Business Research Methods

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Research Meaning Types Nature and scope of research Problem formulation statement of research objective value and cost of information Decision theory -Organizational structure of research - Research process - research designs - exploratory -Descriptive - Experimental research.


To equip the students with the basic understanding of the research methodology and provide an insight into the application of modern analytical tools and techniques for the purpose of management decision making.


· Value of Business Research · Scope of Research

· Types of Research · Structure of Research LEARNING OBJECTIVES

· To understand the importance of business research as a management decision making tool

· To define business research

· To understand the difference between basic and applied research

· To understand when business research in needed and when it should be conducted

· To identify various topics for business research. INTRODUCTION

The task of business research is to generate accurate information for use in decision making. The emphasis of business research is shifting the decision makers from intuitive information that is based on own judgment and gathering information into systematic and objective investigation.


The business research is defined as the systematic and objective process of gathering, recording and analyzing data for aid in making business decisions. Literally, research means to "search again". It connotes patient study and scientific investigation where in the research takes more careful look to discover to know about the subject of study. The


data collected and analyzed are to be accurate, the research need to be very objective. Thus, the role of researcher is to be detached and impersonal rather than engaging in biased attempt. This means without objectivity the research is useless. The definition is restricted to take decision in the aspects of business alone. This generates and provides the necessary qualitative and quantitative information upon which, as base, the

decisions are taken. This information reducing the uncertainly of decisions and reduces the risk of making wrong decisions. However research should be an "aid" to managerial judgment, not a substitute for it. There is more to management than research. Applying research remains a managerial art.

The study of research methods provides with the knowledge and skills that need to solve the problems and meet the challenges of the fast-paced decision making environment. There are two important factors stimulate an interest in scientific approach to decision making:

1. The is an increased need for more and better information, and 2. The availability of technical tools to meet this need.

During the last decade, we have witnessed dramatic changes in the business

environment. These changes have created new knowledge needs for the manager to consider when evaluating any decision. The trend toward complexity has increased the risk associated with business decision, making it more important to have sound

information base. The following are the few reasons which makes the researcher to lookout for newer and better information based on which the decisions are taken:

· There are more variables to consider on every decision · More knowledge exists in every field of management

· The quality and theory and models to explain the tactics and strategic results are improving.

· Better arrangement of information

· Advancement in computer allowed to create better database.

· The power and ease of use of today's computer have given to capability to analyze the data to solve managerial problems

The development of scientific method in business research lags behind the similar developments in physical science research which is more rigorous and much more advanced. But business research is of recent origin and moreover the finding cannot be patented that of physical science research. Business research normally deals with topics such as human attitudes, behavior and performance. Even with these hindrances,

business research is making strides in the scientific arena. Hence, the managers who are not proposed ior this scientific application in business research will be at severe



The prime value of business research is that it reduces uncertainty by providing information that improves decision making process. The decision making process is associated with the development and implementation of a strategy, which involves the following

1. Identifying problems or opportunities

Before any strategy can be developed, an organization must determine where it wants to go and how it will get there. Business research can help managers to plan strategies by determining the nature of situations or by identifying the existence of problems of opportunities that are present in the organization. Business research may be used as a scanning activity to provide information about what business happening or its

environment. Once it defines and indicates problems and opportunities, managers may evaluate alternatives very easily and clear enough to make a decision.

2. Diagnosing and Assessing problems and opportunities

The important aspect of business research is the provision of diagnostic information that clarifies the situation. It there is a problem, they need to specify what happened and why. If an opportunity exists, they need to explore, clarify and refine the nature of

opportunity. This will help in developing alternative courses of action that are practical. 3. Selecting and implementing a course of action

After the alternative course of action has been clearly identified, business research is conducted to obtain scientific information which will aid in evaluating the alternatives and selecting the best course of action.

Need for Research

When a manager faced with two or more possible course if action, the researcher

carefully need to take decision whether or not to conduct the research. The following are the determinants.

1. Time Constraints

In most of the business environment, the decisions most must be made immediately, but conducting research systematically takes time. There will not be much time to relay on research. As a consequence, the decisions are sometimes made without adequate information and through understanding of the situation.

2. Availability of Data

Often managers possess enough information with no research. When they lack adequate information, research must be considered. The managers should think whether the research will be able to generate information needed to answer the basic question about which the decision is to be taken.


3. Nature of Information

The value of research will depend upon the nature of decisions to be made. A routine decision does not require substantial information or warrants. However, for important and strategic decision, more likely research needs to be conducted.

4. Benefits vs. Costs

The decision to conduct research boils down to these important questions. 1. Will the rate of return be worth the investment?

2. Will the information improve the quality of the decision? 3. Is the research expenditure the best use of available funds?

Thus, the cost of information should not exceed the benefits i.e. value of information. What is Good Research?

Good research generates dependable data can be used reliable for making managerial decisions. The following are the tips of good research.

· Purpose clearly defined, i.e., understanding problems clearly · The process described in sufficient details

· The design carefully planned to yield results

· Careful consideration must be given and maintain high ethical standards · Limitations properly revealed

· Adequate analysis of the data and appropriate tools used

· Presentation of data should be comprehensive, early understood and presented unambiguously

· Conclusion should base on the data obtained and justified. Scope of Research

The scope of research on management is limited to business. A researcher conducting research within an organization may be referred as a "marketing researcher" or

"organizational researcher", although business research is specialized and the term encompasses all the functional areas – Production, Finance, Marketing, HR etc. The different functional areas may investigate different phenomenon, but they are

comparable to one another because they use similar research methods. There are many kinds of areas are resembled in the business environment like forecasting, trends

environment, capital formation, portfolio analysis, cost analysis, risk analysis, TQM, job satisfaction, organizational effectiveness, climate, culture, market potential,

segmentation, sales analysis, distribution channel, computer information needs analysis, social values and establish and etc.


Types of Research

Research is to develop and evaluate concepts and theories. In broader sense research can be classified as.

1) Basic Research or Pure Research

It does not directly involve the solution to a particular problem. Although basic research generally cannot be implemented, this is conducted to verify the acceptability of a given theory or to discuss more about a certain concept.

2) Applied Research

It is conducted when a decision must be made about a specific real-life problem. It encompasses those studies undertaken to answer question to specific problems or to make decision about particular course of action.

