Table of Contents
Q.1. Elaborate the stages in Marketing Research Process. (20)
Marketing research exercise may take many forms but systematic inquiry is feature common to all such forms. Being a systematic inquiry it requires careful planning of the orderly investigation process. Though it is an over simplification to assume that all research processes would necessarily follow a given sequence marketing research often follows a generalised pattern which can be broken down and studied as sequential stages.
Stages in Marketing Research1. Defining the Problem
Clear problem definition is of crucial importance in marketing research as in terms of both time and money research is a costly process. Careful attention to problem definition allows the researcher to set the proper research objectives which in turn facilitate relevant and economic data collection.Problem definition in specific terms must precede the determination of the purpose of the research.
2. Statement of Research Objectives
After clarifying and identifying the research problem with or without exploratory research, the researcher must make a formal statement of research objectives.Research objectives may be state in qualitative or quantitative terms and expressed as research question statements or hypothesis. For example, the research objective ” To find out the extent to which the sales promotion programmes affected sales” is a research objective expressed as a statement. A hypothesis on the other hand is a statement that can be refuted or supported by empirical findings. The same research objective could be stated as: “To test the hypothesis that sales are -positively affected by the sales promotion programme undertaken this summer.”
3. Planning the Research Design
Once the research problem has been defined and the objectives decided, the research design must be developed. A research design is a piaster plan specifying the procedure for collecting and analysing the needed information. It represents framework for the research plan of action. The objectives of the study discussed in the preceding step are included in the research design to ensure that data collected are relevant to the objectives.4. Planning the Sample
Although the sample plan is included in the research design, the actual sampling is a separate and important stage in the research process, Sampling involves procedures that use a small number of items or parts of the population to make conclusion regarding the whole population. The first sampling question that needs to be asked is who is to be sampled, which follow from what is the target population. Defining the population may not be as simple as it seems. For example, if you are interested in finding the association between savings and loans, you may survey the people who already have accounts and the selected sample will not represent potential customers.The researcher is also required to know how to select the various unit to make up the sample. There are two basic classes of sampling methods-probabilistic, and non-probabilistic.
5. Data Collection
The data collection process follows the formulation of research design including the sampling plan. Data which can be secondary or primary, can be collected using variety of tools. These tools are classified into two broad categories, the observation methods and the communication methods, all of which have their inherent advantages and disadvantages.6. Data Processing and Analysis
Once the data has been collected it has to be converted to a format that will suggest answers to the problem identified in the first step, Data processing begins with the editing of data and coding. Editing involved inspecting the data collection forms for omission, legibility and consistency in classification.Analysis represents the application of logic to the understanding of data collected about the subject. In its simplest forms, analysis may involve determination of consistent patterns and summarising of appropriate details. The appropriate analytical techniques chosen would depend upon informational requirements of the problem, characteristics of the research designs and the -nature of the data gathered. The statistical analysis may range from simple univariate analysis to very complex multivariate analysis. You will study univariate, bivariate and multivariate analysis and their applications in marketing problem in last three blocks of this course.
7. Formulating Conclusion, Preparing & Presenting the Report
The final sate in the research process is that of interpreting the information mid drawing conclusions for use in managerial decisions. The research report should effectively communicate the research findings and need not necessarily include complicated statements about the technical aspect of the study and research methods.Often the management is not interested in details of research design and statistical analysis but in the concrete findings of the research. If executives are to act on these findings they must be convinced of the value of the findings. Researchers, therefore, must make the presentation technically accurate, understandable and useful.
Q.2. Describe the four major types of Research Designs. (20)
A research design is a broad plan that states objectives of research project and provides the guidelines what is to be done to realize those objectives. It is, in other words, a master plan for executing a research project.
Types of Research Designs
The research design is a broad framework that describes how the entire research project is carried out. Basically, there can be three types of research designs – exploratory research design, descriptive research design, and experimental (or causal) research design. Use of particular research design depends upon type of problem under study.
1. Exploratory Research Design
This design is followed to discover ideas and insights to generate possible explanations. It helps in exploring the problem or situation. It is, particularly, emphasized to break a broad vague problem statement into smaller pieces or sub-problem statements that help forming specific hypothesis.The hypothesis is a conjectural (imaginary, speculative, or abstract) statement about the relationship between two or more variables. Naturally, in initial state of the study, we lack sufficient understanding about problem to formulate a specific hypothesis. Similarly, we have several competitive explanations of marketing phenomenon. Exploratory research design is used to establish priorities among those competitive explanations.
The exploratory research design is used to increase familiarity of the analyst with problem under investigation. This is particularly true when researcher is new in area, or when problem is of different type.
This design is followed to realize following purposes:
1. Clarifying concepts and defining problem
2. Formulating problem for more precise investigation
3. Increasing researcher’s familiarity with problem
4. Developing hypotheses
5. Establishing priorities for further investigation
Exploratory research design is characterized by flexibility to gain insights and develop hypotheses. It does not follow a planned questionnaire or sampling. It is based on literature survey, experimental survey, and analysis of selected cases. Unstructured interviews are used to offer respondents a great deal of freedom. No research project is purely and solely based on this design. It is used as complementary to descriptive design and causal design.
