Master of Business Administration - Semester 3 MB 0050: “Research Methodology” (4 credits) (Book ID: B1206)

ASSIGNMENT- Set 1 Marks 60 Note: Each Question carries 10 marks. Answer all the questions.

NAME=JAGADEESHA.S ROLL NO =511118390

1 how is a research problem formulated ?

Formulating the Problem

The selection of one appropriate researchable problem out of the identified problems

requires evaluation of those alternatives against certain criteria, which may be grouped into:

 Internal Criteria

Internal Criteria consists of:

1) Researcher’s interest: The problem should interest the researcher and be a challenge

to him. Without interest and curiosity, he may not develop sustained perseverance. Even a

small difficulty may become an excuse for discontinuing the study. Interest in a problem

depends upon the researcher’s educational background, experience, outlook and

sensitivity.

2) Researcher’s competence: A mere interest in a problem will not do. The researcher

must be competent to plan and carry out a study of the problem. He must have the ability to

grasp and deal with int. he must possess adequate knowledge of the subject-matter,

relevant methodology and statistical procedures.

3) Researcher’s own resource: In the case of a research to be done by a researcher on

his won, consideration of his own financial resource is pertinent. If it is beyond his means,

he will not be able to complete the work, unless he gets some external financial support.

Time resource is more important than finance. Research is a time-consuming process;

hence it should be properly utilized.

 External Criteria

1) Research-ability of the problem: The problem should be researchable, i.e., amendable

for finding answers to the questions involved in it through scientific method. To be

researchable a question must be one for which observation or other data collection in the

real world can provide the answer.

2) Importance and urgency: Problems requiring investigation are unlimited, but available

research efforts are very much limited. Therefore, in selecting problems for research, their

relative importance and significance should be considered. An important and urgent

problem should be given priority over an unimportant one.

3) Novelty of the problem: The problem must have novelty. There is no use of wasting

one’s time and energy on a problem already studied thoroughly by others. This does not

mean that replication is always needless. In social sciences in some cases, it is appropriate

to replicate (repeat) a study in order to verify the validity of its findings to a different

situation.

4) Feasibility: A problem may be a new one and also important, but if research on it is not

feasible, it cannot be selected. Hence feasibility is a very important consideration.

5) Facilities: Research requires certain facilities such as well-equipped library facility,

suitable and competent guidance, data analysis facility, etc. Hence the availability of the

facilities relevant to the problem must be considered.

6) Usefulness and social relevance: Above all, the study of the problem should make

significant contribution to the concerned body of knowledge or to the solution of some

significant practical problem. It should be socially relevant. This consideration is particularly

important in the case of higher level academic research and sponsored research.

7) Research personnel: Research undertaken by professors and by research

organizations require the services of investigators and research officers. But in India and

other developing countries, research has not yet become a prospective profession. Hence

talent persons are not attracted to research projects.

Each identified problem must be evaluated in terms of the above internal and external

criteria and the most appropriate one may be selected by a research scholar.

2. What are the characteristics of good research design?

Design research investigates the process of designing in all its many fields. It is thus related to Design methods in general or for particular disciplines. A primary interpretation of design research is that it is concerned with undertaking research into the design process. Secondary interpretations would refer to undertaking research within the process of design. The overall intention is to better understand and to improve the design process.

Throughout the design construction task, it is important to have in mind some endpoint, some criteria which we should try to achieve before finally accepting a design strategy. The criteria discussed below are only meant to be suggestive of the characteristics found in good research design. It is worth noting that all of these criteria point to the need to individually tailor research designs rather than accepting standard textbook strategies as is Theory-Grounded. Good research strategies reflect the theories which are being investigated. Where specific theoretical expectations can be hypothesized these are incorporated into the design. For example, where theory predicts a specific treatment effect on one measure but not on another, the inclusion of both in the design improves discriminant validity and demonstrates the predictive power of the theory. 

Situational. Good research designs reflect the settings of the investigation. This was illustrated above where a particular need of teachers and administrators was explicitly addressed in the design strategy. Similarly, intergroup rivalry, demoralization, and competition might be assessed through the use of additional comparison groups who are not in direct contact with the original group. 

Feasible. Good designs can be implemented. The sequence and timing of events are carefully thought out. Potential problems in measurement, adherence to assignment, database construction and the like, are anticipated. Where needed, additional groups or measurements are included in the design to explicitly correct for such problems. 

Redundant. Good research designs have some flexibility built into them. Often, this flexibility results from duplication of essential design features. For example, multiple replications of a treatment help to insure that failure to implement the treatment in one setting will not invalidate the entire study. 