However, the procedures and techniques utilized by both researchers do not differ substantially. Both employ scientific method to answer questions. Broadly, the scientific method refers to techniques and procedures that help researcher to know and

understand business phenomenon. The scientific method requires systematic analysis and logical interpretation of empirical evidence (facts from observation or

experimentation) to confirm or dispose prior conceptions. In basic research, it first tests the prior conceptions or assumptions or hypothesis and then makes inferences and conclusions. In the applied research the use of scientific method assures objectivity in gathering facts and taking decision.

At the outset, it may be noted that there are several ways of studying and tackling a problem. There is no single perfect design. Research designs have been classified by authors in different ways. Different types of research designs have emerged on the account of the different perspectives from which the problem or opportunity is viewed. However, the research designs broadly classified into three categories - exploratory, descriptive, and causal research. The research can be classified on the basis of either technique or function. Experiment, surveys and observation are few common

techniques. The technique may be qualitative of quantitative. Based on the nature of the problems or purpose of study the above three are used invariably used in management parlance.

3) Exploratory Research

The focus is mainly on discovering of ideas. An exploratory research is generally based on secondary data that are already available. It is to be understood that this type of study is conducted to classify ambiguous problems. These studies provide information to use in analyzing situations. This will helps to crystallize a problem and identify

information needs for further research. The purpose of exploration is usually to develop hypotheses or question for further research. The exploration may be accomplished with different techniques. Both qualitative and quantitative techniques are applicable


although exploration studies relies on more heavily a qualitative technique like experience survey, focus group.

4) Descriptive Research

The major purpose is to describe characteristics of a population or phenomenon.

Descriptive research seeks to determine to answers to who, what, when, where and how questions. Unlike explorative, these studies are based on some previous understanding of the nature of the research problem. Descriptive studies can be divided into two broad categories-Cross sectional and Longitudinal. The former type is more frequently used. A cross section study is concerned with the sample of elements from a given population. It is carried out once and represents one point at time. The longitudinal studies are based on panel data or panel methods. A panel is sample of representatives who are

interviewed and then re-interviewed from time to time, that is, longitudinal studies are repeated over an extended period.

5) Causal Research

The main goal of causal research is the identify cause and effect relationship among variables. It attempts to establish that when we do one thing what another thing will happen. Normally explorative and descriptive studies precede causal research.

However, based on the breadth and depth of study, another method is frequently used is management called case study research. This places more emphasis on a full contextual analysis of fever events or conditions and their interrelations. An emphasis on details provides valuable insight for problem solving, evaluation and strategy. The detail is gathered from multiple sources of information.

Value of Information and Cost

Over the part decade many cultural, technological are competitive factors have created a variety of new challenges, problems and opportunities for today's decision makers in business. First, the rapid advances in interactive marketing communication

technologies have increased the need for database management skills. Moreover, advancements associated with the so called information super high ways have created greater emphasis on secondary data collection, analysis and interpretation. Second, there is a growing movement emphasizes quality improvements. This placed more importance on cross sectional information then over before. Third is the expansion of global markets which introduces a new set of multicultural problem and question.

These three factors that influence the research process and it take steps into seeking new information in management perspective. There may be situations where management is sufficiently clear that no additional information is likely to change its decision. In such cases, it is obvious that the value of information is negligible. In contrast, there are situations where the decisions look out for information which is not available easily. Unless the information collected does not led to change or modify a decision, the


is unsure of what is to be done and ii) where extreme profits or losses involved. A pertinent question is-how much information should be collected in a given situation? Since the collected information involves a cost, it is necessary to ensure that the benefit from the information is more than the cost involved in its collection.

Decision Theory

With reference to the above discussion, an attempt is needed to see how information can be evaluated for setting up a limit. The concept of probability is the basis of decision maker under conditions of uncertainly. These are three basic sources of assigning probabilities

1) Based on a logic / deduction: For e.g., when a coin is tossed, the probability of getting a head or tail is 0.5.

2) Past experience / Empirical evidence: The experience gained in the process resolving these problems in the past. On the basis of its past experience, it may be in a better positive to estimate the probability of new decisions.

3) Subjective Estimate: The most frequently used method, it is based on the knowledge and information with respect to researcher for the probability estimates. The above discussion was confined to single stage problem wherein the researcher is required to select the best course of action on the basis of information available at a point at time. However, there are problems with multiple stages wherein a sequence of decisions involved. Each decision leads to a chance event which in turn influences the next decision. In those cases, a Decision Tree Analysis i.e. graphical derives depicting the sequence of action-event combination, will be useful in making a choice between two alternatives. If the decision tree is not helpful, more sophisticated technological known as Bayesian Analysis can be used. Here, the probabilities can be revised on account of the availability of new information using prior, posterior and pre-posterior analysis. There is a great deal between budgeting and value assessment in management decision to conduct research. An appropriate research study should help managers avoid losses and increase sales or profits; otherwise, research can be wasteful. The decision maker wants a cost-estimate for a research project and equally precise assurance that useful information will result from the research. Even if the researcher can give good cost and information estimates, the decision maker or manager still must judge whether the benefits out-weigh the costs.

Conceptually, the value of research information is not difficult to determine. In business situation the research should provide added revenues or reduce expenses. The value of research information may be judge in terms of “the difference between the results of decisions made with the information and the result that would be made without it”. It is simple to state, in actual application, it presents difficult measurement problems.


1. Focus on the most important issues of the project: Identify certain issues as important and others as peripheral to the problems. Unimportant issues are only to drain resources.

2. Never try to do much: There is limit to the amount of information that can be collected. The researcher must take a trade-off between the number of issues that can be dealt with the depth of each issue. Therefore it is necessary to focus on those issues of greatest potential value.

3. Determine whether secondary, primary information or combination is needed: The most appropriate must be selected that should address the stated problem.

4. Analyze all potential methods of collecting information: Alternative data sources and research designs are available that will allow detailed investigation of issues at a relatively low cost.

5. Subjectively asses the value of information: The researcher need to ask some fundamental questions relating to objections. For example, a) Can the information be collected at all? b) Can the information tell something more that already what we have? c) Will the information provide significant insights? d) What benefits will be delivered from this information?

