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focus groups. The best tools for combination research are: O nline Survey - The two tools combined here are online interviews and the use of questionnaires. This is a questionnaire that the target audience can complete over the Internet. It is timely, effective and efficient. Especially since the data to be collected is quantitative in nature. D ual-Moderator - The two tools combined here are focus groups and structured questionnaires. The structured questioners give a direction as to where the research is headed while two moderators take charge of proceedings. Whilst one ensures the focus group session progresses smoothly, the other makes sure that the topics in question are all covered. Dual- moderator focus groups typically result in a more productive session and essentially leads to an optimum collection of data. 8.3 PRIMARY AND SECONDARY DATA 8.3.1 Methods of Collecting Primary Data: D I Primary data may be obtained by applying any of the following methods: I M  S irect Personal Interviews.  ndirect Oral Interviews.  nformation from Correspondents.  ailed Questionnaire Methods.  chedule Sent Through Enumerators. 1. D irect Personal Interviews: A face to face contact is made with the informants (persons from whom the information is to be obtained) under this method of collecting data. The interviewer asks them questions pertaining to the 101 CU IDOL SELF LEARNING MATERIAL (SLM)

survey and collects the desired information. Thus, if a person wants to collect data about the working conditions of the workers of the Tata Iron and Steel Company, Jamshedpur, he would go to the factory, contact the workers and obtain the desired information. The information collected in this manner is first hand and also original in character. There are many merits and demerits of this method, which are discussed as under: Merits: 1. M ost often respondents are happy to pass on the information required from them when contacted personally and thus response is encouraging. 2. T he information collected through this method is normally more accurate because interviewer can clear doubts of the informants about certain questions and thus obtain correct information. In case the interviewer apprehends that the informant is not giving accurate information, he may cross- examine him and thereby try to obtain the information. 3. T his method also provides the scope for getting supplementary information from the informant, because while interviewing it is possible to ask some supplementary questions which may be of greater use later 4. T here might be some questions which the interviewer would find difficult to ask directly, but with some tactfulness, he can mingle such questions with others and get the desired information. He can twist the questions keeping in mind the informant’s reaction. Precisely, a delicate situation can usually he handled more effectively by a personal interview than by other survey techniques. 5. T he interviewer can adjust the language according to the status and educational level of the person interviewed, and thereby can avoid inconvenience and misinterpretation on the part of the informant. Demerits: 1. T his method can prove to be expensive if the number of informants is large and the area is widely spread. 2. T 102 CU IDOL SELF LEARNING MATERIAL (SLM)

here is a greater chance of personal bias and prejudice under this method as compared to other methods. 3. T he interviewers have to be thoroughly trained and experienced; otherwise they may not be able to obtain the desired information. Untrained or poorly trained interviewers may spoil the entire work. 4. T his method is more time taking as compared to others. This is because interviews can be held only at the convenience of the informants. Thus, if information is to be obtained from the working members of households, interviews will have to be held in the evening or on week end. Even during evening only an hour or two can be used for interviews and hence, the work may have to be continued for a long time, or a large number of people may have to be employed which may involve huge expenses. Though there are some demerits in this method of data collection still we cannot say that it is not useful. The matter of fact is that this method is suitable for intensive rather than extensive field surveys. Hence, it should be used only in those cases where intensive study of a limited field is desired. In the present time of extreme advancement in the communication system, the investigator instead of going personally and conducting a face to face interview may also obtain information over telephone. A good number of surveys are being conducted every day by newspapers and television channels by sending the reply either by e-mail or SMS. This method has become very popular nowadays as it is less expensive and the response is extremely quick. But this method suffers from some serious defects, such as (a) those who own a phone or a television only can be approached by this method, (b) only few questions can be asked over phone or through television, (c) the respondents may give a vague and reckless answers because answers on phone or through SMS would have to be very short. 2. I ndirect Oral Interviews: Under this method of data collection, the investigator contacts third parties generally called ‘witnesses’ who are capable of supplying necessary information. This method is generally adopted when the information to be obtained is of a complex nature and informants are not inclined to respond if approached directly. For example, when the researcher is trying to obtain data on drug addiction or the habit of taking liquor, there is high probability that the addicted person will not provide the desired data and hence will disturb the whole research process. In this situation taking the help of such persons or agencies or the neighbors who know them well becomes necessary. Since 103 CU IDOL SELF LEARNING MATERIAL (SLM)

these people know the person well, they can provide the desired data. Enquiry Committees and Commissions appointed by the Government generally adopt this method to get people’s views and all possible details of the facts related to the enquiry. Though this method is very popular, its correctness depends upon a number of factors such as 1. T he person or persons or agency whose help is solicited must be of proven integrity; otherwise any bias or prejudice on their part will not bring out the correct information and the whole process of research will become useless. 2. T he ability of the interviewers to draw information from witnesses by means of appropriate questions and cross-examination. 3. It might happen that because of bribery, nepotism or certain other reasons those who are collecting the information give it such a twist that correct conclusions are not arrived at. Therefore, for the success of this method it is necessary that the evidence of one person alone is not relied upon. Views from other persons and related agencies should also be ascertained to find the real position .Utmost care must be exercised in the selection of these persons because it is on their views that the final conclusions are reached. 3. I nformation from Correspondents: The investigator appoints local agents or correspondents in different places to collect information under this method. These correspondents collect and transmit the information to the central office where data are processed. This method is generally adopted by newspaper agencies. Correspondents who are posted at different places supply information relating to such events as accidents, riots, strikes, etc., to the head office. The correspondents are generally paid staff or sometimes they may be honorary correspondents also. This method is also adopted generally by the government departments in such cases where regular information is to be collected from a wide area. For example, in the construction of a wholesale price index numbers regular information is obtained from correspondents appointed in different areas. The biggest advantage of this method is that, it is cheap and appropriate for extensive investigation. But a word of caution is that it may not always ensure accurate results because of the personal prejudice and bias of the correspondents. As stated earlier, this method is suitable and adopted in those cases where the information is to be obtained at regular intervals from a wide area. 4. M 104 CU IDOL SELF LEARNING MATERIAL (SLM)

ailed Questionnaire Method: Under this method, a list of questions pertaining to the survey which is known as ‘Questionnaire’ is prepared and sent to the various informants by post. Sometimes the researcher himself too contacts the respondents and gets the responses related to various questions in the questionnaire. The questionnaire contains questions and provides space for answers. A request is made to the informants through a covering letter to fill up the questionnaire and send it back within a specified time. The questionnaire studies can be classified on the basis of: i. T he degree to which the questionnaire is formalized or structured. ii. T he disguise or lack of disguise of the questionnaire and iii. T he communication method used. When no formal questionnaire is used, interviewers adapt their questioning to each interview as it progresses. They might even try to elicit responses by indirect methods, such as showing pictures on which the respondent comments. When a researcher follows a prescribed sequence of questions, it is referred to as structured study. On the other hand, when no prescribed sequence of questions exists, the study is non-structured. When questionnaires are constructed in such a way that the objective is clear to the respondents then these questionnaires are known as non- disguised; on the other hand, when the objective is not clear, the questionnaire is a disguised one. On the basis of these two classifications, four types of studies can be distinguished: 1. N on-disguised structured, 2. N on-disguised non-structured, 3. D isguised structured and 4. D isguised non-structured. There are certain merits and demerits of this method of data collection which are discussed below: 105 CU IDOL SELF LEARNING MATERIAL (SLM)

