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Course Code : MS-95 ASSIGNMENT REFERENCE MATERIAL

Course Title : Research Methodology for Management Decisions

Assignment No. : 95/TMA /SEM-II/2010

1. You have drafted a questionnaire to find out the reasons for decline in sales of a product. Pretest your

questionnaire with hypothetical data. Suggest the modifications required based on the results in the drafted

questionnaire.

PRETESTING A QUESTIONNAIRE

The pretest is a valuable indicator of the effectiveness of a questionnaire to collect data. The pretesting ofquestionnaire consists in selecting, approaching and interviewing a small segment in the same manner to be followed in

the full scale operation and then analysing the results in the light of the objectives f the study.

We can understand from the pretest whether the replies provide the type of information needed or whether the

respondents are misinterpreting any of the questions. In addition, results obtained in a pretest can at times suggest new

ideas or hypotheses worthy of further examination.

If a pretest indicates any change of importance, a further pretest may be warranted to review , the questionnaire. Thus, themere fact that the wording of a question originally misunderstood has been changed does not of itself ensure the clarity of

the new form. A few interviews with the new question form are highly desirable.

APPENDIX

Questionnaire for Bike Customers

(The findings of this survey will be used only for academic purposes by the students of Lal Bahadur Shastri

Institute of Management, New delhi)

Q1.Name of the customer

Q2. Age:

18-24

24-30

30-36

ABOVE 36

\Q3.WHICH BIKE DO YOU OWN PRESENTLY ?

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Q4.ANUAL HOUSEHOLD INCOME

10 LAKH

Q5.WHAT IS THE PURPOSE OF YOUR BIKE?

(RATE ON 5 POINT SCALE-5 BEING THE HIGHEST AND 1 BEING THE LOWEST )

a)OFFICE

1 2 3 4 5

LOWEST HIGHEST

b)TRAVELLING

1 2 3 4 5

LOWEST HIGHEST

c)HOUSEHOLD

1 2 3 4 5

LOWEST HIGHEST

d)ADVENTURE

1 2 3 4 5

LOWEST HIGHEST

e)OTHER

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1 2 3 4 5

LOWEST HIGHEST

PLEASE SPECIFY THE OTHER FACTOR

Q6.RATE THE KEY FACTORS WHICH AFFECTED YOUR BUYING DECISION?

(5 BEING THE HIGHEST AND 1 BEING THE LOWEST )

a)MILEAGE

1 2 3 4 5

LOWEST HIGHEST

b)LOOKS

1 2 3 4 5

LOWEST HIGHEST

c)BRAND

1 2 3 4 5

LOWEST HIGHEST

d)SERVICE

1 2 3 4 5

LOWEST HIGHEST

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e)PRICE

1 2 3 4 5

LOWEST HIGHEST

Q7.WHICH OF THE FOLLOWING INFLUENCES YOUR PURCHASE DECISION?


a)FRIENDS

1 2 3 4 5

LOWEST HIGHEST

b)FAMILY

1 2 3 4 5

LOWEST HIGHEST

c)DEALER

1 2 3 4 5

LOWEST HIGHEST

d)ADVERTISEMENT

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1 2 3 4 5

LOWEST HIGHEST

e)OTHER FACTORS

1 2 3 4 5

LOWEST HIGHEST

PLEASE SPECIFY THE OTHER FACTOR

Q8.WHICH OF THE FOLLOWING MEDIA ARE YOU MOST LIKELY TO NOTICE AN

ADVERTISEMENT ?


a)TELEVISION

1 2 3 4 5

LOWEST HIGHEST

b)HOARDINGS

1 2 3 4 5

LOWEST HIGHEST

c)PRINT MEDIA

1 2 3 4 5

LOWEST HIGHEST

d)INTERNET

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1 2 3 4 5

LOWEST HIGHEST

e)OTHERS

1 2 3 4 5

LOWEST HIGHEST

IF OTHERS PLEASE SPECIFY

Q9.WHICH BRAND NAME DO YOU FIND EASY TO RECALL?


a)BAJAJ

1 2 3 4 5

LOWEST HIGHEST

b)HERO HONDA

1 2 3 4 5

LOWEST HIGHEST

c)YAMAHA

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1 2 3 4 5

LOWEST HIGHEST

d)HONDA

1 2 3 4 5

LOWEST HIGHEST

e)TVS

1 2 3 4 5

LOWEST HIGHEST

Q10.RATE THE BIKES ON THE BASIS OF YOUR PREFERENCE?