Structure of Research

Business research can take many forms, but systematic inquiry is a common thread. Systematic inquiry requires an orderly investigation. Business research is a sequence of highly interrelated activity. The steps research process overlap continuously.

Nevertheless, research on management often follows a general pattern. The styles are: 1. Defining the problem

2. Planning a research design 3. Planning a sample

4. Collecting data 5. Analyzing the data

6. Formulating the conclusions and preparing the report SUMMARY

This paper outlined the importance of business research. Difference between basic and applied research have been dealt in detail. This chapter has given the meaning, scope, types, and structure of the research.


KEY TERMS · Research

· Value of research · Need for research · Good Research · Scope of research · Types of research · Basic research · Applied research · Scientific method · Exploratory research · Descriptive research

· Cross - sectional and longitudinal · Causal research

· Decision theory · Structure QUESTIONS

1. What are some examples of business research in your particular field of interest? 2. What do you mean by research? Explain its significance in modem times.

3. What is the difference between applied and basic research? 4. What is good research?

5. Discuss: Explorative, descriptive and causal research.

6. Discuss the value of information and cost using decision theory. 7. Discuss the structure of business research.

End of Chapter -LESSON - 2



· To discuss the nature of decision makers objectives and the role they play in defining the research

· To understand that proper problem definition is essential for effective business research

· To discuss the influence of the statement of the business problem on the specific research objectives

· To state research problem in terms of clear and precise research objectives. STRUCTURE

· Problem definition · Situation analysis · Measurable Symptoms · Unit of analysis

· Hypothesis and Research objectives PROBLEM DEFINITION

Before choosing a research design, manager and researcher need a sense of direction for the investigation. It is extremely important to define the business problem carefully because the definition determines the purposes of the research and ultimately the research design. Well defined problem is half solved problems - hence, researcher must understand how to define problem. The formal quantitative research process should not begin until the problem has been clearly defined. Determination of research problem consists of three important tasks namely,

1. Classifying in argument information needs. 2. Redefining research problem, and

3. Establishing hypothesis & research objectives.

Step 1: To ensure that appropriate information created through this process, researcher must assist decision maker in making sure that the problem or opportunity has been clearly defined and the decision maker is aware of the information requirements. This include the following activities namely,

i) Purpose: Here, the decision maker holds the responsibility of addressing a recognized decision problem or opportunity. The researcher begins the process by asking the decision maker to express his or her reasons for thinking there is a need to undertake research. By this questioning process, the researcher can develop insights as what they believe to be the problems. One method that might be employed to familiarize the decision maker is the iceberg principle. The dangerous part of many problems, like submersed portion of the iceberg, is neither visible nor understood by managers. If the submerged position of the problem is omitted from the problem definition, then result may be less than optimal.

ii) Situation Analysis or understanding the situation: To gain the complete understanding, both should perform a basic situation analysis of the circumstances


surrounding the problem area. A situational analysis is a popular tool that focuses on the informal gathering of background information to familiarize the overall complexity of the decision. A situation analysis attempts to identify the event and factors that have led to the current decision problem situation. To objectively understand the client's domain (i.e., industry, competition, product line, markets etc) the researchers not rely only on the information provided by client but also others. In short the researcher must develop expertise in the client's business.

iii) Identifying and separating measurable symptoms: Once the researcher understands the overall problem situation, they must work with decision maker to separate the problems from the observable and measurable symptoms that may have been initially perceived as the being the decision problem.

iv) Determining unit of analysis: The researcher must be able to specify whether data should be collected about individual, households, organizations, departments, geographical areas, specific object or some contributions of these. The unit of analysis will provide direction in later activities such as scale measurement development and drawing appropriate sample of respondents.

v) Determining relevant variables: Here, the focus is on the identifying the

different independent or dependent variables. It is determination of type of information (i.e., facts, estimates, predictions, relationships) and specific constructs (i.e. concepts or ideas about an object, attributes, or phenomenon that are worth measurement)

Step 2:

Once the problem is understood and specific information requirements are identified, then the researcher must redefine the problem in more specific terms. In reframing the problems and questions as information research questions, they must use their scientific knowledge expertise. Establishing research questions specific to problems will force the decision maker to provide additional information that is relevant to the actual problems. In other situations, redefining problems as research problems can lead to the

establishment of research hypothesis rather than questions. Step 3: (Hypothesis & Research objective)

A hypothesis is basically an unproven started of a research question in a testable format. Hypothetical statement can be formulated about any variable and can express a possible relationship between two or more variables. While research questions and hypotheses are similar in their intent to express relationship, the hypotheses tend to be more specific and declarative, whereas research questions are more interrogative. In other words hypotheses are statement that can be empirically tested.

Research objectives are precise statements of what a research project will attempt to achieve. It indirectly represents a blueprint of research activities. Research objectives allow the researcher to document concise, measurable and realistic events that either increase or decrease the magnitude of management problems. More importantly it


allows for the specification of information required to assist management decision making capabilities.


Nature of decision maker’s objectives, and the role they play in defining the research have been dealt in detail. This chapter has given the steps involved in defining the problem. KEY TERMS · Problem definition · Iceberg principle · Situation analysis · Unit of analysis · Variables · Hypotheses · Research objectives QUESTIONS

1. What is the task of problem definition? 2. What is the iceberg principle?

3. State a problem in your field of interest, and list some variables that might be investigated to solve this problem.

4. What do you mean by hypothesis? 5. What is a research objective?

End of Chapter -LESSON – 3



· To list the stages in the business research process

· To identify and briefly discuss the various decision alternatives available to the researcher during each stage of the research process.


· To classify business research as exploratory, descriptive, and causal research · To discuss categories of research under exploratory, descriptive and causal

research. STRUCTURE

· Research process · Research design

· Types of research designs · Explorative research · Descriptive research · Casual research Research Process

Before discussing the phases and specific steps of research process, it is important to emphasize the need for information and when the research is conducted or not. In this context, the research process may be called as information research process that would be more appropriate in business parlance. The information research is used to reflect the evolving changes occurring within the management and the rapid changes facing many decision makers regarding how firms conduct both internal and external activities. Hence, understanding the process of transforming raw data into usable information from broader information and expanding the applicability of the research process in solving business problems and opportunities is very important.

