Question No. 5 - MMPC -002 - Human Resource Management - MBA and MBA (Banking & Finance)

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                MMPC -002 - Human Resource Management

Question No. 5 

Discuss the relevance of the understanding of job design for developing organisational effectiveness.

One of the most frequent questions people often ask one another when they first meet is “What are you doing?” Instead of saying, “I teach in a college” or “I treat patients”, a person often says, “I am a teacher” or “I am a doctor” like this. This exhibits the occupation of a person. Occupation provides a person his identity. It talks a great deal about ones social position. of occupation also implies a set of social relationships (Hughes, 1945). For instance, the occupation as a professor implies that students , other professors, and publishers’ representatives are part of the set of their social relationships. Finally, occupations are by and large linked to the work of adults (Roe, 1956). The work carried out by teenagers is not often considered as an occupation, because it does not frame a major part of their identities as individuals. Taking into account all of these considerations, occupation is defined as “the social role performed by adult members of society that directly and/or indirectly yields social and financial consequences and that constitutes a major focus in the life of an adult” (Hall, 1975). A job is a person’s occupation at one point in time. A career is the unfolding sequences of jobs that a person has over the life course. 

The term ‘job design’ refers to the way the tasks are combined to form a complete job. It can be defined as building the specifications of the position, contents, method and relationships of the job so as to meet with various technological and organizational requirements as well as meet the personal needs job holders. According to Bowditch and Buono, job design refers to “ any set of activities that involve the alteration of specific jobs or interdependent systems of jobs with the intent of improving the quality of employee job experience and their on- the-job productivity.” 

While designing a job, the following points are to be borne in mind: 

Job redesign is an essential allegiance to quality improvement of the individual, and the organization. It should be performed from either the bottom up, or top down, depending upon the hierarchy and responsibility of the position and its relationships within the organization. 

Job design is a process which integrates work content (tasks, functions, relationships), the reward (extrinsic and intrinsic), and the qualifications required (skills, knowledge, abilities) for each job in a way that meet the needs of employees and the organization. 

Some jobs are routine because the tasks are consistent and repetitive; other are non-routine. Some require a large number of varied and diverse skills; other are narrow in scope. Some jobs constrict employees by requiring them to follow very clear-cut procedures; others allow employees considerable autonomy in how they do their work. 

Some jobs are most effectively accomplished by groups of employees working as a team; whereas other jobs are best done by individuals acting essentially independently. Thus jobs differ in the way their tasks are combined, and different combinations produce a variety of job designs in the organization.

There are three objectives of jobs design which are as follows: 

- to meet the organizational requirements such as higher productivity, operational efficiency, quality of product/service, etc.;

- to satisfy the needs of the individual employees like interest, challenge, achievement or accomplishment, etc.; and

- to integrate the needs of the individual with the organizational requirements.

While designing a job, the following factors are taken into consideration. 

i. The volume of work - it will determine by and large the number of jobs. 

ii. The complexity of the work - to be carried out, both in terms of its variety or breadth and its technical difficulty or depth. 

iii. The work processes involved -It might be desirable for one person to be involved in an entire process, or the work flows may be such that the work process has to be divide between several different people. 

iv. The nature of the people currently employed in the organization- The extent to which jobs can be redesigned depend largely on the kind of people employed 

v. The sequence of flows in the process- the succession of events and their timings affect how the work can be organized. Where activities are carried out over a longer period, this is likely to be the cause of greater complexity. 

vi. The timescales - where immediate responses are required, specific jobs may have to be earmarked to provide such responses. Work requiring longer planning horizons is likely to be more complex and needs therefore to be done at a higher level. 

vii. The geographical scattering of the organization’s activities . 

viii. The involvement of other parts of the organization in the overall process- there may be a need for extensive communication and coordination and the design of jobs should take account of the way this is to be achieved. 

ix. The effect of information technology (Cushway and Lodge, 2001).

In recent years, as computers and high technology become more and more ingrained in the modern workplace, the basic problem arises how to successfully fit technological advancements into job designs. 

Automation 

Highly simplified jobs often cause problems because they offer little intrinsic motivation for the worker. The tasks have been defined so narrowly that they lack challenge and cause boredom when someone repeats them over and over again. Given the high technology available today, one way to deal with this problem is by complete automation-allowing a machine to do the work previously accomplished through human effort. This approach increasingly involves the use of robots, which are becoming more and more useful and reliable. 

Flexible Manufacturing 

Flexible manufacturing cells, teams of workers using special technology, exploit adaptive and integrated job designs to shift work among alternative products. This approach is gradually more widespread. Under this system, a cellular manufacturing system hold a number of automated production machines that cut, shape, drill, and fasten together various metal components. Each machine is attached to the others by convertible conveyor grids that allow quick change from manufacturing one product to another-such as from air-conditioner compressors to engine crankshafts. Workers in the cells perform very few routine assembly-line tasks. As an alternative, they dedicate most time to make certain that operations are carried out correctly and to handling changeovers from one product configuration to another. Above and beyond, to keep production flowing slickly, each worker needs to improve expertise across a wide range of tasks. In this way flexible manufacturing cells comprise jobs that are often enriched on the core characteristics.

Electronic Offices 

Electronic office technology was the key when U.S. Healthcare, a large, private-practice based health maintenance organisation (HMO), became interested in improving the quality of its health-care services. The company installed large electronic bulletin boards that monitored progress toward a range of performance goals. It also installed an electronic main (e-main) system, used robots to dispense paper mail, and installed a computerized telephone answering machine. Fundamentally, the company tried to automate as many tasks as possible to free employees for more challenging work. Continuing development in these electronic offices present many new job opportunities for those with the necessary abilities and interests, but they can be stressful and difficult for those who lack the necessary education or skills. Clearly, today’s high technologies must be carefully integrated with the human factor, and continuing education and training are still needed to equip people to deal with emerging workplace technologies.