a)PULSAR

1 2 3 4 5

LOWEST HIGHEST

b)APACHE

1 2 3 4 5

LOWEST HIGHEST

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c)F-Z 150

1 2 3 4 5

LOWEST HIGHEST

d)HUNK

1 2 3 4 5

LOWEST HIGHEST

e)ANY OTHER

1 2 3 4 5

LOWEST HIGHEST

IF OTHER,PLEASE SPECIFY

Q11.RATE YOUR PERCEPTION ABOUT PULSAR ON THE FOLLOWING FACTORS-


a)STYLE

1 2 3 4 5

LOWEST HIGHEST

b)POWER

1 2 3 4 5

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LOWEST HIGHEST

c)PERFORMANCE

1 2 3 4 5

LOWEST HIGHEST

d)VALUE

1 2 3 4 5

LOWEST HIGHEST

e)BRAND

1 2 3 4 5

LOWEST HIGHEST

2. What is a Semantic Differential Scale? Explain the steps in construction of the scale. When will you use this

scale?

THE SEMANTIC DIFFERENTIAL SCALEThe term Semantic differential scale refers to any collection of rating scales anchored by bipolar adjectives. It is a

very flexible approach to obtaining measures of attitudes. The, object that is rated is called the "concept" and almost

anything can be rated including family planning, cosmetics, Shrikhand, political parties, etc. 107nally, a semantic

differential scale is based on a seven-point rating scale for each of a number of attributes relating to the research topic.

The extreme point represent the bipolar adjectives with the central category representing neutral. In the semantic

differential scale only the extremes have names. The in-between categories have either blank spaces or sometimes a

number. Some examples of the scale are as follows

Good Bad

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Honest .Dishonest

Progressive ..Behind the times

The preparation of a semantic differential scale for a study requires expressing the things that could he used to

describe the object, and thus serve as a bets for attitude formation, in terms of positive and negative statements. The

negative phrase is sometimes put on the left side of the scale and sometimes on the right. This prevents a respondent with

a positive attitude from simply checking either the left or right hand sides without reading the describing words.

The scale can be used for a variety of purposes. 11 can be used to check whether a respondent has a favourable

attitude towards the object, which out of three neighborhood banks has the most appealing profile for housewives, etc.

It is possible to assign points to individual cells in the scale. Then one could arrive at the scores for comparisons

of different objects. The Figure 1 gives an example based on image study of three neighbourhood banks among a sample

of 100 housewives.

The semantic differential provides information on differences (differential) in word usage (semantics) in

subjects. Osgood and Tannenbaum wrote the classic work on using the semantic differential, entitled

The Measurement of Meaning.1 The book is a detailed analysis of this powerful technique. We simply introduce

the procedure here. Osgood and Tannenbaum isolated three major dimensions of word meanings through the use of factor

analysis. These dimensions are evaluative (good or bad),potency (strong or weak) and activity (fast or slow). Their book

contains hundreds of adjective pairs relating to these three dimensions. A subject is presented a sheet of paper with a

single word or term at the top. Below this word are a numberof adjectival pairs, separated by seven blanks. For example

the meanings associated with the term my church might be formatted like this: The first four adjective pairs measure the

evaluative dimension; the next three measure potency; and the last three measure activity. The numbers shown above are

not printed on the instrument, but are shown here to help clarify the scoring procedure. Pairs which are reversed should be

scored in reverse, so that positive is always (1) and negative (7) regardless of which side of the scale they appear. Subjects

check one blank between each pair indicating their opinion of the term on this scale. Blanks are scored 1-7, providing a

numerical score for the meaning of the term in each dimension. Groups of subjects can then be compared on the three

dimensions of meaning for any commonly used word. (Note: the numbering scale 1-7 is true only if the positive term is on

the left; otherwise the scale is labeled 7-1). Results can be plotted in three dimensions to provide a picture of semantic

differences between two or more groups of subjects.

3. Discuss the role of modeling in research in managerial decisions making with an appropriate illustration. How is

model validation done?

MODELS AND MODELLING

A manager, whichever type of organisation he/she works in, very often faces situations where he/she has to

decide/ choose among two or more alternative courses of action. These are called decision-making situations. An

illustration of such a situation would be the point of time when you possibly took the decision to join/take up this

management programme. Possibly, you had a number of alternative management education programmes to choose from.

Or, at worst, maybe you had admission in this programme only. Even in that extreme type of situation you had a choice

-whether to join the programme or not! You have, depending upon your own decision-making process, made the decision

The different types of managerial decisions have been categorized in the following manner

Routine/Repetitive/Programmable vs.

No routine/Nonprogrammable decisions.

