MB0050 Research Methodology Set-1

SMU Apar India College

MB0050 Assignment Set-1

Spring 2011

Master of Business Administration - MBA Semester III MB0050 Research Methodology (Book ID: B1206) Assignment Set- 1 1. a. Differentiate between nominal, ordinal, interval and ratio scales, with an example of each. Answer: Nominal Measurement: In nominal measurement the numerical values just "name" the attribute uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball are measures at the nominal level. A player with number 30 is not more of anything than a player with number 15, and is certainly not twice whatever number 15 is. Ordinal Measurement: In ordinal measurement the attributes can be rank-ordered. Here, distances between attributes do not have any meaning. For example, on a survey you might code Educational Attainment as 0=less than H.S.; 1=some H.S.; 2=H.S. degree; 3=some college; 4=college degree; 5=post college. In this measure, higher numbers mean more education. But is distance from 0 to 1 same as 3 to 4? Of course not. The interval between values is not interpretable in an ordinal measure. Interval Measurement: In interval measurement the distance between attributes does have meaning. For example, when we measure temperature (in Fahrenheit), the distance from 30-40 is same as distance from 70-80. The interval between values is interpretable. Because of this, it makes sense to compute an average of an interval variable, where it doesn't make sense to do so for ordinal scales. But note that in interval measurement ratios don't make any sense - 80 degrees is not twice as hot as 40 degrees (although the attribute value is twice as large). Ratio Measurement: In ratio measurement there is always an absolute zero that is meaningful. This means that you can construct a meaningful fraction (or ratio) with a ratio variable. Weight is a ratio variable. In applied social research most "count" variables are ratio, for example, the number of clients in past six months.Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

Why? Because you can have zero clients and because it is meaningful to say that "...we had twice as many clients in the past six months as we did in the previous six months." It's important to recognize that there is a hierarchy implied in the level of measurement idea. At lower levels of measurement, assumptions tend to be less restrictive and data analyses tend to be less sensitive. At each level up the hierarchy, the current level includes all of the qualities of the one below it and adds something new. In general, it is desirable to have a higher level of measurement (e.g., interval or ratio) rather than a lower one (nominal or ordinal). b. What are the purposes of measurement in social science research? Answer: No discussion of scientific method is complete without an argument for the importance of fundamental measurement - measurement of the kind characterizing length and weight. Yet few social scientists attempt to construct fundamental measures. This is not because social scientists disapprove of fundamental measurement. It is because they despair of obtaining it. The conviction that fundamental measurement is unobtainable in social science and education has such a grip that we do not see our despair is unnecessary. Fundamental measurement is not only obtainable in social science but, in an unaware and hence incomplete form, is widely relied on. Social scientists are practicing fundamental measurement without knowing it and hence without enjoying its benefits or building on its strengths. The realization that fundamental measurements can be made in social science research is usually traced to Luce and Tukey (1964) who show that fundamental measurement can be constructed from an axiomatization of comparisons among responses to arbitrary pairs of quantities of two specified kinds. But Thurstone's 1927 Law of comparative Judgement contains an equivalent idea and his empirical work (e.g., 1928a, 1928b, 1929) contains results which are rough examples of fundamental measurement. FundamentalRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

measurement also occurs in Bradley and Terry 1952 and Rasch 1958, 1960 and 1966. The fundamental measurement which follows from Rasch's 'specific objectivity' is developed in Rasch 1960, 1961, 1967 and 1977. Rasch's specific objectivity and R. A. Fisher's estimation sufficiency are two sides of the same approach to inference. Andersen (1977) shows that the only measuring processes which support specific objectivity and hence fundamental measurement are those which have sufficient statistics for their parameters. It follows that sufficient statistics lead to and are necessary for fundamental measurement. Several authors connect 'additive conjoint' fundamental measurement with Rasch's work (Keats, 1967, 1971; Fischer 1968; Brogden, 1977). Perline, Wright and Wainer (1977) provide two empirical demonstrations of the equivalence of non-metric multidimensional scaling (Kruskal, 1964, 1965) and the Rasch process in realizing fundamental measurement. Wright and Stone (1979) show how to obtain fundamental measurement from mental tests. Wright and Masters (1982) give examples of its application to rating scales and partial credit scoring. In spite of this considerable literature advancing, explaining and illustrating the successful application of fundamental measurement in social science research, most current psychometric practice is either unaware of the opportunity or considers it impractical. MAINTAINING A UNIT Thurstone says "The linear continuum which is implied in all measurement is always an abstraction. . . . All measurement implies the recreation or restatement of the attribute measured to an abstract linear form." and "There is a popular fallacy that a unit of measurement is a thing such as a piece of yardstick. This is not so. A unit of measurement is always a process of some kind which can be repeated without modification in the different parts of the measurement continuum" (Thurstone, 1931, 257). Campbell (1920) specifies an addition operation as the hallmark of fundamental measurement. At bottom, it is maintaining a unit that supports

Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

addition. Let us see how this requirement can be met in psychological measurement. Rasch (1960, 171-172) shows that, if P = e(b - d) / G where G = [1 + e(b - d)] is the way person ability b and item difficulty d combine to govern the probability of a successful outcome and, if Event AB is person A succeeding but person B failing on a particular item, while Event BA is Person B succeeding but person A failing on the same item, then a distance between persons A and B on a scale defined by a set of items of a single kind can be estimated by bA - bB = loge NAB - log NBA where NAB, is the number of times A succeeds but B fails and NBA is the number of times B succeeds but A fails on any subset of these items. This happens because, for Rasch's model, PAB = PA(1-PB) = e(ba-d)/GAGB PBA = PB(1 - PA) = e(bB - d)/GAGB so that d cancels out of PAB/PBA = e(bA leaving loge(PAB/PBA) = bA - bB a distance which holds regardless of the value of d. This result is equivalent to Case 5 of Thurstone's Law of Comparative Judgement of 1927 and to Bradley and Terry of 1952 and conforms to Luce and Tukey of 1964. Since d does not appear in this equation, estimates of the distance between A and B must be statistically equivalent whatever the item difficulty d .Page42 Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

bB)



Spring 2011

Since the unit defined by the distance between A and B holds over the range of the continuum defined by the values d can take and is thus independent of d, Rasch's model for specifying measures is the unit-maintaining process Thurstone requires. Whether a particular kind of data can be disciplined to follow the Rasch process can only be discovered by applying the process to the data and examining the consequences. It is worth noticing, however, that whenever we have deemed it useful to count right answers or to add scale ratings, we have taken it for granted that the data concerned did, in fact, follow the Rasch process well enough to suit our purposes. This is so because counts and additions are exactly the sufficient statistics for the Rasch process and for no other! If we subscribe to Thurstone's requirement, then we want data that we can govern in this way. That means that fitting the Rasch process becomes more than a convenience, it becomes the essential criterion for data good enough to support the construction of fundamental measures. The Rasch process becomes the criterion for valid data. VERIFYING FIT How well does data have to fit the Rasch process in order to obtain fundamental measurement? The only reasonable or useful answer is: "Well enough to serve the practical problem for which the measures are intended, that is, well enough to maintain an invariance sufficient to serve the needs at hand." How can we document the degree of invariance the Rasch process obtains with a particular set of data? (One method is to specify subsets of items in any way that is substantively interesting but also independent of the particular person scores we have already examined (NAB, NBA) and then to see whether the new counts resulting from these item subsets estimate statistically equivalent distances. The extent to which the distance between A and B is invariant over challenging Partitions of items is the extent to which the data succeeds in making use of the Rasch process to maintain a unit.