Merits: 1. Questionnaire method of data collection can be easily adopted where the field of investigation is very vast and the informants are spread over a wide geographical area. 2. This method is relatively cheap and expeditious provided the informants respond in time. 3. This method has proved to be superior when compared to other methods like personal interviews or telephone method. This is because when questions pertaining to personal nature or the ones requiring reaction by the family are put forth to the informants, there is a chance for them to be embarrassed in answering them. Demerits: 1. T his method can be adopted only where the informants are literates so that they can understand written questions and lend the answers in writing. 2. I t involves some uncertainty about the response. Co-operation on the part of informants may be difficult to presume. 3. T he information provided by the informants may not be correct and it may be difficult to verify the accuracy. However, by following the guidelines given below, this method can be made more effective: The questionnaires should be made in such a manner that they do not become an undue burden on the respondents; otherwise the respondents may not return them back. i. P repaid postage stamp should be affixed ii. T he sample should be large iii. I t should be adopted in such enquiries where it is expected that the respondents would return the questionnaire because of their own interest in the enquiry. iv. I t should be preferred in such enquiries where there could be a legal compulsion to provide the 106 CU IDOL SELF LEARNING MATERIAL (SLM)

information. 5. S chedules Sent Through Enumerators: Another method of data collection is sending schedules through the enumerators or interviewers. The enumerators contact the informants, get replies to the questions contained in a schedule and fill them in their own handwriting in the questionnaire form. There is difference between questionnaire and schedule. Questionnaire refers to a device for securing answers to questions by using a form which the respondent fills in himself, whereas schedule is the name usually applied to a set of questions which are asked in a face-to face situation with another person. This method is free from most of the limitations of the mailed questionnaire method. Merits: The main merits or advantages of this method are listed below: 1. I t can be adopted in those cases where informants are illiterate. 2. T here is very little scope of non-response as the enumerators go personally to obtain the information. 3. T he information received is more reliable as the accuracy of statements can be checked by supplementary questions wherever necessary. This method too like others is not free from defects or limitations. The main limitations are listed below: Demerits: 1. I n comparison to other methods of collecting primary data, this method is quite costly as enumerators are generally paid persons. 2. T he success of the method depends largely upon the training imparted to the enumerators. 3. I nterviewing is a very skilled work and it requires experience and training. Many statisticians have the tendency to neglect this extremely important part of the data collecting process and this result 107 CU IDOL SELF LEARNING MATERIAL (SLM)

in bad interviews. Without good interviewing most of the information collected may be of doubtful value. 4. I nterviewing is not only a skilled work but it also requires a great degree of politeness and thus the way the enumerators conduct the interview would affect the data collected. When questions are asked by a number of different interviewers, it is possible that variations in the personalities of the interviewers will cause variation in the answers obtained. This variation will not be obvious. Hence, every effort must be made to remove as much of variation as possible due to different interviewers. 8.3.2 Methods of Collecting Secondary Data: As stated earlier, secondary data are those data which have already been collected and analyzed by some earlier agency for its own use, and later the same data are used by a different agency. According to W.A.Neiswanger, “A primary source is a publication in which the data are published by the same authority which gathered and analyzed them. A secondary source is a publication, reporting the data which was gathered by other authorities and for which others are responsible.” Sources of Secondary Data: The various sources of secondary data can be divided into two broad categories: 1. P ublished sources, and 2. U npublished sources. 1. P ublished Sources: The governmental, international and local agencies publish statistical data, and chief among them are explained below: (a) I nternational Publications: There are some international institutions and bodies like I.M.F, I.B.R.D, I.C.A.F.E and U.N.O who publish regular and occasional reports on economic and statistical matters. (b) O 108 CU IDOL SELF LEARNING MATERIAL (SLM)

fficial Publications of Central and State Governments: Several departments of the Central and State Governments regularly publish reports on a number of subjects. They gather additional information. Some of the important publications are: The Reserve Bank of India Bulletin, Census of India, Statistical Abstracts of States, Agricultural Statistics of India, Indian Trade Journal, etc. (c) S emi-Official Publications: Semi-Government institutions like Municipal Corporations, District Boards, Panchayats, etc. Publish reports relating to different matters of public concern. (d) P ublications of Research Institutions: Indian Statistical Institute (I.S.I), Indian Council of Agricultural Research (I.C.A.R), Indian Agricultural Statistics Research Institute (I.A.S.R.I), etc. Publish the findings of their research programmes. (e) P ublications of various Commercial and Financial Institutions (f) R eports of various Committees and Commissions appointed by the Government as the Raj Committee’s Report on Agricultural Taxation, Wanchoo Committee’s Report on Taxation and Black Money, etc. Are also important sources of secondary data. (g) J ournals and News Papers: Journals and News Papers are very important and powerful source of secondary data. Current and important materials on statistics and socio-economic problems can be obtained from journals and newspapers like Economic Times, Commerce, Capital, Indian Finance, Monthly Statistics of trade etc. 2. U npublished Sources: Unpublished data can be obtained from many unpublished sources like records maintained by various government and private offices, the theses of the numerous research scholars in the universities or institutions etc. 109 CU IDOL SELF LEARNING MATERIAL (SLM)

Precautions in the Use of Secondary Data: Since secondary data have already been obtained, it is highly desirable that a proper scrutiny of such data is made before they are used by the investigator. In fact the user has to be extra-cautious while using secondary data. In this context Prof. Bowley rightly points out that “Secondary data should not be accepted at their face value.” The reason being that data may be erroneous in many respects due to bias, inadequate size of the sample, substitution, errors of definition, arithmetical errors etc. Even if there is no error such data may not be suitable and adequate for the purpose of the enquiry. Prof. Simon Kuznet’s view in this regard is also of great importance. According to him, “the degree of reliability of secondary source is to be assessed from the source, the compiler and his capacity to produce correct statistics and the users also, for the most part, tend to accept a series particularly one issued by a government agency at its face value without enquiring its reliability”. Therefore, before using the secondary data the investigators should consider the following factors: Choice between Primary and Secondary Data: As we have already seen, there are a lot of differences in the methods of collecting Primary and Secondary data. Primary data which is to be collected originally involves an entire scheme of plan starting with the definitions of various terms used, units to be employed, type of enquiry to be conducted, extent of accuracy aimed at etc. For the collection of secondary data, a mere compilation of the existing data would be sufficient. A proper choice between the type of data needed for any particular statistical investigation is to be made after taking into consideration the nature, objective and scope of the enquiry; the time and the finances at the disposal of the agency; the degree of precision aimed at and the status of the agency (whether government- state or central-or private institution of an individual). In using the secondary data, it is best to obtain the data from the primary source as far as possible. By doing so, we would at least save ourselves from the errors of transcription which might have inadvertently crept in the secondary source. Moreover, the primary source will also provide us with detailed discussion about the terminology used, statistical units employed, size of the sample and the technique of sampling (if sampling method was used), methods of data collection and analysis of results and we can ascertain ourselves if these would suit our purpose. Now-a-days in a large number of statistical enquiries, secondary data are generally used because fairly reliable published data on a large number of diverse fields are now available in the publications of governments, private organizations and research institutions, agencies, periodicals and magazines etc. In fact, primary data are collected only if there do not exist any secondary data suited to the investigation under study. In some of the investigations both primary as well as secondary data may be used. 110 CU IDOL SELF LEARNING MATERIAL (SLM)

8.4 SUMMARY In Statistics, the data collection is a process of gathering information from all the relevant sources to find a solution to the research problem. It helps to evaluate the outcome of the problem. The data collection methods allow a person to conclude an answer to the relevant question. Most of the organizations use data collection methods to make assumptions about future probabilities and trends. Once the data is collected, it is necessary to undergo the data organization process. The main sources of the data collections methods are “Data”. A data can be classified into two types, namely primary data and the secondary data. The primary importance of data collection in any research or business process is that it helps to determine many important things about the company, particularly the performance. So, the data collection process plays an important role in all the streams. Depends on the type of data, the data collection method is divided into two categories namely,  P rimary Data Collection methods S  econdary Data Collection methods There are two types of data, primary and secondary. Data which are collected first hand are called Primary data and data which have already been collected and used by somebody are called Secondary data. There are two methods of collecting data: (a) Survey method or total enumeration method and (b) Sample method. When a researcher goes for investigating all the units of the subject, it is called as survey method. On the other hand if he/she resorts to investigating only a few units of the subject and gives the result on the basis of that, it is known as sample survey method. There are different sources of collecting Primary and Secondary data. Some of the important sources of Primary data are—Direct Personal Interviews, Indirect Oral Interviews, Information from Correspondents, Mailed questionnaire method, Schedules sent through enumerators and so on. Though all these sources or methods of Primary data have their relative merits and demerits, a researcher should use a particular method with lot of care. There are basically two sources of collecting secondary data- (a) Published sources and (b) Unpublished sources. Published sources are like publications of different government and semi-government departments, research institutions and agencies etc. Whereas unpublished sources are like records maintained by different government departments and unpublished theses of different universities etc. Editing of secondary data is necessary for different purposes as – editing for completeness, editing for consistency, editing for accuracy and editing for homogeneity. It is always a tough task for the researcher to choose between primary and secondary data. Though primary data are more authentic and accurate, time, money and labor involved in obtaining these 111 CU IDOL SELF LEARNING MATERIAL (SLM)

more often prompt the researcher to go for the secondary data. There are certain amount of doubt about its authenticity and suitability, but after the arrival of many government and semi government agencies and some private institutions in the field of data collection, most of the apprehensions in the mind of the researcher have been removed. 8.5 KEYWORDS  Field notes - A researcher’s notes on the observations made at the setting during a field study  Field study - A qualitative data collection method borrowed from anthropology, also known as field observation. It is carried out in the natural setting where the phenomenon takes place.  Framing - The way the messages of a discourse are regulated and controlled, that shapes how the message is interpreted.  Functiona lism - Research carried under the functionalist paradigm examines why people behave the way they do and assumes it is because people know the consequences and uses (functions) of their behaviour or actions. It is also known as administrative research and is linked to the Columbia School. 8.6 LEARNING ACTIVITY 1. Explain the various tools & techniques you would use to collect the data for the reason in fall in sales. _________________________________________________________________________________ ________________________________________________________________________________ 2. Difference between methods of data collection in primary & secondary data _________________________________________________________________________________ ________________________________________________________________________________ 8.7 UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Questions E 1. xplain the various tools & techniques of data collection in detail 112 CU IDOL SELF LEARNING MATERIAL (SLM)