Work-Flow and Process Reengineering 

One of the most recent approaches for upgrading job designs and organisational performance is based on the concept of process reengineering. Process engineering means the analysis, reshuffling, and reconfiguration of actions and tasks required to reach a work goal. This approach methodically breaks work processes down into their specific components and subtasks, analyses each for relevance and simplicity, and then does everything possible to reconfigure the process to eliminate wasted time, effort, and resources. Job redesign through process reengineering focus on every step in the process, from the seeking out for items and vendors, to the obtaining of bids, to the completion of necessary forms, to the securing of required signatures and approvals, to the actual placing of the order, and so on to the point at which the new computer actually arrives, is checked in, is placed into an equipment inventory, and is finally delivered to the workplace.

Question No. 4 - MMPC -002 - Human Resource Management - MBA and MBA (Banking & Finance)

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                MMPC -002 - Human Resource Management

Question No. 4 - 

Define collective bargaining in your own words. Critically evaluate the state of collective bargaining in the present day business scenario. Explain with examples your answer giving due details of the organisations and the sources you are referring to.

Collective bargaining, the ongoing process of negotiation between representatives of workers and employers to establish the conditions of employment. The collectively determined agreement may cover not only wages but hiring practices, layoffs, promotions, job functions, working conditions and hours, worker discipline and termination, and benefit programs.

Collective bargaining existed before the end of the 18th century in Britain; its development occurred later on the European continent and in the United States, where Samuel Gompers developed its common use during his leadership of the American Federation of Labor. Collective agreements are probably least significant in developing countries that have large labour populations from which to draw.

The degree of centralization in the bargaining process and the functions performed by collective agreements vary. Contract negotiation may occur at the national, regional, or local level, depending on the structure of industry within a country. National agreements, which are more common in smaller countries, usually settle general matters, leaving more detailed issues for local consideration. An agreement may, for example, set actual wage rates, or it might simply establish minimum wage rates.

Collective agreements are not legally binding in all countries. In Britain their application depends on the goodwill of the signatories. In some countries—including Germany, France, and Australia—the government may require that the terms of negotiated settlements be extended to all firms in an industry. In the United States similar results have been achieved, albeit less formally, by unions that select a target employer in a particular industry: the negotiation of a new agreement with the targeted employer then sets the pattern for other labour contracts in the same industry.

Like many other countries, in India, collective bargaining got some impetus from various statutory and voluntary provisions. The Trade Disputes Act 1929, the Bombay Industrial Relations Act 1946, the Industrial Disputes Act 1947, and the Madhya Pradesh Industrial Relations Act 1960, provided a machinery for consultation and paved the way for collective bargaining. Among the voluntary measures, mention may be made to the different tripartite conferences, joint consultative machineries, code of discipline, and Central and State Implementation and Evaluation Units. 

In India, collective bargaining was not very popular till the end of the Second World War. However, there had been a few instances where wages and working conditions were regulated by collective agreements even earlier. The collective bargaining agreements have been concluded at three levels – at plant level, industry level, and national level. Collective bargaining was traditionally conducted at the plant level as in the case of TISCO, Indian Aluminium Company, and Bata Shoe Company. 

In some industrial units, detailed grievance procedures have been laid down by mutual agreements. The collective agreement signed between the TISCO and the Tata Workers’ Union in 1956 embodies a provision for grievance procedure and closer association of employees with management. The Belur Report of 1958, which is a study by Subbiah Kannappan and his associates in the Indian Aluminium Company is one of the best published case studies on collective bargaining in India. It throws light on the factors responsible for creating a favourable bargaining relationship between the management and the union. The best example of an industry level agreement is offered by the textile industry of Bombay and Ahmedabad. The agreement between the Ahmedabad Millowners’ Association and the Ahmedabad Textile Labour Association, which were signed on 27 th June 1955, laid down the procedure to be followed for the grant of bonus and the voluntary settlement of industrial disputes. The practice of industrywise bargaining continues to prevail in the cotton textile industry in Maharashtra, Gujarat, and Tamil Nadu; in Jute Textiles and in the plantation industry in Karnataka, Tamil Nadu, and West Bengal. In most other industries, particularly in modern industry groups, collective agreements are entered into at the plant or enterprise level.

The agreements at the national level are generally bipartite agreements and are finalised at conferences of labour and management convened by the Government of India. The bonus agreement for plantation workers was concluded in January 1956 between the representatives of the Indian Tea Association and the India Tea Planters’ Association on the one hand, and the Hind Mazdoor Sabha (HMS) and the Indian National Trade Union Congress (INTUC) on the other. The agreement was about the payment of bonus to about one million plantation workers. The Employer’s Federation of India (EFI) in a study of collective bargaining in its member organisations in the late sixties (published in a monograph in 1970) classified collective agreements into three categories: (i) agreements which have been drawn up after direct negotiations between the parties and are purely voluntary in character for the purpose of their implementation; (ii) agreements which combine the elements of voluntarism and compulsion, i.e., those negotiated by the parties and registered before a conciliator as settlements; and (iii) agreements which acquire legal status because of successful discussion between the parties when the matters in disputes were under reference to industrial tribunal/ courts and could be considered sub judice, the agreements reached being recorded by the tribunals/courts as consent awards.

