research as: The function which links the consumer, the customer and public to the marketer through INFORMATION.

Redefining Marketing Research : 

Information Redefining Marketing Research

DEFINITIONS OF MARKETING RESEARCH : 

DEFINITIONS OF MARKETING RESEARCH Marketing research is defined as the systematic and objective identification, collection, analysis and dissemination of information for the purpose of assisting management in decision making related to the identification and solution of problems (and opportunities) in marketing

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Identification: Involves defining the marketing research problem (or opportunity) and determining the information that is needed to address it. Collection: Data must be obtained from relevant sources.

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Analysis: Data are analyzed, interpreted, and inferences are drawn. Dissemination of information: The findings, implications, and recommendations are provided in a format that makes this information actionable and directly useful as an input into decision making.

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Thus, marketing research is the function which links the consumer , customer and public to the marketer through information- used to identify and define marketing opportunities and problems, generate, refine and evaluate marketing actions, monitor marketing performance and improve understanding of marketing as a process.

Classification of Marketing Research : 

Classification of Marketing Research Problem Identification Research Research undertaken to help identify problems which are not necessarily apparent on the surface and yet exist or are likely to arise in the future. Examples: market potential, market share, image, market characteristics, sales analysis, forecasting, and trends research. Problem Solving Research Research undertaken to help solve specific marketing problems. Examples: segmentation, product, pricing, promotion, and distribution research.

A Classification of Marketing Research : 

A Classification of Marketing Research Marketing Research Fig 1.1

Problem Solving Research : 

Problem Solving Research Table 1.1

Problem Solving Research : 

Problem Solving Research Table 1.1 cont.

Problem Solving Research : 

Problem Solving Research Table 1.1 cont.

GOAL OF MARKETING RESEARCH : 

GOAL OF MARKETING RESEARCH To provide: Relevant Accurate Valid and Timely Information to facilitate marketing decision making.

WHEN TO DO MARKETING RESEARCH : 

WHEN TO DO MARKETING RESEARCH There is an information gap which can be filled by doing research. The cost of filling the gap through marketing research is less than the cost of taking a wrong decision without doing the research. The time taken for research does not delay decision- making beyond reasonable limits.

APPLICATIONS OF MARKETING RESEARCH : 

APPLICATIONS OF MARKETING RESEARCH Applications of marketing research can be divided into two broad areas: Strategic Tactical Among the strategic areas, marketing research applications would be demand forecasting, sales forecasting, segmentation studies and identification of target markets for a given product.

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In tactical area, there is product testing, pricing research, advertising research, promotional research and distributional research.

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Concept Research- The first stage is the development of a concept and its testing. The concept for a new product may come from several sources- the idea may be brain-storming session consisting of company employees, a focus group conducted among consumers or the brainwave of a top executive, before it goes into prototype or product development stages.

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Product research- Research also helps to identify which alternative packaging is most preferred, or what drives a consumer to buy a brand or product category itself, and specifies of satisfaction or dissatisfaction with elements of a product.

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Pricing Research- It could try to find out how the current price of a product is perceived, whether it is a barrier for purchase, how a brand is perceived with respect to its price and relative to other brand’s prices.

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Distribution Research- Distribution research focuses on various issues related to the distribution of products including service levels provided by current channels, frequency of salespeople visits to distribution points, routing and transport related issues for deliveries to and from distribution points throughout the channel etc.

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Advertising Research- The two major categories of research in advertising are- Copy Media

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Copy testing- This is a broad term that includes research into all aspects of advertising-brand awareness, brand recall etc.

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Media Research- The major activity under this category is research into viewer ship of specific television programmes on various TV channels . There are specialized agencies like A.C. Nielsen worldwide which offer viewer ship data. In India both ORG-MARG and IMRB offer this service. They provide peoplemeter data with brand names of TAM and INTAM which is used by advertising agencies when they draw up media plans for their clients.

Limitations of Marketing Research : 

Limitations of Marketing Research Ever changing market conditions Research reports based on interpretations and individual inferences Biasness of Researcher Higher Costs Inability to draw accurate and reliable information from the respondents

The Role of Marketing Research : 

The Role of Marketing Research

MARKETING RESEARCH DURING DIFFERENT PHASES OF THE ADMINISTRATIVE PROCESS : 

MARKETING RESEARCH DURING DIFFERENT PHASES OF THE ADMINISTRATIVE PROCESS This conceptual framework is called the administrative process, and it consists of the four phases managers frequently go through: Setting goals and establishing strategies Developing a marketing plan Putting the plan into action Evaluating the plan's effectiveness

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Setting goals and establishing strategies- Useful information can be obtained on a variety of topics when managers attempt to select a new strategy to pursue. Changes in the size or trend of demand, changes in the structure or composition of the market, needs, wants and/ or dissatisfactions in relevant market segments may suggest that a problem exists, waiting to be solved, if the right strategy can be identified.

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Developing a marketing plan- When developing marketing plans, managers often use marketing research to identify key market segments. By measuring their attitudes and opinions toward the features of available products and how these products are used, managers can identify important product and advertising considerations to include in their plans.

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Putting the plan into action- When a plan is put into action, management must monitor the effects of the plan to see if it is achieving its objectives.

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Evaluating the Plan’s Effectiveness- At the end of the operating period management will want to reappraise the plan and compare results with the objectives. Such a reappraisal will involve an aggregation and compilation of most of the information obtained during the planning and action phases, with a special emphasis on sales, market share, marketing costs and contribution to profit.

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Such a reappraisal will involve an aggregation and compilation of most of the information obtained during the planning and action phases, with a special emphasis on sales, market share, marketing costs and contribution to profit. It will also measure brand awareness, preferred rates and other measures of marketing results.

The Importance of Information : 

The Importance of Information Why Information Is Needed Marketing Environment Strategic Planning Customer Needs Competition

EVOLUTION OF THE MARKETING INFORMATION SYSTEM CONCEPT : 

EVOLUTION OF THE MARKETING INFORMATION SYSTEM CONCEPT In 1966 Professor Philip Kotler of northwestern University used the terms marketing nerve centre to describe a new unit within marketing to gather and process marketing information. He identified the three types of marketing information: Marketing intelligence- information that flows into the firm from the environment.

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Internal marketing information- information that is gathered within the firm. Marketing communication- information that flows from the firm outward to the environment.