2. W hat is primary and secondary data? L E 3. D ist down methods for collecting primary data. 4. xplain the interview method 5. escribe questionnaire method used in research. B. Multiple Choice Questions (MCQs) 1. A ccurate watching & noting of phenomena as they occur in nature with regard to cause & effect or mutual relations is --- a. O bservation b. E xperiment c. R esearch d. S urvey 2. O bservation does not include------- L a. S istening W b. 113 peaking c. CU IDOL SELF LEARNING MATERIAL (SLM)

atching R d. eading 3. O bservation does not include the process of— S A a. D ensation P b. ttention c. iscussion d. erception 4. W hich of the following is the published source of secondary data J N a. ournals A b. ewspapers c. Reports d. ll of these 5. _ ___________ is the process of collecting data through an instrument consisting of a series of questions and prompts to receive a response from individuals it is administered to 114 CU IDOL SELF LEARNING MATERIAL (SLM)

a. S urvey b. I nterview c. Q uestionnaire d. O bservation Answers: 2. (b) 3. (c) 1. (a), 8.8 REFERENCES  Donald, R. Cooper & Pamela S. Schindler (2014).  Business Research Methods. New Delhi: Tata McGraw-Hill Publishing Co. Ltd.  Gupta, S.C. (2010). Fundamentals of Statistics. 6th Ed. Mumbai: HPH.  Gupta, S. P. (2002). Statistical Methods. New Delhi: Sultan Chand & Sons.  Beri, G. C. (2012). Business Statistics. New Delhi: Tata McGraw-Hill Publishing Co. Ltd.  Zikmund. (2015). Business Research Methods. New Delhi: Cengage Learning  Churchill, Gilbert A (1983) Marketing Research: Methodological Foundations, The Dryden Press, New York.  Kothari C.R. (1990) Research Methodology: Methods and Technique. Wishwa Prakashan, New Delhi.  Mahalotra N.K. (2002) Marketing Research: An Applied Orientation. Pearson Education Asia.  Mustafi, C.K. 1981. Statistical Methods in Managerial Decisions, Macmillan: New Delhi. 115 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT-9 SCALING Structure 9.0. Learning Objectives 9.1. Introduction 9.2. Meaning of scaling 9.3. Important scaling techniques 9.4. Rating and ranking scales 9.4.1. Rating Scale 9.4.2. Ranking Scale 9.5. Scale construction techniques 9.6. Summary 9.7. Keywords 9.8. Learning Activity 9.9. Unit End Questions (Mcq And Descriptive) 9.10. References 9.0 LEARNING OBJECTIVES After studying this Unit, you will be able to:  Explain the different scaling techniques  Discuss different types of scales used in research 9.1 INTRODUCTION Scaling is considered as the extension of measurement. What is Measurement? The measurement is the process of assigning numbers or symbol to the characteristics of the object as per the specified rules. Here, the researcher assigns numbers, not to the object, but to its characteristics such as perceptions, attitudes, preferences, and other relevant traits. In research, usually, the numbers are assigned to the qualitative traits of the object because the quantitative data helps in statistical analysis of the resulting data and further facilitates the communication of measurement rules and results. 9.2 MEANING OF SCALING Scaling is the procedure of measuring and assigning the objects to the numbers according to the 116 CU IDOL SELF LEARNING MATERIAL (SLM)

specified rules. In other words, the process of locating the measured objects on the continuum, a continuous sequence of numbers to which the objects are assigned is called as scaling. All the scales used in scaling techniques can be explained in terms of four basic characteristics., Viz. Description, Order, Distance, and origin. These characteristics collectively define the Levels of Measurement of scale. The level of measurement indicates that what properties of an object are measured or not measured by the scale. 9.3 IMPORTANT SCALING TECHNIQUES The researchers have identified many scaling techniques; today, we will discuss some of the most common scales used by business organizations, researchers, economists, experts, etc. These techniques can be classified as primary scaling techniques and other scaling techniques. Let us now study each of these methods in-depth below: P A. rimary Scaling Techniques The major four scales used in statistics for market research consist of the following: Figure 9.1 Important scaling techniques 117 CU IDOL SELF LEARNING MATERIAL (SLM)

a. Nominal Scale Nominal scales are adopted for non-quantitative (containing no numerical implication) labelling variables which are unique and different from one another. Types of Nominal Scales: D ichotomous: A nominal scale that has only two labels is called ‘dichotomous’; for example, Yes/No. N ominal with Order: The labels on a nominal scale arranged in an ascending or descending order is termed as ‘nominal with order’; for example, Excellent, Good, Average, Poor, Worst. N ominal without Order: Such nominal scale which has no sequence, is called ‘nominal without order’; for example, Black, White. b. O rdinal Scale The ordinal scale functions on the concept of the relative position of the objects or labels based on the individual’s choice or preference. For example, At Amazon.in, every product has a customer review section where the buyers rate the listed product according to their buying experience, product features, quality, usage, etc. The ratings so provided are as follows:  5 Star – Excellent  4 Star – Good  3 Star – Average  2 Star – Poor 118 CU IDOL SELF LEARNING MATERIAL (SLM)

 1 Star – Worst I c. nterval Scale An interval scale is also called a cardinal scale which is the numerical labelling with the same difference among the consecutive measurement units. With the help of this scaling technique, researchers can obtain a better comparison between the objects. For example; A survey conducted by an automobile company to know the number of vehicles owned by the people living in a particular area who can be its prospective customers in future. It adopted the interval scaling technique for the purpose and provided the units as 1, 2, 3, 4, 5, 6 to select from. In the scale mentioned above, every unit has the same difference, i.e., 1, whether it is between 2 and 3 or between 4 and 5. d. R atio Scale One of the most superior measurement techniques is the ratio scale. Similar to an interval scale, a ratio scale is an abstract number system. It allows measurement at proper intervals, order, categorization and distance, with an added property of originating from a fixed zero point. Here, the comparison can be made in terms of the acquired ratio. For example, A health product manufacturing company surveyed to identify the level of obesity in a particular locality. It released the following survey questionnaire: Select a category to which your weight belongs to:  L ess than 40 kilograms  4 0-59 Kilograms  6 0-79 Kilograms  8 0-99 Kilograms 1 119 CU IDOL SELF LEARNING MATERIAL (SLM)