2. Descriptive Research Design
Descriptive research design is typically concerned with describing problem and its solution. It is more specific and purposive study. Before rigorous attempts are made for descriptive study, the well-defined problem must be on hand. Descriptive study rests on one or more hypotheses.For example, “our brand is not much familiar,” “sales volume is stable,” etc. It is more precise and specific. Unlike exploratory research, it is not flexible. Descriptive research requires clear specification of who, why, what, when, where, and how of the research. Descriptive design is directed to answer these problems.
3. Causal or Experimental Research Design
Causal research design deals with determining cause and effect relationship. It is typically in form of experiment. In causal research design, attempt is made to measure impact of manipulation on independent variables (like price, products, advertising and selling efforts or marketing strategies in general) on dependent variables (like sales volume, profits, and brand image and brand loyalty). It has more practical value in resolving marketing problems. We can set and test hypotheses by conducting experiments.Test marketing is the most suitable example of experimental marketing in which the independent variable like price, product, promotional efforts, etc., are manipulated (changed) to measure its impact on the dependent variables, such as sales, profits, brand loyalty, competitive strengths product differentiation and so on.
4. Correlational research design
Correlational research is a non-experimental research design technique that helps researchers establish a relationship between two closely connected variables. This type of research requires two different groups. There is no assumption while evaluating a relationship between two different variables, and statistical analysis techniques calculate the relationship between them.A correlation coefficient determines the correlation between two variables, whose value ranges between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables and -1 means a negative relationship between the two variables.
Q.3. Discuss possible sources of primary data. Highlight the advantages and limitations of use of primary data in marketing research. (20)
The major sources of primary data include respondents, analogous case situations and research experiments.
1. Respondents
Respondents represent by far the most important source of primary marketing data. Marketing decisions are characterised by the fact that they always involve in one way or the other, prediction of the behaviour of the market participants- be it consumer, industrial users, marketing intermediaries or competitors. Decisions as diverse as product introduction, price or channel modification, determination of the advertising budget or reallocation of sales territories would require forecasting of the behaviour of one or more of the above groups. The study of respondents therefore characterises most marketing research situations.The type of information that may be collected from respondents may include data on past behaviour, intentions of likely behaviour, extent of knowledge, attitudes and opinion and socio-economic characteristics and lifestyle data.
a. Past behaviour
It is frequently used as a predictor of future behaviour, acting on the premise that there is a relatively stable relationship between past and future behaviour patterns. Information or past behaviour of respondents is sought on past consumption habits, exposure and awareness of communication tools, experience of competitor’s vis-a vis the company’s products or services etc., in order to get an idea of the consumption of the product or service.b. Intention
It means of likely action are self prediction of planned action to be taken sometime in future. They represent a very frequently sought information from the respondent. However, as intention only represent concurrently valid statements, actual behaviour may get modified by changes over which the respondent has little or no control; for example- price changes or entry of a new attractive competitive brand. The utility of intentions data is far higher as a predictive tool, when intentions, given the conditions of purchase can be assigned probabilities.c. Attitudes and opinions
It represents predisposed mental state to act in a certain way. For example, the statement by a respondent that he prefers fresh ground coffee to the instant” blends, gives a clue to his likely behaviour when faced with a decision to buy coffee. Attitude research in marketing has found the greatest use in product designs and advertising. Both qualitative and quantitative techniques are used to collect Data Collection information on attitudes and opinions.d. Socio-economic characteristics
Whenever in a consumer research situation there is a basis for the belief that some socio-economic characteristics like income, education occupational status etc. are associated with the purchase of a product or service, information is sought on these characteristics. Study on these types of variables may help defining the target market more precisely and arriving at more accurate pricing and promotion decisions.e. Life styles
They are increasingly being used to segment markets reposition products and targeting positioning strategies wore closely to the consumer profile. Life style profiles are developed though systematic study of values attitudes, opinions and interests, as well as by income, all of which can be elicited from the respondents through communication methods or a combination of communication and observation methods.2. Primary data from Analogous situations – Case Study
Evolved from the behavioural sciences, case study or case history is in extensive use in marketing research today. Using situations analogous to or relevant to the problem situation, an in depth investigation is carried out to thoroughly study the case situation. The emphasis of the study is on identifying key variables, defining the nature of their relationships and possibly defining the problem/opportunity in order to suggest alternative courses of action for the decision situation. The source is typically relevant in situations where multiple variables interact to produce the problem or the opportunity. Examples would be research problems involving,• study of changes in sales performance with the entry of a new competitor,
• contrasting performance levels in different markets,
• transitional stages like study of sales territories where the company is altering its distribution channels from indirect to direct sales.