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ENVIRONMENT FIRM Internal Marketing information Marketing Intelligence Marketing communications

What is a Marketing Information System (MIS)? : 

What is a Marketing Information System (MIS)? Consists of people, equipment, and procedures to gather, sort, analyze, evaluate and distribute needed, timely, and accurate information to marketing decision makers.

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The MIS inputs are provided through internal record system( information on market potential, sales, prices ) , marketing intelligence system, marketing research system and analysis of information and turns it into a basis for marketing actions and tactics.

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The term marketing information system has gained prominence in the new era of technological advancement, consumer consciousness and computerised information systems.

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Thus MIS may be defined as “ a structured, interacting complex of persons, machines and procedures designed to generate an orderly flow of pertinent information, collected from both intra and extra firm sources for use as the basis for decision making in specified responsibility areas of marketing management.”

FUNCTIONS OF MIS : 

FUNCTIONS OF MIS Assess, Develop and Distribute Information.

The Marketing Information System : 

Marketing Information System Developing Information The Marketing Information System Information Analysis Internal Data Marketing Research Marketing Intelligence Distributing Information Assessing Information Needs Marketing Managers Marketing Environment Marketing Decisions and Communications

Marketing Information System : 

Marketing Information System

THE DIMENSIONS OF MARKETING INFORMATION SYSTEMS : 

THE DIMENSIONS OF MARKETING INFORMATION SYSTEMS Accessibility- This refers to the ease and speed with which the particular information could be obtained. Faster and easier access will have more value as compared to slow and difficult access. Comprehensiveness- More complete the information in itself, more valuable it becomes. This attribute does not refer to the value of information but refers only to its usefulness.

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Authenticity- If the information is being generated from a formal information system it is authentic and could be measurable Free from Bias- The information if free from any bias towards the preconceived conclusion will have more value than otherwise.

STEPS IN MIS DESIGN : 

STEPS IN MIS DESIGN Define the system- The system for which design is to be made has to be defined; in terms of elements, the relationship and its boundaries. The system may be the complete organization consisting of all functions or only one or several functions.

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Source and identification- Once the information needs have been assessed, the source of this information and the frequency of reporting have to be identified. The source could be both external and internal where as the frequency could be based on the occurrence of the event or by exception.

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Accuracy- The information if free from any error will have more value than otherwise. Timeliness- It takes certain time to generate the information and the value of information depends very much on how timely it is made available to the user manager.

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Formats of MIS- There are two formats which are very important, viz. (a) Research assessment sheet (b) Marketing activity evaluation sheet.

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The research assessment sheet contains information like marketing decisions, parameters, frequency, source and the format code. The marketing activity evaluation sheet will contain the items, relationship, standard, actual, variance and reason. The first format is useful from the information point of view while the second format could be used for control

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Implementation- The steps needed for implementing the newly designed Marketing information system could be: Prepare marketing research plan Train the research staff Prepare operating schedule Evaluate and modify the research system.

ROLE AND IMPORTANCE OF MIS : 

ROLE AND IMPORTANCE OF MIS Information primarily geared to assist marketing decision-making process and control. Marketing communication based on relevance with respect to diverse needs of management at different levels. Flexibility in information system to incorporate future requirements of marketing managers on need basis.

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Marketing information helps an organization update itself with the current and future competition, diversification and / or expansion strategic plans because marketing managers are engrossed in finding solutions to ever increasing operational problems.

SCIENTIFIC METHOD : 

SCIENTIFIC METHOD Concept-Two general traits characterize the scientific method: Validity and Reliability. Validity is the characteristic used to describe research that measures what it claims to measure. Reliability is the characteristic of research methodology that allows it to be repeated again and again always with the same results.

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The chief characteristics of a scientific method are: Verifiability- The conclusions drawn through a scientific method is subjected to verification. Verifiability pre-supposes that the phenomenon must be capable of being measured. E.g. gas expands more than water.

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Generability- Scientific laws are universal in nature. Complete universality is only a myth and is rarely achieved in social science. This is mainly due to heterogeneous nature of social phenomenon.

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Objectivity- The first requisite of all basic knowledge is the ability to get at the real facts and not to be influenced by notions and own wishes. System- It pertains to the method of arriving at the result. The scientific conclusion is not only true but it is based on a systematic mode of investigation.

SCIENTIFIC METHOD IN THE PHYSICAL SCIENCES AND MARKETING : 

SCIENTIFIC METHOD IN THE PHYSICAL SCIENCES AND MARKETING The scientific method, as a method of reducing uncertainty has been developed primarily in the physical sciences. Measuring instruments used in marketing- the questionnaire e.g. do not provide as clear a definition of what is being measured as do., say thermometers. Consumers must interpret questions and find ways to express answers, and in marketing it is often hard to know whether the sample from which information is collected really represents the

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Universe that the researcher desires to study. It is in the area of reliability, however, that the physical sciences appear significantly more ‘scientific’ than marketing. In chemistry , for example an experiment is conducted under controlled conditions. Such variables as temperature, atmospheric pressure, and quantities of chemicals are carefully measured

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And all but one held constant during the experiment. These conditions are reported in detail along with the results of the experiment so that others may reproduce the same conditions and verify the results.

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But in marketing it is difficult, to control all the conditions surrounding a research project so that the same researcher can see if his or her techniques produce the same results at different times and places, or so that other researches can attempt to reproduce the results.

Distinction between scientific and non-scientific method : 

Distinction between scientific and non-scientific method Objectivity of the investigator- Researchers must base their judgments on facts, not on preconceived notions or intuition , if their work is to be scientific. Accuracy of measurement- The scientific method attempts to obtain the most accurate measurements possible. As the factors to be measured and the measuring devices available differ from one field of study to another, the accuracy of measurements differ widely

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Continuing and Exhaustive nature of investigation- The process which is continuous and unending, systematizes knowledge. The scientific method contributes to the accumulation of systematic knowledge while the non-scientific method may not be able to do so.

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Thus if researchers are completely objective, if their measurements are completely accurate, and if their studies are exhaustive, then their results will be reliable.