00-119 Kilograms  1 20 Kilograms and more The following table will better clarify the difference between all the four primary scaling techniques: PARTICULAR NOMINAL ORDINAL INTERVAL RATIO Characteristics SCALE SCALE SCALE SCALE Description, Description Order Distance Order, Distance and Sequential Not Applicable Applicable Applicable Origin Arrangement Applicable Fixed Zero Point Not Applicable Not Applicable Not Applicable Applicable Applicable Multiplication and Not Applicable Not Applicable Not Applicable Division Applicable Addition and Not Applicable Not Applicable Applicable Measurable Subtraction Applicable Applicable Difference Non-Measurable Non-Measurable Measurable Applicable between Variables Mean Not Applicable Not Applicable Applicable Median Not Applicable Applicable Applicable Mode Applicable Applicable Applicable Other Scaling Techniques Scaling of objects can be used for a comparative study between more than one objects (products, services, brands, events, etc.). Or can be individually carried out to understand the consumer’s behavior and response towards a particular object. Following are the two categories under which other scaling techniques are placed based on their comparability: 120 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 9.2 IMPORTANT SCALING TECHNIQUES a. C omparative Scales For comparing two or more variables, a comparative scale is used by the respondents. Following are the different types of comparative scaling techniques: b. P aired Comparison A paired comparison symbolizes two variables from which the respondent needs to select one. This technique is mainly used at the time of product testing, to facilitate the consumers with a comparative analysis of the two major products in the market. To compare more than two objects say comparing P, Q and R, one can first compare P with Q and then the superior one (i.e., one with a higher percentage) with R. For example, A market survey was conducted to find out consumer’s preference for the network service provider brands, A and B. The outcome of the survey was as follows: B 121 CU IDOL SELF LEARNING MATERIAL (SLM)

rand ‘A’ = 57%  B rand ‘B’ = 43% Thus, it is visible that the consumers prefer brand ‘A’, over brand ‘B’. c. R ank Order In rank order scaling the respondent needs to rank or arrange the given objects according to his or her preference. For example, A soap manufacturing company conducted a rank order scaling to find out the orderly preference of the consumers. It asked the respondents to rank the following brands in the sequence of their choice: SOAP BRANDS RANK Brand V 4 Brand X 2 Brand Y 1 Brand Z 3 The above scaling shows that soap ‘Y’ is the most preferred brand, followed by soap ‘X’, then soap ‘Z’ and the least preferred one is the soap ‘V’. d. C onstant Sum It is a scaling technique where a continual sum of units like dollars, points, chits, chips, etc. is given to the features, attributes and importance of a particular product or service by the respondents. For example, the respondents belonging to 3 different segments were asked to allocate 50 points to the following attributes of a cosmetic product ‘P’: ATTRIBUTES SEGMENT 1 SEGMENT 2 SEGMENT 3 Finish 11 8 9 Skin Friendly 11 12 12 122 CU IDOL SELF LEARNING MATERIAL (SLM)

Fragrance 7 11 8 10 Packaging 9 8 11 Price 12 11 From the above constant sum scaling analysis, we can see that:  S egment 1 considers product ‘P’ due to its competitive price as a major factor.  B ut segment 2 and segment 3, prefers the product because it is skin-friendly. e. Q -Sort Scaling Q-sort scaling is a technique used for sorting the most appropriate objects out of a large number of given variables. It emphasizes on the ranking of the given objects in a descending order to form similar piles based on specific attributes. It is suitable in the case where the number of objects is not less than 60 and more than 140, the most appropriate of all ranging between 60 to 90. For example, The marketing manager of a garment manufacturing company sorts the most efficient marketing executives based on their past performance, sales revenue generation, dedication and growth. The Q-sort scaling was performed on 60 executives, and the marketing head creates three piles based on their efficiency as follows: Figure 9.3 Q-Sort Scaling 123 In the above diagram, the initials of the employees are used to denote their names. CU IDOL SELF LEARNING MATERIAL (SLM)

f. N on-Comparative Scales A non-comparative scale is used to analyses the performance of an individual product or object on different parameters. Following are some of its most common types: g. C ontinuous Rating Scales It is a graphical rating scale where the respondents are free to place the object at a position of their choice. It is done by selecting and marking a point along the vertical or horizontal line which ranges between two extreme criteria. For example, A mattress manufacturing company used a continuous rating scale to find out the level of customer satisfaction for its new comfy bedding. The response can be taken in the following different ways (stated as versions here): Figure 9.4 Continuous Rating Scale The above diagram shows a non-comparative analysis of one particular product, i.e. comfy bedding. Thus, making it very clear that the customers are quite satisfied with the product and its features. h. I temized Rating Scale Itemized scale is another essential technique under the non-comparative scales. It emphasizes on choosing a particular category among the various given categories by the respondents. Each class is briefly defined by the researchers to facilitate such selection. The three most commonly used itemized rating scales are as follows: 124 CU IDOL SELF LEARNING MATERIAL (SLM)

i. L ikert Scale: In the Likert scale, the researcher provides some statements and ask the respondents to mark their level of agreement or disagreement over these statements by selecting any one of the options from the five given alternatives. For example, A shoes manufacturing company adopted the Likert scale technique for its new sports shoe range named Z sports shoes. The purpose is to know the agreement or disagreement of the respondents. For this, the researcher asked the respondents to circle a number representing the most suitable answer according to them, in the following representation:  1 – Strongly Disagree  2 – Disagree  3 – Neither Agree Nor Disagree  4 – Agree  5 – Strongly Agree ii. S emantic Differential Scale: A bi-polar seven-point non-comparative rating scale is where the respondent can mark on any of the seven points for each given attribute of the object as per personal choice. Thus, depicting the respondent’s attitude or perception towards the object. For example, A well-known brand for watches, carried out semantic differential scaling to understand the customer’s attitude towards its product. The pictorial representation of this technique is as follows: 125 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 9.5 Semantic Differential Scale From the above diagram, we can analyze that the customer finds the product of superior quality; however, the brand needs to focus more on the styling of its watches. iii. S taple Scale: A Stapel scale is that itemized rating scale which measures the response, perception or attitude of the respondents for a particular object through a unipolar rating. The range of a Stapel scale is between -5 to +5 eliminating 0, thus confining to 10 units. For example, A tours and travel company asked the respondent to rank their holiday package in terms of value for money and user-friendly interface as follows: Figure 9.6 Stapel Scale With the help of the above scale, we can say that the company needs to improve its package in terms of value for money. However, the decisive point is that the interface is quite user-friendly for the 126 CU IDOL SELF LEARNING MATERIAL (SLM)

customers. 9.4 RATING AND RANKING SCALES 9.4.1 RATING SCALE Rating scale is defined as a closed-ended survey question used to represent respondent feedback in a comparative form for specific particular features/products/services. It is one of the most established question types for online and offline surveys where survey respondents are expected to rate an attribute or feature. Rating scale is a variant of the popular multiple-choice question which is widely used to gather information that provides relative information about a specific topic. Researchers use a rating scale in research when they intend to associate a qualitative measure with the various aspects of a product or feature. Generally, this scale is used to evaluate the performance of a product or service, employee skills, customer service performances, processes followed for a particular goal etc. Rating scale survey question can be compared to a checkbox question but rating scale provides more information than merely Yes/No. Types of Rating Scale Broadly speaking, rating scales can be divided into two categories: Ordinal and Interval Scales. A n ordinal scale is a scale the depicts the answer options in an ordered manner. The difference between the two-answer option may not be calculable but the answer options will always be in a certain innate order. Parameters such as attitude or feedback can be presented using an ordinal scale. A n interval scale is a scale where not only is the order of the answer variables established but the magnitude of difference between each answer variable is also calculable. Absolute or true zero value is not present in an interval scale. Temperature in Celsius or Fahrenheit is the most popular example of an interval scale. Net Promoter Score, Likert Scale, Bipolar Matrix Table are some of the most effective types of interval scale. There are four primary types of rating scales which can be suitably used in an online survey:  G raphic Rating Scale  N umerical Rating Scale 127 CU IDOL SELF LEARNING MATERIAL (SLM)

 D escriptive Rating Scale  C omparative Rating Scale (a) G raphic Rating Scale: Graphic rating scale indicates the answer options on a scale of 1-3, 1-5, etc. Likert Scale is a popular graphic rating scale example. Respondents can select a particular option on a line or scale to depict rating. This rating scale is often implemented by HR managers to conduct employee evaluation.5 point Likert scale for satisfaction (b) N umerical Rating Scale: Numerical rating scale has numbers as answer options and not each number corresponds to a characteristic or meaning. For instance, a Visual Analog Scale or a Semantic Differential Scale can be presented using a numerical rating scale (c) D escriptive Rating Scale: In a descriptive rating scale, each answer option is elaborately explained for the respondents. A numerical value is not always related to the answer options in the descriptive rating scale. There are certain surveys, for example, a customer satisfaction survey, which needs to describe all the answer options in detail so that every customer has thoroughly explained information about what is expected from the survey. (d) C omparative Rating Scale: Comparative rating scale, as the name suggests, expects respondents to answer a particular question in terms of comparison, i.e. on the basis of relative measurement or keeping other organizations/products/features as a reference. 9.4.2 RANKING SCALE A ranking scale is a survey question tool that measures people’s preferences by asking them to rank their views on a list of related items. Using these scales can help your business establish what matters and what doesn’t matter to either external or internal stakeholders. You could use ranking scale questions to evaluate customer satisfaction or to assess ways to motivate your employees, for example. Ranking scales can be a source of useful information, but they do have some disadvantages. Businesses typically use ranking scales when they want to establish preferences or levels of importance in a group of items. A respondent completing a scale with five items, for example, will assign a number 1 through 5 to each individual one. Typically, the number 1 goes to the item that is most important to the respondent; the number 5 goes to the one that is of least importance. In some 128 CU IDOL SELF LEARNING MATERIAL (SLM)

