Measurement Scales Measurement : The assignment of Numbers or other symbols to characteristics of...
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Measurement Scales Measurement : The assignment of Numbers or other symbols to characteristics of objects according to certain pre-specified rules. Scaling: The generation of a continuum upon which measured objects are located.
Measurement Scales Measurement : The assignment of Numbers or other symbols to characteristics of objects according to certain pre-specified rules. Scaling:
Measurement Scales Measurement : The assignment of Numbers or
other symbols to characteristics of objects according to certain
pre-specified rules. Scaling: The generation of a continuum upon
which measured objects are located.
Slide 2
Primary Scales of Measurement There are 4 kinds of scales
namely: Nominal scale Ordinal scale Interval scale Ratio scale
Slide 3
Nominal scale In this scale numbers are used to identify
objects. For example University Registration numbers assigned to
students. Have you visited Bangalore? Yes-1, No-2 Yes is coded as
one and No is coded as Two. The numeric attached to the answers has
no meaning and is a mere identification. If the numbers are
interchanged it wont affect the answer.
Slide 4
Example for nominal scale The telephone number is a example of
nominal scale where one number is assigned to one subscriber.
Similarly bus route numbers are examples of nominal scale. How old
are you? This is an example of nominal scale. What is your PAN Card
Number? Arranging the books in the library subject wise, author
wise
Slide 5
Limitations There is no rank ordering. No mathematical
operation is possible. Statistical implication- calculation of
standard deviation and the mean is not possible
Slide 6
Ordinal scale (ranking scale) The ordinal scale is used for
ranking in most of market research studies. Ordinal scales are used
to ascertain the consumer perceptions, preferences etc. This is
also known as ranking scale
Slide 7
Example of ordinal scale The respondents may be given a list of
brands which may be suitable and were asked to rank on the basis of
ordinal scale. Lux Liril Cinthol Lifebuoy Hamam
Slide 8
Example for ordinal scale Rankitem No of Respondents
ICinthol150 IILiril300 IIIHamam250 IVLux200 VLifebuoy100
Total1000
Slide 9
Nominal scale- contd In the previous example II is the mode and
III is the median. In market research the researchers often ask the
respondents to rank the items like for example A soft drink based
upon flavor or Color. In such cases the ordinal scale is used
Slide 10
Interval scale Interval scale is more powerful than the nominal
and ordinal scale. The distance given on the scale represents equal
distance on the property being measured. Interval scale may tell us
How far object are apart with respect to an attribute? This means
that the difference can be compared. The difference between 1 and 2
is equal to the difference between 2 and 3.
Slide 11
Eg for interval scale Eg 1: Suppose we want to measure the
rating of a refrigerator using interval scale it will appear as
follows: 1 Brand name Poor------------Good 2 Price
High-------------Low 3 Service after sales Poor-----------Good 4
Utility Poor----------Good
Slide 12
Interval scale-contd The researcher cannot conclude that the
respondent who gives rating of 6 is 3 times more favorable towards
the product under study than the respondent who awards the rating
of 2. Eg 2: How many hours you spend to do class assignment every
day?
Table 3 use of health drink Income per month 012345 More than 5
No of families 500061457101851 5921445074100152500
Slide 119
E.g.-Contd.. The above table shows that consumption of a health
drink not only depends on income but also on the number of children
per family. Health drinks are also popular among the family with no
children. This shows that even adults consume this drink. It is
obvious from the table that 59 out of 500 families consume health
drinks even though they have no children. The table also shows that
families in the income group of 2001-3000 consume health drink the
most.
Slide 120
Module-4- Data Analysis Multivariate analysis This can be
studied under: Discriminant analysis Factor analysis Cluster
analysis Conjoint analysis Multidimensional scaling
Slide 121
Discriminant Analysis In this analysis 2 or more groups are
compared. In the final analysis, we need to find out whether the
groups differ one from another.
Slide 122
Example of discriminant analysis Where discriminant analysis is
used: Those who buy our brand and those who buy competitors brand.