Efficient. Good designs strike a balance between redundancy and the tendency to overdesign. Where it is reasonable, other, less costly, strategies for ruling out potential threats to validity are utilized. 

This is by no means an exhaustive list of the criteria by which we can judge good research design. nevertheless, goals of this sort help to guide the researcher toward a final design choice and emphasize important components which should be included.

The development of a theory of research   methodology  for the social sciences has largely occurred over the past half century and most intensively within the past two decades. It is not surprising, in such a relatively recent effort, that an emphasis on a few standard research designs has occurred. Nevertheless, by moving away from the notion of "design selection" and towards an emphasis on design construction, there is much to be gained in our understanding of design principles and in the quality of our research.

Exploratory research provides insights into and comprehension of an issue or situation. It should draw definitive conclusions only with extreme caution. Exploratory research is a type of research conducted because a problem has not been clearly defined. Exploratory research helps determine the best research design, data collection method and selection of subjects. Given its fundamental nature, exploratory research often concludes that a perceived problem does not actually exist.

Exploratory research often relies on secondary research such as reviewing available literature and/or data, or qualitative approaches such as informal discussions with consumers, employees, management or competitors, and more formal approaches through in-depth interviews, focus groups, projective methods, case studies or pilot studies. The Internet allows for research   methods  that are more interactive in nature: E.g., RSS feeds efficiently supply researchers with up-to-date information; major search engine search results may be sent by email to researchers by services such as Google Alerts; comprehensive search results are tracked over lengthy periods of time by services such as Google Trends; and Web sites may be created to attract worldwide feedback on any subject.

The results of exploratory research are not usually useful for decision-making by themselves, but they can provide significant insight into a given situation. Although the results of qualitative   research  can give some indication as to the "why", "how" and "when" something occurs, it cannot tell us "how often" or "how many."

Exploratory research is not typically generalizable to the population at large.

Applied research in administration is often exploratory because there is need for flexibility in approaching the problem. In addition there are often data limitations and a need to make a decision within a short time period. Qualitative research methods such as case study or field research are often used in Exploratory research..

There are three types of objective in a marketing   research  project.

Exploratory Research or Formulative Research

Descriptive research

Causal research 

Exploratory Research or Formulative Research 'The objective of exploratory research is to gather preliminary information that will help define problems and suggest hypotheses.'

Descriptive Research 'The objective of descriptive research is to describe things, such as the market potential for a product or the demographics and attitudes of consumers who buy the product.

3 how case study method is useful to business research ?

While case study writing may seem easy at first glance, developing an effective case study (also called a success story) is an art.  Like other marketing communication skills, learning how to write a case study takes time.  What’s more, writing case studies without careful planning usually results in sub optimal results?

Savvy case study writers increase their chances of success by following these ten proven techniques for writing an effective case study:

Involve the customer throughout the process. Involving the customer throughout the case study development process helps ensure customer cooperation and approval, and

results in an improved case study. Obtain customer permission before writing the document, solicit input during the development, and secure approval after drafting the document.

Write all customer quotes for their review. Rather than asking the customer to draft their quotes, writing them for their review usually results in more compelling material.

 

Case Study Writing Ideas

Establish a document   template . A template serves as a roadmap for the case study process, and ensures that the document looks, feels, and reads consistently. Visually, the template helps build the brand; procedurally, it simplifies the actual writing. Before beginning work, define 3-5 specific elements to include in every case study, formalize those elements, and stick to them. Start with a bang. Use action verbs and emphasize benefits in the case study title and subtitle.  Include a short (less than 20-word) customer quote in larger text.  Then, summarize the key points of the case study in 2-3 succinct bullet points.  The goal should be to tease the reader into wanting to read more.

Organize according to problem, solution, and benefits. Regardless of length, the time-tested, most effective organization for a case study follows the problem-solution-benefits flow.  First, describe the business and/or technical problem or issue; next, describe the solution to this problem or resolution of this issue; finally, describe how the customer benefited from the particular solution (more on this below). This natural story-telling sequence resonates with readers.

Use the general-to-specific-to-general approach. In the problem section, begin with a general discussion of the issue that faces the relevant industry.  Then, describe the specific problem or issue that the customer faced.  In the solution section, use the opposite sequence.  First, describe how the solution solved this specific problem; then indicate how it can also help resolve this issue more broadly within the industry.  Beginning more generally draws the reader into the story; offering a specific example demonstrates, in a concrete way, how the solution resolves a commonly faced issue; and concluding more generally allows the reader to understand how the solution can also address their problem.