Difficulties in Applying the Scientific Method to Marketing : 

Difficulties in Applying the Scientific Method to Marketing Investigator involved in use of results Imprecise Measuring Devices Influence of measurement process on results Time pressure for results Difficulty in using Experiments to test hypotheses Great complexity of subject

RESEARCH DESIGN : 

RESEARCH DESIGN “Research design is a blue print for the collection, measurement and analysis of data.” Bernard Philips The overall research design can be split into the following parts: Sampling design Observational design Statistical design Operational design

TYPES OF RESEARCH DESIGNS : 

TYPES OF RESEARCH DESIGNS Research design in case of exploratory research studies Research design in case of descriptive studies Research design in case of casual research studies

RESEARCH DESIGN IN CASE OF EXPLORATORY RESEARCH STUDIES : 

RESEARCH DESIGN IN CASE OF EXPLORATORY RESEARCH STUDIES The exploratory studies are carried out to explore a subject. The main objective is to help in defining a research problem and generate a set of hypothesis or research questions. In such studies: The sample size is small Non-probability sampling techniques are used The objective is general rather than specific

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No definite recommendations are made as a result of the analysis. The design in such studies must be flexible The exploratory research is carried out by using survey of existing literature, survey of experienced individuals and analysis of selected case studies.

RESEARCH DESIGN IN CASE OF DESCRIPTIVE OR DIAGNOSTIC STUDIES : 

RESEARCH DESIGN IN CASE OF DESCRIPTIVE OR DIAGNOSTIC STUDIES In such studies: The data collected here may relate to the demographic or the behavioral variables of the respondents under study. The research has got very specific objective The sample size is large which is drawn through a probability sampling design The design in such studies must be rigid.

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The recommendations/ findings in a descriptive research are definite Descriptive studies can be divided into two broad categories- Cross-sectional and longitudinal

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Cross-sectional studies- A cross- sectional study is concerned with a sample of elements from a given population. Data on a number of characteristics from the sample elements are collected and analyzed. Cross-sectional studies are of two types- Field studies and Surveys

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Field studies- These are ex-post-facto scientific inquiries and are based on behavioral pattern. Survey research- It is data based research and its major strength is its wide scope. Detailed information can be obtained from a sample of large population. A sample survey needs less time than a census enquiry.

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Advantages of field studies- They are close to real life Variables exert their influence fully Field studies are also strong in their heuristic quality.

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Weaknesses- These studies are scientifically inferior to other studies. One of the major weaknesses is their ex-post-facto character. Lack of precision in the measurement of variables.

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Advantages (Survey research) Limitations of Survey Research- Survey research demands more time and money The interview process make the respondent alert and cautious and he may not answer the questions in a natural manner. Survey research needs a good deal of knowledge on the part of the researcher.

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Longitudinal studies- Longitudinal studies are based on panel data and panel methods. A panel is a sample of respondents who are interviewed and then reinterviewed from time to time. Generally panel data relate to the repeated measurements of the same variables.

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Each family included in the panel records its purchases of a number of products at regular intervals ,say, weekly, monthly or quarterly. Over a period of time, such data will reflect changes in the buying behavior of families.

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Advantages- Such data enable the researcher to undertake detailed analysis. More comprehensive data can be obtained as individuals and families included in the panel are those who have accepted to provide data periodically. The information obtained through panel data should be more accurate than the survey method

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Limitations- A major criticism of panels is that they may not be representative samples. Sometimes panel members may deliberately give wrong information to show off their status, annoyance over periodical reporting or repeated interviews.

RESEARCH DESIGN IN CASE OF CASUAL RESEARCH STUDIES : 

RESEARCH DESIGN IN CASE OF CASUAL RESEARCH STUDIES Casual research studies are hypothesis- testing research studies, generally known as experimental studies. Prof. R.A Fisher’s name is associated with experimental designs.

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Basic principles of experimental designs- Prof. Fisher has given three principles of experimental designs- The principle of Replication- According to the principle of replication the experiment should be repeated more than once. Thus each treatment is applied in many experimental units instead of one. By doing so the statistical accuracy of the experiment is increased.

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The principle of Randomization- The principle of randomization provides protection, when we conduct an experiment , against the effects of extraneous factors i.e. through the application of principle of randomization we can have a better estimate of the experimental error.

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The principle of local control- According to the principle of local control, the variability caused due to extraneous factors, can be measured and hence eliminated from the experimental error. For this, we first divide the field into several homogenous parts known as blocks. Blocks are formed in such a manner that each block contains as many plots as there are treatments so that one plot from each is randomly selected for each treatment.

EXPERIMENTAL DESIGNS : 

EXPERIMENTAL DESIGNS Experimental design refers to the framework or structure of an experiment and as such there are several experimental designs. An experiment allows the researcher to alter systematically the variables of interest and observe what changes follow. There is at least one independent variable (IV) and one dependent variable (DV) in a casual relationship.

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We hypothesize that in some way DV causes the IV to occur. The presence or absence of one is assosciated with the presence or absence of the other. Experimental designs can broadly be divided into two groups: Informal Experimental designs Formal Experimental designs

INFORMAL EXPERIMENTAL DESIGNS : 

INFORMAL EXPERIMENTAL DESIGNS Before and after without control group- This is the simplest type of experiment in which the dependent variable is measured before the treatment is given and after the treatment is given the dependent variable is measured again. The effect of the treatment shall be the difference in magnitudes i.e.

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Treatment effect = Y- X Where Y is the magnitude after the treatment is given & X is the magnitude before the treatment is given. Test area : Level of phenomenon Level of phenomenon after before the treatment ( X ) the treatment (Y ) Treatment Effect = (Y) –(X)

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After only with control design- In this design two comparable groups are selected (control group and experimental group) and the treatment is given only in the experimental group. The dependent variable is then measured in both the groups at the same time Treatment Effect = Z-Y

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Where Z is the level of phenomenon in experimental group and Y is the level of phenomenon in control group. Test area: Level of phenomenon after treatment (z) Control area: Level of phenomenon without treatment (y) Treatment Effect = (z) - (y)

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Before- and -after with control design- In this design two areas are selected and the dependent variable is measured in both the areas for an identical time- period after the introduction of the treatment. The treatment effect is determined by subtracting the change in the dependent variable in the control area from the change in the dependent variable in test area.

