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This is the difference between business research and academic research. Research in business subjects is different from business research. Research methodology : Research methodology A method is a sequential process whereas; methodology is the science of method. Therefore, research methodology is the scientific way of carrying out a method for doing research. In business research we may include the Case Study method. Rationale for research : Rationale for research Why do we do research? For finding out the truth. Various questions: Do sales campaigns improve sales? Does FDI lead to economic development? Are Indian stock markets volatile? Does training improve performance? Facts and truth. : Facts and truth. A fact is a thing known to have occurred; to exist or a datum of experience often followed by an explanation. In a way, it is a piece of evidence. (You do not need research for facts) A truth is more fundamental. It is the quality or state of being. Where does this truth belong or reside? In the population? Classical Inference : Classical Inference Population Measurement Econometrics Characteristics Behavior Patterns Choices (Responses) Events Quantities Sample provides imprecise inference about the entire population – Thus, we need sampling theory, asymptotics and hypothesis testing. Studying a trend : Studying a trend Fact vs. truth : Fact vs. truth Median price was Truth: Growth rate of Housing Prices in USA : Truth: Growth rate of Housing Prices in USA Hypothesis : Hypothesis This brings us to hypothesis, which is a proposition made as a basis of reasoning without the assumption of its truth. Since it is a supposition that can only be the starting point of an investigation based on known facts, a hypothesis has to be validated empirically. Every hypothesis can thus be proved or disproved. Hence when a hypothesis is stated, the null (or opposite) hypothesis must be stated alongside and their notations conventionally being H. # 1 and H # 0. Once a hypothesis has been tested and proved it tells us the truth. Overall hypothesis : Overall hypothesis Individual hypothesis : Individual hypothesis Method – Five Ds (steps) : Method – Five Ds (steps) The first D stands for definition. Here the position of the investigator, the subject to be studied, the ambit of inquiry, the purpose of the study and the limitations of the investigator are stated. This is needed in the interest of clarity. For instance, examining stock market anomalies in NSE in post liberalization. The third D stands for design and here the investigator makes the research design i.e. how the study will proceed. This is the stage from where the pilot study is launched within a restricted physical domain and when the environment is controlled. For instance, we used UP (a non-VAT) state to study the influence of VAT on business enterprise. This is setting-up a control. Method contd… : Method contd… The third stage is called development. Based on the results of the pilot study the hypothesis are reformulated and the direction of the inquiry is finalized. Here the investigator develops the study itself and collection of facts begins only to be followed by their systematic documentation. This is the stage for validation. (Face and Content validation). The fourth D stands for diagnosis. Just as a doctor uses the thermometer, the stethoscope and the blood sample test to determine what kind of fever you have so too the investigator uses a set of methods to arrive at a finding. This stage includes empirical analysis and modeling. Cross-referencing of data is very important and so is correlation. For instance: A beta-coefficient may be negative either because Y is falling and X is rising or the other way round. Delivery : Delivery The fifth stage is called delivery when the study analyses the facts and arrives at a conclusion, which has some social, political, economic or technological significance. Policy recommendation are laid out here. Further research questions are also raised. The delivery stage is also called actualizing the findings or implementing the intervention. Here the presentation is a very important criterion. It is also important to reconcile to basic assumptions and show limitations. Assumptions : Assumptions Role of the assumptions. Sharpness of inferences. Parameterizing the model. For reaching the truth we must generalize. For this we must parameterize. Limitations : Limitations We must know to what extent the results can be relied upon. To what extent they can be generalized. Here case study approach has a very severe limitation. It cannot be generalized. It only tells us the how not the why of anything. To this extent it is not scientific. Four axioms : Four axioms There are four axioms borrowed from the Economic Science and increasingly used in both Management as well as Social Science Research. These are:i. The fallacy of composition . What is true of a part is not necessarily true of the whole.ii. The fallacy of accident. What is true of the whole is not necessarily true of the part.iii. The acceptance of post hoc sed non-proctor hoc. Any occurrence after an event is not necessarily because of the event.iv. Understanding