Page42



Spring 2011

A more general way to examine and document fit is to compose for each response x = 0 or 1 the score residual y=x-P in which P = e(b - d)/[1 + e(b - d)] comes from the current estimates of person ability b and item difficulty d and the expected value of x, Ex = P and then to accumulate these score residuals over the item subsets chosen to challenge fit. If (b1 - b0) is defined as the extent to which a subset of items fails to maintain the unit constructed by the full set of items, then that subset score residual sum(y) estimates (b1 - b0) sum(dy/db) . When the data fit the Rasch process, then the differential (slope of the curve) of y with respect to b dy/db = dP/db = P(1 - P) = w equals the score variance so that sum(y) =~ (b1 - b0 ) sum(w) and (b1- b0) =~ sum(y)/sum(w) = g The statistic g = sum(y)/sum(w) estimates the logit discrepancy in scale invariance (b1- b0) due to the item subset specified, with g having expected value of g, Eg = 0, and model variance of g around Eg, Vg = 1/sum(w) when the data fit this unit-maintaining, i.e. Rasch, process.Page42




Spring 2011

Subsets need not be limited to items. Groups of Persons can be used to review the extent' to which any item is vulnerable to bias for or against the type of persons grouped. In general, any combination of items and persons thought to interact in a way that might interfere with the unit-maintaining process can be used to define a subset for calculating g. The resulting value of g estimates the direction and logit magnitude of the putative disturbance to scale invariance. The stability of any particular value of g can be evaluated from the root of its modeled variance, Vg = 1/sum(w). CONSTRUCTING ADDITION The way to build a linear scale is to construct an addition operation. This can be done by finding an operation which answers the question: "If Person A has more ability than person B, then how much 'ability' must be added to B to make the performance of B appear the same as the performance of A ?" To be more specific, "What 'addition' will cause PB = PA?" To answer this question we must realize that the only situation in which we can observe these P's is the one in which we expose the persons to items of the specified kind. This changes the question to: "What change in the situation through which we find out about persons by testing them with items will give B the same probability of success as A ?" In other words: "What 'addition' will cause PBj = PAi?" Or, to be explicit, "What item j will make the performance of person B appear the same as the performance of person A on item i?" The Rasch process specifies that when PBj = PAi then bB - dj = bA - di The 'addition' required to cause B to perform like A is bB + (bA - bB) = bA.Page42

The way this 'addition' is accomplished is to give person B an item j which is di - dj = bA - bB




Spring 2011

easier than item i , namely, an item j with difficulty dj = di - (bA - bB) so that bB + (bA - bB) = bB+ (di - dj) = bA The way the success of this 'addition' is evaluated is to see whether the performance of person B on items like j is observed to be statistically equivalent to the performance of person A on items like i. This, in fact, is the comparison checked in any detailed test of fit. CURRENT PRACTICE It has long been customary in social science research to construct scores by counting answers (scored by their ordinal position in a sequence of ordered response possibilities) and then to use these scores and monotonic transformations of them as measures. When the questions asked have only two answer categories, then we count right answers. When the questions have an ordered series of answer categories, then ye count how many categories from 'least' to 'most' ('worst` to 'best', 'weakest' to strongest') have been surpassed. There is scarcely any quantitative data in social science research not already in this form or easily put so. If there has been any progress in quantitative social science, then this kind of counting must have been useful. But this has implications. Counting in this way implies a measurement process, not any process, but a particular one. Counting implies a process which derives counting as the necessary and sufficient scoring procedure. Well, counting is exactly the sufficient statistic for estimating measures under the Rasch process. Since the Rasch process constructs simultaneous conjoint measures whenever data are valid for such a construction, we have, in our counting, been practicing the first steps of fundamental measurement all along. All we need do is to take this implication of our actions seriously and to complete our data analyses by verifying the extent to which our data fit the Rasch process and so are valid for fundamental measuring. When our data can be organized to fit well enough to be useful, then we can use the results to define Thurstone linear scales and to make Luce and Tukey fundamental measures on them. WHAT OF OTHER MODELS?Page42




Spring 2011

The Rasch process maintains a unit that supports addition. Is that so for the other processes advocated for the construction of psychological measurement systems? Consider the three item parameter process (Lord, 1780, 12) Q = c + (1 - c)P P = e[a(b - d)]/G 1 - Q = (1 - c)(1 - P) G = 1 + e[a(b - d)] Now QAB/QBA = QA(1- QB)/QB(1-QA) = c(1-PB) + (1-c)PA(1-PB) / c(1- PA) + (1-c)PB(1-PA) Is there any way to cancel the three item parameters out of this expression in order to maintain a unit among b's over the range of the item parameters? Is there any way to cancel b out of this expression in order to enable a samplefree estimation of the item parameters? If c were a single constant known beforehand and always the same for all items no matter how much persons differed in their guessing behavior, then we could use (Q-c)/(1-Q) = P/(1-P) to eliminate the influence of this one common c and so concentrate on the problems caused by the interaction of b with a. But when c varies from item to item, then, even if its various values were known, the differential consequences of b variation on [c/(1 - c)](1 - PB) versus [c/(1 - c)](1 - PA) would prevent the Q process from maintaining a fixed distance between persons A and B over the range of d and c . Nor can we construct an addition for the Q process. What shall we 'add' to bB to cause person B to perform like person A, that is, to cause QBj = QAi?Page42




Spring 2011

There is no single 'amount' to add because the amount called for varies with the varying values of c and a. If we abandon c as a variable, then PAB/PBA = e[a(bA- d)]/e[a(bA-d)] and loge(PAB/PBA) = a(bA- bB) . The item parameter d is gone, so that a(bA- bB) is maintained over the range of d . But what shall we do with a? If we advance a as an item parameter, then we have to estimate a different unit for every item. The distance between A and B can only be maintained if every a for every item can be known independently of every b to be compared. But that prevents us from using the behavior of persons to estimate the values of a. This happens because when we try to estimate a we find that we cannot separate it from its interactions with the estimation of the b's used for its estimation. When we try to estimate these b's we find that we cannot separate them from their interactions with a. We can maintain the distance between A and B only when a is a constant over persons and items, that is, when we are back tb the Rasch process. Nor can the process which includes a as a variable support addition. When P = e[a(b - d)]/{1 + e[a(b - d)]} then PBj = PAi . implies that aj(bB-dj) = ai(bA-di) so that bA = di+ (aj/ai)(bB-dj) We see that an 'addition' which will equate the performances of persons A and B is defined in general only over persons and items for which a is a constant so that (aj/ai) = 1 and bA = bB + (di- dj)


Page42



Spring 2011

as in the Rasch process. CONCLUSION If measurement is our aim, nothing can be gained by chasing after extra item parameters like c and a. We must seek, instead, for items which can be managed by an observation process in which any potentially misleading disturbances which might be blamed on variation in possible c's and a's can be kept slight enough not to interfere with the maintenance of a scale stability sufficient for the measuring job at hand. That we have been content to use unweighted raw scores, just the count of right answers, as our 'good enough' statistic for all these eighty years, testifies to our latent conviction that the data with which we work can be usefully managed with a process no more complicated than the Rasch process. A good thing too! Only the Rasch process can maintain units that support addition and so produce results that qualify as fundamental measurement.