Good salesman and poor salesman, medium salesman. Those who go to
food world to buy and those who buy in a kirana shop. Heavy user,
medium user and light user of the product.
Slide 123
Equn for discriminant analysis Z= b1x1+b2x2+b3x3 Z=
Discriminant score B1=Discriminant weight for variable 1 B2=
Discriminant weight for variable 2 B3= Discriminant weight for
variable 3 X=Independent variable
Slide 124
Application of discriminant analysis A company manufacturing
FMCG products introduces a sales contest among its marketing
executives to find out How many distributors can be roped in to
handle the companys product. Assume that this contest runs for 3
months. Each marketing executive is given a target regarding number
of new distributors and they can generate during the period. This
target is fixed and based on the past sales achieved by them about
which, the data is available in the company.
Slide 125
Application of discriminant analysis-Contd.. It is also
announced that the marketing executives who add 15 or more
distributors will be given a maruti omni-van as prize. Those who
generate between 5 and 10 distributor will be given a 2 wheeler as
prize. Those who generate less than 5 distributor will get nothing.
Now assume that 5 marketing executives won a maruti van and 4 won a
2 wheeler.
Slide 126
Application of discriminant analysis-contd.. The company wants
to find out, which activities of the marketing executive made the
difference in terms of winning a prize and not winning the prize.
One can proceed in a number of ways. The company could compare
those who won maruti van against others. Alternatively the company
might compare those who won, one of the 2 prizes, against those who
won nothing.
Slide 127
Application- contd.. Discriminant analysis will highlight the
difference in activities performed by each group members to get the
prize. The activity might include: More number of calls made to the
distributors. More personal visits to the distributors with advance
appointments. Use of better convincing skills.
Slide 128
Conducting Discriminant Analysis The steps involved in
conducting Discriminant Analysis is as follows: Formulate the
problem Estimate the discriminant function coefficients. Interpret
the results Assess the validity of discriminant analysis
Slide 129
Factor analysis The main purpose of factor analysis is to group
large set of variable factors into fewer factors. Each factor will
account for one or more component. Each factor a combination of
many variables.
Slide 130
Factor analysis model Mathematically, factor analysis is
somewhat similar to multiple regression analysis, in that each
variable is expressed as a linear combination of underlying
factors.
Slide 131
Factor Analysis Model- Contd.. If the variables are
standardized, the factor model may be represented as:
Xi=Ai1F1+Ai2F2+Ai3F3+..+AimFm+ViUi Where Xi= ith Standardized
variable Aij= standardized multiple regression coefficient of
variable i on common factor j. F=Common Factor Vi= standardized
regression coefficient of variable i on unique factor i. Ui= the
unique factor for variable i. M= number of common factors.
Slide 132
Statistics associated with factor analysis Bartletts test of
sphericity: is a test of statistics used to examine the hypothesis
that the variables are uncorrelated in the population. In other
words, the population correlation matrix is an identity matrix.
Correlation matrix: A correlation matrix is a lower triangle matrix
showing the simple correlation, r between all the possible pairs of
variables included in the analysis. Communality: is the amount of
variance, a variable shares with all the other variables being
considered. This is also the proportion of variance explained by
the common factors. Eigen value: represents the total variance
explained by each factor.
Slide 133
Statistics associated with factor analysis- Contd.. Factor
loadings: are simple correlations between the variables and the
factors. Factor loading plot: A factor loading Plot is the plot of
original variables using the factor loadings as coordinates. Factor
matrix: A factor matrix contains the factor loadings of all the
variables on the factors extracted. Factor scores: Factor Scores
are composite scores estimated for each respondent on the derived
statistics. KMO: Kaiser Meyer Olkin measure of sampling adequacy:
is an index used to examine the appropriateness of factor analysis.
High values between 0.5 and 1.0 indicate factor analysis is
appropriate. Values below 0.5 imply that factor analysis may not be
appropriate.