· Quantify benefits when possible. No single element in a case study is more compelling than the ability to tie quantitative benefits to the solution. For example, “Using Solution X saved Customer Y over $ZZZ, ZZZ after just 6 months of implementation;” or, “Thanks to Solution X, employees at Customer Y have realized a ZZ% increase in productivity as measured by standard performanceindicators.” Quantifying benefits can be challenging, but not impossible. The key is to present imaginative ideas to the customer for ways to quantify the benefits, and remain flexible during this discussion.  If benefits cannot be quantified, attempt to develop a range of qualitative benefits; the latter can be quite compelling to readers as well.

· Use photos. Ask the customer if they can provide shots of personnel, ideally using the solution. The shots need not be professionally done; in fact, “homegrown” digital photos sometimes lead to surprisingly good results and often appear more genuine. Photos further personalize the story and help form a connection to readers.

· Reward the customer. After receiving final customer approval and finalizing the case study, provide a pdf, as well as printed copies, to the customer.  Another idea is to frame a copy of the completed case study and present it to the customer in appreciation for their efforts and cooperation.

Writing a case study is not easy. Even with the best plan, a case study is doomed to failure if the writer lacks the exceptional writing skills, technical savvy, and marketing experience that these documents require.  In many cases, a talented writer can mean the difference between an ineffective case study and one that provides the greatest benefit. If a qualified internal writer is unavailable, consider outsourcing the task to professionals who specialize in case study writing.

4. Distinguish between Schedules and questionnaires. Difference between a schedule and a questionnaire

There is a difference between a schedule and a questionnaire. A scheduleis a form that the investigator fills himself through surveying the units or individuals. A questionnaire is a form sent (usually mailed) by an investigator to respondents. The respondent has to fill it and then send it back to the investigator.

Questionnaires

Often, information is collected through questionnaires. The questionnaires are filled with questions pertaining to the investigation. They are sent to the respondents with a covering letter soliciting cooperation from the respondents (respondents are the people who respond to questions in thequestionnaire). The respondents are asked to give correct information and to mail the questionnaire back. The objectives of investigation are explained in the covering letter together with assurance for keeping information provided by the respondents as confidential.

Good questionnaire construction is an important contributing factor to the success of a survey. When questionnaires are properly framed and constructed, they become important tools by which statements can be made about specific people or entire populations.

This method is generally adopted by research workers and other official and non-official agencies. This method is used to cover large areas ofinvestigation. It is more economical and free from investigator’s bias. However, it results in many “non-response” situations. The respondent may be illiterate. The respondent may also provide wrong information due to wrong interpretation of questions.

Schedule Filled By Investigators

Information can be collected through schedules filled by investigators through personal contact. In order to get reliable information, the investigator should be well trained, tactful, unbiased and hard working.

A schedule is suitable for an extensive area of investigation through investigator’s personal contact. The problem of non-response is minimised.

There is a difference between a schedule and a questionnaire. A scheduleis a form that the investigator fills himself through surveying the units or individuals. A questionnaire is a form sent (usually mailed) by an investigator to respondents. The respondent has to fill it and then send it back to the investigator.

5. What are the contents of research reports? The outline of a research report is given below:

I. Prefatory Items

· Title page

· Declaration

· Certificates

· Preface/acknowledgements

· Table of contents

· List of tables

· List of graphs/figures/charts

· Abstract or synopsis

II. Body of the Report

· Introduction

· Theoretical background of the topic

· Statement of the problem

· Review of literature

· The scope of the study

· The objectives of the study

· Hypothesis to be tested

· Definition of the concepts

· Models if any

· Design of the study

· Methodology

· Method of data collection

· Sources of data

· Sampling plan

· Data collection instruments

· Field work

· Data processing and analysis plan

· Overview of the report

· Limitation of the study

· Results: findings and discussions

· Summary, conclusions and recommendations

III. Reference Material

· Bibliography

· Appendix

· Copies of data collection instruments

· Technical details on sampling plan

· Complex tables

· Glossary of new terms used.

6. Write short notes on the following: a. Median

b. Standard Deviation

Median

One problem with using the mean, is that it often does not depict the typical outcome.  If there is one outcome that is very far from the rest of the data, then the mean will be strongly affected by this outcome.  Such an outcome is called and outlier.  An alternative measure is the median.  The median is the middle score.  If we have an even number of events we take the average of the two middles.  The median is better for describing the typical value.  It is often used for income and home prices.