2. a. What are the sources from which one may be able to identify research problems? Answer: 1. Follow this general procedure when identifying and defining a problem situation: Start with a simple statement of the problem situation. Add details as you review the literature,review theoretical concepts, and investigate the problem in greater depth. Simplify the focus by identifying the most important aspects of the problem that are researchable. 2. Make a first attempt at identifying the problem situation by using the following format: Problem Situation: Write a small, simple paragraph that identifies the problem. Discrepancy: State the discrepancy between what is and what should be. Problem Question: Write down the central problem question. Possible Answers: Write two or more plausible answers to the problem question.


Page42



Spring 2011

3. From available research literature, health and behavioral theory, current service statistics,educated opinions, the assistance of PLHA, and other sources of information, try to add details to the problem situation you have just identified. Look for theoretical concepts and operational variables that you may have missed. List these concepts and variables on a piece of paper as you come across them. Try to answer the following questions: What are the incidence and prevalence of the problem? Which geographic areas are affected by the problem? Which population groups are affected by the problem? What are the findings of other research studies? What has been done to overcome the problem in the past? How successful were past efforts to overcome the problem? What seem to be major unanswered questions about the problem? 4. With the information you have collected from a literature review and other sources, rewrite your statement identifying and defining the problem. Use the format described above: Problem Situation, Discrepancy, Problem Question, and Possible Answers. Add details that help to define the problem, but organize the information. Try to establish the boundaries of the problem. Focus your attention on the most important, researchable aspects of the problem. 5. Have one or more colleagues read your final statement identifying and defining the problem situation. Have them tell you what he or she thinks the problem is. If they are unclear about the problem situation or cannot describe the discrepancy between what is and what should be, then go back to the beginning and start all over again. Now that you have identified and defined the problem situation, it is necessary next to justify the importance of the problem. Research often is expensive and time consuming. Ask yourself why the problem you wish to study is important. Can you justify your selection of the research problem? Can you convince others that the problem is important? Example for Justifying the Selection of a Research Problem Over time, millions of HIV-infected people in Africa and elsewhere in the world are developing HIV-related illnesses. In most African countries, hospitals, clinics, and other formal health care system institutions simply cannot cope with the large numbers of people in need of physical care and social and psychological support. In some hospitals, well over half of the beds are already occupied by AIDS patients; in some countries the figure is as high as 70 percent of all hospital beds. This is a problem of great concern to health care planners, as well as to the Ministry of Finance, whichRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

simply does not have the resources to build new facilities or train large numbers of new providers. The problem of providing care and support for PLHA is particularly challenging in rural areas because there are relatively few health facilities or adequately trained providers available. In this situation, an alternative is to provide care, support, and treatment in the homes of those with AIDS. How to do this in a cost-effective manner while simultaneously providing high-quality services is a challenge. New models of delivering care and support services in rural areas need to be developed and tested to improve the quality of life for PLHA. Without effective new approaches, large numbers of people with AIDS will suffer physical and psychological pain that might otherwise be avoided or at least lessened. Comments on the Example The first paragraph establishes the dimensions of the problem. The large number of people with AIDS cannot be adequately treated or supported by the formal health care system, which is already overwhelmed in many countries. The second paragraph notes that the problem is particularly acute in rural areas, where health facilities and providers are relatively few in number. An alternative is to provide services to PLHA in their homes. The important question is how to do this. The paragraph ends by saying that without the development of new approaches to care and support, large numbers of PLHA will needlessly suffer. What To Do: Justifying the Selection of a 1. In justifying the importance of a research problem, it is helpful to ask yourself a series of questions and then try to answer each of them. Is the problem you wish to study a current and timely one? Does the problem exist now? How widespread is the problem? Are many areas and many people affected by the problem? Does the problem affect key populations, such as youth, PLHA, mothers, or children? Does the problem relate to ongoing program activities? Does the problem relate to broad social, economic, and health issues, such as unemployment, income distribution, poverty, the status of women, or education? Who else is concerned about the problem? Are top government officials concerned? Are medical doctors or other professionals concerned? 2. Review your answers to these questions, and arrange them into one or two paragraphs that justify the importance of the research problem.Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009 Page42



Spring 2011

Start by discussing the broad issues that justify the problem and then begin to focus on the more specific issues related to a particular population group or geographical setting Involving Program Managers and Others in the Research Process One important way to accomplish the first step in the OR process is to involve not only researchers but also program managers and many other people, such as village chiefs, teachers, health personnel, NGOs, and PLHA organizations, in the entire problem identification, definition, and justification process. This involvement links the program experience of managers with the HIV/AIDS problem experience and understanding of PLHA with the technical and methodological skills of researchers. Teaming researchers, program managers, and PLHA is an educational experience for everyone that can have long-range benefits that go far beyond the mere design and implementation of a single OR study. Researchers, for example, begin to understand more fully the day-to-day administrative concerns of managers, the service delivery problems NGOs face, and the social, economic, psychological, and health concerns that PLHA confront every day. This increased understanding can help sharpen the focus of a study on those aspects of a program problem that could be changed. Administrators begin to appreciate the need to identify and define program problems on the basis of accurate data. They begin to view research as an important tool for decision making and as an ongoing process to which they can contribute. PLHA begin to experience a sense of empowerment and hope for the future as they become involved in identifying problem situations that affect their lives but could be changed through an operations research process. The early involvement of all key stakeholders in the operations research process is more likely to increase their interest later in reviewing and using the results from OR studies. b. Why literature survey is important in research? Answer: A literature review is an evaluative report of studies found in the literature related to your selected area. The review should describe, summarize, evaluate and clarify this literature. It should give a theoretical basis for the research and help you determine the nature of your own research. Select a limited number of works that are central to your area rather than trying to collect a large number of works that are not as closely connected to your topic area. A literature review goes beyond the search for information and includes the identification and articulation of relationships between the literature and your field of research. While the form of the literature review may vary with different types of studies, the basic purposes remain constant:Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

Provide a context for the research Justify the research Ensure the research hasn't been done before (or that it is not just a "replication study") Show where the research fits into the existing body of knowledge Enable the researcher to learn from previous theory on the subject Illustrate how the subject has been studied previously Highlight flaws in previous research Outline gaps in previous research Show that the work is adding to the understanding and knowledge of the field Help refine, refocus or even change the topic 3. a. What are the characteristics of a good research design? Answer: Generally a good research design minimizes bias and maximizes the reliability of the data collected and analyzed. The design which gives the smallest experimental error is reported to be the best design in scientific investigation. Similarly, a design which yields maximum information and provides a opportunity for considering different aspects of a problem is considered to be the most appropriate efficient design . Thus the question of a good design is related to the purpose or objective of the research problem and also with the nature of the problem to be studied. A good research design should satisfy the following four conditions namely objectivity, reliability, validity and generalization of the findings. 1. Objectivity: It refers to the findings related to the method of data collection and scoring of the responses. The research design should permit the measuring instrument which are fairly objective in which every observer or judge scoring the performance must precisely give the same report. In other words, the objectivity of the procedure may be judged by the degree of agreement between the final scores assigned to different individuals by more than one independent observer. This ensures the objectivity of the collected data which shall be capable of analysis and drawing generalizations. 2. Reliability: Reliability refers to consistency throughout a series of measurements. For eg: if a respondent gives out a response to a particular item, he is expected to give the same response to that item even if he is asked repeatedly. If he is changing his response to the same item, the consistency will be lost. So the researcher should frame the items in a questionnaire in such a way that it provides consistency or reliability.Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