Slide 134
Statistics associated with factor analysis- Contd.. Percentage
of variance: This is the percentage of the total variance
attributed to each factor. Residuals: Residuals are the differences
between the observed correlations, as given in the input
correlation matrix, and the reproduced correlations, as estimated
from the factor matrix. Scree plot: A scree plot is a plot of the
eigenvalues against the number of factors in order of
extraction.
Slide 135
Conducting factor analysis The steps involved in conducting
factor analysis is as follows: Formulate the problem Construct of
correlation matrix. Determine the method of factor analysis.
Determine the number of factors. Rotate the factors. Interpret the
factors: calculate the factor scores, select the surrogate
variables. Determine the model fit.
Slide 136
Conducting factor analysis- Contd.. Principal component
analysis: An approach to factor analysis that considers the total
variance in the data. Common factor analysis: An approach to factor
analysis that estimates the factors based on the common
variance.
Slide 137
Conducting factor analysis- Contd.. Determine the number of
factors: The number of factors can be determined using the
following approaches: A priori determination. Determination based
on Eigen values. Determination based on scree plots. Determination
based on percentage of variance. Determination based on split-half
reliability: The sample is split in half and factor analysis is
performed on each half. Determination based on significance
tests.
Slide 138
Conducting factor analysis- Contd.. The rotation of factor can
be done based on; Orthogonal Rotation: Rotation of factors in which
the axes are maintained at right angles. Variance procedure: It is
a commonly used procedure. An orthogonal method of factor rotation
that minimizes the number of variables with high loadings on a
factor, thereby enhancing the interpretability of the factors.
Oblique rotation: Rotation of factors, when the axes are not
maintained at right angles.
Slide 139
Factor analysis contd.. There are 2 most commonly employed
factors analysis procedures. They are: Principle component analysis
Common factor analysis When the objective is to summarize
information from a large set of variables in to a few factors,
principle component factor analysis is used. On the other hand if
the researcher wants to analyze the components of the main factor,
common factor analysis is used.
Slide 140
Example of common factor analysis Example: inconvenience inside
a car. The components may be: Leg room Seat arrangement Entering
the rare seat Inadequate dickey space Door locking mechanism
Slide 141
Example of principle component factor analysis Example:
customer feedback about a 2 wheeler manufactured by a company. The
MR Manager prepares a questionnaire to study the customer feedback.
The researcher has identified 6 variables or factors for this
purpose.
Slide 142
e.g for principle factor analysis- contd.. The factors are as
follows: Fuel efficiency (A) Durability (B) Comfort (C) Spare parts
availability (D ) Breakdown frequency (E) Price (F)
Slide 143
Factor analysis- contd.. The questionnaire may be administered
to 5000 respondents. The opinion of the customer is gathered. Let
us allot points 1 to 10 for the variables factors A to E. 1 is the
lowest and 5 is the highest. Let us assume that the application of
factor analysis has led to grouping the variables as follows.
Slide 144
Factor analysis- contd.. A, B, D,E into factor 1 F into
factor-2 C into factor-3 Factor-1 can be termed as technical
factors Factor-2 can be termed as Price factor. Factor-3 can be
termed as Personal factor.
Slide 145
Applications of factor Analysis It is used for market
segmentation. Product research: can be employed to determine the
brand attributes that influence the consumers choice. Advertising
studies: media consumption habits of target audience. Pricing
studies: to identify characteristics of price sensitive
consumers.
Slide 146
Cluster Analysis Cluster analysis is used to: To classify
persons or objects into small number of clusters or groups. To
identify specific customer segment for the companys brand. Cluster
analysis is a technique used for classifying objects into groups.
This can be used to sort data( a number of people, companies,
cities, brands or any other objects) into homogenous groups based
on their characteristics.
Slide 147
Applications of Cluster Analysis Customer segmentation
Estimation of segment sizes Industries where this technique is
useful includes Automobiles Retail stores Insurance B to B Durables
and packaged goods VALS (consumer Behavior)
Slide 148
Statistics associated with cluster Analysis Agglomeration
schedule: An agglomeration schedule gives information on the
objects or cases being combined at each stage of the hierarchical
clustering process. Cluster centroid: Is the mean values of the
variables for all the cases or objects in a particular cluster.