Example

Suppose you randomly selected 10 house prices in the South Lake Tahoe area.  Your are interested in the typical house price.  In $100,000 the prices were

        2.7,   2.9,   3.1,   3.4,   3.7,  4.1,   4.3,   4.7,  4.7,  40.8

If we computed the mean, we would say that the average house price is 744,000.  Although this number is true, it does not reflect the price for available housing in South Lake Tahoe.  A closer look at the data shows that the house valued at 40.8 x $100,000  =  $4.08 million skews the data.  Instead, we use the median.  Since there is an even number of outcomes, we take the average of the middle two

      3.7 + 4.1                        =  3.9            2

The median house price is $390,000.  This better reflects what house shoppers should expect to spend.

        

There is an alternative value that also is resistant to outliers.  This is called the trimmed mean which is the mean after getting rid of the outliers or 5% on the top and5% on the bottom.  We can also use the trimmed mean if we are concerned with outliers skewing the data, however the median is used more often since more people understand it.

Example:

At a ski rental shop data was collected on the number of rentals on each of ten consecutive Saturdays: 

        44, 50, 38, 96, 42, 47, 40, 39, 46, 50.

 

To find the sample mean, add them and divide by 10:

         44 + 50 + 38 + 96 + 42 + 47 + 40 + 39 + 46 + 50                                                                                        = 49.2                                        10

Notice that the mean value is not a value of the sample.

To find the median, first sort the data:

        38, 39, 40, 42, 44, 46, 47, 50, 50, 96

Notice that there are two middle numbers 44 and 46.  To find the median we take the average of the two.

                             44 + 46        Median  =                      =  45                                  2

Notice also that the mean is larger than all but three of the data points.  The mean is influenced by outliers while the median is robust.

Standard Deviation

The mean, mode, median, and trimmed mean do a nice job in telling where the center of the data set is, but often we are interested in more.  For example, a pharmaceutical engineer develops a new drug that regulates iron in the blood.  Suppose she finds out that the average sugar content after taking the medication is the optimal level.  This does not mean that the drug is effective.  There is a possibility that half of the patients have dangerously low sugar content while the other half have dangerously high content.  Instead of the drug being an effective regulator, it is a deadly poison.  What the pharmacist needs is a measure of how far the data is spread apart.  This is what the variance and standard deviation do.  First we show the formulas for these measurements.  Then we will go through the steps on how to use the formulas.

We define the variance to be 

        

and the standard deviation to be

        

Variance and Standard Deviation: Step by Step

1. Calculate the mean, x. 2. Write a table that subtracts the mean from each observed value.

3. Square each of the differences.

4. Add this column.

5. Divide by n -1 where n is the number of items in the sample  This is the variance.

6. To get the standard deviation we take the square root of the variance.  

 

Example

The owner of the Ches Tahoe restaurant is interested in how much people spend at the restaurant.  He examines 10 randomly selected receipts for parties of four and writes down the following data.

        44,   50,   38,   96,   42,   47,   40,   39,   46,   50

He calculated the mean by adding and dividing by 10 to get

        x  =  49.2

Below is the table for getting the standard deviation:

x x - 49.2 (x - 49.2 )2  

44 -5.2 27.04

50 0.8 0.64

38 11.2 125.44

96 46.8 2190.24

42 -7.2 51.84

47 -2.2 4.84

40 -9.2 84.64

39 -10.2 104.04

46 -3.2 10.24

50 0.8 0.64

Total   2600.4

 

Now 

        2600.4                         =  288.7        10 - 1

Hence the variance is 289 and the standard deviation is the square root of  289 = 17.

Since the standard deviation can be thought of measuring how far the data values lie from the mean, we take the mean and move one standard deviation in either direction.  The mean for this example was about 49.2 and the standard deviation was 17.  We have: 

49.2 - 17 = 32.2

 and 49.2 + 17 = 66.2 

What this means is that most of the patrons probably spend between $32.20 and $66.20.

 

The sample standard deviation will be denoted by s and the population standard deviation will be denoted by the Greek letter .

The sample variance will be denoted by s2 and the population variance will be denoted by 2.

The variance and standard deviation describe how spread out the data is.  If the data all lies close to the mean, then the standard deviation will be small, while if the data is spread out over a large range of values, s will be large.  Having outliers will increase the standard deviation.

One of the flaws involved with the standard deviation, is that it depends on the units that are used.  One way of handling this difficulty, is called the coefficient of variation which is the standard deviation divided by the mean times 100%

                                  CV  =           100%                                

In the above example, it is 

         17                   100%   =  34.6%        49.2

This tells us that the standard deviation of the restaurant bills is 34.6% of the mean.