The EFI study covered 109 collective agreements, relating to 77 companies and 11 industrial associations. Results of the study show that the collective agreements have included all levels. Industry-wide agreements were concluded in engineering, textiles, and tea plantations, and plant-wide or company-wide agreements were the norm in most other industries. The EFI study found “two categories of subjects (which) appear to have figured prominently in the collective agreements, one having a direct bearing on the pay packet and the other relating to leisure and leave.” Wages, dearness allowance, retirement benefits and bonus (appearing in a majority of agreements) are illustrations of the first category, and annual leave, paid holidays, and casual leave are (included in a smaller but substantial number of agreements) of the second. Out of the 109 agreements analysed, 96 dealt with wages and 50 with bonus. As for the duration of the agreements as many as 49 (i.e. 45 percent) were for a period of 3 years, 18 were for a period of 5 years and only a small number were for a period of less than 2 years. There are 31 agreements, which dealt with the whole range of topics comprising wages, conditions of employment, and fringe benefits. The rest of them covered one or more specific subjects. The study makes the following concluding observations:

“Another notable feature of the agreements under reference, which is of considerable importance for the development and maintenance of harmonious industrial relations, is the recognition of their mutual rights and responsibilities by the representatives of management and employees. Under a number of agreements, the unions have recognised the right of the management among other things, to introduce new or improved methods of production, establish production schedules and standards, and make rules for maintaining discipline and securing effective operation of the plant. The right of the management to discharge workers for just cause, including inefficiency and lack of work, has also been conceded. The managements on their part have recognised the unions as bargaining agents and pledged to desist from unfair labour practices such as interference with the right of the workman to organise and join a union and discrimination against them because of their membership of a union. In the same manner, the trade unions have agreed to follow the constitutional methods as laid down in the grievance procedure to redress the grievances of their members and to desist from indulging in or encouraging unfair labour practices.”

A significant development of the 1970s is the emergence of bipartite national industrial committees in certain key industrial sectors such as coalmining, textile, sugar, electrical machinery, steel and cement. These committees comprise representatives of all major trade unions and employers in a given industry, and operate under broad terms of reference, which include a revision of wage structure and conditions of employment. These negotiating committees have covered both public and private sectors. The development of joint negotiating committees is a clear indication of the preference of the parties for collective bargaining over other methods. Although a variety of industries have been covered under this approach, we would confine our examination to two of the most important ones, namely, steel and coal mine. The new experiment, in the form of bipartite negotiating committees was first pioneered in the iron and steel industry in 1970. Early in that year a Joint Steel Wage Negotiating Committee [later on re-christened as National Joint Committee for the Steel Industry (NJCS)] was formed. The NJCS is composed of representatives of employers and employees. The employers’ side is represented by Indian Iron and Steel Company (IISCO) and Tata Iron and Steel Company (TISCO), and from all the public sector steel plants. On the employees’ side, there are three members each from the central trade union organisations, namely, the All India Trade Union Congress (AITUC), the Indian National Trade Union Congress (INTUC), Centre of Indian Trade Unions (CITU), and the Hind Mazdoor Sabha (HMS). Till now, the Committee has signed six agreements.

The coalmines in India were nationalised in 1973. There were agitations by the unions prior to 1973 because wage improvements sanctioned by the Coal Wage Board in 1967 had not been implemented by several (private) mine owners and operators. The government was not in favour of appointing another wage board for the industry and felt, instead, that wages and other matters in coalmining should be settled through negotiations and collective bargaining. With this end in view, a Joint Bipartite Committee for Coal Industry (JBCCI) was constituted on August 14, 1973 with union representatives from INTUC, AITUC and HMS, and management representatives from Coal Mines Authority, Tata Iron and Steel Company, and Indian Iron and Steel Company. 

Subsequently, the committee has signed six bipartite settlements regarding wage revision, working conditions, and other issues relating to coal industry. 

The National Commission on Labour (1966-69) reported the existence of collective agreements at all levels.

Most of the collective agreements have been at the plant level, though in the important textile centres like Bombay and Ahmedabad, industry-level agreements have been common. Such agreements are also to be found in the plantation industry in the South and in Assam, and in the coal industry. Apart from these, in new industries like chemicals, petroleum, oil refining and distribution, aluminium, manufacture of electrical and other equipment, and automobile repairing, arrangements of settlement of disputes through voluntary agreements have been common in recent years. In ports and docks, collective agreements have been the rule at individual centers. 

On certain matters affecting all ports, all India agreements have been reached. In the banking industry, after a series of awards, employers and unions are in recent years coming closer to reach collective agreements. On the whole, the record of reaching collective agreements has not been unsatisfactory, though its extension to a wider area is certainly desirable. For promotion of collective bargaining, the commission recommended, among other things, statutory recognition of representative union as the sole bargaining agent. The following steps may be considered for promoting collective bargaining in our country: 

1) Collective bargaining should be declared as an integral part of India’s national industrial relations policy. In order to give it a constitutional sanctity, it should be incorporated in the Directive Principles of State Policy. 

2) The two relevant instruments setting international standards regarding collective bargaining, namely, Convention 98 concerning the application of principles of the right to organise and to bargain collectively adopted in 1949, and Recommendation 91 concerning collective agreements adopted in 1951, should be ratified/implemented. 

3) Collective bargaining should be adopted as a part of the corporate personnel policy in all public sector enterprises, departmental undertakings, and in public utility services. 

4) There should be drastic trade union reforms such as: 

(a) recognition of the majority union as a bargaining agent; 

(b) development of a trained and educated cadre of worker-leaders through strong, enlightened, responsible and democratic trade unions; and 

(c) gradual delinking of trade unions from political parties. 

5) More emphasis should be given on mutual settlement of industrial disputes through collective bargaining rather than adjudication. A beginning has to be made in this direction by declaring that collective bargaining will acquire primacy in the procedure for settling industrial disputes.

Question No. 5 - MCO-03 - Research Methodology and Statistical Analysis

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                               MCO-03 - 

    Research Methodology and Statistical Analysis

                           Mcom - 2nd Year

Question No. 5

Distinguish between the following: 

(a) Primary and Secondary Data 

In statistical analysis, collection of data plays a significant part. The method of collecting information is divided into two different sections, namely primary data and secondary data. In this process, the primary data is assembling data or information for the first time, whereas the secondary data is the data that has already been gathered or collected by others.

The most important characteristics of the primary data is that it is original and first-hand, whereas the secondary data is the interpretation and analysis of the primary data.