3. Simulation
A marketing simulation is an incomplete representation of -the marketing system or some aspect of the system. It is a relatively new data source and is largely computer based. It involves the study of dynamics of the marketing system by manipulating independent variables like marketing mix or situational factors and observing their influence on the dependent variables. It would therefore need characteristics of the phenomenon and the relationships between variables to be represented as data inputs.4. Experimentation
Experimentation also represents a fairly rich source of primary data and is used mostly to study cause and effect relationships among marketing variables. Marketing experiments are conducted almost like other scientific experiments. One or more, independent variables are consciously manipulated or controlled and their impact on indendent variables is studied. All the same effort is made to control those variables which may hamper the ability to interpret valid causality. Various experimental designs have already been discussed in the preceding unit under the subtitle of experimental research design.Advantages of Primary data
Primary sources usually provide more detailed information than the secondary sources. This is partly because methods of data collection and the tools used can be tailored more precisely to the informational needs of the researcher. This also contributes to the flexibility of analysis for the research purpose at hand.
Terms and units can be more precisely defined and the researcher can choose the appropriate unit for this purpose with a much greater degree of fit since he has greater degree of flexibility in choosing the appropriate unit. In case of secondary data, as the data have been collected for some other purpose, the researcher may not have much of control over the choice of units used in the secondary data source.
The user in case of primary data can judge the degree of confidence that he may place on the data because he has an accurate idea of the tools and methodology used and their limitations.
In addition to the above general advantages, there are other merits which characterise the different methods of primary data collection.
Limitations of Primary Data in Marketing Research
1. Collection of primary data is a time consuming affair.
2. It may be expensive too.
3. It is difficult to find sincere and honest interviewers or enumerators.
4. In the case of questionnaire method, the researcher might face the problem of non-response.
5. The respondents may not be prepared for an interview when the enumerator approaches them with the schedule.
The major limitations of primary data relates itself to costs in terms of time and money involved, availability of skilled investigators or interviewers and the errors that may creep in various stages i.e. sample selection, sample size selection, tools chosen etc.
Q.4. Write short notes on the following: (4×5=20)
a. Nominal Scale
A Nominal Scale is a measurement scale, in which numbers serve as “tags” or “labels” only, to identify or classify an object. A nominal scale measurement normally deals only with non-numeric (quantitative) variables or where numbers have no value.
Below is an example of Nominal level of measurement.
Please select the degree of discomfort of the disease:
1-Mild
2-Moderate
3-Severe
In this particular example, 1=Mild, 2=Moderate, and 3=Severe. Here numbers are simply used as tags and have no value.
There are four variable measurement scales: nominal, ordinal, interval and ratio. These measurement scales are ways to categorize different variables (an element, feature or factor that is likely to vary). By default, all variables fall in one of the four scales mentioned above. Understanding their properties and assigning variables to one of the four measurement scales is important mathematically because they determine what mathematical operations are allowed.
Nominal scale possesses only the description characteristic which means it possesses unique labels to identify or delegate values to the items. When nominal scale is used for the purpose of identification, there is a strict one-to-one correlation between an object and the numeric value assigned to it. For example, numbers are written on cars in a racing track. The numbers are there merely to identify the driver associated with the car, it has nothing to do with characteristics of the car.
But when nominal scale is used for the purpose of classification, then the numbers assigned to the object serve as tags to categorize or arrange objects in class. For example, in the case of a gender scale, an individual can be categorized either as male or female. In this case, all objects in the category will have the same number, for example, all males can be no. 1 and all females can be no. 2. Please note, that nominal is purely used for counting purposes.
From a statistics point of view nominal scale is one of the easiest to understand measurement scale. As mentioned earlier, nominal scale is assigned to items that are not quantitative or number oriented.
For example, let’s assume we have 5 colors, orange, blue, red, black and yellow. We could number them in any order we like either 1 to 5 or 5 to 1 in ascending or descending order. Here numbers are assigned to colors only to identify them. Another example of nominal scale from a research activity point to view is YES/NO scale. It essentially has no order.
Characteristics of Nominal Scale
• In nominal scale a variable is divided into two or more categories, for example, agree/disagree, yes or no etc. It’s is a measurement mechanism in which answer to a particular question can fall into either category.
• Nominal scale is qualitative in nature, which means numbers are used here only to categorize or identify objects. For example, football fans will be really excited, as the football world cup is around the corner! Have you noticed numbers on a jersey of a football player? These numbers have nothing to do with the ability of players, however, they can help identify the player.
• In nominal scale, numbers don’t define the characteristics related to the object, which means each number is assigned to one object. The only permissible aspect related to numbers in a nominal scale is “counting.”
b. Ordinal Scale
Ordinal scale is the 2nd level of measurement that reports the ranking and ordering of the data without actually establishing the degree of variation between them. Ordinal level of measurement is the second of the four measurement scales.
“Ordinal” indicates “order”. Ordinal data is quantitative data which have naturally occurring orders and the difference between is unknown. It can be named, grouped and also ranked.
For example:
“How satisfied are you with our products?”
1- Totally Satisfied
2- Satisfied
3- Neutral
4- Dissatisfied
5- Totally Dissatisfied
Survey respondents will choose between these options of satisfaction but the answer to “how much?” will remain unanswered. The understanding of various scales helps statisticians and researchers so that the use of data analysis techniques can be applied accordingly.