3. Validity: Any measuring device or instrument is said to be valid when it measures what it is expected to measure. For eg: an intelligence test conducted for measuring the I.Q should measure only the intelligence and nothing else, and the questionnaire shall be framed accordingly. 4. Generalizability: It means how best the data collected from the samples can be utilized for drawing certain generalisations applicable to a large group from which sample is drawn. Thus a research design helps an investigator to generalize his findings provided he has taken due care in defining the population, selecting the sample, deriving appropriate statistical analysis etc. while preparing the research design. Thus a good research design is one which is methodologically prepared and should ensure that: a) The measuring instrument can yield objective, reliable and valid data. b) The population is clearly defined. c) Most appropriate techniques of sample selection is used to form an appropriate sample. d) Appropriate statical analysis has been carried out, and e) The findings of the study is capable of generalisations. b. What are the components of a research design? Answer: Twelve Components of Research: 1. Purpose/Goals/Questions A. Appropriate - Does topic apply to the discipline selected? Need to judge by "intrinsic merit", not evaluators bias or disciplinary biases. B. Clear - If sufficient focus can summarize purpose in short paragraphs. Succinct, terms clearly defined. Parsimonious. C. Comprehensive - Do question/s and purpose really describe all it could for a complete study? Are all of the relevant areas related to the topic included? D. Credible - Questionable if topic is already studied thoroughly. Need to be familiar with related literature. What groups special interests are served or ignored? Whose values are emphasized in the goals and purposes? E. Significant - Will it significantly contribute to literature? Will it provide a unique or distinctive perspective on existing issues? Can it refine concepts, perspectives, or verify current understandings? Will it be likely to be accepted and used for significant insight and/or change of policies and practices? 2. Research PhilosophyRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

A. Appropriate - What philosophical view is reflected in the study itself, not just the philosophy of researcher. B. Clear - Specify philosophical tradition: e.g. 1) realism 2) idealism 3) existentialism 4) pragmatism 5) phenomenology 6) positivist 7) a combination of these. C. Comprehensive - Not just selecting a philosophy and using associated guidelines in a study "replacing one positivist orthodoxy with a group of neopositivist orthodoxies." (LeCompte and Preissle, 1993, p.326). Is there consistency in philosophical views? D. Credible - Describe in detail, relate to existing research philosophies in literature. E. Significant - Show how philosophy affected choices of theories, data collection, interpretation. 3. Conceptual/Theoretical Frame - Heart of study To some degree specified at the beginning of the study, can be multiple theories, very broad theories (e.g. Lewin, Gaffman). A. Appropriate Concepts apply to setting; fits what was experienced. How do concepts and constructs interrelate with each other/interactions. Concepts related to questions asked "securely integrated" B. Clear - Well defined. Data level of questions clearly related to theory. Empirical descriptions of possible relationships and presuppositions articulated. C. Comprehensive - Scope of theory fits research questions novices often restrict scope to current status of discipline need to broaden to include cross disciplinary concerns multiple theories and views. D. Credible - Not an afterthought emergent (not imposedneed to suspend preconceptions at least for awhile during data collection) and found in data. Address rival explanations of data; not just trying to support one perspective. Results of data, not results of researchers norm based or value-based judgements that are presumed to be factual. E. Significant - Thoroughly addresses relevant theories or adds to them. Most significant when pit one theory against another (like a critical experiment). 4. Research Design/Model A. Appropriate Ethical, do-able. Must assess clarity before can tell if it is appropriate design. Does design fit questions/goals? Ethnography good for 4 situations: 1. Sociocultural system analysis 2. Using culture to analyze social events 3. Participantcentered analysis and reconstruction of events or actions 4. Obtaining process and values data B. Clear Often not clear what was intended to be done in contrast with what actually was done "Ethnography" defined in different ways sometimesRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

synonymous with qualitative research. Use terminology consistently within study and accurate terms (good to reference using an accepted methods text). C. Comprehensive Adaptations and variations of standard methods appropriate to answer questions comprehensively. Need variety of methods (one form of triangulation). D. Credible 6-8 months in field more credible than one shot observation in classroom. Need sufficient training and funding. Has the design been used before? E. Significant Complete ethnography vs. mixed design will affect interpretation. Takes time and sufficient resources. Results may be inaccessible for longer time due to analysis requirements may require compromise. (e.g. use samples of video data). 5. Setting/Circumstances (Description of Setting) A. Appropriate need to identify the range of circumstances to be sure all that are relevance to questions are included in study. B. Clear Description is clear. Ideal is to describe setting so thoroughly it is virtually equal to photograph. C. Comprehensive - My list compiled from numerous studies: 1. How you chose and entered site 2. Detailed description (Maps and diagrams may be good supplements if they dont compromise confidentiality) a) Surrounding community b) School/church/organization catchment area c) Buildings d) Politics e) Church or other organization social, legal, denominational, district and local church levels f) Leadership style/roles, social class ethnicity, areas of responsibility and power g) History of church, or other organization: local, district, denomination h) Annual schedule i) Teachers roles, backgrounds (e.g. Ethnicity, class) j) Classroom/s or other areas used k) Staff roles, background l) Students ethnicity, classes, roles. Gatekeeper of this crucial to access these details. D. Credible Degree to which it could be a guidebook/manual for a newcomer. Show how setting and situation could bias study. Include documents, manuals if available. (e.g. photo books, policy manual). E. Significant Not necessarily representative. Describe insufficient detail that reader can determine if research site is significantly like their own site.Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

6. Sampling Procedure A. Appropriate (The procedure itself is appropriate, not just the number or the sample itself.) Did specific people chosen for detailed study bias study? Sufficient number and variety of people in sample/s to represent the group to be described in results and conclusions section of report. B. Clear The written summary of the sampling process is described fully what kind of sampling/selection used (use terms from reputable source). C. Comprehensive Also need to describe participants in thorough detail. Who declined to participate and who dropped out? Does this reflect a loss of extremes that might give distinctive input? Or was it a loss of those in midrange that might give more typical responses. (Thus were conclusions skewed toward views of one or several extremes?) Who were the participants? Characteristics in detail (My compilation from studies): 1. race/ethnicity 2. age 3. number of participants 4. gender 5. socioeconomics 6. student/teacher ratio 7. teacher typologies of students 8. home environment (family, residential area, living area/s) 9. common experiences with peers 10. personality characteristics D. Credible Most likely biasing factors described in detail. Are comparisons and generalizations warranted from sample/selections? (Highlight areas of possible bias and explicate in detail). E. Significant Convincing rationales provided for sampling procedure. 7. Background and Experience of Researcher A. Appropriate Researcher characteristics affect rapport: sex, age, ethnicity, values/morals, social and emotional characteristics, other physical aspects. These also affect degree to which researcher can participate, degree of acceptance by participants, and thus the resulting data. B. Clear Language, including dialect, will affect entry and later access to people during research (congruence with their world-view). C. Comprehensive Extensive reflections of past experiences that might potentially relate to setting, participants reflect before, during and after research. (Personal notes). D. Credible If identify personal characteristics, reader can possibly judge likelihood participants were reactive E. Significant May find setting or participants repugnant and withdraw reject values of those studied or go to other extreme: "go native" and lose perspective.Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