cases, scales do not force respondents to rank all items, asking them to choose their top three out of the five, for example. Online surveys may remove the need to key in numbers, allowing respondents to drag and drop items into order. Advantages of Ranking Scales Ranking scales give you an insight into what matters to your respondents. Each response to an item has an individual value, giving results that you can easily average and rank numerically. This can be a valuable business tool, as it gives a statistical breakdown of your audience’s preferences based on what you need to know. If you are making business decisions and have various options to choose from, data from a ranking scale might give you a clearer insight into how to satisfy your audience based on what is important to them. 9.5 SCALE CONSTRUCTION TECHNIQUES Scale construction techniques in research methodology helps in social science studies, while measuring attitudes of the people we generally follow the technique of preparing the opinionnaire* (or attitude scale) in such a way that the score of the individual responses assigns him a place on a scale. Under this approach, the respondent expresses his agreement or disagreement with a number of statements relevant to the issue. While developing such statements, the researcher must note the following two points: T hat the statements must elicit responses which are psychologically related to the attitude being measured; T hat the statements need be such that they discriminate not merely between extremes of attitude but also among individuals who differ slightly. Researchers must as well be aware that inferring attitude from what has been recorded in opinionnaires has several limitations. People may conceal their attitudes and express socially acceptable opinions. They may not really know how they feel about a social issue. People may be unaware of their attitude about an abstract situation; until confronted with a real situation, they may be unable to predict their reaction. Even behavior itself is at times not a true indication of attitude. For instance, when politicians kiss babies, their behavior may not be a true expression of affection toward infants. Thus, there is no sure method of measuring attitude; we only try to measure the expressed opinion and then draw inferences from it about people’s real feelings or attitudes. With all these limitations in mind, psychologists and sociologists have developed several scale construction techniques for the 129 CU IDOL SELF LEARNING MATERIAL (SLM)

purpose. The researcher should know these techniques so as to develop an appropriate scale for his own study. Some of the important approaches, along with the corresponding scales developed under each approach to measure attitude are as follows: Different Scales for Measuring Attitudes of People Figure 9.7 Different Scales for Measuring Attitudes of People Arbitrary scale in Research Methodology Arbitrary scales are developed on ad hoc basis and are designed largely through the researcher’s own subjective selection of items. The researcher first collects few statements or items which he believes are unambiguous and appropriate to a given topic. Some of these are selected for inclusion in the measuring instrument and then people are asked to check in a list the statements with which they agree. The chief merit of such scales is that they can be developed very easily, quickly and with relatively less expense. They can also be designed to be highly specific and adequate. Because of these benefits, such scales are widely used in practice. Differential Scales (or Thurstone-type Scales) The name of L.L. Thurstone is associated with differential scales which have been developed using consensus scale approach. Under such an approach the selection of items is made by a panel of judges who evaluate the items in terms of whether they are relevant to the topic area and unambiguous in implication. The detailed procedure is as under: T he researcher gathers a large number of statements, usually twenty or more, that express various points of view toward a group, institution, idea, or practice (i.e., statements belonging 130 CU IDOL SELF LEARNING MATERIAL (SLM)

to the topic area). T hese statements are then submitted to a panel of judges, each of whom arranges them in eleven groups or piles ranging from one extreme to another in position. Each of the judges is requested to place generally in the first pile the statements which he thinks are most unfavorable to the issue, in the second pile to place those statements which he thinks are next most unfavorable and he goes on doing so in this manner till in the eleventh pile he puts the statements which he considers to be the most favorable. T his sorting by each judge yields a composite position for each of the items. In case of marked disagreement between the judges in assigning a position to an item, that item is discarded. F or items that are retained, each is given its median scale value between one and eleven as established by the panel. In other words, the scale value of any one statement is computed as the ‘median’ position to which it is assigned by the group of judges. A final selection of statements is then made. For this purpose a sample of statements, whose median scores are spread evenly from one extreme to the other is taken. The statements so selected, constitute the final scale to be administered to respondents. The position of each statement on the scale is the same as determined by the judges. After developing the scale as stated above, the respondents are asked during the administration of the scale to check the statements with which they agree. The median value of the statements that they check is worked out and this establishes their score or quantifies their opinion. It may be noted that in the actual instrument the statements are arranged in random order of scale value. If the values are valid and if the opinionnaire deals with only one attitude dimension, the typical respondent will choose one or several contiguous items (in terms of scale values) to reflect his views. However, at times divergence may occur when a statement appears to tap a different attitude dimension. The Thurstone method has been widely used for developing differential scales which are utilized to measure attitudes towards varied issues like war, religion, etc. Such scales are considered most appropriate and reliable when used for measuring a single attitude. But an important deterrent to their use is the cost and effort required to develop them. Another weakness of such scales is that the values assigned to various statements by the judges may reflect their own attitudes. The method is not completely objective; it involves ultimately subjective decision process. Critics of this method also opine that some other scale designs give more information about the respondent’s attitude in 131 CU IDOL SELF LEARNING MATERIAL (SLM)

comparison to differential scales. Summated Scales (or Likert-type Scales) Summated scales (or Likert-type scales) are developed by utilizing the item analysis approach wherein a particular item is evaluated on the basis of how well it discriminates between those persons whose total score is high and those whose score is low. Those items or statements that best meet this sort of discrimination test are included in the final instrument. Thus, summated scales consist of a number of statements which express either a favorable or unfavorable attitude towards the given object to which the respondent is asked to react. The respondent indicates his agreement or disagreement with each statement in the instrument. Each response is given a numerical score, indicating its favorableness or unfavourableness, and the scores are totaled to measure the respondent’s attitude. In other words, the overall score represents the respondent’s position on the continuum of favorable-unfavourableness towards an issue. Most frequently used summated scales in the study of social attitudes follow the pattern devised by Likert. For this reason they are often referred to as Likert-type scales. In a Likert scale, the respondent is asked to respond to each of the statements in terms of several degrees, usually five degrees (but at times 3 or 7 may also be used) of agreement or disagreement. For example, when asked to express opinion whether one considers his job quite pleasant, the respondent may respond in any one of the following ways:  s trongly agree,  a gree,  u ndecided,  d isagree,  s trongly disagree. We find that these five points constitute the scale. At one extreme of the scale there is strong agreement with the given statement and at the other, strong disagreement, and between them lie intermediate points. We may illustrate this as under: 132 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 9.8 Summated Scales (or Likert-type Scales) Each point on the scale carries a score. Response indicating the least favorable degree of job satisfaction is given the least score (say 1) and the most favorable is given the highest score (say 5). These score—values are normally not printed on the instrument but are shown here just to indicate the scoring pattern. The Likert scaling technique, thus, assigns a scale value to each of the five responses. The same thing is done in respect of each and every statement in the instrument. This way the instrument yields a total score for each respondent, which would then measure the respondent’s favorableness toward the given point of view. If the instrument consists of, say 30 statements, the following score values would be revealing.  3 0 × 5 = 150 Most favorable response possible  3 0 × 3 = 90 A neutral attitude  3 0 × 1 = 30 Most unfavorable attitude. The scores for any individual would fall between 30 and 150. If the score happens to be above 90, it shows favorable opinion to the given point of view, a score of below 90 would mean unfavorable opinion and a score of exactly 90 would be suggestive of a neutral attitude. Procedure: The procedure for developing a Likert-type scale is as follows: A s a first step, the researcher collects a large number of statements which are relevant to the attitude being studied and each of the statements expresses definite favorableness or unfavourableness to a particular point of view or the attitude and that the number of favorable and unfavorable statements is approximately equal. A fter the statements have been gathered, a trial test should be administered to a number of subjects. In other words, a small group of people, from those who are going to be studied finally, are asked to indicate their response to each statement by checking one of the categories of agreement or disagreement using a five point scale as stated above. 133 CU IDOL SELF LEARNING MATERIAL (SLM)