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Time Period 1 Time Period 2 Test area: Level of phenomenon Level of phenomenon before treatment (x) after treatment (y) Control area: Level of phenomenon Level of phenomenon without treatment (A) without treatment (z) Treatment Effect = (Y-X) – (Z-A)

FORMAL EXPERIMENTAL DESIGNS : 

FORMAL EXPERIMENTAL DESIGNS Completely randomized design ( C.R. Design)- Involves only two principles, the principle of replication and the principle of randomization. The essential characteristic of this design is that subjects are randomly assigned to experimental treatments. C.R design is divided into two broad categories:

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Two- group simple randomized design- In a two- group simple randomized design, first of all the population is defined and then from the population a sample is selected randomly. Further, requirement of this design is that items after being selected randomly from the population be randomly assigned to the experimental and control group.

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Population Sample Randomly Selected Randomly assigned Control group Experimental group Treatment A Treatment B Independent variable

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Merits- It is simple and randomizes the differences among sample items. Limitations- It does not control the extraneous variable and as such the result of the experiment does not depict a correct picture.

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Random replication design- There are two population in the replication design. The sample is taken randomly from the population available for study and is randomly assigned to four experimental and four control groups.

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Similarly sample is taken randomly from the population available to conduct experiments and the eight individuals so selected should be randomly assigned to the eight groups. Equal number of items are put in each group so that the size of the group is not likely to affect the result of the study.

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Merits- It randomizes any individual differences among those conducting the treatments. It is an extension of the two group simple randomized design. It provides control for the differential effects of the extraneous independent variable.

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Population available For study Random selection Sample ( to be studied) Random assignment Population available To conduct experiments Random selection Sample ( to conduct treatments) Random assignment Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7 Group 8 E E E E C C C C Treatment A Treatment B Independent variable

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RANDOMIZED BLOCK DESIGN ( R.B DESIGN)- In the R.B design, subjects are first divided into groups, known as blocks, such that within each group the subjects are relatively homogenous in respect to some related variable.

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The number of subjects in a given block would be equal to the number of treatments and one subject in each block would be randomly assigned to each treatment . E.g. Suppose four different forms of a standardized test in statistics were given to each of five students and following are the scores which they obtained.

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If each student separately randomized the order in which he or she took the four tests, the design of such type of experiments is known as R.B design.

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Latin Square design (L.S design)- This type of design is very frequently used in agricultural research. The treatments in a L.S design are so allocated among the plots that no treatment occurs more than once in any row or any one column.

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The diagrammatic form of such a design, say, five type of fertilizers i.e. A, B, C, D and E and the two blocking factors i.e. the varying soil fertility and the varying seeds.

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FERTILITY LEVEL Seed Differences

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Thus the diagram shows that in a L.S design the field is divided into as many blocks as there are varieties of fertilizers and then each block is again divided into as many parts as there are varieties of fertilizers in such a way that each of the fertilizer variety is used in each of the block only once.

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Factorial designs- Factorial designs are used in experiments where the effects of varying more than one factor are to be determined. They are specially important in social and economic phenomenon where usually a large number of factors affect a particular problem. Factorial designs can be of two types:

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Simple factorial designs- In case of simple factorial designs, we consider the effects of varying two factors on the dependent variable. Simple factorial design is also known as ‘ two- factorial design.’ A 2 *2 simple factorial design can be represented as:

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2 * 2 Simple Factorial design Experimental variable Treatment A Treatment B Control variables Level I Level II

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In this type of experiment there are two treatments of the experimental variable and two levels of the control variable. As such there are four cells into which the sample is divided. Each of the four combinations would provide one treatment or experimental condition.

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Subjects are assigned at random to each treatment in the same manner as in a randomized group design. The means for different cells may be obtained along with the means for different rows and columns. Thus through this design we can study the main effects of treatments as well as the main effects of levels.

STUDY I DATA : 

STUDY I DATA

STUDY II DATA : 

STUDY II DATA

COMPLEX FACTORIAL DESIGNS : 

COMPLEX FACTORIAL DESIGNS Experiments with more than two factors at a time involves the use of complex factorial design. In case of three factors with one experimental variable having two treatments and two control variables, each one of which having two levels, the design used will be termed 2* 2* 2 complex factorial design which will contain a total of eight cells.

2* 2*2 COMPLEX FACTORIAL DESIGN : 

2* 2*2 COMPLEX FACTORIAL DESIGN Level1 Level2

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From this design, it is possible to determine the main effects for three variables i.e. one experimental and two control variables They permit various other comparisons of interest.

PRIMARY DATA AND SECONDARY : 

PRIMARY DATA AND SECONDARY Primary data is original in character and are generated in large number of surveys conducted mostly by government and also by some individuals, institution and research bodies. Data which are not originally collected but rather obtained from published or unpublished sources are known as secondary data.

CHOICE BETWEEN PRIMARY AND SECONDARY DATA : 

CHOICE BETWEEN PRIMARY AND SECONDARY DATA Nature and scope of the enquiry Availability of financial resources. Availability of time Degree of accuracy desired, and The collecting agency i.e. whether an individual an institution or a government body.

ADVANTAGES OF SECONDARY DATA : 

ADVANTAGES OF SECONDARY DATA Economical- As the cost of collecting original data is saved It saves the time of the researcher. This leads to the prompt completition of the research project.

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Such data indicates deficiencies and gaps. As a result , the researcher can make his primary data collection more specific and more relevant to study Secondary data can be used as a basis for comparison with the primary data that the researcher has just collected.

DATA DISADVANTAGES OF SECONDARY : 

DATA DISADVANTAGES OF SECONDARY The unit in which secondary data are expressed may not be the same as is required in the research project. The desired class boundaries are not same

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Secondary data is not always accurate. In case, the degree of inaccuracy is high, the use of such dubious data would undermine the utility of a research study Secondary data is somewhat out of date. A good deal of time is spent in the collection, processing, tabulation and publishing of such data and by the time the data are available to the researcher, they are already two to three years old.

SOURCES OF SECONDARY DATA : 

SOURCES OF SECONDARY DATA Internal sources – Accounting records, sales force, reports etc. External sources- Office of the economic adviser, Govt. of India, Reserve Bank of India Bulletin.

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Govt. publications- Registrar General of India [Population Census every ten years], Central Statistical Organization (CSO), Annual publication of the department of Economic Affairs, Ministry of Finance, Govt. of India. It is published on the eve of the presentation of the national budget and contains a detailed review of the different sectors of the economy.

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Non-Government publications- Indian Cotton Mills Federation [ data on cotton textile industry], Annual report of Bombay Mill owners, India’s production Exports and Internal Consumption of coir and goods etc.