Cluster membership: Indicates the cluster to which each case or
object belongs. Dendrogram: A Dendrogram or tree graph is a
graphical dev ice for displaying clustering results. Distances
between cluster centers: These distances indicate how separated the
individual pairs of clusters are. Icicle diagram: An icicle diagram
is a graphical display of clustering results, so called because it
resembles a row of icicles hanging from the eaves of the house.
Similarity/distance coefficient matrix: Is a lower triangle matrix
containing pair wise distances between objects or cases.
Slide 149
Conducting Cluster Analysis Formulate the problem Select a
distance measure Select a clustering procedure Decide on the number
of clusters Interpret and profile clusters. Assess the validity of
clustering.
Slide 150
Select a clustering Procedure Hierarchical Clustering: A
Clustering procedure characterized by the development of hierarchy
or tree like structure. Agglomerative clustering: hierarchical
clustering procedure where each object starts out in a separate
cluster. Divisive clustering: Hierarchical clustering procedure
where all the objects start out in one giant cluster. Clusters are
formed by dividing this cluster into smaller and smaller clusters.
Linkage methods: Agglomerative methods of hierarchical clustering
that cluster objects are based on computation of distances between
them. Single linkage: Linkage method that is based on minimum
distance or the nearest neighbor approach. Complete linkage:
Linkage method that is based on maximum distance or the farthest
neighbor approach. Average linkage: A Linkage method based on the
average distance between all the pairs of objects, where one member
of the pair is from each of the clusters.
Slide 151
Select a clustering Procedure- Contd.. Variance methods: An
agglomerative method of hierarchical clustering in which clusters
are generated to minimize the within cluster variance. Wards
procedure: variance method in which the squared Euclidean distance
to the cluster means is minimized. Centroid methods: A Variance
method of hierarchical clustering in which the distance between 2
clusters is the distance between their centroids.
Slide 152
Select a clustering Procedure- Contd.. Non-hierarchical
clusters: A Procedure that first assigns or determines a cluster
center and then groups all objects within a prespecified threshold
value from the center. Sequential threshold method: A
non-hierarchical clustering method in which a cluster center is
selected and all the objects within a prespecified threshold value
from the center are grouped together. Parallel threshold method:
Non-hierarchical clustering method that specifies several cluster
centers at once. All objects that are within a prespecified
threshold value from the center are grouped together. Optimizing
partitioning method: Non-hierarchical clustering method that allows
for later reassignment of objects to clusters to optimize an
overall criterion.
Slide 153
Cluster analysis is applicable An FMCG company wants to map the
profile of its target audience in terms of lifestyle, attitude, and
perceptions. A consumer durable company wants to know the features
and services a consumer takes into account, when purchasing through
catalogues. A housing finance corporation wants to identify and
cluster the basic characteristics, lifestyles and mindset of
persons who would be availing housing loans. Clustering can be done
based on parameters such as interest rates, documentation,
processing fee, number of installments
Slide 154
Process There are 2 ways in which cluster analysis is carried
out: First, objects/respondents are segmented into a pre- decided
number of clusters. In this case a method called non-hierarchical
method can be used which partitions data into the specified number
of clusters. The second method is called the hierarchical
method.
Slide 155
Interpretation of Results Ideally the variables should be
measured on an interval or ratio scale. This is because the
clustering techniques use the distance measure to find the closest
objects to group into clusters. An example of its use can be
clustering of towns similar to each other which will help decide
where to locate new retail stores.
Slide 156
Slide 157
Interpretation of Results-Contd.. If clusters of customers are
found based on their attitudes towards new products and interest in
different kinds of activities an estimate of the segment size for
each segment of the population can be obtained by looking at the
number of objects in each cluster. Names can also be given to
clusters to describe each one. Marketing strategies for each
segment are based on segment characteristics.
