Welcome friends again to the session of marketing
research and analysis we were in the last
session we were discussing about the experimentation
or the causal effect basically in which we
talked about causal research has two basic
parts one is the field experiment.
Right and the other being the lab experiment
right so where research is being done either
in the lab or on a set like the field right
on let us say people at large in the public
domain so you can experiment to see how a
variable effects right and the variables being
the we spoke about was the dependent variable
and the independent variable right so the
dependent variable is basically the Y which
we say is the effect right and the dependent
independent variable being the X which we
say is the cause right.
So this is the cause which effects which and
that is the effect right so what is the effect
let's say of let us say the film's income
let us say income is how income is affected
by the popularity of actor popularity of the
actors right so this we are trying to measure
and we see if we suppose change the actor
is there a change in the revenue right or
the if suppose you must have seen in sitcoms
and all this the program’s basically where
the change in actor in between the serial
right after some time.
So what happens is basically why does it do
it they try to see whether they feel they
have hypothesis that if we change this actor
maybe our program will do better right they
will pull in more you know consumers more
eyeballs to the program and people's interest
for the program will increase and that is
why they try to change and sometimes it also
helps them in breaking the boredom right.
So we did with the started with the experimental
design right so the pre -experimental and
the true experimental.
We said in which we had there was pre-experimental
this is the one shot case study not and this
is a true experimental where we had pretest
and posttest both this has got an advantage
but the third is basically what we are now
discussing is the quasi experimental so what
is the quasi experimental and why it is different
from the others right for example the pre
experimental and the true experimental the
basic difference between Quasi and the others
is that a quasi-experiment right basically
does not have the element of randomization
or randomness right so that randomness is
missing in the quasi experiment so why does
it what how can we explain this let us say
there are situations where we do not have
the ability to randomly place somebody because
in fact the biggest advantage of experimentation
is to have randomization right that means
what you can say as I was showing in the last
session.
So let us say let us say there is a field
in which you have divided the field into several
levels right and you want to randomly put
in the let us say fertilizer so you may put
it here and here and you do not want to put
here so this may be like a control group and
this is your treatment group right so this
thing is very helpful in research because
it helps us to find out what exactly is the
effect of the treatment or is there any real
effect of the treatment or there is no effect
by just measuring the difference between this
and this group and this group okay.
So but the quasi experimental group is something
where I have given an example if you see the
time series and the multiple time series let
us take an example very few a very nice example
suppose you want to check what happens when
you know people are given different doses
of medicine right so in those cases when medicine
dosages are given it is impossible for you
to have attest like for example let us say
suppose we give treatment right.
And we will not give any treatment so this
is not possible why it is not possible this
is not possible because the simple reason
is unethical how will you allow somebody not
to be treated right just to make an experiment
we can do that right so in such conditions
this is the difficulty which we have and that
is why studies which fall into this category
where you are not able to do and random experiment
right in those cases are called come under
the quasi integration sorry quasi experimentation
okay quasi experimentation.
So let us take these two basic examples so
as I said one you and I think I hope you understood
that you when you do not give a treatment
always it is not possible so randomly you
are you that random factor is missing in this
Quasi right.
So as it says look these are the this is a
time series design which you have taken right
there is no randomization of the test units
through the treatments so this is the biggest
difficulty for example one of classes is suppose
you want to test it on the person right you
want to see the effect of a particular drug
or a particular promotion on people's age
right here the biggest problem is that suppose
we try to effect on we try to test it on.
A let us say A being an individual right we
can do one thing suppose A is the particular
age group of let us say 40 to 50 let us assume
and we want to see how it varies for 40 to
50 and 20 to 30 let us say okay now the problem
which what kind of studies is that 40 to 50
if we do and this is supposed to arrange that
to different people please understand this
there are two different people so twenty to
thirty is one percent whose behavior although
we are saying age-wise they are different
but there are two different people whose characteristics
might be entirely different but we don't have
an option why we do not have an option.
We cannot do is that we will we will try to
you bring this man back to 20 or 30 and do
the experiment again yes one thing is possible
that if you can do like for example 20 to
30 when at this time period you do it and
then again you do it this time period maybe
it is possible but otherwise the reverse is
not possible okay so the timing of the treatment
presentation as well as which test units are
exposed to the treatment may not be within
the researchers control so why I will be saying
time-series designs if you seen times these
designs are basically where let us say the
in a typical time series design.