Operating vs. Strategic decisions.

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The routine/ repetitive/ programmable decisions are those which can be taken care of by the manager by resorting

to standard operating procedures (also called "sops" in managerial parlance). Such decisions the manager has to take fairly

often and he/she knows the information required to facilitate them. Usually the decision maker has knowledge in the formof "this is what you do" or "this is how you process" for such decision-making situations. Examples of these decisions

could be processing a loan application in a financial institution and supplier selection by a materials manager in a

manufacturing organisation.

The non-repetitive/ non-programmable/ strategic decisions are those which have a fairly long-term effect in anorganisation. Their characteristics are such that no routine methods, in terms of standard operating procedures, can be

developed for taking care of them. The element of subjectivity/judgement in such decision-making is fairly high. Since the

type of problem faced by the decision maker may vary considerably from one situation to another, the information needs

and the processing required to arrive at the decision may also be quite different.

The decision-making process followed may consist, broadly, of some or all of the steps given below:

Problem definition; Identifying objectives, criteria and goals; Generation/ Enumeration of alternative courses of

action; Evaluation of alternatives; Selection/ choosing the "best" alternative; Implementation of the selected alternative

All the above steps are critical in decision-making situations. However, in the fourth and fifth steps; i.e., evaluation andselection, models play a fairly important role. In this unit we will concentrate on Model Building and Decision-making.

Many managerial decision-making situations in organisations are quite complex. So, managers often take recourse

to models to arrive at decisions.

Model:

The term `model' has several connotations. The dictionary meaning of this word is "a representation of a thing". It

is also defined as the body of information about a system gathered for the purpose of studying the system. It is also stated

as the specification of a set of variables and their interrelationships, designed to represent some real system or process in

whole or in part. All the above given definitions are helpful to us of Modeling Models can be understood in terms of their

structure and purpose. The purpose of modeling for managers is to help them in decision-making. The term `structure ' in

models refers to the relationships of the different components of the model. In case of large, complex and untried

problem situations the manager is vary about taking decisions based on intuitions. A wrong decision can possibly land the

organisation in dire straits. Here modeling comes in handy. It is possible for the manager to model the decision-making

situation and try out the alternatives on it to enable him to select the best" one. This can be compared to non-destructive

testing in case of manufacturing organisations.

Presentation of Models:

There are different forms through which Models can be presented. They are as follows:

Verbal or prose models.

Graphical/ conceptual models.

Mathematical models.

Logical flow models.

Verbal Models:

The verbal models use everyday English as the language of representation. An example of such model from the

area of materials management would be as follows:

The price of materials is related to the quantum of purchases for many items. As the quantum of purchases

increases, the unit procurement price exhibits a decrease in a step-wise fashion. However, beyond a particular price level

no further discounts are available."

Graphical Models:

The graphical models are more specific than verbal models. They depict the interrelationships between thedifferent variables or parts of the model in diagrammatic or picture form. They improve exposition, facilitate discussions

and guide analysis. The development of mathematical models usually follows graphical models.

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between the variables in terms of mathematical equations or inequalities. Most of these include clearly the objectives, the

uncertainties and the variables. These models have the following advantages:

They can be used for a wide variety of analysis.

They can be translated into computer programs.

The example of a mathematical model that is very often used by materials managers is the Economic Order

Quantity (EOQ). It gives the optimal order quantity (Q) for a product in terms of its annual demand (A), the ordering cost

per order (Co), the inventory carrying cost per unit (Ci) and the purchase cost per unit (Cp). The model equation is asfollows :

Q = (2 * A * Co/Ci * Cp)

Logical Flow Models:

The logical flow models are a special class of diagrammatic models. Here, the model is expressed in form of

symbols which are usually used in computer programming and software development. These models are very useful for

situations which require multiple decision points and alternative paths. These models, once one is familiar with the

symbols used, are fairly easy to follow.

ROLE OF MODELLING IN RESEARCH IN MANAGERIAL DECISION-MAKING: AN ILLUSTRATION

In the previous sections of this unit we have tried to explore the topics of model building and decision-making.

However, we confined ourselves to bits and pieces of each concept and their illustration in a comprehensive decision-

making situation has not been attempted. In this section we will look at a managerial decision-making situation in totality

and try to understand the type of modelling which may prove of use to the decision maker.

The example we will consider here is the case of co-operative state level milk and milk products marketing

federation. The federation has a number of district level dairies affiliated to it, each having capacity to process raw milkand convert it into a number of milk products like cheese, butter, milk powders, ghee, shrikhand, etc. The diagrammatic

model of the processes in this set up is depicted in the typical problems faced by the managers in such organisations are

that : (a) the amount of milk procurement by the individual district dairies is uncertain, (b) there are limited processing

capacities for different products, and (c) the product demands are uncertain and show large fluctuations across seasons,

months and even weekdays.