Thus, an ordinal scale is used as a comparison parameter to understand whether the variables are greater or lesser than one another using sorting. The central tendency of the ordinal scale is Median.
Likert Scale is an example of why the interval difference between ordinal variables cannot be concluded. In this scale the answer options usually polar such as, “Totally satisfied” to “Totally dissatisfied”.
The intensity of difference between these options can’t be related to specific values as the difference value between totally satisfied and totally dissatisfied will be much larger than the difference between satisfied and neutral. If someone loves Mercedes Benz cars and is asked “How likely are you to recommend Mercedes Benz to your friends and family?” will be troubled to choose between Extremely likely and Likely. Thus, an ordinal scale is used when the order of options is to be deduced and not when the interval difference is also to be established.
Ordinal Scale Characteristics
• Along with identifying and describing the magnitude, the ordinal scale shows the relative rank of variables.• The properties of the interval are not known.
• Measurement of non-numeric attributes such as frequency, satisfaction, happiness etc.
• In addition to the information provided by nominal scale, ordinal scale identifies the rank of variables.
• Using this scale, survey makers can analyze the degree of agreement among respondents with respect to the identified order of the variables.
c. Interval Scale
The interval scale is a quantitative measurement scale where there is order, the difference between the two variables is meaningful and equal, and the presence of zero is arbitrary. It measures variables that exist along a common scale at equal intervals. The measures used to calculate the distance between the variables are highly reliable.
The interval scale is the third level of measurement after the nominal scale and the ordinal scale. Understanding the first two levels will help you differentiate interval measurements. A nominal scale is used when variables do not have a natural order or ranking. You can include numbered or unnumbered variables, but common survey examples include gender, location, political party, pets, and so on.
In contrast, on an ordinal scale, the rank of variables matters, but the difference or distance between the variables doesn’t. Think about price range filters for online shopping. You can select “less than $25,” “$26 up to $50,” and so forth, but the difference between them is not relevant. Likewise, the ranking of variables such as “Would not recommend” and “Would highly recommend” matters, but the difference between them does not unless that difference is represented by another variable.
Characteristics of interval scale
• The interval scale is preferred to nominal scale or ordinal scale because the latter two are qualitative scales. The interval scale is quantitative in the sense that it can quantify the difference between values.
• Interval data can be discrete with whole numbers like 8 degrees, 4 years, 2 months, etc., or continuous with fractional numbers like 12.2 degrees, 3.5 weeks or 4.2 miles.
• You can subtract values between two variables that help understand the difference between two variables.
• Interval measurement allows you to calculate the mean and median of variables.
• Interval data is especially useful in business, social, and scientific analysis and strategy because it is straightforward and quantitative.
• This is a preferred scale in statistics because you can assign a numerical value to any arbitrary assessment, such as feelings and sentiments.
d. Ratio Scale
Ratio scale is a type of variable measurement scale which is quantitative in nature. Ratio scale allows any researcher to compare the intervals or differences. Ratio scale is the 4th level of measurement and possesses a zero point or character of origin. This is a unique feature of ratio scale. For example, the temperature outside is 0-degree Celsius. 0 degree doesn’t mean it’s not hot or cold, it is a value.
Following example of ratio level of measurement to help understand the scale better.
Please select which age bracket do you fall in?
• Below 20 years
• 21-30 years
• 31-40 years
• 41-50 years
• 50 years and above
Ratio scale has most of the characteristics of the other three variable measurement scale i.e nominal, ordinal and interval. Nominal variables are used to “name,” or label a series of values. Ordinal scales provide a sufficiently good amount of information about the order of choices, such as one would be able to understand from using a customer satisfaction survey. Interval scales give us the order of values and also about the ability to quantify the difference between each one. Ratio scale helps to understand the ultimate-order, interval, values, and the true zero characteristic is an essential factor in calculating ratios.
A ratio scale is the most informative scale as it tends to tell about the order and number of the object between the values of the scale. The most common examples of ratio scale are height, money, age, weight etc. With respect to market research, the common examples that are observed are sales, price, number of customers, market share etc.
Characteristics of Ratio Scale
• Ratio scale, as mentioned earlier has an absolute zero characteristic. It has orders and equally distanced value between units. The zero point characteristic makes it relevant or meaningful to say, “one object has twice the length of the other” or “is twice as long.”
• Ratio scale doesn’t have a negative number, unlike interval scale because of the absolute zero or zero point characteristic. To measure any object on a ratio scale, researchers must first see if the object meets all the criteria for interval scale plus has an absolute zero characteristic.
• Ratio scale provides unique possibilities for statistical analysis. In ratio scale, variables can be systematically added, subtracted, multiplied and divided (ratio). All statistical analysis including mean, mode, the median can be calculated using ratio scale. Also, chi-square can be calculated on ratio scale variable.