8. Role/s of Researcher A. Appropriate Roles will facilitate acquisition of some data and limit other data so choose roles that will best provide data related to goals and purpose. B. Clear Need to be resocialized on personal level yet retain outside view. Did you specify a role rather than leave it vague (ambiguous roles tend to be seen as threatening) C. Comprehensive Relationships vitally affect breadth of data. Were you able to flex with people shift role if it meant getting more and better quality data? D. Credible Need some externality-value of etic perspective. ("Social Science framework") (See Lofland & Lofland text, or even Jim Lee). Did they believe your role or did you try to portray a role you could not genuinely assume? Need authenticity of role (we can assume several possible roles, but some roles fall outside our potential range). E. Significant Must assess degree of participation full to none to help assess influence of researcher assumptions and biases on results. Were you a significant person to them by virtue of the assumed role/s, or were you peripheral? 9. Data Collections Methods A. Appropriate Methods are plausibly related to the research questions. Beware of irrelevant or unneeded data (too many rabbit trails followed). Careful planning needed and regular follow up reviews. What constraints on the kinds of data collected were there? Also characteristics of participants can affect methods chosen. B. Clear Parsimony needed. Describe amount of time taken for each phase. Low inference descriptors needed. Audit trail each decision, change, and other aspects can be traced. C. Comprehensive Be exhaustive as possible without exhausting participants or researcher. Can over saturate redundancy because you are there too long. Sufficient time must be allotted for data collection at least a few weeks. (3 to 36 months in literature surveyed). Deal with all questions proposed. D. Credible Describe the initial planning and ongoing review of plans so it can be judged for appropriateness. Must be dense and represent what was researched fairly. Direct quotes needed from participants and researchers notes. Use the language participants use. Higher validity with interactive methods if methods used correctly. Naturalistic setting also adds to validity, as does the use of categories emic to or emergent from participants. Time facilitates likelihood of matching participant categories to researcher categories. Multiple sources of data needed to verify and refineor eliminate preliminary findings. Readers will judge if alternative sources were availableRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

(i.e. what could have been asked or examined, but wasnt?) Were techniques of data collection related to results justified conclusions, not just speculation or superficial data. E. Significant Data hard to replicate lower reliability (as with projective instruments) and concepts of participants are more tied to time, place, and those studied. Reliability of confirmation surveys with standardized items can be controlled to greater extent than can reliability of observations using field note data. (One can make a case for very high reliability in transcriptions of interviews). Validity of interviews and observations depends on researchers role, specific techniques used, and degree of clarity of constructs. 10. Data Analysis/Interpretation A. Appropriate Name the formal method/s of data analysis and changes/adaptations made. Informal methods should be described in detail (preferably illustrated). Identify the basis for interpretations. B. Clear Almost seems mystical how data is transformed because of complexity of data, as well as shifts of data and kinds of analysis throughout study. Needs to document how initial codes pushed researcher to more elaborate codes and linkages and finally to formal data analysis. (Use theoretical notes for this documentation). Describe member check and how the results of the check elaborated or restricted conclusions. C. Comprehensive Need to describe: 1) abstraction process 2) units of analysis 3) codes used 4) methods of quantification, if any 5) corroborating evidence 6) how synthesized in the results 7) limitations and advantages to a given formal method 8) usage of additional formal methods with given data and triangulation of formal methods of analysis (and potential future formal methods and triangulation). D. Credible Often a weak area in qualitative research either cavalier or not described sufficiently. Why did the researcher choose one analytic method and not an alternative? Need to trace ongoing shifts in analysis and describe in the report and justify those changes, so reader can judge analysis approach chosen. Distinguish analysis using participant categories from analysis using researcher categories including those derived from existing theories so they can be evaluated for appropriateness. E. Significant Discuss how key constructs evolved over time of study, so they can be assessed for adequate relationship to initial questions as well as the collected data. While unwarranted conjectures are possible using qualitative analytic procedure, it is also possible that analysis will be too narrow and simplistic, interfering with the development of theory that adequately explains. Describe derivation of meanings of emergent terms and constructs. 11. Applications/RecommendationsPage42




Spring 2011

A. Appropriate What is intended audience? Relates to purpose and goals. What policies need revising? May be parallels discovered between unrelated groups e.g. comparisons made across cultures. B Clear Careful specification of implications. Implications for the research setting, and potential applications for other settings clearly state limitations of conclusions and applications. Application is a shared responsibility with the reader. C. Comprehensive If initial framing is more comprehensive, it will apply more broadly. Multiple applications of results possible, not just one situation or level (at least implied, not absolute). (This could be a unique case.) D. Credible Degree of tentativeness/confidence is appropriate to purpose, goals, and breadth of study. Convincing arguments for conclusions, implications, and applications. E. Significant May not need to specify immediate changes needed if the outcome of study is new or revised theory, or information about a group or context that is rare. The ultimate objective of a study is to document what occurred and preserve information. Tentative conclusions can generate as much research in future as very confident conclusions (perhaps even generate more follow-up research). Move beyond concepts that initially informed research what does the research suggest about additional areas needing research and potential areas have been uncovered that need new constructs and categories that subsequent research can address? What issues do we confirmatory follow-up? 12. Presentation Format and Sequence A. Appropriate Wide variations: 1) chronological 2) topical 3) descriptions of problem-solving. Many qualitative methodologists prefer the review of the literature that is embedded throughout, rather than a separate chapter (requires a high level of integrative thinking). B. Clear Clarity of description is a strong indication of validity. Sometimes difficult to separate data and interpretations; theoretical notes help in this area. C. Comprehensive Represent multiple perspectives via dense narrative. Several criteria (McCutcheon): 1) logical 2) orderly 3) feasible 4) alternative possibilities are noted and reasons for discarding them are described in detail 5) sufficient data to merit interpretations made quotes allow reader to assess this 6) results are consistent with what has been found in similar groups and, if not, there is a plausible explanation for discrepancies 7) results contribute to theory, general understanding, or current controversies and issues. Results apply to multiple theories and perspectives D. Credible direct quotations of participants and field notes convincing, but addresses major questions you began with. Include findings that areRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

discrepant and deviant cases (exceptions) helps separate data and interpretations. Time oriented or other consequential influences must be demonstrated and justified. Note results that were not anticipated (surprises) and show how those were integrated into results and why they are significant. E. Significant Need to describe different levels of confidence for various data and interpretations. Integrate results broadly, relate significance to original goals and purposes. 4. a. Distinguish between Doubles sampling and multiphase sampling. Answer: Multiphase sampling Multiphase sampling plays a vital role in forest surveys with its application extending over continuous forest inventory to estimation of growing stock through remote sensing. The essential idea in multiphase sampling is that of conducting separate sampling investigations in a sequence of phases starting with a large number of sampling units in the first phase and taking only a subset of the sampling units in each successive phase for measurement so as to estimate the parameter of interest with added precision at relatively lower cost utilizing the relation between characters measured at different phases. In order to keep things simple, further discussion in this section is restricted to only two phase sampling. A sampling technique which involves sampling in just two phases (occasions) is known as two phase sampling. This technique is also referred to as double sampling. Double sampling is particularly useful in situations in which the enumeration of the character under study (main character) involves much cost or labour whereas an auxiliary character correlated with the main character can be easily observed. Thus it can be convenient and economical to take a large sample for the auxiliary variable in the first phase leading to precise estimates of the population total or mean of the auxiliary variable. In the second phase, a small sample, usually a sub-sample, is taken wherein both the main character and the auxiliary character may be observed and using the first phase sampling as supplementary information and utilising the ratio or regression estimates, precise estimates for the main character can be obtained. It may be also possible to increase the precision of the final estimates by including instead of one, a number of correlated auxiliary variables. For example, in estimating the volume of a stand, we may use diameter or girth of trees and height as auxiliary variables. In estimating the yield of tannin materials from bark of trees certain physical measurements like the girth, height, number of shoots, etc., can be taken as auxiliary variables.