Slide 158
Steps in Cluster Analysis Selection of the sample to be
clustered (buyers, products, employees) Definition on which the
measurement to be made. (e.g. Product attributes, buyer behavior,
characteristics) Clusters should be arranged in hierarchy. Cluster
comparison and validation.
Slide 159
Steps in Cluster Analysis-Contd.. Selection of the sample to be
clustered (buyers, products, employees) Definition on which the
measurement to be made. (e.g. product attributes, buyer
characteristics). Computing the similarities among the entities.
Arrange the clusters in hierarchy. Cluster comparison and
validation.
Slide 160
Conjoint Analysis A technique that attempts to determine the
relative importance consumers attach salient attributes and the
utilities they attach to the level of attributes. Conjoint analysis
is concerned with the measurement of the joint effect of the 2 or
more attributes that are important from the consumers point of
view.
Slide 161
Statistics associated with conjoint analysis Part worth
functions: The part worth functions or utility functions describe
the utility consumers attach to the levels of each attribute.
Relative importance weights: The relative important weights are
estimated and indicate which attributes are important in
influencing consumer choice. Attribute levels: The attribute levels
denote the values assumed by the attributes. Full profiles: Full
profiles or complete profiles of brands are constructed in terms of
all the attributes by using the attribute levels specified by the
design. Pair wise tables: In Pair wise tables, the respondents
evaluate two attributes at the same time until all the required
pairs of attributes have been evaluated.
Slide 162
Statistics associated with conjoint analysis-Contd.. Cyclical
designs: Cyclical designs are designs employed to reduce the number
of paired comparisons. Fractional factorial designs: Fractional
factorial designs are designs employed to reduce the number of
stimulus profiles to be evaluated in the full profile approach.
Orthogonal arrays: Orthogonal arrays are a special class of
factorial designs that enable the efficient estimation of all main
effects. Internal validity: This involves correlations of the
predicted evaluations for the holdout or validation stimuli with
those obtained from the respondents.
Slide 163
Steps in Conducting Conjoint Analysis Formulate the Problem
Construct the Stimuli. Decide on the form of Input data. Select a
Conjoint analysis procedure. Interpret the results. Assess
reliability and validity.
Slide 164
Conjoint Analysis Model Conjoint analysis model: The
mathematical model expressing the fundamental relationship between
attributes and utility in conjoint analysis.
Slide 165
Conjoint Analysis Model-Contd.. The model estimated may be
represented by: m ki U(X)= aij xij i=1 j=1 Where U(X)= overall
utility of an alternative aij= the part worth contribution or
associated with the jth level. (j, j= 1,2..ki) of the ith attribute
(i, i = 1,2m) Ki = number of levels of attribute i m = number of
attributes Xij = 1 if the jth level of ith attribute is present = 0
otherwise
Slide 166
Hybrid Conjoint Analysis A form of conjoint analysis that can
simplify the data collection task and estimate selected
interactions as well as all its main effects. It has been developed
to serve 2 main purposes: Simplify data collection task by imposing
less burden on each respondent. Permit the estimation of selected
interactions at the subgroup level as well as all main effects at
individual level.
Slide 167
Conjoint Analysis-Contd.. In a situation where the company
would like to know the most desirable attributes or their
combination for a new product or service, the use of conjoint
analysis is not appropriate.
Slide 168
Example for conjoint analysis An airline would like to know,
which is the most desirable combination of attributes to a frequent
traveller: Punctuality Airfare Quality of food served on the flight
Hospitality and empathy shown
Slide 169
Conjoint analysis.. Contd Conjoint analysis is a multivariate
technique that captures the exact levels of utility that an
individual consumer places on various attributes of the product
offering. Conjoint analysis enables direct comparison.
Slide 170
Example of conjoint analysis Designing an automobile loan or
insurance plan in the insurance industry. Designing a complex
machine for business customers.