The type of decisions which have to be made in such a set up can be viewed as a combination of short/

intermediate term and long-term ones. The short-term decisions are typically product-mix decisions like deciding : (1)

whereto produce which product and (2) when to produce it. The profitability of the organisation depends to a great extent

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on the ability of the management to make these decisions optimally. The long-term decisions relate to (1) the capacity

creation decisions such as which type of new capacity to create, when, and at which location(s) and (2) which new

products to go in for. Needless to say, this is a rather complex decision-making situation and intuitive or experience baseddecisions.

We have in this section seen a real life, complex managerial decision-making situation and looked at the possible

models the researcher could propose to improve the decision-making. Similar models could be built for other decision-

making situations.

4. Write short note on the following

a) Factor loading and factor analysis

FACTOR ANALYSIS

Factor analysis is a generic name given to a class of techniques whose purpose is data reduction and

summarization. Very often market researchers are overwhelmed by the plethora of data. Factor analysis comes to their

rescue in reducing the number of variables. Factor analysis does not entail partitioning the data matrix into criterion and

predictor subsets; rather interest is centred on relationships involving the whole set of variables. In factor analysis:

The analyst is interested in examining the "strength" of the overall association among variables. The sense that hewould like to account for this association in terms of a smaller set of linear composites of the original variables tha

preserve most of the information in the full data set. Often his interest will emphasize description of the data rather than

statistical inference. No attempt is made to divide the variables into criterion versus prediction sets. The models are

primarily based on linear relationships.

Factor analysis is a "search" technique. The researcher-decision maker does not typically have a clear priori

structure of the number of factors to be identified. Cut off points with respect to stopping rules for the analysis is often ad

hoc as the output becomes available. Even where the procedures and rules are stipulated in advance, the results are more

descriptive than inferential.

The procedure involved in computation of factor analysis is extremely complicated and cannot be carried out

effectively without the help of computer. Packages like SPSS, SAS and Biomedical programs (BMD) can be used to

analyse various combinations leading to factor reduction. We will make an attempt to conceptualise the scenario of factor

analysis with emphasis on the interpretation of figures.

The term "factor analysis" embraces a variety of techniques. Our discussion focuses on one procedure: principal

component analysis and the factors derived from the analysis are expressed as linear equations. These linear equations are

of the form

The factors are derived, and each variable appears in each equation. The a-co-efficients indicate the importance ofeach variable with respect to a particular factor. Co-efficient of zero indicating the variable is of no significance for the

factor. In principal component analysis, the factors are derived sequentially, using criteria of maximum reduction in

variance and non-correlation among factors.

b. Different types of experimental design

Experiments are much more effective than descriptive techniques in establishing the casual relationships. First,

the units to be studied are selected by the researcher and each unit is assigned to the group determined by the researcher.

The units do not select their groups, thus avoiding the self-selection bias. Second, a necessary consequence of the first, the

researcher administers the predetermined treatment or treatments to the units with in each group.

The use of a control group is almost mandatory in experimental designs. The inclusion of a control group permits

a better isolation of the treatment component through a proper design like a simple cross sectional design.

A major contribution that the statisticians have made to experimental design is the development of randomization

concept which enables the researcher to reduce the effect of the uncontrolled variables on comparative measures of

response to the variables that are under the experimenter's control. Randomization is a useful device for ensuring on the

average, that uncontrolled variables do not favour one treatment versus others.

Completely Randomized Design

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Randomized Complete Block Design

Latin Square Design

Factorial Design

Analysis. of Covariance

5) A sample of 48 tools produced by a machine shows the following sequence of good (G) and defective

(D) tools

G G G G G G D D G G G G G G G G

G G D D D D G G G G G G D G G G

G G G G G G D D G G G G G D G G

Test the randomness at the 0.05 significance level.

Solution:

The numbers of Ds and Gs are N1= 10 and N

2= 38, respectively, and the number of runs is V = 11.

Thus the mean and variance are given by

2 (10) (38)

v = _______ +1 = 16.83

10 + 38

2v = 2 (10) (38) [2 (10) (38) - 10 - 38]

_________________

(10 + 38)2 (10 + 38 - 1)

= 4.997

So that v = 2.235

For a two-tailed test at the 0.05 level, we would accept the Hypothesis Ho of randomness if -1.96 < z

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