Page42



Spring 2011

Like many other kinds of sampling, double sampling is a technique useful in reducing the cost of enumerations and increasing the accuracy of the estimates. This technique can be used very advantageously in resurveys of forest areas. After an initial survey of an area, the estimate of growing stock at a subsequent, period, say 10 or 15 years later, and estimate of the change in growing stock can be obtained based on a relatively small sample using double sampling technique. Another use of double sampling is in stratification of a population. A first stage sample for an auxiliary character may be used to sub-divide the population into strata in which the second (main) character varies little so that if the two characters are correlated, precise estimates of the main character can be obtained from a rather small second sample for the main character. It may be mentioned that it is possible to couple with double sampling other methods of sampling like multistage sampling (sub-sampling)known for economy and enhancing the accuracy of the estimates. For example, in estimating the availability of grasses, canes, reeds, etc., a two-stage sample of compartments (or ranges) and topographical sections (or blocks) may be taken for the estimation of the effective area under the species and a subsample of topographical sections, blocks or plots may be taken for estimating the yield. Selection of sampling units In the simplest case of two phase sampling, simple random sampling can be employed in both the phases. In the first step, the population is divided into well identified sampling units and a sample is drawn as in the case of simple random sampling. The character x is measured on all the sampling units thus selected. Next, a sub-sample is taken from the already selected units using the method of simple random sampling and the main character of interest (y) is measured on the units selected. The whole procedure can also be executed in combination with other modes of sampling such as stratification or multistage sampling schemes. Parameter estimation Regression estimate in double sampling : Let us assume that a random sample of n units has been taken from the population of N units at the initial phase to observe the auxiliary variable x and that a random sub-sample of size m is taken where both x and the main character y are observed.


Page42



Spring 2011

Let

= mean of x in the first large sample =

(5.35)

= mean of x in the second sample =

(5.36)

= mean of y in the second sample =

(5.37)

We may take as an estimate of the population mean . However utilising the previous information on the units sampled, a more precise estimate of can be obtained by calculating the regression of y on x and using the first sample as providing supplementary information. The regression estimate of is given by (5.38) where the suffix (drg) denotes the regression estimate using double sampling and b is the regression coefficient of y on x computed from the units included in the second sample of size m. Thus

(5.39) The variance of the estimate is approximately given by,

(5.40)

where

(5.41)

(ii) Ratio estimate in double samplingRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42

(5.42)



Spring 2011

Ratio estimate is used mainly when the intercept in the regression line between y and x is understood to be zero. The ratio estimate of the population mean is given by

(5.43) where denotes the ratio estimate using double sampling. The variance of the estimate is approximately given by

(5.44) where

(5.45)

(5.46)

(5.47)

(5.48) An example of analysis of data from double sampling using regression and ratio estimate is given below. Table 5.5 gives data on the number of clumps and the corresponding weight of grass in plots of size 0.025 ha, obtained from a random sub-sample of 40 plots taken from a preliminary sample of 200 plots where only the number of clumps was counted. Table 5.5. Data on the number of clumps and weight of grass in plots selected through a two phase sampling procedure. < TD WIDTH="16%" VALIGN="TOP">Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

60 Serial number Number of clumps (x) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 459 388 314 35 120 136 367 568 764 607 886 507 417 389 258 214 674 395 260 281 68 65 44 15 34 30 54 69 72 65 95 32 72 60 50 30 70 57 45 36 21 22 23 24 25 26 27 28 29 30 31 648 33 34 35 36 37 38 39 40 Weight in kg (y) Serial number Number of clumps (x) 245 185 59 114 354 476 818 709 526 329 169 74 446 86 191 342 227 462 592 402 61 32 35 40 40 66Page42

Weight in kg (y) 25 50 16 22 59 63 92 64 72 46 33

68 55




Spring 2011

Here, n = 200, m = 40. The mean number of clumps per plot as observed from the preliminary sample of 200 plots was = 374.4.

,

,

,

,

=

= Mean number of clumps per plot from the sub-sample of 40 plots is

Mean weight of clumps per plot from the sub-sample of 40 plots

The regression estimate of the mean weight of grass in kg per plot is obtained by using Equation (5.38) where the regression coefficient b obtained using Equation (5.39) isPage42 Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

b



Spring 2011

Hence, = 52.6 - 0.89 = 51.7 kg /plot

= 82.9

=376.297 The variance of the estimate is approximately given by Equation (5.40)

(5.40) = 3.5395 The ratio estimate of the mean weight of grass in kg per plot is given by Equation (5.43)

= 51.085

= 3827.708

= 46175.436Page42 Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009



Spring 2011

= 0.1364 The variance of the estimate is approximately given by Equation (5.44) is

= 5.67 b. What is replicated or interpenetrating sampling? [ 5 marks] There are k interviewers and they are each different in their manner of interviewing and hence may obtain slightly different responses. To make notation simple, we assume that each interviewer conducts the same number of interviews. Let n denote the total sample size and n = k* m. There are k subsamples and each interviewer will be assigned m subjects. Objective: to use simple random sampling to estimate Interviewer 1 - y11, y12, y13, ... , y1m Interviewer 2 - y21, y22, y23, ... , y2m Interviewer 3 - y31, y32, y33, ... , y3m Interviewer k - yk1, yk2, yk3, ... , ykm The average for the ith interviewer is denoted as:

The grand average is denoted as:

The grand average

is unbiased for and the estimated variance of

is:


Page42

The technique of interpenetreting the subsample gives an estimate of the variance of ybar that accounts for interviewer biases. In practice, the estimated variance given in the above formula is usually larger than the standard estimate of the variance by using simple random sampling.



Spring 2011

Example for interpenetreting subsample A researcher has 10 research assistants, each with his/her own equipment that they use to measure the time (in seconds) it take for people to respond to a command. A simple random sample of 80 people are taken. Since the researcher believes the assistants will produce slightly biased measurements, he decides to randomly divide the 80 people into 10 subsamples of 8 persons each. Each assistant is then assigned to one subsample. The measurements are given in the following table. assistants time it takes to respond 73 65 54 64 76 71 65 43 52 65 62 73 52 63 69 63 77 58 59 79 75 67 48 59 83 75 69 62 63 69 71 78 56 71 85 68 74 42 69 72 68 71 51 78 66 72 82 61 72 68 55 67 62 67 74 69 73 53 64 71 65 59 57 76 73 60 67 61 58 67

1 2 3 4 5 6 7 8 9 10

52 62 43 73 88 55 72 55 62 77

Minitab output:


Page42



Spring 2011

5. a. How is secondary data useful to researcher? Answer: Secondary data is information gathered for purposes other than the completion of a research project. A variety of secondary information sources is available to the researcher gathering data on an industry, potential product applications and the market place. Secondary data is also used to gain initial insight into the research problem. Secondary data is classified in terms of its source either internal or external. Internal, or in-house data, is secondary information acquired within the organization where research is being carried out. External secondary data is obtained from outside sources. The two major advantages of using secondary data in market research are time and cost savings.