Primary Data Definition

Primary data is the data that is collected for the first time through personal experiences or evidence, particularly for research. It is also described as raw data or first-hand information. The mode of assembling the information is costly, as the analysis is done by an agency or an external organisation, and needs human resources and investment. The investigator supervises and controls the data collection process directly.

The data is mostly collected through observations, physical testing, mailed questionnaires, surveys, personal interviews, telephonic interviews, case studies, and focus groups, etc.

Secondary Data Definition

Secondary data is a second-hand data that is already collected and recorded by some researchers for their purpose, and not for the current research problem. It is accessible in the form of data collected from different sources such as government publications, censuses, internal records of the organisation, books, journal articles, websites and reports, etc.

This method of gathering data is affordable, readily available, and saves cost and time. However, the one disadvantage is that the information assembled is for some other purpose and may not meet the present research purpose or may not be accurate.

The differences between the primary and secondary data are represented in a comparison format as follows:

 

Primary Data	Secondary Data
Definition
Primary data are those that are collected for the first time.	Secondary data refer to those data that have already been collected by some other person.
Originality
These are original because these are collected by the investigator for the first time.	These are not original because someone else has collected these for his own purpose.
Nature of Data
These are in the form of raw materials.	These are in the finished form.
Reliability and Suitability
These are more reliable and suitable for the enquiry because these are collected for a particular purpose.	These are less reliable and less suitable as someone else has collected the data which may not perfectly match our purpose.
Time and Money
Collecting primary data is quite expensive both in the terms of time and money.	Secondary data requires less time and money; hence it is economical.
Precaution and Editing
No particular precaution or editing is required while using the primary data as these were collected with a definite purpose.	Both precaution and editing are essential as secondary data were collected by someone else for his own purpose.

(b) Estimation and testing of hypothesis

 

(c) Sampling and Non-Sampling Errors 

Sampling error is one which occurs due to unrepresentativeness of the sample selected for observation. Conversely, non-sampling error is an error arise from human error, such as error in problem identification, method or procedure used, etc.

An ideal research design seeks to control various types of error, but there are some potential sources which may affect it. In sampling theory, total error can be defined as the variation between the mean value of population parameter and the observed mean value obtained in the research. The total error can be classified into two categories, i.e. sampling error and non-sampling error.

BASIS FOR COMPARISON	SAMPLING ERROR	NON-SAMPLING ERROR
Meaning	Sampling error is a type of error, occurs due to the sample selected does not perfectly represents the population of interest.	An error occurs due to sources other than sampling, while conducting survey activities is known as non sampling error.
Cause	Deviation between sample mean and population mean	Deficiency and analysis of data
Type	Random	Random or Non-random
Occurs	Only when sample is selected.	Both in sample and census.
Sample size	Possibility of error reduced with the increase in sample size.	It has nothing to do with the sample size.

The significant differences between sampling and non-sampling error are mentioned in the following points:

a. Sampling error is a statistical error happens due to the sample selected does not perfectly represents the population of interest. Non-sampling error occurs due to sources other than sampling while conducting survey activities is known as non-sampling error.

b. Sampling error arises because of the variation between the true mean value for the sample and the population. On the other hand, the non-sampling error arises because of deficiency and inappropriate analysis of data.

c. Non-sampling error can be random or non-random whereas sampling error occurs in the random sample only.

d. Sample error arises only when the sample is taken as a representative of a population.As opposed to non-sampling error which arises both in sampling and complete enumeration.

e. Sampling error is mainly associated with the sample size, i.e. as the sample size increases the possibility of error decreases. On the contrary, the non-sampling error is not related to the sample size, so, with the increase in sample size, it won’t be reduced.

(d) Bibliography and footnote 

The Footnote

Content footnotes give additional information about the content, and bibliographic notes provide additional sources related to the content. The footnote is found at the bottom, or foot, of the page. It is marked by a superscript number within the body of the text. The superscript number also appears at the bottom of the page, along with the additional explanatory or bibliographic information.

If specific sources are used to write content footnotes, this information should be cited through parenthetical citations within the footnote and then with full citation information within the Works Cited, or Bibliography, page. Bibliographic footnotes point your readers to specific, related outside texts without providing much commentary on them. Full citation information for these sources should also be included on the Works Cited page.

The Bibliography

The Bibliography, or Works Cited, page is the last section of a paper. It compiles the full citation information for any source cited in or consulted for the paper into one location and allows your readers to get an overview of the works informing your thinking.

The full citation information found in this section tells your readers when and where a source was published, whereas a footnote might only include the title of the work. Additionally, no information besides the citation information is included within the bibliography.

Question No. 4 - MCO-03 - Research Methodology and Statistical Analysis

Solutions to Assignments

                               MCO-03 - 

    Research Methodology and Statistical Analysis

Question No. 4 

Write short notes on the following: 

(a) Comparative Scales

Scaling emerged from the social sciences in an attempt to measure or order attributes with respect to quantitative attributes or traits.  Scaling provides a mechanism for measuring abstract concepts.

A comparative scale is an ordinal or rank order scale that can also be referred to as a non-metric scale.  Respondents evaluate two or more objects at one time and objects are directly compared with one another as part of the measuring process.

For example you could ask someone if they prefer listening to MP3s through a Zune or an iPod. You could take it a step further and add some other MP3 player brands to the comparison.  MP3 players would be scaled relative to each other and the scale position of any one player would depend on the the scale position of the remaining players.  Because they are being compared differences such as who has the click wheel are effectively forced.  Where this is limiting is evident when you find no standard of comparison outside the objects being compared. No generalizations are made outside of these objects.  Often used when physical characteristics of objects are being compared.

1. Guttman Scaling

This can also be referred to as a cumulative scoring or scalogram analysis.  The intent of this survey is that the respondent will agree to a point and their score is measured to the point where they stop agreeing.  For this reason questions are often formatted in dichotomous yes or no responses.