 

Master of Business Administration - Semester 3 MB 0050: “Research Methodology” (4 credits) (Book ID: B1206)

ASSIGNMENT- Set 2Marks 60 Note: Each Question carries 10 marks. Answer all the questions.

NAME=JAGADEESHA.S ROLL NO =511118390

1. What is the significance of research in social and business sciences?

Significance of Research in Social and Business Sciences

According to a famous Hudson Maxim, “All progress is born of inquiry. Doubt is often better

than overconfidence, for it leads to inquiry, and inquiry leads to invention”. It brings out the

significance of research, increased amounts of which makes progress possible. Research

encourages scientific and inductive thinking, besides promoting the development of logical

habits of thinking and organization.

The role of research in applied economics in the context of an economy or business is

greatly increasing in modern times. The increasingly complex nature of government and

business has raised the use of research in solving operational problems. Research

assumes significant role in formulation of economic policy, for both the government and

business. It provides the basis for almost all government policies of an economic system.

Government budget formulation, for example, depends particularly on the analysis of needs

and desires of the people, and the availability of revenues, which requires research.

Research helps to formulate alternative policies, in addition to examining the consequences

of these alternatives. Thus, research also facilitates the decision making of policy-makers,

although in itself it is not a part of research. In the process, research also helps in the

proper allocation of a country’s scare resources. Research is also necessary for collecting

information on the social and economic structure of an economy to understand the process

of change occurring in the country. Collection of statistical information though not a routine

task, involves various research problems. Therefore, large staff of research technicians or

experts is engaged by the government these days to undertake this work. Thus, research

as a tool of government economic policy formulation involves three distinct stages of

operation which are as follows:

· Investigation of economic structure through continual compilation of facts

· Diagnoses of events that are taking place and the analysis of the forces underlying them;

and

· The prognosis, i.e., the prediction of future developments

Research also assumes a significant role in solving various operational and planning

problems associated with business and industry. In several ways, operations research,

market research, and motivational research are vital and their results assist in taking

business decisions. Market research is refers to the investigation of the structure and

development of a market for the formulation of efficient policies relating to purchases,

production and sales. Operational research relates to the application of logical,

mathematical, and analytical techniques to find solution to business problems such as cost

minimization or profit maximization, or the optimization problems. Motivational research

helps to determine why people behave in the manner they do with respect to market

characteristics.

More specifically, it is concerned with the analyzing the motivations underlying consumer behaviour. All these researches are very useful for business and industry, which are responsible for business decision making.

Research is equally important to social scientist for analyzing social relationships and

seeking explanations to various social problems. It gives intellectual satisfaction of knowing

things for the sake of knowledge. It also possesses practical utility for the social scientist to

gain knowledge so as to be able to do something better or in a more efficient manner. This,

research in social sciences is concerned with both knowledge for its own sake, and

knowledge for what it can contribute to solve practical problems.

2. What is the meaning of hypothesis? Discuss the types of hypothesis. According to Theodorson and Theodorson, “a hypothesis is a tentative statement asserting a relationship between certain facts. Kerlinger describes it as “a conjectural statement of the relationship between two or more variables”. Black and Champion have described it as “a tentative statement about something, the validity of which is usually unknown”. This statement is intended to be tested empirically and is either verified or rejected. It the statement is not sufficiently established, it is not considered a scientific law. In other words, a hypothesis carries clear implications for testing the stated relationship, i.e., it contains variables that are measurable and specifying how they are related. A statement that lacks variables or that does not explain how the variables are related to each other is no hypothesis in scientific sense.

Types of Hypothesis

There are many kinds of hypothesis the researcher has to be working with. One type of hypothesis asserts that something is the case in a given instance; that a particular object, person or situation has particular characteristics. Another type of hypothesis deals with the frequency of occurrence or of association among variables; this type of

hypothesis may state that X is associated with Y. A certain Y proportion of items e.g. urbanism tends to be accompanied by mental disease or than something are greater or lesser than some other thing in specific settings. Yet another type of hypothesis asserts that a particular characteristics is one of the factors which determine another characteristic, i.e. X is the producer of Y. hypothesis of this type are called causal hypothesis.