• Ratio scale has ratio scale units which have several unique and useful properties. One of them is they allow unit conversion. Take an example of calculation of energy flow. Several units of energy occur like Joules, gram-calories, kilogram-calories, British thermal units. Still more units of energy per unit time (power) exist kilocalories per day, liters of oxygen per hour, ergs, and Watts.
Q.5. Explain the steps in the Sampling process and differentiate between Sampling and Non-sampling Errors. (20)
The sampling process consists of five sequential steps.
Steps in Sampling ProcessStep 1
The primary step is to define the population. It should be defined in terms of sampling (i) the elements, (ii) the sampling units, (iii) the extent, and (iv) the time.Step 2
The next step is to specify the sampling frame.Step 3
The third step is to choose an appropriate sampling design for selecting the same .The sampling design describes the procedure by which a sample is selected.There are two types of procedures namely non-probability sampling procedures and probability sampling procedures. Under probability sampling procedure, each element has a known chance of being selected in the sample. In the non-probability sampling procedure, there is no known chance of an element of the population being -selected in the sample. The selection of an element of population in the sample depends upon judgement of the researcher or of the field interviewer.
Step 4
Once we have decided the procedure by which sample would be selected, the next step is to determine how large sample should be taken.Step 5
The interviewers who are to go to field to collect actual data need very clear and accurate instructions as to how to do this job. The sampling planning is therefore not complete until these instructions are prepared and handed over to the investigators.Difference between Sampling and Non-sampling errors
Sampling Error
The error which arises due to drawing inferences about population parameter on the basis of observations drawn from a sample (a part of the population) is called sampling error. In other words a sampling error is made while selecting a sample which is not representative of the population. It represents the difference between sample value and true value of population parameters. A sampling error is bound to occur while selecting a sample as it is difficult, if not impossible, for a sample (a small part of the population) to be exactly representative of the population. This occurs no matter how careful the researcher is randomly choosing the sample.
Sampling error, therefore, is a result of chance. The sampling error usually decreases with increase in sample size and it is non-existent in a complete enumeration survey.
Non-sampling Error
The main reasons for the occurrence of non- sampling errors are the improper specification of the scope of study, inadequate coverage of population and sample, vague definition of variables involved and wrong methods of data collection. A combination of one or more of following factors could always result into non-sampling errors.
1. Inadequate specification of data which may not be consistent with the objectives of survey.
2. Inaccurate methods of interview.
3. Inaccurate reporting by the respondents.
4. Lack of trained and experienced interviewers.
5. Plain lying by the respondents,
6. Inability to locate proper respondents due to improper instructions or wrong address.
7. Inadequate scrutiny of the basic data.
8. Errors in coding, editing and tabulation of data.
9. Errors in presenting the tabulated results, graphs etc
The non-sampling error can occur in the case of complete enumeration as well as in sample survey. However, non-sampling error increases with the increase in sample size. Therefore, the size of non-sampling error is much larger in case of complete enumeration than that of sample surveys. This, is because in a study involving complete enumeration of the units of the population, we need more interviewers, more staff to supervise these interviewers and more manpower to convert raw data to computer input. This would involve more training of the interviewers, which would be both, costly and time consuming. Further, as the size of staff becomes larger their quality falls. It also becomes more difficult to control and supervise their activities. All this would result into more errors And less accurate results thereby contributing to increased non-sampling errors.
Q.6. What are the various ways in which non-probability sampling can be done ? Elaborate them. (20)
Non-Probability Sampling Methods
1. Convenience Sampling
Under convenience sampling, as the name implies, the samples are selected at the convenience of the researcher or investigator. Here, we have no way of determining the representativeness of the sample. This results into biased estimates. Therefore, it is not possible to make an estimate of sampling error as the difference between sample estimate and population parameter is unknown, both in terms of magnitude and direction. It is therefore suggested that convenience sampling should not be used in both, descriptive and causal studies as it is not possible to make any definitive statements about the results from such a sample.This method may be quite useful in exploratory designs as a basis for generating hypotheses. The method is also useful in testing of questionnaire etc. at the pertest phase of the study. Convenience sampling is extensively used in marketing studies and otherwise. This would be clear from the following examples.
a. Suppose a marketing research study aims at estimating the proportion ` of Pan (beetle leave) shops in Delhi which store a particular drink say Maaza. It is decided to take a sample size of 100. What investigator does is to visit 100 pan shops near his place of residence as it is very convenient to him and observe whether a Pan shop stores maaza or not. This definitely is not a representative sample as most pan shops in Delhi had no chance of being selected. It is only those pan shops which were near the residence of the investigator that had a chance of being selected.
b. The other example where convenience sampling is often used is in test marketing. There might be some cities whose demographic make tips are approximately the same as national average. While conducting marketing tests for new products; the researcher may take samples of consumers from such cities and obtain `consumer evaluations, about these products as these are supposed to represent “national” tastes.
c. A ball pen manufacturing company is interested in knowing the opinions about the ball pen (like smooth flow of ink, resistance to breakage of the cover etc.) it is presently manufacturing with a view to modify it to suit customers needs. The job is given to a marketing researcher who visits a college near his place of residence and asks a few students (a convenient sample) their opinion about the ball pen in question.
d. As another example, a researcher might visit a few shops to observe what brand of vegetable oil people are buying so as to make inference about the share of a particular brand he is interested in.