The survey may start out with a question that is easy to agree with and then get increasingly sensitive to the point where the respondent starts to disagree.  You may start out with a question that asks if you like music at which point you mark yes.   Four questions later it may ask if you like music without a soul and which is produced by shady record labels only out to make money at which point you may say no. If you agreed with the first 5 questions and then started disagreeing you would be rated a 5.  The total of questions you agreed to would be added up and your final score would say something about your attitude toward music.

2. Rasch Scaling

This probabilistic model provides a theoretical basis for obtaining interval level measurements based on counts from observations such as total scores on assessments. This analyzes individual differences in response tendencies as well as an item’s discrimination and difficulty.  It measures how respondents interact with items and then infers differences between items from responses to obtain scale values.  This model is typically used analyze data from assessments and to measure abilities, attitudes, and personality traits.

3. Rank-Order Scaling

This gives the respondent a set of items and then asks the respondent to put those items in some kind of order. The “order” could be something like preference, liking, importance, effectiveness, etc.  This can be a simple ordinal structure such as A is higher than B or be done by relative position (give each letter a numerical value as in A is 10 and B is 7).   You could present five items and ask the respondent to order each one A-E in order of preference.  In Rank-Order scaling only (n-1) decisions need to be made.

4. Constant Sum Scaling

With this ordinal level technique respondents are given a constant sum of units such as points, money, or credits and then asked to allocate them to various items.   For example,  you could ask a respondent to reflect on the importance of features of a product and then give them 100 points to allocate to each feature of the product based on that.   If a feature is not important then the respondent can assign it zero.   If one feature is twice as important as another then they can assign it twice as much.   When they are done all the points should add up to 100.

5. Paired Comparison Scale

This is an ordinal level technique where a respondent is presented with two items at a time and asked to choose one.   This is the most widely used comparison scale technique.   If you take n brands then [n (n-1)/2] paired comparisons are required.  A classic example of when paired comparison is used is during taste tests.  For example you could have a taste test in which you have someone try both Coke and Pepsi and then ask them which one they prefer.

6. Bogardus Social Distance Scale

This is a cumulative score that is a variant of the Guttman scale, agreement with any item implies agreement with the preceding items. This scale is used to measure how close or distant people feel toward other people.  Social distance is a concern when it comes to issues related to racial integration or other forms of equality.  This is applicable to team formation in the work place for example.  Some people accept other people easily and use trustworthiness as the basis of their relationship with other people.  Other people do not accept people who are not like them and tend to keep those that are not like them at arms length.

7. Q-Sort Scaling

This is a rank order procedure where respondents are asked to sort a given number of items or statements and classify them into a predetermined number of sets (usually 11) according to some criterion such as preference, attitude, or behavioral intent.  Using cards that note an item to be ranked is the most popular and simplest method to use in the sorting process.  In order to increase statistical reliability at least 60 cards should be used and no more than 140.  This is good for discriminating among a large group of items in a relatively short amount of time.

 

(b) Purpose of a Report 

No research is complete unless the report is written and communicated. It is necessary for you as the researcher to maintain proper notes on progress made, e.g., problem statement objectives, justifications for the study, review of literature, development of instruments for data collection, hypothesis, sample description and sampling technique, pilot study, problems faced in data collection, the data and the analysis. These notes help in preparing the research report. 

The main purpose of research report is to let others interested in the subject know the findings of the research. The researcher himself/ herself may have definite purpose of writing the research report. Examples of purposes are listed below: 

1) Research is conducted for the partial fulfillment of the degree like M.Sc., Ph.D. Therefore writing report is a part sf the academic programme. 

2) Research is conducted to find an answer to the problems faced by the practitioner, teacher or administrator. Here the report is written to communicate the findings to others in the profession for critiquing, application of result or future investigation in the area of research. 

3) When the research is funded by the government or a research foundation. They stipulate the requirements of the report. 

The reports are usually written as a thesis, monographs or article for publication in journal or magazine. The content outline can be broadly divided as introduction, review of literature, methodology, data analysis and interpretation, summary and conclusions. The introduction section include: background of the study, need and justification for the study, problem statement, variables, objectives and hypothesis, scope and limitations, and assumptions. Methodology includes: justification and explanation of research approach, sampling technique and size of sample, setting of the study, construction or selection of instrument for data collection, procedure for data collection and the plan of data analysis. Style of writing references and bibliography is recommended by the publishers ' and the research departments of institutions. The writer has to follow them strictly. Reference are works that are referred in the text whereas bibliography includes all the relevant literature reviewed irrespective of "referred" or "not referred" status. 

(c) Binomial Distribution 

A binomial distribution can be thought of as simply the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times. The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice). For example, a coin toss has only two possible outcomes: heads or tails and taking a test could have two possible outcomes: pass or fail. 

A binomial distribution can be thought of as simply the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times. The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice). For example, a coin toss has only two possible outcomes: heads or tails and taking a test could have two possible outcomes: pass or fail. 

Binomial distributions must also meet the following three criteria:

a. The number of observations or trials is fixed. In other words, you can only figure out the probability of something happening if you do it a certain number of times. This is common sense—if you toss a coin once, your probability of getting a tails is 50%. If you toss a coin a 20 times, your probability of getting a tails is very, very close to 100%.

b. Each observation or trial is independent. In other words, none of your trials have an effect on the probability of the next trial.

c. The probability of success (tails, heads, fail or pass) is exactly the same from one trial to another.

The binomial distribution is closely related to the Bernoulli distribution. According to Washington State University, “If each Bernoulli trial is independent, then the number of successes in Bernoulli trails has a binomial Distribution. On the other hand, the Bernoulli distribution is the Binomial distribution with n=1.”