T he response to various statements is scored in such a way that a response indicative of the most favorable attitude is given the highest score of 5 and that with the most unfavorable attitude is given the lowest score, say, of 1. T hen the total score of each respondent is obtained by adding his scores that he received for separate statements. T he next step is to array these total scores and find out those statements which have a high discriminatory power. For this purpose, the researcher may select some part of the highest and the lowest total scores, say the top 25 per cent and the bottom 25 per cent. These two extreme groups are interpreted to represent the most favorable and the least favorable attitudes and are used as criterion groups by which to evaluate individual statements. This way we determine which statements consistently correlate with low favorability and which with high favorability. O nly those statements that correlate with the total test should be retained in the final instrument and all others must be discarded from it. Advantages: The Likert-type scale has several advantages. Mention may be made of the important ones. I t is relatively easy to construct the Likert-type scale in comparison to Thurstone-type scale because Likert-type scale can be performed without a panel of judges. L ikert-type scale is considered more reliable because under it respondents answer each statement included in the instrument. As such it also provides more information and data than does the Thurstone-type scale. E ach statement, included in the Likert-type scale, is given an empirical test for discriminating ability and as such, unlike Thurstone-type scale, the Likert-type scale permits the use of statements that are not manifestly related (to have a direct relationship) to the attitude being studied. L 134 CU IDOL SELF LEARNING MATERIAL (SLM)

ikert-type scale can easily be used in respondent-centred and stimulus-centred studies i.e., through it we can study how responses differ between people and how responses differ between stimuli. L ikert-type scale takes much less time to construct, it is frequently used by the students of opinion research. Moreover, it has been reported in various research studies* that there is high degree of correlation between Likert-type scale and Thurstone-type scale. Limitations: There are several limitations of the Likert-type scale as well. One important limitation is that, with this scale, we can simply examine whether respondents are more or less favorable to a topic, but we cannot tell how much more or less they are. There is no basis for belief that the five positions indicated on the scale are equally spaced. The interval between ‘strongly agree’ and ‘agree’, may not be equal to the interval between “agree” and “undecided”. This means that Likert scale does not rise to a stature more than that of an ordinal scale, whereas the designers of Thurstone scale claim the Thurstone scale to be an interval scale. One further disadvantage is that often the total score of an individual respondent has little clear meaning since a given total score can be secured by a variety of answer patterns. It is unlikely that the respondent can validly react to a short statement on a printed form in the absence of real-life qualifying situations. Moreover, there “remains a possibility that people may answer according to what they think they should feel rather than how they do feel.” In spite of all the limitations, the Likert-type summated scales are regarded as the most useful in a situation wherein it is possible to compare the respondent’s score with a distribution of scores from some well-defined group. They are equally useful when we are concerned with a programme of change or improvement in which case we can use the scales to measure attitudes before and after the programme of change or improvement in order to assess whether our efforts have had the desired effects. We can as well correlate scores on the scale to other measures without any concern for the absolute value of what is favorable and what is unfavorable. All this accounts for the popularity of Likert-type scales in social studies relating to measuring of attitudes. Cumulative scales: Cumulative scales or Louis Guttman’s scalogram analysis, like other scales, consist of series of statements to which a respondent expresses his agreement or disagreement. The special feature of this type of scale is that statements in it form a cumulative series. This, in other words, means that the statements are related to one another in such a way that an individual, who replies favorably to say item No. 3, also replies favorably to items No. 2 and 1, and one who replies favorably to item No. 4 also replies favorably to items No. 3, 2 and 1, and so on. This being so an individual whose attitude is at a certain point in a cumulative scale will answer favorably all the items on one side of this point, and answer unfavorably all the items on the other side of this point. The individual’s score is worked 135 CU IDOL SELF LEARNING MATERIAL (SLM)

out by counting the number of points concerning the number of statements he answers favorably. If one knows this total score, one can estimate as to how a respondent has answered individual statements constituting cumulative scales. The major scale of this type of cumulative scales is the Guttman’s scalogram. We attempt a brief description of the same below. The technique developed by Louis Guttman is known as scalogram analysis, or at times simply ‘scale analysis’. Scalogram analysis refers to the procedure for determining whether a set of items forms a unidimensional scale. A scale is said to be unidimensional if the responses fall into a pattern in which endorsement of the item reflecting the extreme position results also in endorsing all items which are less extreme. Under this technique, the respondents are asked to indicate in respect of each item whether they agree or disagree with it, and if these items form a unidimensional scale, the response pattern will be as under: Response Pattern in Scalogram Analysis Figure 9.9 Response Pattern in Scalogram Analysis A score of 4 means that the respondent is in agreement with all the statements which is indicative of the most favorable attitude. But a score of 3 would mean that the respondent is not agreeable to item 4, but he agrees with all others. In the same way one can interpret other values of the respondents’ scores. This pattern reveals that the universe of content is scalable. Procedure: The procedure for developing a scalogram can be outlined as under: T he universe of content must be defined first of all. In other words, we must lay down in clear terms the issue we want to deal within our study. T he next step is to develop a number of items relating the issue and to eliminate by inspection the items that are ambiguous, irrelevant or those that happen to be too extreme items. 136 CU IDOL SELF LEARNING MATERIAL (SLM)

T he third step consists in pre-testing the items to determine whether the issue at hand is scalable (The pretest, as suggested by Guttman, should include 12 or more items, while the final scale may have only 4 to 6 items. Similarly, the number of respondents in a pretest may be small, say 20 or 25 but final scale should involve relatively more respondents, say 100 or more). I n a pretest the respondents are asked to record their opinions on all selected items using a Likert-type 5-point scale, ranging from ‘strongly agree’ to ‘strongly disagree’. The strongest favorable response is scored as 5, whereas the strongest unfavorable response as 1. The total score can thus range, if there are 15 items in all, from 75 for most favorable to 15 for the least favorable. R espondent opinionnaires are then arrayed according to total score for analysis and evaluation. If the responses of an item form a cumulative scale, its response category scores should decrease in an orderly fashion as indicated in the above table. Failure to show the said decreasing pattern means that there is overlapping which shows that the item concerned is not a good cumulative scale item i.e., the item has more than one meaning. Sometimes the overlapping in category responses can be reduced by combining categories. After analyzing the pretest results, a few items, say 5 items, may be chosen. T he next step is again to total the scores for the various opinionnaires, and to re-array them to reflect any shift in order, resulting from reducing the items, say, from 15 in pretest to, say, 5 for the final scale. The final pretest results may be tabulated in the form of a table given in Table below. The Final Pretest Results in a Scalogram Analysis Pretest Results in a Scalogram Analysis 137 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 9.10 The Final Pretest Results in a Scalogram Analysis Pretest Results in a Scalogram Analysis The table shows that five items (numbering 5, 12, 3, 10 and 7) have been selected for the final scale. The number of respondents is 25 whose responses to various items have been tabulated along with the number of errors. Perfect scale types are those in which the respondent’s answers fit the pattern that would be reproduced by using the person’s total score as a guide. Non-scale types are those in which the category pattern differs from that expected from the respondent’s total score i.e., non-scale cases have deviations from one-dimensionality or errors. Whether the items (or series of statements) selected for final scale may be regarded a perfect cumulative (or a unidimensional scale), we have to examine on the basis of the coefficient of reproducibility. Guttman has set 0.9 as the level of minimum reproducibility in order to say that the scale meets the test of one-dimensionality. He has given the following formula for measuring the level of reproducibility: Guttman’s Coefficient of Reproducibility = 1 – e/n(N) where e = number of errors n = number of items N = number of cases For the above table figures, Coefficient of Reproducibility = 1 – 7/5(25) = .94 138 CU IDOL SELF LEARNING MATERIAL (SLM)