Slide 171
Process of conjoint analysis Design attributes for the product
are first identified. For a shirt manufacturer, these could be
design such as designer shirts Vs plain shirts, this price of Rs400
versus Rs.800. The outlets can have exclusive distribution. All
possible combinations of these attributes level are then listed
out. Each design combination will be ranked by customers and used
as input data for conjoint analysis. Then the utility of the
products relative to the price are measured.
Slide 172
Process of conjoint analysis The output is a part-worth or
utility for each level of each attribute. For example the design
may get a utility level of 5 and plain as 7.5. Similarly, the
exclusive distribution may have a part utility of 2, and mass
distribution, 5.8. We then put together the part utilities and come
up with a total utility for any product combination we want to
offer and compare that with the maximum utility combination for
this customer segment.
Slide 173
Approach to conjoint analysis From a discussion with the
client, identify the design attributes to be studied and the levels
at which they can be offered. Then build a list of product concepts
on offer. These product concepts are then ranked by customers. Once
this data is available, use conjoint analysis to derive the part
utilities of each attribute level. This is then used to predict the
best product design for the given customer segment. Use the SPSS
Conjoint procedure to analyse the data.
Slide 174
Uses of Conjoint Analysis The uses of Conjoint analysis is as
follows: Determining the relative importance of attributes in the
consumer choice process. Estimating market share of brands that
differ in attribute levels. Determining the composition of most
preferred brand. Segmenting the market based on similarity of
preferences for attribute levels. Applications of conjoint analysis
have been made in consumer goods, industrial goods, financial and
other services.
Slide 175
MDS The most common and useful marketing application of
multidimensional scaling is product positioning or brand
positioning. Positioning is essentially concerned with mapping a
consumers mind and placing all the competing brands of a product
category in appropriate slots or positions on it. One obvious way
to do that is to ask customers what they think of competing brands
or say 6 attributes with a rating scale of 5 to 10 points. This
would result in rating for all the brands on all attributes which
could be taken as 2 attributes at a time and plotted on a
graph.
Slide 176
MDS A class of procedures for representing perceptions and
preferences of respondents spatially by means of a virtual display.
Perceived or psychological relationship among stimuli are
represented as geometric relationships among points in a
multidimensional space.
Slide 177
Statistics and terms associated with MDS Similarity judgments:
are ratings on all possible pairs of brands or other stimuli in
terms of their similarity using a likert- type scale. Preference
rankings: are rank ordering of the brands or other stimuli from the
most preferred to least preferred. They are normally obtained from
the respondents. Stress: This is lack of fit-measure; higher the
values of stress indicates poor fits
Slide 178
Statistics and terms associated with MDS- Contd.. R-Square: R
Square is a squared correlation Index that indicates the proportion
of variance of the optimally scaled data that can be accounted for
by the MDS Procedure. This is a goodness of fit measure. Spatial
map: Perceived relationship among brands or other stimuli are
represented as geometric relationship among points in a Multi
Dimensional space called spatial map. Coordinates: indicate the
positioning of a brand or a stimulus in a spatial map. unfolding:
The representation of both brands and respondents as points in the
same space is referred to as unfolding.
Slide 179
Conducting MDS Formulate the problem Obtain input data Select
an MDS Procedure Decide on the number of dimensions. Label the
dimensions and interpret the configuration. Assess reliability and
validity.
Slide 180
Conducting MDS-Contd.. Obtain Input Data: Perception Data:
Direct Approaches: In Direct Approaches to gathering perception
data, the respondent, the respondents are asked to judge how
similar or dissimilar the various brands or stimuli are, using
their own criteria. Respondents are often required to rate all
possible pairs of brands or stimuli in terms of similarity on a
likert scale. These data are referred to as similarity
judgements.
Slide 181
Example Similarity judgments on all the possible pairs of
toothpaste brands may be obtained in the following manner: very
very Dissimilar similar Colgate vs. Crest 1 2 3 4 5 6 7 Aqua fresh
vs,crest 1 2 3 4 5 6 7 Colgate vs aquafresh 1 2 3 4 5 6 7 The
number of pairs to be evaluated is n(n-1)/2, where n is the number
of stimuli. Other procedures are also available.