A Bernoulli distribution is a set of Bernoulli trials. Each Bernoulli trial has one possible outcome, chosen from S, success, or F, failure. In each trial, the probability of success, P(S) = p, is the same. The probability of failure is just 1 minus the probability of success: P(F) = 1 – p. (Remember that “1” is the total probability of an event occurring…probability is always between zero and 1). Finally, all Bernoulli trials are independent from each other and the probability of success doesn’t change from trial to trial, even if you have information about the other trials’ outcomes.

Many instances of binomial distributions can be found in real life. For example, if a new drug is introduced to cure a disease, it either cures the disease (it’s successful) or it doesn’t cure the disease (it’s a failure). If you purchase a lottery ticket, you’re either going to win money, or you aren’t. Basically, anything you can think of that can only be a success or a failure can be represented by a binomial distribution.

The binomial distribution formula is:

b(x; n, P) = nCx * Px * (1 – P)n – x

Where:

b = binomial probability

x = total number of “successes” (pass or fail, heads or tails etc.)

P = probability of a success on an individual trial

n = number of trials

The binomial distribution formula can calculate the probability of success for binomial distributions. Often you’ll be told to “plug in” the numbers to the formula and calculate. This is easy to say, but not so easy to do—unless you are very careful with order of operations, you won’t get the right answer. If you have a Ti-83 or Ti-89, the calculator can do much of the work for you. If not, here’s how to break down the problem into simple steps so you get the answer right—every time.

(d) Skewness

Skewness is a measure of asymmetry or distortion of symmetric distribution. It measures the deviation of the given distribution of a random variable from a symmetric distribution, such as normal distribution. A normal distribution is without any skewness, as it is symmetrical on both sides. Hence, a curve is regarded as skewed if it is shifted towards the right or the left.

Types of Skewness

 

1. Positive Skewness

If the given distribution is shifted to the left and with its tail on the right side, it is a positively skewed distribution. It is also called the right-skewed distribution. A tail is referred to as the tapering of the curve differently from the data points on the other side.

As the name suggests, a positively skewed distribution assumes a skewness value of more than zero. Since the skewness of the given distribution is on the right, the mean value is greater than the median and moves towards the right, and the mode occurs at the highest frequency of the distribution.

 

2. Negative Skewness

If the given distribution is shifted to the right and with its tail on the left side, it is a negatively skewed distribution. It is also called a left-skewed distribution. The skewness value of any distribution showing a negative skew is always less than zero. The skewness of the given distribution is on the left; hence, the mean value is less than the median and moves towards the left, and the mode occurs at the highest frequency of the distribution.

 

Measuring Skewness

Skewness can be measured using several methods; however, Pearson mode skewness and Pearson median skewness are the two frequently used methods. The Pearson mode skewness is used when a strong mode is exhibited by the sample data. If the data includes multiple modes or a weak mode, Pearson’s median skewness is used.

The formula for Pearson mode skewness:

Where:

X = Mean value

Mo = Mode value

s = Standard deviation of the sample data

 

The formula for Person median skewness:

Where:

Md = Median value

How to Interpret

- Skewness also includes the extremes of the dataset instead of focusing only on the average. Hence, investors take note of skewness while estimating the distribution of returns on investments. The average of the data set works out if an investor holds a position for the long term. Therefore, extremes need to be looked at when investors seek short-term and medium-term security positions.

- Usually, a standard deviation is used by investors in forecasting returns, and it presumes a normal distribution with zero skewness. However, because of skewness risk, it is better to obtain the performance estimations based on skewness. Moreover, the occurrence of return distributions coming close to normal is low.

- Skewness risk occurs when a symmetric distribution is applied to the skewed data. The financial models seeking to estimate an asset’s future performance consider a normal distribution. However, skewed data will increase the accuracy of the financial model.

- If a return distribution shows a positive skew, investors can expect recurrent small losses and few large returns from investment. Conversely, a negatively skewed distribution implies many small wins and a few large losses on the investment.

- Hence, a positively skewed investment return distribution should be preferred over a negatively skewed return distribution since the huge gains may cover the frequent – but small – losses. However, investors may prefer investments with a negatively skewed return distribution. It may be because they prefer frequent small wins and a few huge losses over frequent small losses and a few large gains.

Question No. 3 - MCO-03 - Research Methodology and Statistical Analysis

Solutions to Assignments

                               MCO-03 - 

    Research Methodology and Statistical Analysis

Question No. 3

Briefly comment on the following: 

(a) The recognition or existence of a problem motivates research. 

Without a problem, research cannot proceed, because there is nothing to proceed from and proceed towards. Therefore, the first step in research is to perceive a problem - either practical or theoretical. The recognition or existence of a problem motivates research. It may be noted that research is the process of repeated search for truth/facts. Unless there is a problem to search for, investigation cannot proceed. Thus, a problem sets the goal or direction of research. A problem in simple words is “some difficulty experienced by the researcher in a theoretical or practical situation. Solving this difficulty is the task of research”. A problem exists when we do not have enough information to answer a question (problem). The answer to the question or problem is what is sought in the research. By problem we mean “any condition or circumstance in which one does not know how to act and what to accept as true”. In our common usage when we are unable to assess a thing correctly, we often say ‘it is problematic’. 

Thus the researcher who selects a problem formulates a hypothesis or postulates a theoretical assumption that this or that is true, this or that thing to do. He/she collects proof (facts/data) of his/her hypothesis. Based on the analysis of the data collected he/she asserts the truth or answers the question/solves the problem.

A topic of study may be selected by some institution or by some researcher or researchers having intellectual interests. In the former case there could be a wide variety of problems in which institutions are interested. The institution could be a local body, or government or corporate enterprises or a political party. For example, the government may be interested in assessing the probable consequences of various courses of action for solving a problem say rural unemployment. A firm may be interested in assessing the demand for something and predicting the future course of events so as to plan appropriate action relating to marketing, production, consumer behaviour and so on.