Which you say the people are you know there
are what you say treated you can say that
treated to for example on a particular there
is said that the income of people are being
measured for example this is the income of
people what we are doing for year-wise we
are doing it 16, 17, 18, 19, 20 so when we
are doing it basically right we are keeping
the same people right so keeping the same
people helps us to atleast do our research
in a better way to understand because this
researcher has not changed he has been more
or less the same right so that is why we have
been able to control it to some measure otherwise
we cannot most of the studies in economics
you cannot put it in truly experimental condition
it is very difficult to place in a very truly
experimental condition see what happens if
they are not given and what happens if they
are given so if you do that there will be
two different elements altogether right so
that becomes end and on the same person you
cannot make the experiment twice it is impossible
because you cannot tear or regress the person
the age of the person right so that is one
problem but one multiple time series design
is one where we have two groups.
So one what the observation is taken for the
experimental group and there is a control
group what is saying if the control group
is carefully selected this design my can be
an improvement although the flaw will remain.
What I said right you cannot take back the
person to is to age of coordinator 30 again
when it is already 40 can be an improvement
over the simple time series experiment right
can test the treatment effect twice against
the pretreatment measures pretreatment measures
in the experimental group and against the
control group in the experimental group you
had that precondition and post condition which
exactly might not be able to replicate but
at least we can figure out what is the best
possible solution that we can achieve.
So quasi integration quasi experimentation
are basically experiments where you do not
have that randomness or to user of the randomness
characteristics okay ,the fourth is it comes
to these three which comes in the randomized
block right.
Let us see what a statistical design statistical
designs for example are basically used for
a series of basic experience that allow for
statistical control and analysis of the variables
and offer the following what are the advantages
the effect of more than one independent variable
can be measured now please understand when
you hear something called like a factorial
design.
Factorial design right a factorial design
is nothing but a one factor is nothing but
an independent variable one factor is nothing
but an independent variable so if you are
doing factorial design or even a Latin square
which is nothing but kind of factorial design
what we do is basically we are saying the
effect of here more than one independent variable
can be measured what are the let us say independent
variables you want to measure let us say you
want to see whether below score you know gender
right and plus hard work right first let us
take one gender affects the let us say score
of a person how much is scoring in mathematics
the math score or any score for example math
score example we will say that gender has
an effect on the math score that means girls
let us say in terms of math we say boys score
better than girls in math and it had it been
let us say English or something or a subject
like that with a lot of abstracts and theory
is required and the you know the logical understanding
is required.
So there may be a girls would have better
but in math’s we say suppose that lets hypothesis
it could be wrong also that gender does an
effect that means male we are saying are having
a bigger better role to score better than
the girls okay but that is no enough so gender
has two levels let us say male and female
okay plus we are saying suppose we want to
also know okay the amount of hard work let
us say amount of hard work or hard work also
plays an important or you can say the school
in which school we are studying the kind of
school they are studying they say public school
private school.
So school right so the school they are studying
also we are having a say public medium and
a state medium let us say or public and private
the simple because I do not want to get into
this thing so public and private so when I
am having I want to check the effect of the
people score I feel one that gender has an
effect on the mascot second is the type of
school also has an effect on the score people
do get in math right so when I am doing this
is basically what falls you know where we
are trying to test more than one independent
variable okay and here the advantage is that
extraneous variable.
Which if you remember in the last session
we are discussed extraneous variables are
those variables which are basically not exactly
the independent variables but they are still
variables which affects the entire you know
experiment or the entire study right so okay
so these are basically the statistical design
have been done to bring in the randomness
factored into it and reduce the number of
experiments to a limited number okay the most
common statistical designs are the randomized
block design the Latin square design and the
factorial design okay.
Let us see it is useful when only one major
external variable such as store size might
influence the dependent variable now what
it is saying there is only one factor in the
randomized block design you have only one
factor you are taking let us say the factor
could be Store size.
The size of store could be a big store medium-size
store small stores right that can have an
effect on the let us say the dependent variable
that is sales for example okay so when you
have such kind of a block you are trying to
study the impact of store size then the what
we do here is the test.
Units are blocked or group right and then
they are tested the blocking the researcher
ensures that previous the various experimental
and control groups are matched closely on
the external variable right so basically what
is happening if you look at this randomized.