METHODS OF COLLECTING PRIMARY DATA : 

METHODS OF COLLECTING PRIMARY DATA Observation Method Direct personal Interviews Indirect oral Interview Information from correspondents Mailed questionnaire Method Schedules sent through enumerators

Observation Method : 

Observation Method Observation is one of the method of collecting data and is used to get current information. In marketing experimentation observation method is most frequently used e.g. One of the factors influencing the sale of a branded product is how readily it is kept in stock.

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An interested manufacturer may send some observers to a sample of stores to find out how frequently the product is out of stock. Today certain mechanical devices are used for observation. A device known as audiometer is attached to radio sets for recording automatically the station to which the radio-set is tuned.

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Advantages- Observation technique enables a researcher to record behavior as it occurs. It can be used regardless of whether the respondent is willing to report or not. It can be used even when it pertains to those who are unable to respond such as infants and animals.

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Limitations- Only the current behavior of a person or a group of person’s can be observed. One is unable to observe the past behaviour nor can one observe a person’s future behaviour because the act of observation takes place in the present.

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One cannot observe a person’s attitude or opinion on a certain subject nor his knowledge of the same. This method is very slow and as such , when a large number of person’s are to be contacted it becomes unsuitable because of the long time required for this purpose.

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Direct personal interview- Under this method of collecting data, there is a face to face contact with the persons from whom the information is to be obtained. The interviewer asks them questions pertaining to the survey and collects the desired information. The information thus obtained is first hand or original in character.

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Merits- Response is more encouraging as most people are willing to supply information when approached personally. The information obtained by this method is likely to be more accurate. It is also possible through personal interview to collect supplementary information about the

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Informants personal characteristics and environment A delicate situation can usually be handled more effectively by a personal interview than by other survey techniques. The language of communication can be adjusted to the status and educational level of the person interviewed.

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Limitations- It is very costly and time consuming. The chances of biasness are greater under this method. It is totally based on the convenience of the informants.

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Indirect oral Interview- Under this method of collecting data, the investigator contacts third parties called witness capable of supplying the necessary information.

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This method is generally adopted in those cases where the information to be obtained is of a complex nature and the informants are not inclined to respond if approached directly. E.g. in an enquiry regarding addiction to drugs, alcohol etc.

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Information from correspondents- Under this method the investigator appoints local agents or correspondents in different places to collect information. These correspondents collect and transmit the information to the central office where the data are processed. Newspaper agencies generally adopt this method.

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Suitability- This method is generally adopted in those cases where the information to be obtained at regular intervals from a wide area.

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Questionnaire- The questionnaire can be classified into four main types- Structured non-disguised- A structured non-disguised questionnaire is one where the listing of questions is in a pre- arranged order and where the object of the enquiry is revealed to the respondents.

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Structured- disguised- A structured disguised questionnaire is one where the researcher does not disclose the object of the survey. Non-structured-non-disguised- A non-structured questionnaire is one in which they are to be asked from the respondent is left entirely to the researcher but the researcher revealed the objective of the enquiry.

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Non-structured- disguised DESIGNING A QUESTIONNAIRE The success of the questionnaire method of collecting information depends largely on the proper drafting of the questionnaire. Drafting of a questionnaire is a highly specialized job and requires a great deal of skill and experience.

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Covering letter Types of questions- Questions can be classified into various types such as- Open-ended questions Dichotomous questions Multiple- choice questions

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Phrasing of the questions Order of questions Layout of the questionnaire

MAIL QUESTIONNAIRE METHOD : 

MAIL QUESTIONNAIRE METHOD Under this method, a list of questions pertaining to the survey is prepared and sent to various informants by post. The questionnaire contains questions and provide space for answers. Request is made to the informants through a covering letter to fill up the questionnaire and send it back within a specified time.

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Merits- This method of collecting the data can be easily adopted where the field of investigation is very vast and the informants are spread over a wide geographic area.

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Limitations- This method can be adopted only where the informants are literate. The information supplied by the informants may not be correct and it may be difficult to verify the accuracy.

MAJOR QUALITATIVE RESEARCH TECHNIQUES : 

MAJOR QUALITATIVE RESEARCH TECHNIQUES Depth interview Focus group Projective techniques

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Depth Interview- This is an unstructured and longish interview on the given subject. The interviewer has to be skilled to be able to get the respondent to give a free-wheeling interview. Most of the questions are open ended, and ask for opinions, feelings ,associations, reasons for behavior of a consumer of a product category or brand.

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Focus Group- This is essentially a group discussion on a given subject conducted by a trained moderator. The purpose of this is to create a less than formal situation, where people can exchange views, bringing out their opinion, attitudes, feelings about the given subject.

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The sample is selected as usual from a target population which is specified by the needs of the study. Usually a group consists of 6-10 persons. The length of the discussion canbe about an hour or more.

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Projective Techniques- There are many different techniques which can be called ‘projective’. One popular method is to show a respondent a picture and ask him to describe the persons or objects in the picture. A particular product or brand can be shown being used or displayed, and the respondents can be asked to guess the type of consumer who would use the product shown.

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This is essentially a technique which seeks to get indirectly at the underlying motivations, attitudes or emotions of the respondents, which he would not reveal under direct questioning.

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Word Associations- Another variation of projective techniques is to ask respondents to associate brands with one word that they can think of when they think of the brand. It could be a person, a celebrity, or an animal, depending on the interviewers or the analyst viewpoint.

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Sentence completion- Another type of projective technique is giving an incomplete sentence to the respondent, and asking him to complete it. For example, People who use brand B coffee tend to be……..” This method is similar to word associations, and may result in surprising or unexpected associations.

SCALING TECHNIQUES : 

SCALING TECHNIQUES Nominal scale Ordinal scale Interval scale Ratio scale

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Nominal scale- In such a scale , the numbers serve as labels to identify persons, objects or events. Thus, numbers may be assigned to students in a class or patients in a hospital . E.g. we may use the nominal scale by counting students with a certain characteristics or attribute such as those who reside in the university hostels and others.

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In this example, students have been identified and counted by two characteristics namely, whether they are pursuing an undergraduate or post-graduate course of study and their place of residence.

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Ordinal scales- Ordinal scales as the name implies, are ranking scales. These scales also indicate the order. E.g. one may rank two or more households according to their annual income or expenditure. Suppose we have five households with annual incomes as shown below:

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If the household with the highest income is to given No. 1 and the next to it as No.2, and so on, then the following order will emerge.