Overview: The research process has been described anywhere from 6 to 11

standardized stages. Here, the process consist of four distinct inter related phases that have logical, hierarchical ordering depicted below.

Diagram: Four phases of Research Process

However, each phase should be viewed as a separate process that consists of combination of integrated steps and specific procedures. The four phases and corresponding step guided by the principles of scientific method, which involves formalized research procedures that can be characterized as logical, objective, systematic, reliable, valid and ongoing.


The following exhibit represents the interrelated steps of the 4 phases of the research process. Generally researchers should follow the steps in order. However, the

complexity of the problem, the level of risk involved and management needs will determine the order of the process.


Phase 1: Determination of Research Problem

Step 1: Determining management information needs

Step 2: Redefining the decision problem as research problem. Step 3: Establishing research objectives.

Phase 2: Development of Research Design

Step 4: Determining to evaluate research design. Step 5: Determining the data source.

Step 6: Determining the sample plan and sample size. Step 7: Determining the measurement scales.

Phase 3: Execution of the Research Design

Step 8: Data collection and processing data. Step 9: Analysing the data.

Phase 4: Communication of the Results

Step 10: Preparing and presenting the final report to management. Step 1: Determining management information needs

Before the researcher becomes involved usually, the decision maker has to make a formal statement of what they believe is the issue. At this point, the researcher's responsibility is to make sure management has clearly and correctly specified the opportunity or question. It is important for the decision maker and the researcher to agree on the definition of the problem so that the result of the research process will produce useful information. Actually the researcher should assist the decision maker in determining whether the referred problem is really a problem or just a symptom or a yet unidentified problem. Finally the researchers list the factors that could have a direct or indirect impact on the defined problem or opportunity.


Step 2: Redefining the decision problem as research problem.

Once the researcher and decision makers have identified the specific information needs, the researcher must redefine the problem in scientific terms since the researcher feel more comfortable using a scientific framework. This is very critical because, it influences many other steps. It is the researcher's responsibility to state the initial variables

associated with the problem in the form of one or more question formats (how, what, where, when or why). In addition, the researcher need to focus on determining what specific information is required (i.e., facts, estimates, predictions, relationships or some combination) and also of quality of information which includes the value of information. Step 3: Establishing research objectives.

The research objective should follow from the definition of research problem established in Step 2. Formally stated objective provides the guidelines for determining other steps to be undertaken. The undertaking assumption is that, if the objectives are achieved, the decision maker will have information to solve the problem.

Step 4: Determining to evaluate research design.

The research design serves as a master plan of methods and procedures that should be used to collect and analyze the data needed by the decision maker. In this master plan, the researcher must consider the design technique (survey, observation, and

experiment), the sampling methodology and procedures, the schedule and the budget. Although every problem is unique, but most of the objectives can be met using one of three types of research designs: exploratory, descriptive, and casual. Exploratory focuses on collecting either secondary or primary data and using unstructured formal or

informal procedures to interpret them. It is often used simply classify problems or opportunity and it is not intended to provide conclusive information. Some examples of exploratory studies are focus group interview, expensive surveys and pilot studies. Descriptive studies that describe the existing characteristic which generally allows to draw inference and can lead to a course of action. Causal studies are designed to collect information about cause and effect relationship between two or more variables.

Step 5: Determining the data source.

This can be classified as being either secondary or primary. Secondary data can usually be gathered faster and at less cost than primary data. Secondary data are historical data previously collected and assembled for some research problem other than the current situation. In contrast primary data represent firsthand data, and yet to have meaningful interpretation and it employs either surveyor observation.

Step 6: Determining the sample plan and sample size.

To make inference or prediction about any phenomenon we need to understand where or who is supplying the raw data and how representative those data are. Therefore, researchers need to identify the relevant defined target population. The researcher can


choose between a sample (small population) and census (entire population). To achieve this research objective, the researcher needs to develop explicit sampling plan which will serve as a blueprint for defining the target population. Sampling plans can be classified into two general types: probability (equal chance) and non probability. Since sampling size affects quality and general ability, researchers, must think carefully about how many people to include or how many objects to investigate.

Step 7: Determining the measurement scales.

This step focuses on determining the dimensions of the factors being investigated and measuring the variables that underlie the defined problem. This determines how much raw data can be collected and the amount of data to be collected. The level of

information (nominal, ordinal, interval, and ratios), the reliability, the validity and dimension (uni vs. multi) will determine the measurement process.

Step 8: Data collection and processing data.

There are two fundamental approaches to gather raw data. One is to ask questions about variables and phenomena using trained interviewers or questionnaires. The other is to observe variables or phenomena using professional observers or high tech mechanical devices. Self - administered surveys, personal interviews, computer simulations, telephone interviews are some of the tools to collect data. The questioning allows a wider variety of collective of data about not only past, present but also the state of mind or intentions. Observation can be characterized as natural, contrived, disguised or undisguised, structured or unstructured, direct or indirect, human or mechanical, and uses the devices like video camera, tape recorders, audiometer, eye camera, psycho-galvanometer or pupil meter. After the raw data collected, a coding scheme is needed so that the raw data can be entered into computers. It is assigning logical numerical

description to all response categories. The researcher must then clean the raw data of either coding or data entry error.

Step 9: Analysing the data.

Using a variety of data analysis technique, the researcher can create new, complex data structure by continuing two or more variables into indexes, ratios, constructs and so on. Analysis can vary from simple frequency distribution (percentage) to sample statistic measures (mode, median, mean, standard deviation, and standard error) to multivariate data analysis.

Step 10: Preparing and presenting the final report to management.

This step is to prepare and present the final research report to management. The report should contain executive summary, introduction, problem definition and objectives, methodology, analysis, results and finding, finally suggestions and recommendation. It also includes appendix. Any researcher is expected not only submit well produced written report but also oral presentation.


Research Design

Kerlinger, in his "Foundations of Behavioral Research" book defines, "Research design is the plan structure, and strategy of investigation conceived so as to obtain answers to research questions and to control variance". The plan is overall scheme or program of the research. It includes an outline of what the investigator will do from writing

hypotheses and their operational implication to the final analysis of data. A structure is the framework, and the relations among variables of a study. According to Green & Tull, a research design is the specification of methods and procedures for acquiring the information needed. It is the overall operational pattern or framework of the project that stipulates what information is to be collected from which sources by what procedures.