This shows that items number 5, 12, 3, 10 and 7 in this order constitute the cumulative or unidimensional scale, and with this we can reproduce the responses to each item, knowing only the total score of the respondent concerned. Scalogram, analysis, like any other scaling technique, has several advantages as well as limitations. One advantage is that it assures that only a single dimension of attitude is being measured. Researcher’s subjective judgement is not allowed to creep in the development of scale since the scale is determined by the replies of respondents. Then, we require only a small number of items that make such a scale easy to administer. Scalogram analysis can appropriately be used for personal, telephone or mail surveys. The main difficulty in using this scaling technique is that in practice perfect cumulative or unidimensional scales are very rarely found and we have only to use its approximation testing it through coefficient of reproducibility or examining it on the basis of some other criteria. This method is not a frequently used method for the simple reason that its development procedure is tedious and complex. Such scales hardly constitute a reliable basis for assessing attitudes of persons towards complex objects for predicting the behavioral responses of individuals towards such objects. Conceptually, this analysis is a bit more difficult in comparison to other scaling methods. Factor Scales Factor scales are developed through factor analysis or on the basis of intercorrelations of items which indicate that a common factor accounts for the relationships between items. Factor scales are particularly “useful in uncovering latent attitude dimensions and approach scaling through the concept of multiple-dimension attribute space.” More specifically the two problems viz., how to deal appropriately with the universe of content which is multi-dimensional and how to uncover underlying (latent) dimensions which have not been identified, are dealt with through factor scales. An important factor scale based on factor analysis is Semantic Differential (S.D.) and the other one is Multidimensional Scaling. We give below a brief account of these factor scales. Semantic differential scale: Semantic differential scale or the S.D. scale developed by Charles E. Osgood, G.J. Suci and P.H. Tannenbaum (1957), is an attempt to measure the psychological meanings of an object to an individual. This scale is based on the presumption that an object can have different dimensions of connotative meanings which can be located in multidimensional property space, or what can be called the semantic space in the context of S.D. scale. This scaling consists of a set of bipolar rating scales, usually of 7 points, by which one or more respondents’ rate one or more concepts on each scale item. For instance, the S.D. scale items for analyzing candidates for leadership position may be shown as under: 139 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 9.11 Factor Scales Candidates for leadership position (along with the concept—the ‘ideal’ candidate) may be compared and we may score them from +3 to –3 on the basis of the above stated scales. (The letters, E, P, A showing the relevant factor viz., evaluation, potency and activity respectively, written along the left side are not written in actual scale. Similarly the numeric values shown are also not written in actual scale.) Osgood and others did produce a list of some adjective pairs for attitude research purposes and concluded that semantic space is multidimensional rather than unidimensional. They made sincere efforts and ultimately found that three factors, viz., evaluation, potency and activity, contributed most to meaningful judgements by respondents. The evaluation dimension generally accounts for 1/2 and 3/4 of the extractable variance and the other two factors account for the balance. Procedure: Various steps involved in developing S.D. scale are as follows: F irst of all the concepts to be studied are selected. The concepts are usually chosen by personal judgement, keeping in view the nature of the problem. T he next step is to select the scales bearing in mind the criterion of factor composition and the criterion of scale’s relevance to the concepts being judged (it is common practice to use at least three scales for each factor with the help of which an average factor score has to be worked out). One more criterion to be kept in view is that scales should be stable across 140 CU IDOL SELF LEARNING MATERIAL (SLM)

subjects and concepts. T hen a panel of judges are used to rate the various stimuli (or objects) on the various selected scales and the responses of all judges would then be combined to determine the composite scaling. T o conclude, “the S.D. has a number of specific advantages. It is an efficient and easy way to secure attitudes from a large sample. These attitudes may be measured in both direction and intensity. The total set of responses provides a comprehensive picture of the meaning of an object, as well as a measure of the subject doing the rating. It is a standardized technique that is easily repeated, but escapes many of the problems of response distortion found with more direct methods.” Multidimensional scaling: Multidimensional scaling (MDS) is relatively more complicated scaling device, but with this sort of scaling one can scale objects, individuals or both with a minimum of information. Multidimensional scaling (or MDS) can be characterized as a set of procedures for portraying perceptual or affective dimensions of substantive interest. It “provides useful methodology for portraying subjective judgements of diverse kinds.” MDS is used when all the variables (whether metric or non-metric) in a study are to be analyzed simultaneously and all such variables happen to be independent. The underlying assumption in MDS is that people (respondents) “perceive a set of objects as being more or less similar to one another on a number of dimensions (usually uncorrelated with one another) instead of only one.” Through MDS techniques one can represent geometrically the locations and interrelationships among a set of points. In fact, these techniques attempt to locate the points, given the information about a set of interpoint distances, in space of one or more dimensions such as to best summarize the information contained in the interpoint distances. The distances in the solution space then optimally reflect the distances contained in the input data. For instance, if objects, say X and Y, are thought of by the respondent as being most similar as compared to all other possible pairs of objects, MDS techniques will position objects X and Y in such a way that the distance between them in multidimensional space is shorter than that between any two other objects. Two approaches, viz., the metric approach and the non-metric approach, are usually talked about in the context of MDS, while attempting to construct a space containing m points such that m(m – 1)/2 interpoint distances reflect the input data. The metric approach to MDS treats the input data as interval scale data and solves applying statistical methods for the additive constant* which minimizes the dimensionality of the solution space. This approach utilizes all the information in the data in obtaining a solution. The data (i.e., the metric similarities of the objects) are often obtained on a 141 CU IDOL SELF LEARNING MATERIAL (SLM)

bipolar similarity scale on which pairs of objects are rated one at a time. If the data reflect exact distances between real objects in an r-dimensional space, their solution will reproduce the set of interpoint distances. But as the true and real data are rarely available, we require random and systematic procedures for obtaining a solution. Generally, the judged similarities among a set of objects are statistically transformed into distances by placing those objects in a multidimensional space of some dimensionality. The non-metric approach first gathers the non-metric similarities by asking respondents to rank order all possible pairs that can be obtained from a set of objects. Such non-metric data is then transformed into some arbitrary metric space and then the solution is obtained by reducing the dimensionality. In other words, this non-metric approach seeks “a representation of points in a space of minimum dimensionality such that the rank order of the interpoint distances in the solution space maximally corresponds to that of the data. This is achieved by requiring only that the distances in the solution be monotone with the input data.”9 The non-metric approach has come into prominence during the sixties with the coming into existence of high speed computers to generate metric solutions for ordinal input data. The significance of MDS lies in the fact that it enables the researcher to study “the perceptual structure of a set of stimuli and the cognitive processes underlying the development of this structure. Psychologists, for example, employ multidimensional scaling techniques in an effort to scale psychophysical stimuli and to determine appropriate labels for the dimensions along which these stimuli vary.” The MDS techniques, in fact, do away with the need in the data collection process to specify the attribute(s) along which the several brands, say of a particular product, may be compared as ultimately the MDS analysis itself reveals such attribute(s) that presumably underlie the expressed relative similarities among objects. Thus, MDS is an important tool in attitude measurement and the techniques falling under MDS promise “a great advance from a series of unidimensional measurements (e.g., a distribution of intensities of feeling towards single attribute such as color, taste or a preference ranking with indeterminate intervals), to a perceptual mapping in multidimensional space of objects ... company images, advertisement brands, etc.” In spite of all the merits stated above, the MDS is not widely used because of the computation complications involved under it. Many of its methods are quite laborious in terms of both the collection of data and the subsequent analyses. However, some progress has been achieved (due to the pioneering efforts of Paul Green and his associates) during the last few years in the use of non- metric MDS in the context of market research problems. The techniques have been specifically applied in “finding out the perceptual dimensions, and the spacing of stimuli along these dimensions, that people, use in making judgments about the relative similarity of pairs of Stimuli.” But, “in the long run, the worth of MDS will be determined by the extent to which it advances the behavioral sciences.” 142 CU IDOL SELF LEARNING MATERIAL (SLM)

9.6 SUMMARY S caling techniques provide a clear picture of the product life cycle and the market acceptability of the products offered. It facilitates product development and benchmarking through rigorous market research. S caling technique is a method of placing respondents in continuation of gradual change in the pre-assigned values, symbols or numbers based on the features of a particular object as per the defined rules. All the scaling techniques are based on four pillars, i.e., order, description, distance and origin. S caling is the procedure of measuring and assigning the objects to the numbers according to the specified rules. In other words, the process of locating the measured objects on the continuum, a continuous sequence of numbers to which the objects are assigned is called as scaling. The measurement is the process of assigning numbers or symbol to the characteristics of the object as per the specified rules. Here, the researcher assigns numbers, not to the object, but to its characteristics such as perceptions, attitudes, preferences, and other relevant traits. I n research, usually, the numbers are assigned to the qualitative traits of the object because the quantitative data helps in statistical analysis of the resulting data and further facilitates the communication of measurement rules and results. A ll the scales used in scaling techniques can be explained in terms of four basic characteristics., Viz. Description, Order, Distance, and origin. These characteristics collectively define the Levels of Measurement of scale. The level of measurement indicates that what properties of an object are measured or not measured by the scale. 9.7 KEYWORDS  Hypothesis - A formal statement made about the predicted relationship between variables in a research study, which is directly tested by the researcher. Generally linked to deductive reasoning.  Ideographic explanations - Only valid for a specific situation or ‘case’ and not generalizable 143 CU IDOL SELF LEARNING MATERIAL (SLM)