Null Hypothesis and Alternative Hypothesis

In the context of statistical analysis, we often talk null and alternative hypothesis. If we are to compare method A with method B about its superiority and if we proceed on the assumption that both methods are equally good, then this assumption is termed as null hypothesis. As against this, we may think that the method A is superior, it is alternative hypothesis. Symbolically presented as:

Null hypothesis = H0 and Alternative hypothesis = Ha

Suppose we want to test the hypothesis that the population mean is equal to the hypothesis mean (µ H0) = 100. Then we would say that the null hypotheses are that the population mean is equal to the hypothesized mean 100 and symbolical we can express as: H0: µ= µ H0=100If our sample results do not support these null hypotheses, we should conclude that something else is true. What we conclude rejecting the null hypothesis is known as alternative hypothesis. If we accept H0, then we are rejecting Ha and if we reject H0, then we are accepting Ha. For H0: µ= µ H0=100, we may consider three possible alternative hypotheses as follows:

The null hypothesis and the alternative hypothesis are chosen before the sample is drawn (the researcher must avoid the error of deriving hypothesis from the data he collects and testing the hypothesis from the same data). In the choice of null hypothesis, the following considerations are usually kept in view:

· Alternative hypothesis is usually the one which wishes to prove and the null hypothesis are ones that wish to disprove. Thus a null hypothesis represents the hypothesis we are trying to reject, the alternative hypothesis represents all other possibilities.

· If the rejection of a certain hypothesis when it is actually true involves great risk, it is taken as null hypothesis because then the probability of rejecting it when it is true is α (the level of significance) which is chosen very small.

· Null hypothesis should always be specific hypothesis i.e., it should not state about or approximately a certain value.

· Generally, in hypothesis testing we proceed on the basis of null hypothesis, keeping the alternative hypothesis in view. Why so? The answer is that on assumption that null hypothesis is true, one can assign the probabilities to different possible sample results, but this cannot be done if we proceed with alternative hypothesis. Hence the use of null hypothesis (at times also known as statistical hypothesis) is quite frequent.

3. Explain the sampling process  Sampling Procedure

The decision process of sampling is complicated one. The researcher has to first identify the limiting factor or factors and must judiciously balance the conflicting factors. The various criteria governing the choice of the sampling technique:

1. Purpose of the Survey: What does the researcher aim at? If he intends to generalize the findings based on the sample survey to the population, then an appropriate probability sampling method must be selected. The choice of a particular type of probability sampling depends on the geographical area of the survey and the size and the nature of the population under study.

2. Measurability: The application of statistical inference theory requires computation of the sampling error from the sample itself. Probability samples only allow such computation. Hence, where the research objective requires statistical inference, the sample should be drawn by applying simple random sampling method or stratified random sampling method, depending on whether the population is homogenous or heterogeneous.

3. Degree of Precision: Should the results of the survey be very precise, or even rough results could serve the purpose? The desired level of precision as one of the criteria of sampling method selection. Where a high degree of precision of results is desired, probability sampling should be used. Where even crude results would serve the purpose (E.g., marketing surveys, readership surveys etc) any convenient non-random sampling like quota sampling would be enough.

4. Information about Population: How much information is available about the population to be studied? Where no list of population and no information about its nature are available, it is difficult to apply a probability sampling method. Then exploratory study with non-probability sampling may be made to gain a better idea of population. After gaining sufficient knowledge about the population through the exploratory study, appropriate probability sampling design may be adopted.

5. The Nature of the Population: In terms of the variables to be studied, is the population homogenous or heterogeneous? In the case of a homogenous population, even a simple random sampling will give a representative sample. If the population is heterogeneous, stratified random sampling is appropriate.

6. Geographical Area of the Study and the Size of the Population: If the area covered by a survey is very large and the size of the population is quite large, multi-

stage cluster sampling would be appropriate. But if the area and the size of the population are small, single stage probability sampling methods could be used.

7. Financial resources: If the available finance is limited, it may become necessary to choose a less costly sampling plan like multistage cluster sampling or even quota sampling as a compromise. However, if the objectives of the study and the desired level of precision cannot be attained within the stipulated budget, there is no alternative than to give up the proposed survey. Where the finance is not a constraint, a researcher can choose the most appropriate method of sampling that fits the research objective and the nature of population.

8. Time Limitation: The time limit within which the research project should be completed restricts the choice of a sampling method. Then, as a compromise, it may become necessary to choose less time consuming methods like simple random sampling instead of stratified sampling/sampling with probability proportional to size; multi-stage cluster sampling instead of single-stage sampling of elements. Of course, the precision has to be sacrificed to some extent.