From the above definitions it can be understood that the research design is more or less a blueprint of research, which lays down the methods and procedure for requisite collection of information and measurement and analysis with a view to arrive at meaningful conclusions of the proposed study.

Types of Research Design

The different types of design are explained in the previous section (refer types of research). There are three frequently used classification is give below.

I. Explorative II. Descriptive III. Casual

Here the focus will be how these studies are conducted and methods are explained: I. Categories of Explorative Research

There are four general categories of explorative research methods. Each category provides various alternative ways of getting information.

1. Experience Surveys

It is an attempt to discuss issues and ideas with top executive and knowledge people who have experience in the field. This research in the form of experience survey may be quite informal. This activity intends only to get ideas about the' problems. Often an experience survey may consist of interviews with a small number of people who have been carefully selected. The respondents will generally be allowed to discuss the questions with few constraints. Hence, the purpose of such experts is to help formulate the problem and classify concepts rather than develop conclusive evidence.


2. Secondary Data Analysis

Another quick source of background information is trade literature. Using secondary data may equally important to applied research. Investigating date that has been completed for some purpose other than the present one is one of the frequent forms of exploratory research. Also, it is to remember that this method often used in descriptive analysis.

3. Case Studies

It is to obtain information one or few situation that are similar to the present one. The primary advantage of case study is that entire entity can be investigated in depth and with meticulous attention in details. The results from this type should be seen as tentative. Generalizing from a few cases can be dangerous, because more situations are not typical in same sense. But even if situations are not directly comparable, a number of insights can be gained and hypothesis suggested for future research.

4. Pilot Studies

In the context of exploratory research, a pilot study implies that some aspect of the research will be on a small scale. This generates primary data usually for qualitative analysis. The major categories are discussed below:

a. Focus Group Interview

The popular method in the qualitative research is an unstructured free-flowing interview with a small group of people. It is not rigid, but flexible promote that encourages discussions. The primary advantages are that they are relatively brief, easy to execute, quickly analyzed and inexpensive. However, a small group will rarely be a representative sample, no matter how carefully it is selected.

b. Projective Techniques

It is an indirect means of questioning that enable the respondent to project beliefs and feeling onto a third party, an inanimate object or a situation.

Respondents are not required to provide answer to a structural format. They are encouraged to describe a situation in their own words, with little prompting by the researcher, within the context of their own experiences, attitudes, personality and to express opinions and emotions. The most common techniques are used associations, sentence completion, Thematic Apperception Test (TAT) and role playing.

c. Depth interview

It is similar to focus group but in the interviewing session the researcher asks many questions and probes for elaboration. Here the role of researcher


(interviewers) is more important. He must be highly skillful who can influence respondents to talk freely without disturbing the direction. It may last more hours, hence it is expensive.

II. Categories of Descriptive Research

In contrast to exploratory, descriptive studies are more formalized and typically structured with clearly stated hypothesis. When the researcher interested in knowing the characteristics of certain groups such as age, sex, educational level, occupation or income, a descriptive study may be necessary. Descriptive studies can be divided into two broad categories - cross sectional and longitudinal. The following are the methods used in descriptive studies:

1. Secondary Data Analysis 2. Primary Data Analysis 3. Case studies

Several methods are available to collecting the information (i.e., observation, questionnaire, and examination of records with the merits and limitations, the

researcher may use one or more of these methods which have been discussed in details in later chapters. Thus the descriptive studies methods must be selected keeping in view the objectives of the study and the resources available. The said design can be

appropriately referred to as a survey design using observing or questioning process, lit. III. Categories of Causal Research

As the name implies, a causal design investigates the cause and effect relationship between two or more variables. The causal studies may be classified as informal and formal or quasi / true and complex designs. The methods used in experimental research are discussed hereunder:

1. The one-shot case study (after-only design) 2. Before-after without control group.

3. After-only with control group. 4. Before-after with one control group

5. Four-group, Six- study design (Solomon four group design) 6. Time series design


8. Randomized block design. 9. Factorial design

10. Latin square design

The first two methods are called as quasi experimental designs, next 3 methods are called as true experimental designs and last 4 methods are called complex designs. In this experimental design the following symbols are used in describing the various experimental designs:

X = Exposure of a group to an experimental treatment. O = Observation or measurement of the dependent variable.

1. The one-shot case study design (after only design): The one-shot design, there should be a measure of what would happen when test units were not exposed to X to compare with the measure when subjects were exposed to X. This is diagrammed as follows:

X O1

2. Before - after without control group design: In this, the researcher is likely to conclude that the difference between O2 and O1 (O2 – O1) is the measure of

the influence of the experimental treatment. The design is as follows: O1 X O2

3. After only with control group design: The diagram is as follows: Experimental Group: X O1

Control Group: X O2

The design is to randomly selected subjects and randomly assign to experimental or the control group. The treatment is then measured in both groups at same time. The effect is calculated as follows:

O2 - O1

4. Before-after with one control group design: This is explained as follows: Experimental Group: O1 X O2


As the diagram above indicated, the subjects of the experimental group are tested before and after being exposed to the treatment. The control group is tested twice, at the same time of experimental group, but these subjects are not exposed to the treatment. The effect is calculated as follows:

(O2 - O1) - (O4 – O3)

5. Four-group, six- study design: (Solomon four groups design) Combining, the before - after with control group design and the after-only with control group design provides a means for controlling testing affects, as well as other sources of extraneous variations. The diagram as follows:

Experimental Group 1 O1 X O2

Control Group 1 O3 X O4

Experimental Group 2: X O5

Control Group 2: X O6

6. Time series design: When experiments are conducted over long periods of time, they are more vulnerable to history effects due to changes in population, attitudes, economic patterns and the like. Hence, this is also called quasi-experimental design. This design can be diagrammed as follows:

O1 O2 O3 X O4 O5 O6

Several observations have been taken before and after the treatment to determine the patterns after the treatment are similar to the pattern of before the treatments. 7. Completely randomized design (CRD): CRD is an experimental design that uses a random process to assign experimental units to treatments. Here, randomization of experimental units to control extraneous variables while manipulating a single independent variable, the treatment variable.