The secondary research process can be completed rapidly generally in 2 to 3 week. Substantial useful secondary data can be collected in a matter of days by a skillful analyst. When secondary data is available, the researcher need only locate the source of the data and extract the required information. Secondary research is generally less expensive than primary research. The bulk of secondary research data gathering does not require the use of expensive, specialized, highly trained personnel. Secondary research expenses are incurred by the originator of the information.Page42

There are also a number of disadvantages of using secondary data. These include:Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009



Spring 2011

Secondary information pertinent to the research topic is either not available, or is only available in insufficient quantities. Some secondary data may be of questionable accuracy and reliability. Even government publications and trade magazines statistics can be misleading. For example, many trade magazines survey their members to derive estimates of market size, market growth rate and purchasing patterns, then average out these results. Often these statistics are merely average opinions based on less than 10% of their members. Data may be in a different format or units than is required by the researcher. Much secondary data is several years old and may not reflect the current market conditions. Trade journals and other publications often accept articles six months before appear in print. The research may have been done months or even years earlier.

As a general rule, a thorough research of the secondary data should be undertaken prior to conducting primary research. The secondary information will provide a useful background and will identify key questions and issues that will need to be addressed by the primary research. Internal data sources Internal secondary data is usually an inexpensive information source for the company conducting research, and is the place to start for existing operations. Internally generated sales and pricing data can be used as a research source. The use of this data is to define the competitive position of the firm, an evaluation of a marketing strategy the firm has used in the past, or gaining a better understanding of the companys best customers. There are three main sources of internal data. These are: 1. Sales and marketing reports. These can include such things as:


Page42

Type of product/service purchased Type of end-user/industry segment Method of payment Product or product line Sales territory Salesperson Date of purchase Amount of purchase Price



Spring 2011

Application by product Location of end-user

2. Accounting and financial records. These are often an overlooked source of internal secondary information and can be invaluable in the identification, clarification and prediction of certain problems. Accounting records can be used to evaluate the success of various marketing strategies such as revenues from a direct marketing campaign. There are several problems in using accounting and financial data. One is the timeliness factor it is often several months before accounting statements are available. Another is the structure of the records themselves. Most firms do not adequately setup their accounts to provide the types of answers to research questions that they need. For example, the account systems should capture project/product costs in order to identify the companys most profitable (and least profitable) activities. Companies should also consider establishing performance indicators based on financial data. These can be industry standards or unique ones designed to measure key performance factors that will enable the firm to monitor its performance over a period of time and compare it to its competitors. Some example may be sales per employee, sales per square foot, expenses per employee (salesperson, etc.). 3. Miscellaneous reports. These can include such things as inventory reports, service calls, number (qualifications and compensation) of staff, production and R&D reports. Also the companys business plan and customer calls (complaints) log can be useful sources of information. External data sources There is a wealth of statistical and research data available today. Some sources are:


Page42

Federal government Provincial/state governments Statistics agencies Trade associations General business publications Magazine and newspaper articles



Spring 2011

Annual reports Academic publications Library sources Computerized bibliographies Syndicated services.

A good place to start your search is the local city, college or university library. Most reference librarians are very knowledgeable about what data is available, or where to look to find it. Also contact government libraries and departments for research reports/publications they may have done. b. What are the criteria used for evaluation of secondary data? [ 5 marks] Answer: Research using Secondary Data Sources Secondary data is information gathered for purposes other than the completion of a research project. A variety of secondary information sources is available to the researcher gathering data on an industry, potential product applications and the market place. Secondary data is also used to gain initial insight into the research problem. Secondary data is classified in terms of its source either internal or external. Internal, or in-house data, is secondary information acquired within the organization where research is being carried out. External secondary data is obtained from outside sources. The two major advantages of using secondary data in market research are time and cost savings.


Page42

The secondary research process can be completed rapidly generally in 2 to 3 week. Substantial useful secondary data can be collected in a matter of days by a skillful analyst. When secondary data is available, the researcher need only locate the source of the data and extract the required information. Secondary research is generally less expensive than primary research. The bulk of secondary research data gathering does not require the use of expensive, specialized, highly trained personnel. Secondary research expenses are incurred by the originator of the information.



Spring 2011

There are also a number of disadvantages of using secondary data. These include:

Secondary information pertinent to the research topic is either not available, or is only available in insufficient quantities. Some secondary data may be of questionable accuracy and reliability. Even government publications and trade magazines statistics can be misleading. For example, many trade magazines survey their members to derive estimates of market size, market growth rate and purchasing patterns, then average out these results. Often these statistics are merely average opinions based on less than 10% of their members. Data may be in a different format or units than is required by the researcher. Much secondary data is several years old and may not reflect the current market conditions. Trade journals and other publications often accept articles six months before appear in print. The research may have been done months or even years earlier.

As a general rule, a thorough research of the secondary data should be undertaken prior to conducting primary research. The secondary information will provide a useful background and will identify key questions and issues that will need to be addressed by the primary research. Internal data sources Internal secondary data is usually an inexpensive information source for the company conducting research, and is the place to start for existing operations. Internally generated sales and pricing data can be used as a research source. The use of this data is to define the competitive position of the firm, an evaluation of a marketing strategy the firm has used in the past, or gaining a better understanding of the companys best customers. There are three main sources of internal data. These are: 1. Sales and marketing reports. These can include such things as:


Page42

Type of product/service purchased Type of end-user/industry segment Method of payment Product or product line Sales territory Salesperson Date of purchase Amount of purchase



Spring 2011

Price Application by product Location of end-user

2. Accounting and financial records. These are often an overlooked source of internal secondary information and can be invaluable in the identification, clarification and prediction of certain problems. Accounting records can be used to evaluate the success of various marketing strategies such as revenues from a direct marketing campaign. There are several problems in using accounting and financial data. One is the timeliness factor it is often several months before accounting statements are available. Another is the structure of the records themselves. Most firms do not adequately setup their accounts to provide the types of answers to research questions that they need. For example, the account systems should capture project/product costs in order to identify the companys most profitable (and least profitable) activities. Companies should also consider establishing performance indicators based on financial data. These can be industry standards or unique ones designed to measure key performance factors that will enable the firm to monitor its performance over a period of time and compare it to its competitors. Some example may be sales per employee, sales per square foot, expenses per employee (salesperson, etc.). 3. Miscellaneous reports. These can include such things as inventory reports, service calls, number (qualifications and compensation) of staff, production and R&D reports. Also the companys business plan and customer calls (complaints) log can be useful sources of information. External data sources There is a wealth of statistical and research data available today. Some sources are:


Page42

Federal government Provincial/state governments Statistics agencies Trade associations General business publications Magazine and newspaper articles Annual reports Academic publications Library sources



Spring 2011

Computerized bibliographies Syndicated services.