9. Economy: It should be another criterion in choosing the sampling method. It means achieving the desired level of precision at minimum cost. A sample is economical if the precision per unit cost is high or the cost per unit of variance is low.

The above criteria frequently conflict and the researcher must balance and blend them to obtain to obtain a good sampling plan. The chosen plan thus represents an adaptation of the sampling theory to the available facilities and resources. That is, it represents a compromise between idealism and feasibility. One should use simple workable methods instead of unduly elaborate and complicated techniques.

4. Distinguish between Schedules and Questionnaires.

Difference between a schedule and a questionnaire

There is a difference between a schedule and a questionnaire. A scheduleis a form that the investigator fills himself through surveying the units or individuals. A questionnaire is a form sent (usually mailed) by an investigator to respondents. The respondent has to fill it and then send it back to the investigator.

Questionnaires

Often, information is collected through questionnaires. The questionnaires are filled with questions pertaining to the investigation. They are sent to the respondents with a covering letter soliciting cooperation from the respondents (respondents are the people who respond to questions in thequestionnaire). The respondents are asked to give correct information and to mail the questionnaire back. The objectives of investigation are explained in the covering letter together with assurance for keeping information provided by the respondents as confidential.

Good questionnaire construction is an important contributing factor to the success of a survey. When questionnaires are properly framed and constructed, they become important tools by which statements can be made about specific people or entire populations.

This method is generally adopted by research workers and other official and non-official agencies. This method is used to cover large areas ofinvestigation. It is more economical and free from investigator’s bias. However, it results in many “non-response” situations. The respondent may be illiterate. The respondent may also provide wrong information due to wrong interpretation of questions.

Schedule Filled By Investigators

Information can be collected through schedules filled by investigators through personal contact. In order to get reliable information, the investigator should be well trained, tactful, unbiased and hard working.

A schedule is suitable for an extensive area of investigation through investigator’s personal contact. The problem of non-response is minimised.

There is a difference between a schedule and a questionnaire. A scheduleis a form that the investigator fills himself through surveying the units or individuals. A questionnaire is a form sent (usually mailed) by an investigator to respondents. The respondent has to fill it and then send it back to the investigator.

5. What are the problems encountered in the interview?

Interview Problems

In personal interviewing, the researcher must deal with two major problems, inadequate response, non-response and interviewer’s bias.

Inadequate response

Kahn and Cannel distinguish five principal symptoms of inadequate response. They are:

· partial response, in which the respondent gives a relevant but incomplete answer

· non-response, when the respondent remains silent or refuses to answer the question

· irrelevant response, in which the respondent’s answer is not relevant to the question asked

· inaccurate response, when the reply is biased or distorted and

· verbalized response problem, which arises on account of respondent’s failure to understand a question or lack of information necessary for answering it.

Interviewer’s Bias

The interviewer is an important cause of response bias. He may resort to cheating by ‘cooking up’ data without actually interviewing. The interviewers can influence the responses by inappropriate suggestions, word emphasis, tone of voice and question rephrasing. His own

attitudes and expectations about what a particular category of respondents may say or think may bias the data. Another source of response of the interviewer’s characteristics (education, apparent social status, etc) may also bias his answers. Another source of response bias arises from interviewer’s perception of the situation, if he regards the assignment as impossible or sees the results of the survey as possible threats to personal interests or beliefs he is likely to introduce bias.

As interviewers are human beings, such biasing factors can never be overcome completely, but their effects can be reduced by careful selection and training of interviewers, proper motivation and supervision, standardization or interview procedures (use of standard wording in survey questions, standard instructions on probing procedure and so on) and standardization of interviewer behaviour. There is need for more research on ways to minimize bias in the interview.

Non-response

Non-response refers to failure to obtain responses from some sample respondents. There are many sources of non-response; non-availability, refusal, incapacity and inaccessibility.

Non-availability

Some respondents may not be available at home at the time of call. This depends upon the nature of the respondent and the time of calls. For example, employed persons may not be available during working hours. Farmers may not be available at home during cultivation season. Selection of appropriate timing for calls could solve this problem. Evenings and weekends may be favourable interviewing hours for such respondents. If someone is available, then, line respondent’s hours of availability can be ascertained and the next visit can be planned accordingly.

Refusal

Some persons may refuse to furnish information because they are ill-disposed, or approached at the wrong hour and so on. Although, a hardcore of refusals remains, another try or perhaps another approach may find some of them cooperative. Incapacity or inability may refer to illness which prevents a response during the entire survey period. This may also arise on account of language barrier.