2. Judgement Sampling
Judgement sampling is also called purposive sampling. Under this sampling procedure, a researcher deliberately or purposively draws a sample from the population which he thinks is a representative of the population.Needless to mention, all members of the population are not given chance to be selected in the sample. The personal bias of the investigator has a great chance of entering the sample and if the investigator chooses a sample to give results which favours his view point, the entire sutdy may be vitiated.
However, if personal biases are avoided, then the relevant experience and the acquaintance of the investigator with the population may help to choose a relatively representative sample from the population. It is not possible to make an estimate of sampling error as we cannot determine how, precise our sample estimates are.
Judgement sampling is used in a number of cases, some of which are mentioned below:
a. Suppose we have a panel of experts to decide about the launching of a new product in the next year. If for some reason or the other, a member drops out from the panel, the chairman of the panel may suggest the name of another person whom he thinks has the same expertise and experience to be a member of the said panel. This new member was chosen deliberately – a case of Judgment sampling.
b. The method could be used in a study involving the performance of salesmen. The salesmen could be grouped into top-grade and low-grade performer according to certain specified qualities. Having done so, the sales manager may indicate who in his opinion would fall into which category. Needless to mention, this is a biased method. However, in the absence of any objective data, one might have to resort to this type of sampling.
3. Quota Sampling
This is one of the most commonly used sampling method in marketing research studies. Here the sample is selected of the basis of certain basic parameters such as age, sex, income and occupation that describe the nature of a population so as to make it representative of the population. The investigators or field workers are instructed to choose a sample that conforms to these parameters. The field workers are assigned quotas of the numbers of units satisfying the required characteristics on which data should be collected. However, before collecting data on these units the investigators are supposed to verify that the units qualify these characteristics.Suppose we are conducting a survey to study the buying behaviour of a product and it is believed that the buying behaviour is greatly influenced by the income level of the consumers. We assume that it is Possible to divide our population into three income strata such as high income group, middle income group and low income group.
Further, it is known that 20% of the population is in high income group, 35% in the middle income group and 45% in the love income group. Suppose it is decided to select a sample of size 200 from the population. Therefore, samples of size 40, 70 and 90 should come from high income, middle income and low income groups respectively. Now the various field workers are assigned quotas to select the sample from each group in such a way that a total sample of 200 is selected in the same proportion as mentioned above. For example, the first field worker may be assigned a quota of 10 consumers from the high income group, 25 from the middle income group and 40 from the low income group. Similarly the 2nd field worker may be given a different quota and so on such that a total sample of 200 is obtained in the same proportion as discussed earlier.
Q.7. (a) Construct a Likert scale with 10 statements that may be used to measure customer’s satisfaction level of services provided by a 5 star hotel. (10)
Likert Scale
This scale comprises of a series of evaluative statements/items concerning an attitude object. Each statement has ‘a five point agree disagree scale. The number of statements/items could vary from study to study.
However a typical Likert scale has generally 25 to 30 items. The scores on the individual items are summed to produce a total score for the respondent. For this reason it is also called a summated scale. The assumption made here is that each of the items/ statements measures some aspect of a single common factor otherwise the items cannot be legitimately summed.
Likerts scale Questions
1. How satisfied you are with Hotel staff hotel staff behaviour ?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
2. How satisfied you are with hotel staff politeness ?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
3. How satisfied you are with the check-in process?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
4. How satisfied you are with the cleanliness of your room upon arrival?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
5. How satisfied you are with the essential items in your room?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
6. How satisfied you are with the time taken by the hotel staff to respond on your requests?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
7. How satisfied you are with the food quality of the hotel?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
8. How satisfied you are with the services?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
9. How satisfied with your stay at the hotel?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
10. How satisfied you are with the location of the hotel ?
a. Strongly Disagree
b. Disagree
c. Neither Disagree nor Agree
d. Agree
e. Strongly Agree
(b) Prepare a questionnaire with 5 close-ended questions which may be used for profiling customer availing services of a 5 star hotel. (10)
Questionnaire
1. How was the services of the hotel?
a. Good
b. Somewhat good
c. Bad
2. How was the experience of your stay in the hotel?
a. Good
b. Satisfied
c. Bad
3. Did the hotel staff responded well?
a. Yes
b. No
4. Would you recommend the hotel to your friends or relatives?
a. Yes
b. May be
c. Not
5. Would you visit us again?
a. Yes
b. May be
c. Not
Q.8. List characteristic features and uses of Qualitative research. How does Qualitative research differ from Quantitative research ? (20)
Features of Qualitative Research
1. Qualitative Research is exploratory or diagnostic in nature.
2. It involves a small number of people who are not sampled on any probabilistic basis.
3. Qualitative Research is impressionistic rather than definitive.
4. Qualitative Research is used to generate hypothesis for further research.
5. Qualitative Research is used to get better insights into consumer behaviour, and to understand underlying behaviour of the consumer in the buying process.