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House hold Order of household on the basis of annual income A D B E C B D A E C

Interval Scales- The interval scale is stronger than the ordinal scale because it possesses not only the magnitude attribute but also the equal intervals attribute as it measures the values of the quantitative random variables and identifies not only as to which category is greater or better but also by ‘how much’.

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For example- measures of height, weight and time are all examples of interval scale. Let us take the example of temperature. If the temperature are 100 degree C, 80 degree C, 120 degree C, then we can say that a temperature of 100 degrees is 20 degrees warmer than 80 degree C and 20 degrees colder than 120 degrees.

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Ratio Scale- The ratio scale is also used for measurement of quantitative random variables. Example are physical measurements such as height, weight, distance etc. Equal ratio on the ratio scale indicates the equal ratio among the elements being measured.

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For example 10 kg and 50 kg are in the ratio of 1:5, if we convert these weights into grams then we have 10,000 grams and 50,000 grams and is in the ratio of 1:5. One can change from one unit to another by using the relevant conversion factor.

Difficulty of measurement : 

Difficulty of measurement Unlike physical sciences, measurement in social sciences is quite difficult. The measurement of length, height, weight is a simple task involving the use of ratio scale. But this type of situations is normally found in physical sciences. In contrast the measurement in marketing is more difficult. For example-

Difficulty of the measurement process : 

Difficulty of the measurement process Easy Very difficult Length Weight Happiness Creativity Preference Attitude Ratio Scale Interval Scale Ordinal Scale Nominal Scale

Sources of error : 

Sources of error There are four major sources of error in measurement. These are: The respondent The situation The measurer The instrument for data collection

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The respondent- Measurement may get distorted on account of different opinions of the respondent on a given issue. These differences arise on account of status of the respondent, level of education, social class etc. Further some respondents may be reluctant to express their negative opinion on some issue.

CRITERIA FOR A GOOD SCALE : 

CRITERIA FOR A GOOD SCALE There are two important criteria for ascertaining whether the scale developed is good or not. These are reliability and validity. There are three major methods of estimating the reliability of measurement. These are:

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Test- Retest reliability The Alternative forms reliability Split- Half reliability

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Test- Retest reliability- This form of reliability involves repeated measurement of the same respondent or group using the same scaling technique under similar conditions. This would involve administering a test at two points of time top the same person or a group of persons. The score of the two tests would then be correlated. If the correlation is low, then the reliability too is less.

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Alternative- Forms Reliability- Alternative forms reliability involves the same respondent being given a set of two forms. The forms are considered equivalent but are not identical. The results obtained on the basis of these forms are compared to ascertain whether there is considerable difference between the two scores.

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Split-Half Reliability- Another approach to reliability involves administering the measuring instrument only once to test the internal consistency. The split- half technique can be used in case of a multi-item instrument. It involves splitting of a multi-item measurement into two equivalent groups. The item responses of the two groups are then correlated to estimate reliability. A high degree of correlation indicates that there is similarity or homogeneity among the items.

VALIDITY : 

VALIDITY There are four approaches that can be commonly distinguished. Content Validity Construct Validity Predictive Validity Concurrent validity

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Content validity- The first is the content validity, which implies that the content of the scale correspond to the contents of the attitude system. The researcher should first define the problem clearly, identify the items to be measured, and evolve a suitable scale for the purpose

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Construct Validity- One of the difficulties arising in attitude measurement is that it is perhaps impossible to measure attitude directly. It can be measured only indirectly on the basis of answers given by the respondents. In a situation of this type , the test of construct validity is used. In order to apply construct validity, the researcher postulates the nature

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And extent of association between the attitude and other specified variables. He then examines whether these relationship exists. If not there could be two possible explanations. First, his scale might be invalid as it does not satisfactorily measure what it set out to measure. Second, his theory might be deficient in some way and it may be difficult for him to identify it.

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Predictive Validity- Predictive validity signifies how best the researcher can guess the future performance, from his knowledge of the attitude score. For example, an opinion questionnaire which forms the basis for correctly forecasting the demand for a product has predictive validity. The procedure for predictive validity first measures the attitude and then predicts the future behavior on the basis of this measurement.

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Concurrent validity- In the case of concurrent validity, an attitude scale on one variable can be used to estimate scores on another variable. For example , one may decide the social status of the respondents on the basis of their attitude toward savings.

DEVELOPMENT OF MARKETING MEASURES : 

DEVELOPMENT OF MARKETING MEASURES Specify domain of concept- It is very important to develop a sound conceptual definition. Generate sample of items Collect data Improve the measure Collect data for reliability and validity Assessments Assess Reliability Assess Validity Develop Norms

THE CONCEPT OF ATTITUDE : 

THE CONCEPT OF ATTITUDE Definition- The sum total of man’s inclinations and feelings, prejudice or bias, preconceived notions, ideas and convictions about any specific topic.

COMPONENTS OF ATTITUDE : 

COMPONENTS OF ATTITUDE There are three main components of attitudes: A cognitive component An effective component A behavioral component

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A cognitive component indicates that the respondent is aware of and knows about a given object or phenomenon. In marketing research, we want to know whether the respondent has some idea of product features, advertising campaigns, pricing of the product as also its availability, competitive product and such other aspects.

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Affective component indicates the respondent’s liking and preference for an object or phenomenon. When the respondent states: I like this product,” “Advertisement for product X is not good,", he is showing affective component.

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The behavior component indicates the respondent’s intention to buy and his actual purchase behavior. Prior to buy a product or service, the respondent must be inclined to do so. Hence intention to purchase precedes the actual purchase. A company would very much like to know whether the respondent’s intention is to buy its product. If this is so, it is reasonably assured about the product’s sale in the future.

SELECTED ATTITUDE SCALES : 

SELECTED ATTITUDE SCALES Paired- comparison Scale- Under it the respondent can express his attitude by making a choice between two objects. Suppose the respondent is asked to show his preference from amongst the five brands of tea A,B,C,D and E with respect to the flavors. He is then required to show his preference for a particular brand out of each of the possible pairs.