Slide 182
Conducting MDS- Contd.. Derived approach: In MDS attribute
based approach to collecting perception data requiring the
respondents to rate the stimuli on the identified attributes using
semantic differential or likert scale For example different brands
of toothpaste may be rated on attributes such as: Whitens
------------------------------------------Does not teeth whiten
teeth
Slide 183
Conducting MDS- Contd.. Direct Vs Derived Approach: Direct
approaches have the advantage that the researcher does not have to
identify a set of salient attributes. Respondents make similarity
judgments using their own criteria, as they would under normal
circumstances. The disadvantages are that the criteria are
influenced by the brands or stimuli being evaluated. If various
brands of automobiles being evaluated are in the same price range,
then price will not emerge as an important factor. It may be
difficult to determine before analysis if and how the individual
respondents judgment should be combined.
Slide 184
Conducting MDS- Contd.. Direct Vs Derived Approach: The
advantage of Derived or Attribute based approach is that it is easy
to identify respondents with homogenous perceptions. The
respondents can be clustered based on the attribute ratings. It is
also easier to label the dimensions. A disadvantage is that the
researcher must identify all the salient attributes a difficult
task. The spatial map obtained depends on the attributes
identified.
Slide 185
Conducting MDS- Contd.. Select an MDS Procedure Non-metric MDS-
A type of MDS method that assumes that the input data are ordinal.
Metric MDS- A MDS method that assumes that the input data are
metric.
Slide 186
Conducting MDS- Contd.. Decide on Number of Dimensions: The
following guidelines are suggested for determining the number of
dimensions: A priori knowledge: theory or past research may suggest
a particular number of dimensions. Interpretability of the spatial
map: Generally it is difficult to interpret configurations or maps
derived in more than 3 dimensions. Elbow criterion: A plot of
stress versus dimensionality should be examined. The point in this
plot usually form a convex pattern. The point at which a n elbow or
a sharp bend occurs indicates appropriate no of dimensions. Ease of
use: it is generally easier to work with 2 dimensional maps or
configurations than with those involving more dimensions.
Statistical approach: It is used for determining
dimensionality.
Slide 187
Conducting MDS- Contd.. Scaling Preference Data: Internal
Preference Data: Takes into account both brands stimuli and
respondent points. External analysis of preference: vectors based
on preference data.
Slide 188
Example of MDS A product category of shampoos could be
identified as having 5 attributes important to consumers- price,
lather, fragrance, consistency, and favorable effects on hair. If
this were to be rated on a 7-point scale for say six leading brands
of shampoo A, B, C,D,E, and F, then we could pick up any 2
attributes and plot the six brands on a map according to consumer
ratings.
Slide 189
Example of MDS- Contd.. For example if we plotted rating on
price Versus rating on favorable effect on hair, we may find that
all the 6 brands are positioned in different places based on
consumer ratings or perceptions. This is called perceptual map of
consumer perception about competing brands in a product
category.
Slide 190
Methods of MDS Attribute based approach
Similarity/dissimilarity based approach
Slide 191
Recommended Usage Knowing particular attribute Number of
dimensions as well as interpretation. Naming of attributes of the
brands and their target segment such as age, price, quality, or
attempted positioning through brand communication and so on.
Slide 192
Research report There are 2 types of report Oral report Written
report Oral report: This type of reporting is required, when the
researchers are asked to make an oral presentation. Making an oral
presentation is somewhat difficult compared to written report. This
is because the reporter has to interact directly with the audience.
Any faltering during an oral presentation can leave a negative
impression on the audience.
Slide 193
Nature of an oral presentation Opening Finding/Conclusion
Recommendation Method of presentation.
Slide 194
Points to remember in oral presentation Language used must be
simple and understandable. Time Management should be adhered. Use
of charts, graphs etc, will enhance understanding by the audience.