The topic of study may be selected by some individual researcher having intellectual or scientific interests. The researcher may be interested in exploring some general subject matter about which relatively little is known. And its purpose is just for scientific curiosity. Person may also be interested in a phenomenon which has already been studied in the past, but now it appears that conditions are different and, therefore, it requires further examination. Person may also be interested in a field in which there is a highly developed theoretical system but there is need for retesting the old theory on the basis of new facts, so as to test its validity in the changed circumstances. 

The topic of research may be of a general nature or specifically needed by some institution, organization or government. It may be of intellectual interest or of practical concern, “A wide variety of practical concerns may present topics for research”. For example, one may want to study the impact of television on children’s education, performance of regulated agricultural markets, profitability of a firm, impact of imports on Indian economy, a comparative study of accounting practices in public and private undertakings, etc.

If the researcher / research organization has a ready problem on hand, he/she can proceed further in the research process or else you have to search for a problem. Where can you search for research problems? Your own mind, where else? You have to feel the problem and think about it. However, the following sources may help you in identifying the problem / problem areas. 

1) Business Problems: A research problem is a felt need, the need may be an answer, or a solution or an improvement in facilities / technology eg. Cars Business experiences, various types of problems. They may be business policy problems, operational problems, general management problems, or functional area problems. The functional areas are Financial Management, Marketing Management, Production Management and Human Resources Management. Every business research problem is expected to solve a management problem by facilitating rational decision-making. 

2) Day to Day Problems: A research problem can be from the day to day experience of the researcher. Every day problems constantly present some thing new and worthy of investigation and it depends on the keenness of observation and sharpness of the intellect of the researcher to knit his daily experience into a research problem. For example, a person who travels in city buses every day finds it a problem to get in or get out of the bus. But a Q system (that is the answer to the problem) facilitates boarding and alighting comfortably. 

3) Technological Changes: Technological changes in a fast changing world are constantly bringing forth new problems and thus new opportunities for research. For example, what is the impact or implications of a new technique or new process or new machine? 

4) Unexplored Areas: Research problems can be both abstract and of applied interest. The researcher may identify the areas in which much work has been done and the areas in which little work has been done or areas in which no work has been done. He may select those areas which have not been explored so far/explored very little. 

5) Theory of One’s Own Interest: A researcher may also select a problem for investigation from a given theory in which he has considerable interest. In such situations the researcher must have a thorough knowledge of that theory and should be able to explore some unexplained aspects or assumptions of that theory. His effort should revalidate, or modify or reject the theory.

6) Books, Theses, Dissertation Abstracts, Articles: Special assignments in textbooks, research theses, investigative reports, research articles in research journals etc., are rich sources for problem seekers. These sources may suggest some additional areas of needed research. Many of the research theses and articles suggest problems for further investigation which may prove fruitful. 

7) Policy Problems: Government policy measures give rise to both positive and negative impact. The researcher may identify these aspects for his research. For example, what is the impact of the Government’s new industrial policy on industrial development? What is the impact of Export - Import policy on balance of payments? What is the impact of Securities Exchange Board of India Regulations on stock markets? 

8) Discussions with Supervisor and Other Knowledgeable Persons: The researcher may find it fruitful to have discussions with his/her proposed supervisor or other knowledgeable persons in the area of the topic.

The selection of a topic for research is only half-a-step forward. This general topic does not help a researcher to see what data are relevant to his/her purpose. What are the methods would he/she employ in securing them? And how to organize these? Before he/she can consider all these aspects, he/she has to formulate a specific problem by making the various components of it (as explained above) explicit. 

A research problem is nothing but a basic question for which an answer or a solution is sought through research. The basic question may be further broken down into specifying questions. These “simple, pointed, limited, empirically verifiable questions are the final result of the phased process, we designate as the formulation of a research problem”. Specification or definition of the problem is therefore a process that involves a progressive narrowing of the scope and sharpening of focus of questions till the specific challenging questions are finally posed. If you can answer the following questions, you have clearly specified/defined the problem.

(b) Quantitative data has to be condensed in a meaningful manner, so that it can be easily understood and interpreted. 

Quantitative data has to be condensed in a meaningful manner, so that it can be easily understood and interpreted. One of the common methods for condensing the quantitative data is to compute statistical derivatives, such as Percentages, Ratios, Rates, etc. These are simple derivatives. Further, it is necessary to summarise and analyse the data. The first step in that direction is the computation of Central Tendency or Average, which gives a bird's-eye view of the entire data. In this Unit, we will discuss computation of statistical derivatives based on simple calculations. Further, numerical methods for summarizing and describing data ñ measures of Central Tendency ñ are discussed. The purpose is to identify one value, which can be obtained from the data, to represent the entire data set.

Statistical derivatives are the quantities obtained by simple computation from the given data. Though very easy to compute, they often give meaningful insight to the data. Here we discuss three often-used measures: percentage, ratio and rate. These measures point out an existing relationship among factors and thereby help in better interpretation.

1. Percentage 

As we have noted earlier, the frequency distribution may be regarded as simple counting and checking as to how many cases are in each group or class. The relative frequency distribution gives the proportion of cases in individual classes. On multiplication by 100, the percentage frequencies are obtained. Converting to percentages has some advantages - it is now more easily understood and comparison becomes simpler because it standardizes data. Percentages are quite useful in other tables also, and are particularly important in case of bivariate tables.

2. Ratio 

Another descriptive measure that is commonly used with frequency distribution (it may be used elsewhere also) is the ratio. It expresses the relative value of frequencies in the same way as proportion or percentages but it does so by comparing any one group to either total number of cases or any other group. For instance, in table 6.3, Unit 6, the ratio of all labourers to their daily wages between Rs 30ñ35 is 70:14 or 5:1. Where ever possible, it is convenient to reduce the ratios in the form of n1: n2, the most preferred value of n2 being 1. Thus, representation in the form of ratio also reduces the size of the number which facilitates easy comparison and quick grasp. As the number of categories increases, the ratio is a better derivative for presentation as it will be easy and less confusing. 