Inaccessibility

Some respondents may be inaccessible. Some may not be found due to migration and other reasons. Non-responses reduce the effective sample size and its representativeness.

Methods and Aims of control of non-response

Kish suggests the following methods to reduce either the percentage of non-response or its effects:

1. Improved procedures for collecting data are the most obvious remedy for non-response. Improvements advocated are (a) guarantees of anonymity, (b) motivation of the respondent to co-operate (c) arousing the respondents’ interest with clever opening remarks and questions, (d) advance notice to the respondents.

2. Call-backs are most effective way of reducing not-at-homes in personal interviews, as are repeated mailings to no-returns in mail surveys.

3. Substitution for the non-response is often suggested as a remedy. Usually this is a mistake because the substitutes resemble the responses rather than the non-responses. Nevertheless, beneficial substitution methods can sometimes be designed with reference to important characteristics of the population. For example, in a farm management study, the farm size is an important variable and if the sampling is based on farm size, substitution for a respondent with a particular size holding by another with the holding of the same size is possible.

Attempts to reduce the percentage or effects on non-responses aim at reducing the bias caused by differences on non-respondents from respondents. The non-response bias should not be confused with the reduction of sampled size due to non-response. The latter effect can be easily overcome, either by anticipating the size of non-response in designing the sample size or by compensating for it with a supplement. These adjustments increase the size of the response and the sampling precision, but they do not reduce the non-response percentage or bias.

6. Write short notes on the following: a. Dispersion

b. Mathematical averages Dispersion

            A modern student of statistics is mainly interested in the study of variability and uncertainty. In this section we shall discuss variability and its measures and uncertainty will be discussed in probability. We live in a changing world. Changes are taking place in every sphere of life. A man of statistics does not show much interest in those things which are constant. The total area of the earth may not be very important to a research minded person but the area under different crops, area covered by forests, area covered by residential and commercial buildings are figures of great importance because these figures keep on changing form time to time and from place to place. Very large number of experts is engaged in the study of changing phenomenon. Experts working in different countries of the world keep a watch on forces which are responsible for bringing changes in the fields of human interest. The agricultural, industrial and mineral production and their transportation from one part to the other parts of the world are the matters of great interest to the economists, statisticians, and other experts. The changes in human population, the changes in standard living, and changes in literacy rate and the changes in price attract the experts to make detailed studies about them and then correlate these changes with the human life. Thus variability or variation is something connected with human life and study is very important for mankind.

Dispersion:            The word dispersion has a technical meaning in statistics. The average measures the

center of the data. It is one aspect observations. Another feature of the observations is as to how the observations are spread about the center. The observation may be close to the center or they may be spread away from the center. If the observation are close to the center (usually the arithmetic mean or median), we say that dispersion, scatter or variation is small. If the observations are spread away from the center, we say dispersion is large. Suppose we have three groups of students who have obtained the following marks in a test. The arithmetic means of the three groups are also given below:

Group A: 46, 48, 50, 52, 54        

Group B: 30, 40, 50, 60, 70        

Group C: 40, 50, 60, 70, 80        

In a group A and B arithmetic means are equal i.e. . But in group A the observations are concentrated on the center. All students of group A have almost the same level of performance. We say that there is consistence in the observations in group A. In group B the mean is 50 but the observations are not closed to the center. One observation is as small as 30 and one observation is as large as 70. Thus there is greater dispersion in group B. In group C the mean is 60 but the spread of the observations with respect to the center 60 is the same as the spread of the observations in group B with respect to their own center which is 50. Thus in group B and C the means are different but their dispersion is the same. In group A and C the means are different and their dispersions are also different. Dispersion is an important feature of the observations and it is measured with the help of the measures of dispersion, scatter or variation. The word variability is also used for this idea of dispersion.            The study of dispersion is very important in statistical data. If in a certain factory there is consistence in the wages of workers, the workers will be satisfied. But if some workers have high wages and some have low wages, there will be unrest among the low paid workers and they might go on strikes and arrange demonstrations. If in a certain country some people are very poor and some are very high rich, we say there is economic disparity. It means that dispersion is large. The idea of dispersion is important in the study of wages of workers, prices of commodities, standard of living of different people, distribution of wealth, distribution of land among framers and various other fields of life. Some brief definitions of dispersion are:

1. The degree to which numerical data tend to spread about an average value is called the dispersion or variation of the data.

2. Dispersion or variation may be defined as a statistics signifying the extent of the scatteredness of items around a measure of central tendency.

3. Dispersion or variation is the measurement of the scatter of the size of the items of a series about the average.