6. Through Qualitative Research, one can get subtle clues about products/brands/ services, that very few quantitative studies can replicate.
7. In Qualitative Research, no attempt is made to draw hard and fast conclusions about facts that emerge.
8. In a market research project, one normally uses qualitative research and quantitative research methods together.
9. Though Qualitative Research is useful to study consumer behaviour, any attempt to generalize the findings for the entire population is very risky, as the findings are based on a very small sample size.
10. Qualitative Research is highly subjective in nature, and one would need trained psychologists and interviewers to conduct the research work and also for analysis and interpretation of data.
Uses of Qualitative Research
1. It is used in `concept generation and evaluation’ e.g., whenever company wants to develop new product, or modify existing product, it would conduct Qualitative Research among target consumers to understand new emerging consumer needs, or problems which consumer has with the existing product.
Therefore, every time when one goes into the market place, one would find newer models of the same product with addition/deletion of features, new and better external appearance, better finish of the product etc.
2. It is used to define the problem areas more fully – in marketing research one normally starts with qualitative research which is validated further by quantitative research.
3. It is used to formulate hypothesis for further investigation/quantification.
4. It is used to obtain large amount of data about beliefs, attitudes, etc. as data input for developing questionnaires, attitude scales, which would be used as input for multivariate analysis studies.
5. It is also used to conduct post-research study i.e., to amplify or explain same points that emerge from a major study, without having to repeat on a large scale.
6. In some areas of marketing research — especially understanding of consumer behaviour, a more flexible approach is required, rather than a rigid approach as provided by a structured questionnaire – hence qualitative research could be used.
7. In studies of distribution channels, sales, pricing strategies quantitative approach is most suitable, whereas in concept development, product development (needs of 4 consumer), advertising research -” qualitative approach is more suitable.
Difference between Qualitative & Quantitative Research
Qualitative Research
• Exploratory or diagnostic in nature – used to understand behaviour and generate hypothessis.• No calculation of sample size possible – size of sample depends upon dine available to conduct research, cost, variation in the population.
• Sample selected is such that it represents different sections of the population.
• It is dangerous to generalize conclusions for the entire population.
Quantitative Research
• Once hypothesis has been generated, used to test out hypothesis.• Generally a probabilistic approach is used to calculate sample size, using the sample size formula.
• Random selection of respondents to be part of research work – may or may not represent different population segments, depending upon the sampling method utilized.
• Conclusions are generalized to the universe, of which the sample purports to be representative.
Q.9. Write notes on the following: (2×10=20)
a. Editing and Coding of data
Editing
This is concerned with removal of redundant data, filling of missing data completeness of data substance & reliability of data. The data obtained from variance sources are not always complete-sometimes fields remain black due to the human errors also this requires to be corrected. It also corrects the entries present at wrong positions. Many techniques like filling the empty values by frequent values, average values, random value, lowest value etc are common. The editing must be performed just after the data have been collected. This ensures that consistency is maintained various details like editor. data of editing etc are recorded.Information gathered during data collection may lack uniformity. Example: Data collected through questionnaire and schedules may have answers which may not be ticked at proper places, or some questions may be left unanswered. Sometimes information may be given in a form which needs reconstruction in a category designed for analysis, e.g., converting daily/monthly income in annual income and so on. The researcher has to take a decision as to how to edit it.
Editing also needs that data are relevant and appropriate and errors are modified. Occasionally, the investigator makes a mistake and records and impossible answer. “How much red chilies do you use in a month” The answer is written as “4 kilos”. Can a family of three members use four kilo chilies in a month? The correct answer could be “0.4 kilo”.
Coding
Coding is performed to assign a predefined meaning to the data captured. The records that satisfy a given constraints are often marked with some Alphabets numerals etc. so that while sorting. Searching such records are taken out by a single search command. This concept is mutually exclusive.Now-a-days, codes are assigned before going to the field while constructing the questionnaire/schedule. Pose data collection; pre-coded items are fed to the computer for processing and analysis. For open-ended questions, however, post-coding is necessary. In such cases, all answers to open-ended questions are placed in categories and each category is assigned a code.
Manual processing is employed when qualitative methods are used or when in quantitative studies, a small sample is used, or when the questionnaire/schedule has a large number of open-ended questions, or when accessibility to computers is difficult or inappropriate. However, coding is done in manual processing also.
b. Classification of data and data Presentation devices
Classification of Data
This step of data sorting involves the segregation of data into various classified forms. The step of-classification makes the data analysis easy often a research study conducted. Serves not only single but multiple purposes. The various departments in organisation are interested in different aspects of the same data collected by the researches. These data correspond to the various form of analysis to make a particular decision. Data classification helps in making-comparisons & design strategies & policies for future action classification is performed accounting to various criterias like year wise, caste wise income group wise, department wise etc. During the problem study itself the classification criteria must be mentioned. Data classified into various forms may be further summed up to form an aggregated plan & even the aggregated data may be drilled down to achieve the desired results as per specifications.Classification can be classified in two types
1.Classification According to Attributes
a. Simple Classification
b. Manifold Classification
2. Classification According to Numerical Characteristic
Table as Data Presentation Devices
Statistical data can be presented in the form of tables and graphs. In the tabular foam, the classification of data is made with reference to time or some other variables. The graphs are used as a visual form of presentation of data.