8. Randomized block design (RBD): The RBD is an extension of the CRD. A form of randomization is utilized to control for most extraneous variation. Here an attempt is made to isolate the effects of the single variable by blocking its effects. 9. Factorial design: A FD allows for testing the effects of two or more

treatment (factors) at various levels. It allows for the simultaneous manipulation of 2 or more variable at various levels. This design will measure main effect (i.e., the influence on the dependent variable by each independent variable and also interaction effect.


10. Latin Square Design (LSD): The LSD attempts to control or block out the effect of two or more confounding extraneous factors. This design is so named because of the layout of the table that represents the design.


This chapter has outlined the stages in business research prawn. Various types of research design have been dealt in detail.

KEY TERMS · Exploratory research · Descriptive research · Causal research · Focus group · Projective techniques · Case studies · Depth Interview · Experimental designs · Quasi experimental design · True experimental designs · Complex designs


1. Explain the different phases of research process.

2. Briefly describe the different steps involved in a research process. 3. What are major types researches in business?

4. Discuss the categories of exploratory and descriptive research. 5. Explain different experimental designs.


1. Bellenger and et al, Marketing Research, Home Wood Illinois, Inc. 1978.

2. Boot, John C.G. and Cox., Edwin B., Statistical Analysis for Managerial Decisions, 2nd ed. New Delhi: McGraw Hill Publishing Co. Ltd.

3. Edwards, Allen, Statistical Methods, 2nd ed., New York. 1967.





· Meaning and definition of Statistics · Nature of Statistical study

· Importance of Statistics in business and also its limitations STRUCTURE

· Nature of statistical study

· Importance of statistics in business · Statistical quality control method · Limitation of statistics


At the outset, it may be noted that the word 'Statistics' is used rather curiously in two senses-plural and singular. In the plural sense, it refers to a set of figures. Thus, we speak of production and sale of textiles, television sets, and so on. In the singular sense, Statistics refers to the whole body of analytical tools that are used to collect the figures, organize and interpret them and, finally, to draw conclusions from them.

It should be noted that both the aspects of Statistics are important if the quantitative data are to serve their purpose. If Statistics, as a subject, is inadequate and consists of poor methodology, we would not know the right procedure to extract from the data the information they contain. On the other hand, if our figures are defective in the sense that they are inadequate or inaccurate, we would not reach the right conclusions even though our subject is well developed. With this brief introduction, let us first see how Statistics has been defined.

Statistics has been defined by various authors differently. In the initial period the role of Statistics was confined to a few activities. As such, most of the experts gave a narrow definition of it. However, over a long period of time as its role gradually expanded, Statistics came to be considered as much wider in its scope and, accordingly, the experts gave a wider definition of it.

Spiegal, for instance, defines Statistics, highlighting its role in decision-making particularly under uncertainty, as follows:


"Statistics is concerned with scientific method for collecting, organising, summarising, presenting and analysing data as well as drawing valid conclusions and making

reasonable decisions on the basis of such analysis."

This definition covers all the aspects and then tries to link them up with decision-making. After all, Statistics as a subject must help one to reach a reasonable and appropriate decision on the basis of the analysis of numerical data collected earlier. Using the term 'Statistics' in the plural sense, Secrist defines Statistics as

"Aggregate of facts, affected to a marked extent by multiplicity of causes, numerically expressed, enumerated or estimated according to reasonable standards of accuracy, collected in a systematic manner for a predetermined purpose, and placed in relation to each other".

This definition of Secrist highlights a few major characteristics of statistics as given below:

1. Statistics are aggregates of facts. This means that a single figure is not statistics. 2. Statistics are affected by a number of factors. For example, sale of a product depends on a number of factors such as its price, quality, competition, the income of the

consumers, and so on.

3. Statistics must be reasonably accurate. Wrong figures, if analysed, will lead to erroneous conclusions. Hence, it is necessary that conclusions must be based on accurate figures.

4. Statistics must be collected in a systematic manner. If data are collected in a haphazard manner, they will not be reliable and will lead to misleading conclusions. 5. Finally, statistics should be placed in relation to each other. If one collects data

unrelated to each other, then such data will be confusing and will not lead to any logical conclusions. Data should be comparable over time and over space.


Having briefly looked info the definition of Statistics, we should know at this stage as to what the nature of a Statistical study is. Whether a given problem pertains to business or to some other field, there are some well defined steps that need to be followed in order to reach meaningful conclusions.

1. Formulation of the Problem: To begin with, we have to formulate a problem on which a study is to be done. We should understand the problem as clearly as possible. We should know its scope so that we do not go beyond it or exclude some relevant aspect.


2. Objectives of the Study: We should know what the objectives of the proposed study are. We should ensure that the objectives are not extremely ambitious or else the study may fail to achieve them because of limitations of time, finance or even

competence of those conducting the study.

3. Determining Sources of Data: The problem and the objectives, thus properly understood, will enable us to know as to what data are required to conduct the study. We have to decide whether we should collect primary data or depend exclusively on secondary data. Sometimes the study is based on both the secondary and the primary data. When study is to be based on secondary data, whether partly or fully, it is

necessary to ensure that the data are quite suitable and adequate for the objectives of the study.

4. Designing Data Collection Forms: Once the decision in favour of collection of primary data is taken, one has to decide the mode of their collection. The two methods available are: (i) observational method, and (ii) survey method. Suitable questionnaire is to be designed to collect data from respondents in a field survey.

5. Conducting the Field Survey: Side by side when the data collection forms are being designed, one has to decide whether a census surveyor a sample survey is to be conducted. For the latter, a suitable sample design and the sample size are to be chosen. The field survey is then conducted by interviewing sample respondents. Sometimes, the survey is done by mailing questionnaires to the respondents instead of contacting them personally.

6. Organising the Data: The field survey provides raw data from the respondents. It is now necessary to organise these data in the form of suitable tables and charts so that we may be aware of their salient features.

7. Analysing the Data: On the basis of the preliminary examination of the data collected as well as the nature and scope of our problem, we have to analyse data. As several statistical techniques are available, we should take special care to ensure that the most appropriate technique is selected for this purpose.