that correlation does not amount to causation. Because two variables are statistically correlated, it does not follow that one causes the other or is caused by it. Fallacies and solutions : Fallacies and solutions i. The fallacy of composition . Case study approach - limitations. Results can apply only to that firm. Do not generalize. ii. The fallacy of accident. Production function tells the average not of each firm. We have Dummy Variables for knowing about each firm or each period. iii. The acceptance of post hoc sed non-proctor hoc. E.g, Rice production in Punjab (2009 drought)Y (production) follows X (humidity) but X is not the cause; But Z causes X and Z causes Y. It appears that X causes Y. This leads to single equation bias. Low rainfall (Z) caused low humidity (X). Low rainfall (Z) also caused low pests (and high water management)=>high production (Y). iv. Understanding that correlation does not amount to causation. Granger causality (dynamic) or regression. Qualitative research : Qualitative research Qualitative research design is a method of experimentation used extensively by scientists and researchers studying human behavior and habits. It is also very useful for product designers who want to make a product that will sell. This helps in identifying the questions, issues, dimensions, etc. Qualitative research... example : Qualitative research... example For example, a designer generating some ideas for a new product might want to study people’s habits and preferences, to make sure that the product is commercially viable. Quantitative research is then used to assess whether the completed design is popular or not. Qualitative research - a precursor : Qualitative research - a precursor Qualitative research is often regarded as a precursor to quantitative research, in that it is often used to generate possible leads and ideas which can be used to formulate a realistic and testable hypothesis. These hypotheses can then be comprehensively tested and mathematically analyzed, with standard quantitative research methods. Qualitative methods : Qualitative methods For these reasons, these qualitative methods are often closely allied with survey design techniques and individual case studies, as a way to reinforce and evaluate findings over a broader scale. Quantitative research : Quantitative research Quantitative research design is the standard experimental method of most scientific disciplines. These experiments are sometimes referred to as true science, and use traditional mathematical and statistical means to measure results conclusively. They are most commonly used by physical scientists, although social sciences, education and economics have been known to use this type of research. It is the opposite of qualitative research. Quantitative experiments : Quantitative experiments Quantitative experiments all use a standard format, with a few minor inter-disciplinary differences, of generating a hypothesis to be proved or disproved. These hypotheses must be measureable and must be capable of being proved or disproved by mathematical and statistical means. These hypotheses are the basis around which the whole experiment is designed. Research Problem : Research Problem Research forms a cycle. It starts with a problem and ends with a solution to the problem. The problem statement is therefore the axis which the whole research revolves around, because it explains in short the aim of the research. Each thesis creates an anti-thesis and then a synthesis. Therefore, research is incremental, unlike discovery or invention which is radical, breakthrough, sudden, etc. WHAT IS A RESEARCH PROBLEM? : WHAT IS A RESEARCH PROBLEM? A research problem is the situation that causes the researcher to feel apprehensive, confused and ill at ease. It is the demarcation of a problem area within a certain context involving the WHO or WHAT, the WHERE, the WHEN and the WHY of the problem situation. Who are mutual fund investors? (Finance) What is the relationship between volatility and FII flows? (IB) Where do we expect to find supply gaps in Retail business? (Marketing) When does attrition take place? (OBD) Why do reported profits differ from operating profits? (Accounting) IDENTIFICATION OF THE PROBLEM : IDENTIFICATION OF THE PROBLEM The prospective researcher should think on what caused the need to do the research (problem identification). The question that he/she should ask is: Are there questions about this problem to which answers have not been found up to the present? Research originates from a need that arises. A clear distinction between the PROBLEM and the PURPOSE should be made. The problem is the aspect the researcher worries about, thinks about, and wants to find a solution for. What is the relationship between dividend payout and value of the firm? The purpose is to solve the problem, i.e. find answers to the questions. To examine the relationship…. If there is no clear problem formulation, the purpose and methods are meaningless. SUBPROBLEM(S) : SUBPROBLEM(S) Sub problems are problems related to the main problem identified. Sub problems flow from the main problem and make up the main problem. It is the means to reach the set goal in a manageable