30+mba-第73章
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。 Carrying out surveys
Finance； marketing； operations and HRM （human resource management）
collect an inordinate amount of data and the IT （information technology）
department processes it。 However； it falls to the application of analysis
techniques to interpret the data and explain its significance or otherwise。
Bald information on its own is rarely of much use。 If staff turnover goes
up； customers start plaining and bad debts are on the rise； these facts
on their own may tell you very li。。le。 Are these figures close to average；
or should it be the mean or the weighted average that will reveal their
true importance？ Even if the figures are bad； you need to know if they are
outside the range you might reasonably expect to occur in any event。
Generally； managers prefer to rely on quantitative methods for analysis
and there are always plenty of numbers to be obtained。 Figures are efficient；
easy to manipulate and you should use them whenever you can。 But there is
11
Quantitative and Qualitative Research and Analysis 247
also a rich seam of qualitative methods to get valuable information that you
cannot obtain well with quantitative methods。 These qualitative methods
can be used to study human behaviour and more importantly changes in
behaviour。 plex feelings and opinions； such as why employee morale
is low； customers are plaining or shareholders dissatisfied； cannot be
prehensively captured by quantitative techniques。 Using qualitative
methods it is possible to study the variations of plex； human behaviour
in context。 By connecting quantitative data to behaviour using qualitative
methods； a process known as triangulation； you can add an extra dimension
to your analysis with people’s descriptions； feelings and actions。
In business schools these two methods of analysis are rarely taught together
and are even less likely to be taught in the same department； though
some marketing professors will manage joined…up analysis in areas such as
surveys。 At Ro。。erdam School of Management； Erasmus University （
rsm。nl）； for example； in ‘Quantitative Platform for Business’ students
investigate the qualitative as well as the quantitative methods available for
problem solving within an organization。 But EM Lyon （em…lyon/
english） confines its teaching to ‘Business Statistics’ covering ‘the essential
quantitative skills that will be required of you throughout the programme’。
MIT Sloan School of Management （h。。p：//mitsloan。mit。edu/mba/program/
firstsem。php） has a teaching module； ‘Data； Models； and Decision’； in its
first semester that ‘Introduces students to the basic tools in using data to
make informed management decisions’。 That seems heavy on quantitative
analysis； covering probability； decision analysis； basic statistics； regression；
simulation； linear and nonlinear optimization； and discrete optimization；
but devoid of much qualitative teaching ma。。er。 But MIT does uses cases；
and examples drawn from marketing； finance； operations management；
and other management functions； in teaching this subject。
QUANTITATIVE RESEARCH AND ANALYSIS
The purpose of quantitative research and analysis is to provide managers
with the analytical tools necessary for making be。。er management decisions。
The subject； while not rocket science； requires a reasonable grasp
of mathematical concepts。 It is certainly one area that many a。。ending business
school find challenging。 But as figures on their own are o。。en of li。。le
help in either understanding the underlying facts or choosing between
alternatives； some appreciation of probability； forecasting and statistical
concepts is essential。 It is an area where； with a modicum of application；
an MBA can demonstrate skills that will make them stand out from the
crowd。
248 The Thirty…Day MBA
Decision theory
Blaise Pascal （1623–62）； the French mathematician and philosopher who
with others laid the foundations for the theory of probability； is credited
with inaugurating decision theory； or decision making under conditions
of uncertainty。 Until Pascal’s time； the outes of events were considered
to be largely in the hands of the gods； but he instigated a method for using
mathematical analysis to evaluate the cost and residual value of various
alternatives so as to be able to choose the best decision when all the relevant
information is not available。
Decision trees
Decision trees are a visual as well as valuable way to organize data so as
to help make a choice between several options with different chances of
occurring and different results if they do occur。 Trees （see Figure 11。1） were
first used in business in the 1960s but became seriously popular from 1970
onwards when algorithms were devised to generate decision trees and
automatically reduce them to a manageable size。
Making a decision tree requires these steps to be carried out initially；
from which the diagram can be drawn：
。 Establish all the alternatives。
。 Estimate the financial consequences of each alternative。
。 Assign the risk in terms of uncertainty allied with each alternative。
Figure 11。1 shows an example decision tree。 The convention is that squares
represent decisions and circles represent uncertain outes。 In this example；
the problem being decided on is whether to launch a new product
or revamp an existing one。 The uncertain outes are whether the result
of the decision will be successful （￡10 million profit）； just ok （￡5 million
profit） or poor （￡1 million）。 In the case of launching a new product there is；
in the management’s best estimate； a 10 per cent （0。1 in decimals） chance
of success； a 40 per cent chance it will be ok and a 50 per cent chance it
will result in poor sales。 Multiplying the expected profit arising from each
possible oute by the probability of its occurring gives what is termed
an ‘expected value’。 Adding up the expected values of all the possible
outes for each decision suggests； in this case； that revamping an old
product will produce the most profit。
The example is a very simple one and in practice decisions are much
more plex。 We may have intermediate decisions to make； such as
should we invest heavily and bring the new product to market quickly； or
should we spend money on test marketing。 This will introduce more decisions
and more uncertain outes represented by a growing number of
‘nodes’； the points at which new branches in the tree are formed。
Quantitative and Qualitative Research and Analysis 249
If the outes of the decision under consideration are spread over several
years； you should bine this analysis with the net present value of the
monetary values concerned。 （See Discounted Cash Flow in Chapter 2；
Finance。）
Statistics
Statistics is the set of tools that we use to help us assess the truth or otherwise
of something we observe。 For example； if the last 10 phone calls a pany
received were all cancelling orders； does that signal that a business has a
problem； or is that event within the bounds of possibility？ If it is within the
bounds of possibility； what are the odds that we could still be wrong and
really have a problem？ A further issue is that usually we can’t easily examine
the entire population； so we have to make inferences from samples and；
unless those samples are representative of the population we are interested
in and of sufficient size； we could still be very wrong in our interpretation
of the evidence。 At the time of writing； there was much debate as to how
much of a surveillance society Britain had bee。 The figure of 4。2 million
cameras； one for every 14 people； was the accepted statistic。 However； a
diligent journalist tracked down the evidence to find that extrapolating a
survey of a single street in a single town arrived at that figure！
Central tendency
The most mon way statistics are considered is around a single figure
that purports in some way to be representative of a population at large。
Figure 11。1 Example decision tree
Activity
fork
Event
fork
Event
fork
Launch
new product
Revamp
old product
Successful
Successful
OK
OK
Poor
Poo