8. Reaching Statistical bindings: The analysis in the preceding step will bring out some statistical findings of the study. Now we have to interpret these findings in terms of the concrete problem with which we started our investigation.

9. Presentation of Findings: Finally, we have to present the findings of the study, properly interpreted, in a suitable form. Here, the choice is between an oral presentation and a written one. In the case of an oral presentation, one has to be extremely selective in choosing the material, as in a limited time one has to provide a broad idea of the study as well as its major findings to be understood by the audience in proper

perspective. In case of a written presentation, a report has to be prepared. It should be reasonably comprehensive and should have graphs and diagrams to facilitate the reader in understanding it in all its ramifications.



There is an increasing realisation of the importance of Statistics in various quarters. This is reflected in the increasing use of Statistics in the government, industry, business, agriculture, mining, transport, education, medicine, and so on. As we are concerned with the use of Statistics in business and industry here, the description given below is confined to these areas only.

Three major functions where statistics can be found useful in a business enterprise: A. The planning of operations - This may relate to either special projects or to the recurring activities of a firm over a specified period.

B. The setting up of standards - This may relate to the size of employment, volume of sales, fixation of quality norms for the manufactured product, norms for the daily output, and so forth.

C. The function of control - This involves comparison of actual production achieved against the norm or target set earlier. In case the production has fallen short of the target, it gives remedial measures so that such a deficiency does not occur again. A point worth noting here is that although these three functions - planning of

operations, setting standards, and control-are separate, but in practice they are very much interrelated.

Various authors have highlighted the importance of Statistics in business. For instance, Croxton and Cowden give numerous uses of Statistics in business such as project

planning, budgetary planning and control, inventory planning and control, quality control, marketing, production and personnel administration. Within these also they have specified certain areas where Statistics is very relevant. Irwing W. Burr, dealing with the place of Statistics in an industrial organisation, specifies a number of areas where Statistics is extremely useful. These are: customer wants and market research, development design and specification, purchasing, production, inspection, packaging and shipping, sales and complaints, inventory and maintenance, costs, management control, industrial engineering and research.

It can be seen that both the lists are extremely comprehensive. This clearly points out that specific statistical problems arising in the course of business operations are

multitudinous. As such, one may do no more than highlight some of the more important ones to emphasise the relevance of Statistics to the business world.

Personnel Management

This is another sphere in business where statistical methods can be used. Here, one is concerned with the fixation of wage rates, incentive norms and performance appraisal of individual employee. The concept of productivity is very relevant here. On the basis of measurement of productivity, the productivity bonus is awarded to the workers.


Comparisons of wages and productivity are undertaken in order to ensure increases in industrial productivity.

Seasonal Behaviour

A business firm engaged in the sale of certain product has to decide how much stock of that product should be kept. If the product is subject to seasonal fluctuations then it must know the nature of seasonal fluctuations in demand. For this purpose, seasonal index of consumption may be required. If the firm can obtain such data or construct a seasonal index on its own then it can keep a limited stock of the product in lean months and large stocks in the remaining months. In this way, it will avoid the blocking of funds in maintaining large stocks in the lean months. It will also not miss any opportunity to sell the product in the busy season by maintaining adequate stock of the product during such a period.

Export Marketing

Developing countries have started giving considerable importance to their exports. Here, too, quality is an important factor on which exports depend. This apart, the concerned firm must know the probable countries where its product can be exported. Before that, it must select the right product, which has considerable demand in the overseas markets. This is possible by carefully analysing the statistics of imports and exports. It may also be necessary to undertake a detailed survey of overseas markets to know more precisely the export potential of a given product.

Maintenance of Cost Records

Cost is an important consideration for a business enterprise. It has to ensure that cost of production, which includes cost of raw materials, wages, and so forth, does not mount up or else this would jeopardize its competitiveness in the market. This implies that it has to maintain proper cost records and undertake an analysis of cost data from time to time.

Management of Inventory

Closely related to the cost factor is the problem of inventory management. In order to ensure that the production process continues uninterrupted, the business firm has to maintain an adequate inventory. At the same time, excessive inventory means blocking of funds that could have been utilized elsewhere. Thus, the firm has to determine a magnitude of inventory that is neither excessive nor inadequate.

While doing so, it has to bear in mind the probable demand for its product. All these aspects can be well looked after if proper statistics are maintained and analyzed. Expenditure on Advertising and Sales


A number of times business firms are interested to know whether there is an association between two or more variables such as advertising expenditure and sales. In view of increasing competitiveness, business and industry spend a large amount on advertising. It is in their interest to find out whether such advertising expenditure promotes the sales. Here, by using correlation and regression techniques it can be ascertained that the advertising expenditure is worthwhile of not.

Mutual Funds

Mutual funds which have come into existence in recent years, provide an avenue to a person to invest his savings so that he may get a reasonably good return. Different mutual funds have different objectives as they have varying degrees of risk involved in the companies they invest in. Here, Statistics provides certain tools or techniques to a consultant or financial adviser through which he can provide sound advice to a

prospective investor.

Relevance in Banking and Insurance Institutions

Banks and insurance companies frequently use varying statistical techniques in their respective areas of operation. They have to maintain their accounts and analyze these to examine their performance over a specified period.

The above discussion is only illustrative and there are numerous other areas where the use of Statistics is so common that without its use they may have to close down their operations.


In the sphere of production, for example, statistics can be useful in various ways to ensure the production of quality goods. This is achieved by identifying and rejecting defective or substandard goods. The sale targets can be fixed on the basis of sale forecasts, which are done by using varying methods of forecasting. Analysis of sales done against the targets set earlier would indicate the deficiency in achievement, which may be on account of several causes: (i) targets were too high and unrealistic (ii)

salesmen's performance has been poor (iii) emergence of increase in competition, and (iv) poor quality of company's product, and so on. These factors can be further



The preceding discussion highlighting the importance of Statistics in business should not lead anyone to conclude that Statistics is free from any limitation. As we shall see here, Statistics has a number of limitations.

There are certain phenomena or concepts where Statistics cannot be used. This is because these phenomena or concepts are not amenable to measurement. For example,


beauty, intelligence, courage cannot be quantified. Statistics has no place in all such cases where quantification is not possible.