A good place to start your search is the local city, college or university library. Most reference librarians are very knowledgeable about what data is available, or where to look to find it. Also contact government libraries and departments for research reports/publications they may have done. 6. What are the differences between observation and interviewing as methods of data collection? Give two specific examples of situations where either observation or interviewing would be more appropriate. Answer: Observation vs. interviewing as Methods of Data Collection: Collection of data is the most crucial part of any research project as the success or failure of the project is dependent upon the accuracy of the data. Use of wrong methods of data collection or any inaccuracy in collecting data can have significant impact on the results of a study and may lead to results that are not valid. There are many techniques of data collection along a continuum and observation and interviewing are two of the popular methods on this continuum that has quantitative methods at one end while qualitative methods at the other end. Though there are many similarities in these two methods and they serve the same basic purpose, there are differences that will be highlighted in this article.

Observation: Observation, as the name implies refers to situations where participants are observed from a safe distance and their activities are recorded minutely. It is a time consuming method of data collection as you may not get the desired conditions that are required for your research and you may have to wait till participants are in the situation you want them to be in. Classic examples of observation are wild life researchers who wait for the animals of birds to be in a natural habitat and behave in situations that they want to focus upon. As a method of data collection, observation has limitations but produces accurate results as participants are unaware of being closely inspected and behave naturally.


Page42



Spring 2011

Interviewing: Interviewing is another great technique of data collection and it involves asking questions to get direct answers. These interviews could be either one to one, in the form of questionnaires, or the more recent form of asking opinions through internet. However, there are limitations of interviewing as participants may not come up with true or honest answers depending upon privacy level of the questions. Though they try to be honest, there is an element of lie in answers that can distort results of the project.

Though both observation and interviewing are great techniques of data collection, they have their own strengths and weaknesses. It is important to keep in mind which one of the two will produce desired results before finalizing.

Observation vs. interviewing: Observation Observation requires precise analysis by the researcher and often produces most accurate results although it is very time consuming. Interviewing Interviewing is easier but suffers from the fact that participants may not come up with honest replies.

Interview format: Interviews take many different forms. It is a good idea to ask the organisation in advance what format the interview will take.Page42

Competency/criteria based interviews:




Spring 2011

These are structured to reflect the competencies or qualities that an employer is seeking for a particular job, which will usually have been detailed in the job specification or advert. The interviewer is looking for evidence of your skills and may ask such things as: Give an example of a time you worked as part of a team to achieve a common goal.

Technical interviews: If you have applied for a job or course that requires technical knowledge, it is likely that you will be asked technical questions or has a separate technical interview. Questions may focus on your final year project or on real or hypothetical technical problems. You should be prepared to prove yourself, but also to admit to what you do not know and stress that you are keen to learn. Do not worry if you do not know the exact answer - interviewers are interested in your thought process and logic.

Academic interviews: These are used for further study or research positions. Questions are likely to centre on your academic history to date.

Structured interviews: The interviewer has a set list of questions, and asks all the candidates the same questions.

Formal/informal interviews:

Some interviews may be very formal, while others will feel more like an informal chat about you and your interests. Be aware that you are still being assessed, however informal the discussion may seem.

Portfolio based interviews: If the role is within the arts, media or communications industries, you may be asked to bring a portfolio of your work to the interview, and toRanjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

have an in-depth discussion about the pieces you have chosen to include.

Senior/case study interviews:

These ranges from straightforward scenario questions (e.g. What would you do in a situation where to the detailed analysis of a hypothetical business problem. You will be evaluated on your analysis of the problem, how you identify the key issues, how you pursue a particular line of thinking and whether you can develop and present an appropriate framework for organising your thoughts.

Specific types of interview

The Screening Interview: Companies use screening tools to ensure that candidates meet minimum qualification requirements. Computer programs are among the tools used to weed out unqualified candidates. (This is why you need a digital resume that is screening-friendly. See our resume centre for help.) Sometimes human professionals are the gatekeepers. Screening interviewers often have honed skills to determine whether there is anything that might disqualify you for the position. Remember they do not need to know whether you are the best fit for the position, only whether you are not a match. For this reason, screeners tend to dig for dirt. Screeners will hone in on gaps in your employment history or pieces of information that look inconsistent. They also will want to know from the outset whether you will be too expensive for the company.

Some tips for maintaining confidence during screening interviews:Page42 Highlight your accomplishments and qualifications.




Spring 2011

Get into the straightforward groove. Personality is not as important to

the screener as verifying your qualifications. Answer questions directly and succinctly. Save your winning personality for the person making hiring decisions! Be tactful about addressing income requirements. Give a range, and try

to avoid giving specifics by replying, "I would be willing to consider your best offer." If the interview is conducted by phone, it is helpful to have note cards

with your vital information sitting next to the phone. That way, whether the interviewer catches you sleeping or vacuuming the floor, you will be able to switch gears quickly The Informational Interview: On the opposite end of the stress spectrum from screening interviews is the informational interview. A meeting that you initiate, the informational interview is underutilized by job-seekers who might otherwise consider themselves savvy to the merits of networking. Jobseekers ostensibly secure informational meetings in order to seek the advice of someone in their current or desired field as well as to gain further references to people who can lend insight. Employers that like to stay apprised of available talent even when they do not have current job openings, are often open to informational interviews, especially if they like to share their knowledge, feel flattered by your interest, or esteem the mutual friend that connected you to them. During an informational interview, the jobseeker and employer exchange information and get to know one another better without reference to specific job opening.

This takes off some of the performance pressure, but be intentional nonetheless:

Come prepared with thoughtful questions about the field and the company.Page42 Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009



Spring 2011

Gain references to other people and make sure that the interviewer would be comfortable if you contact other people and use his or her name. Give the interviewer your card, contact information and resume. Write a thank you note to the interviewer. The Directive Style: In this style of interview, the interviewer has a clear agenda that he or she follows unflinchingly. Sometimes companies use this rigid format to ensure parity between interviews; when interviewers ask each candidate the same series of questions, they can more readily compare the results. Directive interviewers rely upon their own questions and methods to tease from you what they wish to know. You might feel like you are being steam-rolled, or you might find the conversation develops naturally. Their style does not necessarily mean that they have dominance issues, although you should keep an eye open for these if the interviewer would be your supervisor. Either way, remember: Flex with the interviewer, following his or her lead. Do not relinquish complete control of the interview. If the interviewer does not ask you for information that you think is important to proving your superiority as a candidate, politely interject it.

The Meandering Style: This interview type, usually used by inexperienced interviewers, relies on you to lead the discussion. It might begin with a statement like "tell me about yourself," which you can use to your advantage. The interviewer might ask you another broad, open-ended question before falling into silence. This interview style allows you tactfully to guide the discussion in a way that best serves you. The following strategies, which are helpful for any interview, are particularly important when interviewers use a non-directive approach:

Come to the interview prepared with highlights and anecdotes of your

skills, qualities and experiences. Do not rely on the interviewer to spark your memory-jot down some notes that you can reference throughout the interview.Ranjit Singh Kumar, MBA Part-3, Roll. No. 511139149, LC \ Code:- Dwarka\2009

Page42



Spring 2011

Remain alert to the interviewer. Even if you feel like you can take the

driver's seat and go in any direction you wish, remain respectful of the interviewer's role. If he or she becomes more directive during the interview, adjust. Ask well-placed questions. Although the open format allows you

significantly to shape the interview, running with your own agenda and dominating the conversation means that you run the risk of missing important information about the company and its needs.


Page42

MB0050 Research Methodology Set-1

Documents

capture project product costs

ranjit singh kumar

mb0050 assignment set

highly trained personnel

market growth rate

direct marketing campaign

trade magazines statistics

user industry segment method