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In this case , the paired comparisons will involve: A and B B and D A and C B and E A and D C and D A and E C and E B and C D and E

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Each brand has been put in comparison with the others, one at a time. Since there are 5 brands to be evaluated, these are all in 10 paired comparisons. The total number of paired comparisons can be derived by a formula [ n(n-1) / 2 ] In this case 5( 5-1 ) /2 = 10

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Semantic Differential Scale or Thurstone Scales- L.L Thurstone is associated with differential scales. The semantic differential asks the respondents to express their feelings about whatever is being evaluated by recording their responses on a scale of adjectives.

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The semantic differential scale can be used in a number of cases such as comparison of brands, determining the effectiveness of advertising on attitude change, comparison of companies images etc. An example of semantic differential is given below:

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How would you describe the quality of service of ABC bank? Very efficient Efficient Moderately efficient Neither efficient nor inefficient Moderately inefficient Inefficient Very inefficient

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Summated Scales- In the likert scale the problem of choosing adjectives is avoided. Here a statement is made or a description is given on whatever is to be evaluated. The respondent is then given a scale whose positions range from ‘strongly agree’ to ‘strongly disagree’.

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At one end of this scale is strong approval and the other end is strong disapproval, between them there are many intermediate points. The respondent indicates with reference to each statement, where he stands on his scale.

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The total of his score on all statements is taken as the measurement of his attitude. Statement may be either favorable or unfavorable. For favorable statements values are 5,4,3,2,1 and for unfavorable statements values are 1,2,3,4,5.

CORRELATION : 

CORRELATION If two quantities vary in such a way that movement in one is accompanied by movement in the other, these quantities are correlated. For example, there exists some relationship between age of husband and age of wife, price of commodity and amount demanded etc.

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“Correlation analysis deals with the association between two or more variables”. The measure of correlation is known as correlation coefficient. It tells us the direction as well as degree of correlation. Simpson and Kafka

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Correlation and causation- Correlation analysis helps in determining the degree of relationship between two or more variables. It does not tell us anything about cause and effect relationship. Even a high degree of correlation does not necessarily mean that a relationship of correlation exists between the variables. It establishes a relationship of co variation only.

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Types of correlation- Positive or negative Simple and multiple Partial and total Linear and Non-linear

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Positive or Negative- If both the variables vary in the same direction then correlation is positive, if on the other hand the variables vary in opposite direction the correlation is –ve.

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Positive Correlation X : 10 12 15 18 20 Y : 15 20 22 25 37 Negative Correlation X: 20 30 40 60 80 Y: 40 30 22 15 10

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Simple and Multiple Correlation- When only two variables are studied it is known as simple correlation .eg If we are given only two variables concerning the heights and weights of a few individuals then the relation between them will be known as simple correlation between the variables.

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Multiple Correlation- Multiple correlation refers to the relation between more than two variables at a time. e.g. When we study the yield of rice per acre and both the amount of rainfall and the amount of fertilizers used, it is a problem of multiple correlation. Symbolically if x, y, z are three variables then coefficient expressing relationship of one say X with y and z will be

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Expressed r x. y z or the relation of y with z and x will be represented by r y. z x

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Partial and Total correlation Partial Correlation- In partial correlation we recognize more than two variables, but consider only two variables to be influencing each other the effect of other influencing variables being kept constant e.g. If x, y, z are the three variables then partial correlation between x & y excluding z will be represented by r x y. z.

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Similarly , r y z. x , r z x .y are the symbols for other partial correlation coefficients.

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Total Correlation- Total correlation refers to the relation between all the variables at a time without ignoring any variable. If x, y, z are the three variables then r x y z will represent total correlation between x, y & z

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Linear and Non-Linear correlation Linear Correlation- The two variables X and Y will be related by a correlation known as linear correlation if they are concerned by a relation of the form Y= a+ b x, then we can say that there exists a linear correlation between the variables. When such a line is plotted on a graph paper, the graph will always be a straight line.

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There will be +v e correlation if b is + v e and V e if b is –v e. Whenever the variables are correlated by such a relation the value of the coefficient of correlation is always +1 or - 1

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o X X’ Y Y’ O X X’ y Y’ Linear & positive correlation Linear & Negative correlation

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Non-Linear Correlation- The correlation between two variables X and Y is said to be non-linear if the rate of change of one with regard to another is not always the same. In other words, there will not exist a linear relationship between X and Y.

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Mathematically, the relationship between X and Y will never be of the form Y= a +b x. When such points will be plotted on a graph paper there will be no straight line. They will form curves.

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o x X’ y Y’ o y Y’ Non-linear negative Correlation

Degree of correlation : 

Degree of correlation Degree of Positive limits Negative limits Correlation Perfect correlation 1 -1 Extremely high degree correlation .9 & more -.9 & less Sufficient high degree correlation .75 to .9 -9 to-.75 Moderate high degree Of correlation .6 to .75 -.75 to -.6

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Possibility of Correlation Possibility of no Correlation Absence of correlation .3 to .6 -.6 to -.3 0<r<.3 0 -.3<r<0 0

METHODS : 

METHODS Graphic Methods Scatter diagram Simple graph Mathematical Methods Karl Pearson's coefficient of correlation or product Moment coefficient of correlation or co-variance Method

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Spearman’s coefficient of correlation or rank correlation Concurrent deviation method of finding the coefficient of correlation

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If the value is 0 it means that there is no relationship between the variables. Methods of calculating coefficient of correlation Direct Method Short- cut Method

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Karl Pearson’s coefficient of correlation This formula is named after Karl Pearson and is popularly known as Karl Pearson Coefficient of correlation. It is generally represented by ‘r’. Karl Pearson coefficient of correlation has two extreme values. It always lies between -1 and +1.

METHODS OF CALCULATING KARL-PEARSON COEFFICIENT OF CORRELATION : 

METHODS OF CALCULATING KARL-PEARSON COEFFICIENT OF CORRELATION Direct Method Short-cut Method

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Direct Method

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(II) In this method deviations are taken both in X & Y series from the respective actual arithmetic means

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(iii) In this method the product of the deviations from the actual means of X and Y series are summed up and the sum so obtained is divided by where N is the number of items, are the standard deviations of X and Y series respectively.