The tabulation is used for summarization and condensation of data. It aids in analysis of relationships, trends and other summarization of the given data. The tabulation may be simple or complex. Simple tabulation results in one-way tables, which can be used to answer questions related to one characteristic of the data. The complex tabulation usually results in two way tables, which give information about two interrelated characteristics of the data; three way tables which give information about three interrelated characteristics of data; and still higher order tables, which supply information about several interrelated characteristics of data.
Following are the important characteristics of a table:
a. Every table should have a clear and concise title to make it understandable without reference to the text. This title should always be just above the body of the table.
b. Every table should be given a distinct number to facilitate easy reference.
c. Every table should have captions (column headings) and stubs (row headings) and they should be clear and brief
d. The units of measurements used must always be indicated.
e. Source or sources from where the data in the table have been obtained must be indicated at the bottom of the table.
f. Explanatory footnotes, if any, concerning the table should be given beneath the table along with reference symbol.
g. The columns in the tables may be numbered to facilitate reference.
h. Abbreviations should be used to the minimum possible extent.
i. The tables should be logical, clear, accurate and as simple as possible.
j. The arrangement of the data categories in a table may be a chronological, geographical, alphabetical or according to magnitude to facilitate comparison.
k. Finally, the table must suit the needs and requirements of the research study.
Graphical presentation of data
Several types of graphs and charts are used to present statistical data. Of them; the following are commonly used: bar chart, two dimensional diagrams, pictograms, pie charts and arithmetic chart or line chart.Q.10. What do you understand by Multi-Dimensional Scaling (MDS) technique ? Explain its advantage and application areas.
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate “information about the pairwise ‘distances’ among a set of n objects or individuals” into a configuration of n points mapped into an abstract Cartesian space.
More technically, MDS refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. It is a form of non-linear dimensionality reduction.
Given a distance matrix with the distances between each pair of objects in a set, and a chosen number of dimensions, N, an MDS algorithm places each object into N-dimensional space such that the between-object distances are preserved as well as possible. For N=1, 2, and 3, the resulting points can be visualized on a scatter plot.
Core theoretical contributions to MDS were made by James O. Ramsay of McGill University, who is also regarded as the father of functional data analysis.
Some of the typical marketing applications that emerge from the Multi Dimensional Scaling technique are
1. Market Segmentation
Market segmentation is the technique of trying to identify groups of consumers who exhibit commonality of perception of products and preferences, One can use MDS techniques to identify present perceptions of products by consumers, and use it modify the company’s product, package, advertising, additional features, so that the product offering of the company moves more and more closer to the `ideal’ requirement of the consumer.2. Advertisement Evaluation
The MDS technique could be used at the stage of advertisement pre-testing. Once an advertisement has been developed, it could ‘be tested for similarity/dissimilarity with other advertisements in the same product category. As the ultimate objective of an advertisement is to communicate, with the target consumer effectively, and this is. possible only if the advertisement is distinct in its message from the other competing advertisements.3. Product Re-positioning Studies
If &-company is interested in re-positioning its product/service (in the mind of the consumer), the first and foremost activity to be done is to assess the. current perception of the product in the mind of the consumer. The classic re-positioning case is that of Cadbury chocolates, which kept on assessing its positioning platform, and successfully moved Chocolates from a product perceived. as one for children, to a product which could be consumed by a person of any age,, at any time, of the day, and for varied occasions.4. New Product Development
MDS technique shows us the various perceived perceptions of the different brands. Spaces/ Gaps in the product perceptions could be used’ to, develop new offerings for the target consumer.5. Test Marketing
MDS technique can be used to identify cities that have similar demographic characteristics, and one could then identify a city which could represent a national character, and use that city for test marketing. One can thus `observe that MDS is a very useful technique to help understand the market place and develop strategies for the future.Advantage of Multi Dimensional Scaling
The advantage of NOS methods is not in the measurement of physical distances, but rather “psychological distances”, also called as `dissimilarities’. In MDS, we assume that every individual pawn has a ‘metal map’ of products, people, places, events, companies, and individuals keep on evaluating their external environment on a continuous basis.
We also assume that the respondent is able to provide either numerical measure of his or her perceived degree of similarity/dissimilarity between pairs of objects, or can rank pairs of objects (ordinal scale of measurement) in terms of similarity/dissimilarity to each other.
We can then make use of methodology, of MDS to construct a physical map in one or more dimensional whose inter-point distances (or ranks of distances) are most consistent with input data.
Now-a-days a number of software programmes are available for conducting MDS analysis. These programmes provide for a variety of input data. Some of the widely used softwares include MDPREF, MDSCAL SM, INDSCAL, PREFMAM, PROFIT, KUST.