There are several types of ratios used in statistical work. Let us discuss them. 

a. The Distribution Ratio: It is defined as the ratio of a part to a total which includes that part also. For example, in an University there are 600 girls out of 2,000 students. Than the distribution ratio of girls to the total number of students is 3:10. We can say 30% of the total students are girls in that University. 

b. Interpret ratio: It is a ratio of a part in a total to another part in the same total. For example, sex ratio is usually expressed as number of females per 1,000 males (not against population). 

c. Time ratio: This ratio is a measure which expresses the changes in a series of values arranged in a time sequence and is typically shown as percentage. Mainly, there are two types of time ratios : 

i) Those employing a fixed base period: Under this method, for instance, if you are interested in studying the sales of a product in the current year, you would select a particular past year, say 1990 as the base year and compare the current yearís production with the production of 1990.

 

ii) Those employing a moving base: For example, for computation of the current year's sales, last year's sales would be assumed as the base (for 1991, 1990 is the base. For 1992, 1991 is the base and so on.

 Ratios are more often used in financial economics to indicate the financial status of an organization.

3. Rate 

The concept of ratio may be extended to the rate. The rate is also a comparison of two figures, but not of the same variable, and it is usually expressed in percentage. It is a measure of the number of times a value occurs in relation to the number of times the value could occur, i.e. number of actual occurrences divided by number of possible occurrences. Unemployment rate in a country is given by total number of unemployed person divided by total number of employable persons. It is clear now that a rate is different from a ratio. For example, we may say that in a town the ratio of the number of unemployed persons to that of all persons is 0.05: 1. The same message would be conveyed if we say that unemployment rate in the town is 0.05, or more commonly, 5 per cent. Sometimes rate is defined as number of units of a variable corresponding to a single unit of another variable; the two variables could be in different units. For example, seed rate refers to amount of seed required per unit area of land. The following table gives some examples of rates.

(c) Decomposition and analysis of a time series is one and the same thing. 

Decomposition and analysis of a time series are one and the same thing. The original data or observed data ‘O’ is the result of the effects generated by the long-term and short-term causes, namely, (1) Trend = T, (2) cyclical = C, (3) seasonal = S, and (4) Irregular = I. Finding out the values for each of the components is called decomposition of a time series. Decomposition is done either by the Additive model or the Multiplicative model of analysis. Which of these two models is to be used in analysis of time series depends on the assumption that we might make about the nature and relationship among the four components.

Additive Model: It is based on the assumption that the four components are independent of one another. Under this assumption, the pattern of occurrence and the magnitude of movements in any particular component are not affected by the other components. In this model the values of the four components are expressed in the original units of measurement. Thus, the original data or observed data, ‘Y’ is the total of the four component values, 

that is, 

Y = T + S + C + I

where, T, S, C and I represent the trend variations, seasonal variations cyclical variations, and erratic variations, respectively. 

Multiplicative Model: It is based on the assumption that the causes giving rise to the four components are interdependent. Thus, the original data or observed data ‘Y’ is the product of four component values, 

that is : 

Y = T × S × C × I 

In this model the values of all the components, except trend values, are expressed as percentages. In business research, normally, the multiplicative model is more suited and used more frequently for the purpose of analysis of time series. Because, the data related to business and economic time series is the result of interaction of a number of factors which individually cannot be held responsible for generating any specific type of variations.

(d) Research reports are the product of slow, painstaking and accurate work. 

Reporting simply means communicating or informing through reports. The researcher has collected some facts and figures, analyzed the same and arrived at certain conclusions. He has to inform or report the same to the parties interested. Therefore “reporting is communicating the facts, data and information through reports to the persons for whom such facts and data are collected and compiled”. 

A report is not a complete description of what has been done during the period of survey/research. It is only a statement of the most significant facts that are necessary for understanding the conclusions drawn by the investigator. Thus, “ a report by definition, is simply an account”. The report thus is an account describing the procedure adopted, the findings arrived at and the conclusions drawn by the investigator of a problem.

Research report is a channel of communicating the research findings to the readers of the report. A good report is one which does this task efficiently and effectively. As such it should have the following characteristics/qualities. 

i) It must be clear in informing the what, why, who, whom, when, where and how of the research study. 

ii) It should be neither too short nor too long. One should keep in mind the fact that it should be long enough to cover the subject matter but short enough to sustain the reader’s interest.

iii) It should be written in an objective style and simple language, correctness, precision and clarity should be the watchwords of the scholar. Wordiness, indirection and pompous language are barriers to communication. 

iv) A good report must combine clear thinking, logical organization and sound interpretation. 

v) It should not be dull. It should be such as to sustain the reader’s interest. 

vi) It must be accurate. Accuracy is one of the requirements of a report. It should be factual with objective presentation. Exaggerations and superlatives should be avoided. 

vii) Clarity is another requirement of presentation. It is achieved by using familiar words and unambiguous statements, explicitly defining new concepts and unusual terms. 

viii) Coherence is an essential part of clarity. There should be logical flow of ideas (i.e. continuity of thought), sequence of sentences. Each sentence must be so linked with other sentences so as to move the thoughts smoothly. 

ix) Readability is an important requirement of good communication. Even a technical report should be easily understandable. Technicalities should be translated into language understandable by the readers. 

x) A research report should be prepared according to the best composition practices. Ensure readability through proper paragraphing, short sentences, illustrations, examples, section headings, use of charts, graphs and diagrams. 

xi) Draw sound inferences/conclusions from the statistical tables. But don’t repeat the tables in text (verbal) form. 

xii) Footnote references should be in proper form. The bibliography should be reasonably complete and in proper form. 

xiii) The report must be attractive in appearance, neat and clean whether typed or printed. 

xiv) The report should be free from mistakes of all types viz. language mistakes, factual mistakes, spelling mistakes, calculation mistakes etc., 

The researcher should try to achieve these qualities in his report as far as possible