to others.  Individual fallacy - Taking an exception to a general rule and considering it as cancelling the rule.  Informants - A person who helps a researcher in a field study by helping them gain access to the setting, introduce them to the members of the setting, answer questions the researcher may have and provide clarifications. Often it is a member of the setting. Interactions - Factors that influence each other within a system.  Inter-coder reliability - Recoding of (randomly selected) 10% of the units of analysis coded by one coder, by another in a content analysis, to examine the agreement between the two for reliability and consistency.  Interrelationships - Relationships between factors within a system 9.8 LEARNING ACTIVITY 1. Explain using examples primary scaling techniques _________________________________________________________________________________ _________________________________________________________________________________ 2. Explain meaning of scaling. _________________________________________________________________________________ _________________________________________________________________________________ 9.9 UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Questions Explain State 1. Describe rating & ranking scales Discuss 2. various scale construction techniques 3. primary scaling techniques 4. important scaling techniques B. Multiple Choice Questions (MCQs) 144 CU IDOL SELF LEARNING MATERIAL (SLM)

1. In _____________ the concept of the relative position of the objects or labels based on the individual’s choice or preference a. Nominal b. Ordinal c. Interval d. Ratio 2. One of the most superior measurement technique is the ___________ a. Nominal b. Ordinal c. Interval d. Ratio 3. ________ ______ symbolizes two variables from which the respondent needs to select one a. Rank Order b. Paired comparison c. Q-sort d. Likert 4. ________ ______ symbolizes two variables from which the respondent needs to select one a. Rank Order b. Paired comparison c. Q-sort d. Likert 5. ________ _____ emphasizes on choosing a particular category among the various given categories by the respondents. a. Comparat ive scale 145 CU IDOL SELF LEARNING MATERIAL (SLM)

b. Itemized rating scaling Raking Q-sort c. scale d. Answers: (b) 1. 3. (c) 4. (b) 5. (b) 2. (d) 9.10 REFERENCES  Donald, R. Cooper & Pamela S. Schindler (2014).  Business Research Methods. New Delhi: Tata McGraw-Hill Publishing Co. Ltd.  Gupta, S.C. (2010). Fundamentals of Statistics. 6th Ed. Mumbai: HPH.  Gupta, S. P. (2002). Statistical Methods. New Delhi: Sultan Chand & Sons.  Beri, G. C. (2012). Business Statistics. New Delhi: Tata McGraw-Hill Publishing Co. Ltd.  Zikmund. (2015). Business Research Methods. New Delhi: Cengage Learning  Abrams, M.A., Social Surveys and Social Action, London: Heinemann, 1951.  Arthur, Maurice, Philosophy of Scientific Investigation, Baltimore: John Hopkins University Press, 1943.  RS. Bhardwaj, Business Statistics, Excel Books, New Delhi, 2008.  S.N. Murthy and U. Bhojanna, Business Research Methods, Excel Books, 2007. 146 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT-10 DATA ANALYSIS Structure 10.0. Learning Objectives 10.1. Introduction 10.2. Tabulation of Data 10.3. Data Preparation –frequency tables, bar charts, pie charts, percentages 10.4. Summary 10.5. Keywords 10.6. Learning Activity 10.7. Unit End Questions (Mcq And Descriptive) 10.8. References 10.0 LEARNING OBJECTIVES After studying this Unit, you will be able to:  Explain data tabulation  State significance of tabulated data  Identify types of data analysis  Discuss the various data preparation methods 10.1 INTRODUCTION According to LeCompte and Schensul, research data analysis is a process used by researchers for reducing data to a story and interpreting it to derive insights. The data analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense. Three essential things take place during the data analysis process — the first data organization. Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps in finding patterns and themes in the data for easy identification and linking. Third and the last way is data analysis – researchers do it in both top-down or bottom-up fashion. Marshall and Rossman, on the other hand, describe data analysis as a messy, ambiguous, and time- consuming, but a creative and fascinating process through which a mass of collected data is being brought to order, structure and meaning. 147 CU IDOL SELF LEARNING MATERIAL (SLM)

We can say that “the data analysis and interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.” 10.2 TABULATION OF DATA Tabulation comprises sorting of the data into different categories and counting the number of cases that belong to each category. The simplest way to tabulate is to count the number of responses to one question. This is also called univariate tabulation. The analysis based on just one variable is obviously meagre. Where two or more variables are involved in tabulation, it is called bivariate or multivariate tabulation in marketing research, projects generally both types of tabulation are used. Definition: Prof. Neiswanger has defined a statistical table as “In a systemic organisation of data in columns and rows.” L. K Connor has defined tabulation as the orderly and systematic presentation of numerical data in a form designed to elucidate the problem under consideration. The tabular presentation of data is one of the techniques of presentation of data. The tabular presentation means arranging the collected data in an orderly mariner in rows and in columns. The horizontal arrangement of the data is known as rows, whereas the vertical arrangement is called columns. The classified facts are recorded in rows and columns to give then tabular form. Objects of Tabulation: The following are the main objects of tabulation. a. To make the purpose of enquiry clear tabulation in the general scheme of statistics investigation is to arrange in easily accessible form. b. To make significance clear by arranging in form of table the significance of data, is made very clear. This is because table permits the observation of the whole data in one glance. The total information is clear to the view and the significance of different parts can easily be followed. c. To express the data in the least space table also permits the data to be represented in least possible space, making the whole information clear to the view. If it is expressed in form of a passage it would not only be difficult to follow, but would require more space too. d. To make comparison easy mainly because of the arrangement of figures in it. When two sets of figures are given side by side, it is much easier to form a comparative idea of their significance classification of tabulation: A. Simple Tabulation B. Complex Tabulation. 148 CU IDOL SELF LEARNING MATERIAL (SLM)

A. Simple Tabulation: It gives information about one or more groups of independent questions. This result, in one-way table, provides information of one characteristics of data. B. Complex Tabulation: In this type of tabulation, the data is divided in two or more categories which gives information regarding more sets of interrelated question. Importance of Tabulation a. S ystematic Presentation of Data: Generally the collected data is in fragmented form. The mass of data is presented in a concise and simple manner by means of statistical tables. Thus, tabulation helps in presenting the data in an orderly manner. b. F acilitates Comparison of Data: If the data is in the raw form, it is very difficult to compare. Comparison is possible when the related items of data are presented in simple and concise form. The presentation of complete and unorganized data in the form of tables facilitates the comparison of the various aspects of the data. c. I dentification of the Desired Values: In tabulation, data is presented in an orderly manner by arranging it in rows and columns. Therefore, the desired values can be identified without much difficulty. In the absence of tabulated data, it would be rather difficult to locate the required values. d. P rovides a Basis for Analysis: Presentation of data in tabular form provides a basis for analysis of such data. The statistical methodology suggests that analysis follows presentation of data. A systematic presentation of data in tabular form is a prerequisite for the analysis of data Statistical tables are useful aids in analysis. e. E xhibits Trend of Data: By presenting data in a condensed form at one place, tabular presentation exhibits the trend of data. By looking at a statistical table, we can identify the overall pattern of the data. Data Analysis 149 CU IDOL SELF LEARNING MATERIAL (SLM)

The data collected may or may not in numerical form. Even if data is not in numerical form still, we can carry out qualitative analysis based on the experiences of individual participants. When data is collected in numerical form than through descriptive statistics findings can be summarised. This includes measure of central tendency like mean range etc. Another way to summarised finding is by means of graphs and charts. In any of the research study there is experimental hypothesis or null hypothesis one the basis of data of both hypothesis, various test has been devised to take decision. Where decision is taken on the basis statistical test, it is subject to error, and such correct decision is difficult. But some standard procedures followed to arrive at proper decision. Analysis involves estimating the values of unknown parameters of the population and testing hypothesis for drawing inferences. Types of Analysis:  Q ualitative analysis  C ontent analysis  Q uantitative analysis  D escriptive analysis  B ivariate analysis  S equential analysis  C asual analysis  M ultivariate analysis  I nferential analysis  S tatistical analysis. a. Qualitative Analysis: It is less influenced by theoretical assumption. The limitation of this type of 150 CU IDOL SELF LEARNING MATERIAL (SLM)