This is for example this is a treatment group
let us seek basic commercials okay commercial
A, B, C now we are saying patronage is heavy
medium low and none basically patronage means
loyalty okay none simply understand so what
we are saying does the commercial have an
effect on the loyalty of a store now what
loyalty is loyalty or patronage is our dependent
variable so we are saying that means the equation
would look something like this P is equal.
Let us say a plus B of the type of commercial
from the expert experience commercial may
be right so the patronages affecting is getting
affected by the type of commercial we are
trying to see this so this is one block this
is the second block this is the third block
but this although this is a randomized block
design sometimes we also use before which
is not there in this slide is something we
call a completely randomized design which
is not shown here but I will show you on the
board there is something called a completely
randomized design.
What is a completely randomized design in
suppose in this case only suppose you would
have taken left a heavy medium and low let
us forget the fourth word and let us say we
take the commercials okay the commercials
what we do in a randomly design random is
we try to put it randomly anything right the
first a b c a c b b a c so we do anything
so what happens is when we randomly assign
the values it is very simple this is going
to completely randomized it is very simple
this free of bias everything is good.
But the problem here comes is that when you
do a completely random there is a problem
that two elements might be tested at one a
point of time and maybe some other element
is not checked equally so there might be unequal
weightage given so that can be one of the
worrisome factors okay look at this for high
there is ABC for in terms of everyone is there
but here there are two A’s right and here
there are two C's right so this could be a
problem which can happen so then comes the
randomized block design where what it was
done was to randomly the one block is made
and then they are tested okay completely random
first block A block B Block C so by doing
this a complete block is been tested for example
okay the second is the Latin square design
which is very close to the you can say similar
to the factorial design so here we are able
to manipulate.
The independent variable right to non interacting
external variables the statistically the researcher
is able to control the two non external non
controlling statistically controlling two
non interacting external variables so the
inter interaction effect what you mean by
interaction effect first let us understand
interaction effect means A effects B let us
say .
Something like this let us say A affects B
or B is affected by A okay A C effects B right
although these are independently affecting
together may B but which there can be third
thing that A and C together also have a way
of affecting B what why are we doing it sometimes
in life it happens that two things which is
sometimes you talk it in terms of synergy
also right – or let us go to science we
understand it like a reaction in which a fusion
reaction where when two elements are meeting
together the interaction effect is larger
than the main effect right or sometimes it
could be also that the interaction effect
is actually negative their values and it is
pulling the overall effect down right so the
presence of any more than one variable is
generally one should test for the interacting
effects interaction effects which is very
important in research and in most of the research
publications and all or you will see that
there will be question why the researcher
has not gone for a test of variance why they
had not gone for a interact you okay, I am
checking the interaction effects of the variables.
So that is basically to understand key is
a simple zero it is like when you and your
wife is there and your mother-in-law is there
and they independently have an effect on you
but when they come together in the same house
the effect becomes either very detrimental
or very humorous in humorous side or very
highly positive okay so that is what okay.
So each blocking variable is divided into
an equal number of blocks or levels.
So let us see the how it looks like this Latin
square design the simplest way of making a
Latin square design is you just have to remember
is have this way suppose B A C then you start
from here C A B right then C A B so what is
left from you start again from here so suppose
you would have you do not Okay, how to design
a Latin square design you can just remember
like this ABC.
Now leave A come B C A right so B C A leaves
A so C A B so this is how simply you can design
Latin square so that every item is given an
opportunity okay so when you do this in the
Latin square design a Latin Square design
is called an improper factorial design sometimes
it is said okay.
So basically it is nothing but a kind of a
factorial design only right so you what has
happened every element you see has been given
there is a kind of equal opportunity so B
A C, C B A nothing is being repeated okay
so Latin Square has got one requirement please
understand that is why the name square comes
into here so a Latin square design.
Whenever you make you have to have equal number
of rows and columns suppose. you make three
rows then three columns four rows four columns
to rows two columns five rows five colors
but you cannot take it to a very large extent
because the test will become to clumsy and
complicated okay .Now this is also this is
the factorial design I have got a larger you
know benefit than the others what is happening
here it is used to measure the effect of two
or more independent variables or factors at
various levels see that is the basic advantage
in other studies and experimentations we are
not we were missing out the labels so in these
statistical designs we are measuring the labels.