way and contribute to solving the problem. For instance: Width of share market (main problem) during a recession or during a boom (sub-problem). STATEMENT OF THE PROBLEM : STATEMENT OF THE PROBLEM The statement of the problem involves the demarcation and formulation of the problem, i.e. the WHO/WHAT, WHERE, WHEN, WHY. Who are mutual fund investors? (Finance); Demarcation (In which market?) What is the relationship between volatility(σ) and FII flows (Y)? (IB) Formulation: Volatility(σ) causes FII flows (Y). Where do we expect to find supply gaps in Retail business? (Marketing) Is stock out more in rich areas? Demarcation. When does attrition take place? (OBD). During boom: for better jobs. OR Recession: for further studies. Why do reported profits differ from operating profits? (Accounting) Due to accounting policies? OR Due to financial position? It usually includes the statement of the hypothesis. DESIGN OF EXPERIMENTS : DESIGN OF EXPERIMENTS I. Definition: An event is one or more possible outcomes of doing a thing (e.g., getting a head in three tosses of a coin). An experiment is an activity that produces such an event. Pulse Polio campaign. It produces positive and negative results. II. Planning: It is the way in which the experiment is designed. Location, size, method, funds, etc. III. Phases of Experimental Design: : III. Phases of Experimental Design: Stating the hypothesis or claim that is made. E.g., “The Polio Eradication Program of the Government is success.” Setting-up the objective: The objective is set-up: To evaluate the Polio Eradication Program of the Government. Choosing response variables: What is to be measured? Number of children who have taken Polio drops – absolute. Proportion of children who have taken Polio drops in each family - relative. Number of new cases of Polio registered - incremental. Selecting sample size. : Selecting sample size. As sample size increases results become unbiased. However, if the size is too large the cost of sample data collection and analysis becomes prohibitive. Greater the accuracy required greater size requirement. E => Allowable error. S = stdev. Z = z-score; N= sample size.  N = (Z2S2)/E2 Keeping other things constant : Keeping other things constant Suppose during the same period some private agency (like Red Cross) is also carrying out a campaign against Polio our results would be biased. Fixing the control factor: Data must be collected from places where the government Polio campaign is not on. This will give a basis (or benchmark) for comparison during the next step. Data Collection : Data Collection Data is of three types - Primary, Secondary and Tertiary data. Primary is first hand data collected from a source. That is unpublished and obtained from a field survey or a primary survey. It is in raw form. For instance, a market survey, done before launching a product, yields primary data. Secondary data : Secondary data Secondary data is published and is tabulated. It could be either Census data or Sample data. Examples of Census are Population Census of India. Also Annual Survey of Industries, published by Central Statistical Organization. The ASI consists of Census Sector and Factory Sector. Tertiary data : Tertiary data Tertiary data is somewhat processed data based on other secondary sources. The data published by United Nations is obtained by collecting it from different governments. This could be called tertiary. However, ‘tertiary’ is rarely used as a nomenclature. Tertiary data sources : Tertiary data sources Bank for International Settlements European Central Bank Eurostat International Monetary Fund Organization for Economic Cooperation and Development The World Bank UNCTAD WTO World PENN Tables Bank for International Settlements European Central Bank Eurostat International Monetary Fund Organization for Economic Cooperation and Development The World Bank UNCTAD WTO World PENN Tables Methods of Sample Survey : Methods of Sample Survey Personal interview: This limits the possible size and increases the time taken if the survey is carried out personally by the experimenter. On the other hand, if investigators are employed they need training and yet responses may be inaccurately recorded. Responses are better, quantitatively and qualitative, if the survey responses collected personally. Exit Poll: This method may have a large investigator bias. It cannot be used for all problems. (e.g., testing a new flavor of an ice cream). Mailing (including e-mail): The process is faster and less expensive. But the Non-response rate is very high. The sample size can be much higher. The reliability of responses is less that in personal interviews Mixed method: Some of the above methods can be combined. Data Analysis : Data Analysis The data is processed to arrive at a summary results and measures that enable comparisons. Here the sampling method and the control factors are important. Some names in Qualitative Research : Some names in Qualitative Research Pranee Sayre Lindolf Silverman Some names in Quantitative Research : Some names in Quantitative Research Pearson Nueman Bernard Most of all read: Damodar Gujrati: Introduction Basic You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.