SHORT-CUT METHOD : 

SHORT-CUT METHOD

SPEARMAN’S COEEFICIENT OF CORRELATION OR RANK CORRELATION : 

SPEARMAN’S COEEFICIENT OF CORRELATION OR RANK CORRELATION Sometimes it so happens that the series X and Y are not measured in the quantitative form. This problem arises when the series involve qualitative characteristics such as honesty, beauty, poverty etc. We do not have any measures to ascertain the magnitude of such phenomenon. We can only assign numerical grades to them with the help of Spearman’s Coefficient of correlation or popularly known as ‘Rank Correlation’

STEPS FOR THE CALCULATION OF RANK CORRELATION : 

STEPS FOR THE CALCULATION OF RANK CORRELATION If the data is already ranked we have to do grading of the values in both X and Y series according to their magnitude. There are two ways to rank the aeries. We may rank the least value as number 1 and go on assigning ranks 2,3,4,5……… in the ascending order till all the items are ranked.

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If two or more items have the same value either in one or in the other series or in both the series, then we have to follow the ‘Average Ranking Method’. After assigning ranks, the difference in ranks are calculated and are represented by ‘d’. When there is no repetition in the items Spearman’s Coefficient of correlation is calculated by the formula

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When there is repetition in the items it is calculated by the formula

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Where m1, m2 , m3…..etc. represent the number of times the items are repeated in either X or Y or in both the series.

CONCURRENT DEVIATION METHOD OF FINDING THE COEFFICIENT OF CORRELATION : 

CONCURRENT DEVIATION METHOD OF FINDING THE COEFFICIENT OF CORRELATION By this method we simply observe the direction of each value in each series with reference to the preceding value. If the next value rises we put +ve sign before it. If the value remains stationary i.e. there is no change at any stage we put ‘0’ before it . Here ‘N’ is always one less than the number of pairs of X and Y values because we have to leave the first pair because of the inability to establish the trend.

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After recording the +ve and –ve movements we multiply the two. After this the resultant should be added and it is represented by C i.e. the concurrent deviation. The formula for calculating the coefficient of correlation is

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Where +ve and – ve signs should be determined by 2C-N is +ve or –ve. N

REGRESSION ANALYSIS : 

REGRESSION ANALYSIS The concept of Regression analysis was first given by a great statistician named Sir Francis Galton who first studied it with reference to the heights of fathers and sons. As per his analysis taller fathers have taller sons. In addition the mean height of son of tall father is less than the mean height of their fathers and the mean height of son of short father is more than the mean height of their fathers. Regression lines are thus to study the average relationship between two series.

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According to M.M Blair, “Regression is the measure of average relationship between two or more variables in terms of the original units of the data.

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Correlation It means the relationship between two or more variables. It studies the co variability between two variables. It tells us whether the two variables move in the same direction or in the opposite direction. Regression It expresses the average relationship between two or more variables.

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Correlation does not establish cause and effect relationship between the variables Regression analysis is based on cause and effect relationship between the variables. The variables expressing cause is taken as independent variable and those expressing effect is taken as dependent variable.

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Correlation coefficient between the variables X and Y or Y and X is always taken as the same. Any one of them can be taken as dependent and other as an independent variable in both ways Coefficient of correlation is a relative measure Regression equation in general deal with the functional relationships between X and Y or Y and X. In these two relations the dependent and independent variable change and as such their regression coefficients also change i.e. bxy=byx Regression coefficients are absolute measures.

REGRESSION LINES : 

REGRESSION LINES We have two regression lines i.e. Regression line Yon X and Regression line X on Y . Regression line Y on X is Y- Y= b y x (X- X) Regression Coefficient of Y on X is

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Regression line X on Y is X- X = b x y( Y- Y)

Chi-Square Test And Goodness of fit : 

Chi-Square Test And Goodness of fit The Chi-Square test is one of the simplest and most widely used non-parametric test. The chi-Square test was first used by Karl-Pearson in the year 1990. It is defined as

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Where O refers to the observed frequencies and E refers to the expected frequencies. Degree of freedom v = (r-1) (c-1)

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Steps- Calculate the expected frequencies E = RT .CT N Where E= Expected frequency RT= the row total for the row containing the cell CT= The column total for the column containing the cell N = The total number of observations

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Take the difference between observed and expected frequencies and obtain the squares of these differences i.e. obtain the value of

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Divide the values of by the respective expected frequency and obtain the total If chi- square is zero it means that the observed and expected frequencies completely coincide. The greater the discrepancy between the observed and expected frequencies the greater shall be the value of chi- square.

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The calculated value of chi-square is compared with the table value of chi-square for a given degree of freedom at a certain specified level of significance.

USES OF CHI-SQUARE TEST : 

USES OF CHI-SQUARE TEST Chi-Square test as a test of independence- With the help of chi-square test we can find out whether two or more attributes are associated or not. In order to test whether or not the attributes are associated we take the null hypothesis that there is no association in the attributes or in other words the attributes are independent. If the calculated value of chi-square is less than the table value at a certain

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Level of significance, the attributes are not associated. The chi-square is not a measure of degree or form of relationship, it only tells us whether two principles of classification are or are not significantly related, without reference to any assumptions concerning the form of relationship.

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Chi-Square test as a test of goodness of fit- Chi-Square test is very popularly known as test of goodness of fit for the reason that it enables us to ascertain how appropriately the theoretical distributions such as binomial, Poisson , Normal etc., fit empirical distributions i.e., those obtained from sample data. When an ideal frequency curve whether normal or some other type is fitted to the data, we are interested in

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In finding out how well this curve fits the observed facts. Chi- Square test as a test of homogeneity- Tests of homogeneity are designed to determine whether two or more independent random samples are drawn from the same population or from different populations.

ANALYSIS OF VARIANCE : 

ANALYSIS OF VARIANCE Analysis of variance also known as ANOVA is a statistical technique designed to test whether the means of more than two quantitative populations are equal. The analysis of variance technique given by R.A. Fisher is being divided into – One-Way Classification Two-Way Classification

ONE-WAY AND TWO-WAY CLASSIFICATION : 

ONE-WAY AND TWO-WAY CLASSIFICATION In a one-factor analysis of variance treatments constitute different levels of a single factor which is controlled in the experiment. There are, however, many situations in which the response variable of interest may be affected by more than one factor. E.g. petrol mileage may be affected by the type of car driven, the way it is driven, road conditions and other factors in addition to the brand of petrol used.

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When it is believed that two independent factors might have an effect on the response variable of interest, it is possible to design the test so that an analysis of variance can be used to test for the effects of the two factors simultaneously. Such a test is called a two factor analysis of variance. With the two factor analysis of variance, we can test two sets of hypothesis with the same data at the same time.

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