So let us say gender had two levels one or
two let say one female two male right school
so we had two levels it could be 2 by 2 it
could be three levels right so one two three
let us say government something you okay,
NGO run schools let us say that private schools
so the number of labels at various levels
factorial design helps you in improving exactly
allowing you for the interaction effects basically
so that interaction effect was not possible
in the Latin square design a factorial design
may be conceptualized as a table in a two-factor
design each label of one variable represents
a row and each level of one variable represents
a column.
So what is saying each level of one variable
right represents a row and each level represents
a column now for example let us say this is
a factorial design in which what we have done
is the although it looks very similar like
it looks very similar but the advantage is
that in every element has been given an equal
opportunity right has been given an equal
opportunity so in a factorial design what
happens the interaction effect also comes
into play and every the randomization happens
right it is randomly the randomization factor
comes into play into the system and this becomes
more richer in experimenting and getting values
okay so a researcher can get a better output
right when he uses a factorial design because
each factor or each independent variable with
its level is being considered as a table in
the form of a table okay.
So well this is what is basically what we
had for the experimentation so I hope you
must have been able to understand that means
in during this process we had in the experiment
we try to understand you what are the types
of experimental designs and why should you
do it because every marketer every marketing
application has a requirement of has a requirement
of causal research and the experimentation
right so when you do any field test or anything
you require it right.
So okay let us see when you are doing any
experiment after the experiment you need to
also understand you whom are you going to
work on right who are your samples right so
what is the sample and how what does it mean
basically its sample basically let us understand
a sample you have to understand it as a true
representative of a population so what it
is saying it true representative of a population
okay so our samples.
I just brief maybe we will continue in the
next session so the samples are basically
in any study in any study you need to understand
that you cannot you cannot measure the whole
population suppose this board is the population
you cannot measure the whole population so
in order to avoid that we need to have a sample
which actually is a part of this board you
cannot take this part as your sample because
this is not a part of the board right so it
is it to be part of that population it is
represent this population and sample basically
how does it help the samples are used to make
estimation of the larger group.
That means a sample tells exactly whether
it is actually defining the population or
not right and what we do is basically we try
to find out the difference between the sample
and the population through its mean or something
and then we say okay whether the sample is
actually explaining what the population should
explain or what the population would explain
or not okay so two key elements it is faster
and cheaper.
Obviously you cannot the population being
larger selecting the right people so when
we are talking about a sample a sample could
be a person in marketing research we will
obviously take in terms of value but a sample
does not mean people only my way of putting
it might not be correct sample could be anything.
It could be as a sample could be a product
also so it is from a population of elements
let us say machines or chairs or tables or
let us say this pointer so what we have suppose
a company's manufacturing we can take out
pull out a sample a few samples and tests
what should be the standard specifications
or the characteristics of the sample and whether
these samples are actually defining properly
the meeting those requirements and if yes
then we say that actually the ten if the sample
is good then the population was really good
right so our sample basically helps you to
identify from a small way whether the population
is correct or not correct right so this is
what basically is explained but in terms of
marketing will say selecting the right people
and selecting the right number of the right
people two things very important so sampling
says you have to first let us say if you are
conducting a study let's say you want to check
let us say the use of this marker right so
whether this marker is good effective or not
to test it you can ask anybody right but the
best people to talk about it would be people
like me who are regularly using it right so
although age might not be a factor but yes
the profession could be a factor in deciding
who this product should be using so the sample
when you this when you when you take out a
sample or pull as ample one issue understand.
That the sample should be a correct sample
you should be able to explain the thing the
phenomena and then after that if you take
only maybe about 10 people that might not
be justified to give a true representative
rate so in such situations how many what should
be the some sample size right before why we
can use also formula to calculate right so
we say this is how from the z-score or I think
this is not be visible I need to so what we
will do is Z is equal to X minus so this is
one this is my standard error so through this
also that this being this is equal to root
or n so from here also we can find out but
there are some thumb rules also which I will
explain in the next session how do you understand
sample how to use sample if you get a wrong
sample then there is a large obviously your
study would be a fruitless and it will be
a waste so and misrepresentative.
So the point is you have to understand that
who is my right sample and how many samples
should be taken as a researcher or a also
in a marketing research study you should be
able to understand how many samples should
I take collect so that my whatever I am stating
at the end about the population is coming
true right well this is what we will have
in this session we'll meet in the next session
Thanks.