1. Statistics reveal the average behaviour, the normal or the general trend. An

application of the 'average' concept if applied to an individual or a particular situation may lead to a wrong conclusion and sometimes may be disastrous. For example, one may be misguided when told that the average depth of a river from one bank to the other is four feet, when there may be some points in between where its depth is far more than four feet. On this understanding, one may enter those points having greater depth, which may be hazardous.

2. Since Statistics are collected for a particular purpose, such data may not be relevant or useful in other situations or cases. For example, secondary data (i.e., data originally collected by someone else) may not be useful for the other person.

3. Statistics is not 100 percent precise as is Mathematics or Accountancy. Those who use Statistics should be aware of this limitation.

4. In Statistical surveys, sampling is generally used as it is not physically possible to cover all the units or elements comprising the universe. The results may not be appropriate as far as the universe is concerned. Moreover, different surveys based on the same size of sample but different sample units may yield different results.

5. At times, association or relationship between two or more variables is studied in Statistics, but such a relationship does not indicate 'cause and effect' relationship. It simply shows the similarity or dissimilarity in the movement of the two variables. In such cases, it is the user who has to interpret the results carefully, pointing out the type of relationship obtained.

6. A major limitation of Statistics is that it does not reveal all pertaining to a certain phenomenon.

7. There is some background information that Statistics does not cover. Similarly, there are some other aspects related to the problem on hand, which are also not covered. The user of Statistics has to be well informed and should interpret Statistics keeping in mind all other aspects having relevance on the given problem.


This chapter outlined the importance and growth of statistics. Various applications of statistics in the domain of management have been dealt in detail.

KEY TERMS · Statistics

· Statistical quality control methods · Seasonal behaviour


IMPORTANT QUESTIONS 1. Define statistics

2. What do you mean by statistical quality methods?

3. Explain the application of statistics in various business domains.

End of Chapter -LESSON – 5



· To acquire knowledge of estimation of parameter STRUCTURE

· Estimation of population parameters · Measure of central tendency

· Mean, median, mode · Geometric mean · Harmonic mean

A population is any entire collection of people, animals, plants or things from which we may collect data. It is the entire group we are interested in, which we wish to describe or draw conclusions about.

In order to make any generalizations about a population, a sample, that is meant to be representative of the population, is often studied. For each population there are many possible samples. A sample statistic gives information about a corresponding population parameter. For example, the sample mean for a set of data would give information about the overall population mean.

It is important that the investigator carefully and completely defines the population before collecting the sample, including a description of the members to be included. Example

The population for a study of infant health might be all children born in the India, in the 1980's. The sample might be all babies born on 7th May in any of the years.


Population Parameters

Parameter is a value, usually unknown (and which therefore has to be estimated), used to represent a certain population characteristic. For example, the population mean is a parameter that is often used to indicate the average value of a quantity.

Within a population, a parameter is a fixed value which does not vary. Each sample drawn from the population has its own value of any statistic that is used to estimate this parameter. For example, the mean of the data in a sample is used to give information about the overall mean in the population from which that sample was drawn.


The most important objective of statistical analysis is to determine a single value for the entire mass of data, which describes the overall level of the group of observations and can be called a representative set of data. It tells us where the centre of the distribution of data is located on the scale that we are using. There are several such measures, but we shall discuss only those that are most commonly used. These are: Arithmetic Mean, Mode and Median. These values are very useful in not only presenting the overall

picture of the entire data, but also for the purpose of making comparisons among two or more sets of data.

As an example, questions like, "How hot is the month of June in Mumbai?" can be answered, generally, by a single figure of the average temperature for that month. For the purpose of comparison, suppose that we want to find out if boys and girls at the age 10 differ in height. By taking the average height of boys of that age and the average height of the girls of the same age, we can compare and note the difference.

While, arithmetic mean is the most commonly used measure of central location, mode and median arc more suitable measures under certain set of conditions and for certain types of data. However, all measures of central tendency should meet the following requisites:

· It should be easy to calculate and understand.

· It should be rigidly defined. It should have one and only one interpretation so that the personal prejudice or bias of the investigator does not affect the value or its usefulness.

· It should be representative of the data. If it is calculated from a sample, then the sample should be random enough to be accurately representing the population. · It should have sampling stability. It should not be affected by sampling

fluctuations. This means that if we pick 10 different groups of college students at random and we compute the average of each group, then we should expect to get approximately the same value from these groups.

· It should not be affected much by extreme values. If a few very small or very large items are presented in the data, they will unduly influence the value of the


really typical of the entire series. Hence, the average chosen should be such that it is not unduly influenced by extreme values.

Let us consider these three measures of the central tendency: MODE

In statistics, mode means the most frequent value assumed by a random variable, or occurring in a sampling of a random variable. The term is applied both to probability distributions and to collections of experimental data.

Like the statistical mean and the median, die mode is a way of capturing important information about a random variable or a population in a single quantity. The mode is in general different from mean and median, and may be very different for strongly skewed distributions.

The mode is not necessarily unique, since the same maximum frequency may be attained at different values. The worst case is given by so-called uniform distributions, in which all values are equally likely.

Mode of a probability distribution

The mode of a probability distribution is the value at which its probability density function attains its maximum value, so, informally speaking; the mode is at the peak. Mode of a sample

The mode of a data sample is the element that occurs most often in the collection. For example, the mode of the sample [1, 3, 6, 6, 6, 6, 7, 7, 12, 12, 17] is 6. Given the list of data [1, 1, 2, 4, 4] the mode is not unique.

For a sample from a continuous distribution, such as [0.935..., 1.211..., 2.430..., 3.668..., 3.874...], the concept is unusable in its raw form, since each value will occur precisely once. The usual practice is to discreteise the data by assigning the values to equidistant intervals, as for making a histogram, effectively replacing the values by the midpoints of the intervals they are assigned to. The mode is then the value where the histogram reaches its peak. For small or middle-sized samples the outcome of this procedure is sensitive to the choice of interval width if chosen too narrow or too wide; typically one should have a sizable fraction of the data concentrated in a relatively small number of intervals (5 to 10), while the fraction of the data falling outside these intervals is also sizable.

Comparison of mean, median and mode

For a probability distribution, the mean is also called the expected value of the random variable. For a data sample, the mean is also called the average.