Vital data such as figures, may be printed and circulated to the
audience, so that their ability to comprehend increases. The
presenter should know his target audience well in advance. The
presenter should know the purpose of the report.
Slide 195
Guidelines for oral report Employ visual aids Avoid reading the
report KYA- Know Your Audience Plan and deliver.
Slide 196
Types of written reports On the basis of time interval reports
can be classified as: Daily, Weekly, Monthly, Quarterly, Yearly
Types of Report: Short Report, long Report, Formal Report, Informal
Report, Government Report.
Slide 197
Preparation of written reports Preparation of research report:
The following is the format of research report: Title Page Page
contents/Table of contents Executive Summary Introduction
Methodology Data collection and Analysis Conclusions Suggestions
and Recommendations Bibliography. Appendix
Slide 198
Explanation of contents of reports Executive summary: This
includes a brief detail of what the report consists of. It should
be written in one or two pages. Body: this section include:
Introduction: the introduction should clearly explain the decision
problem. Sometimes it consists of details about the topic, company
profile etc.
Slide 199
Contents of report- contd.. Methodology: this includes the
following: Statement of objectives Data collection method: whether
primary, secondary data or both. Questionnaire design, ie tools for
data collection. Sample design: which includes sample type, sample
size etc.
Slide 200
Contents of report- contd.. Analysis and interpretation: this
should include analysis of question in the questionnaire by using
tables and graphs and other statistical tools.
Slide 201
Contents of report- contd.. Conclusions: this includes the
conclusions drawn from the study. Suggestions and recommendations:
based on the conclusions, suggestions and recommendations are made.
Appendix: the purpose of appendix is to provide a place for
material which is not absolutely necessary in the body of the
report: such as questionnaire, broucher etc.
Slide 202
Bibliography If portions of the report is based on secondary
data, use bibliography section to list the publications or sources
that you have consulted. It includes: Title of the book Name of the
journal in case of article Volume no Page number Edition
Slide 203
Writing the Report- Contd.. Pre writing considerations: The
outline : I. Major Topic Heading A Major subtopic heading 1. Sub
topic a. Minor subtopic (1) Further details (a) Even further
details
Slide 204
Writing the Report- Contd.. Writing Considerations: Contd.. The
Bibliography Writing the Draft Readability Comprehensibility Tone
Final proof.
Slide 205
Presenting the research report Carrying out professional
approach Use short paragraphs Use headings and subheadings Use
vertical listings of points. Incident part of the text that
represents listings, long quotations or examples.
Slide 206
Presenting the research report Presentation of statistics
involves 4 ways: A text paragraph Semi tabular form Tables Graphics
Pie charts
Slide 207
Presenting the research report Preparation Opening Findings and
conclusions Recommendations. Delivery
Slide 208
Presenting the research report- Contd.. Common Research
Problems Speaker problems Vocal characteristics: Should not speak
too softly Do not speak to rapidly Vary volume tone quality Do not
use overworked pet phrase, uhs, etc. Do not stare into space Do not
misuse visuals Do not hitch or tug on clothing, scratch or fiddle
with pocket. Do not rock back and forth or twist from side to side,
or lean too much on the lectern.
Slide 209
Presenting the research report- Contd.. Other problems Cost
considerations Limitations on time Quality of research report
Effectiveness of research.
Slide 210
Presenting the research report Audio-Visuals Low tech: Chalk
board and white boards Hand out materials Flip charts Overhead
transparencies. Slides High Tech Computer drawn visuals Computer
animations
Slide 211
Writing the research Report- Contd.. Other guidelines: Consider
the audience Attitude 1: adopt fresh mind approach Kiss Approach
(Keep it short and simple).
Slide 212
Oral and written report Distinguish between oral and written
report: oral Report No rigid standard format Remembering all that
is said is difficult if not impossible. This is because the
presenter cannot be interrupted frequently for clarification. Tone,
voice modulation, comprehensibility and several other communication
factors play an important role. Correcting mistakes if any is
difficult. The audience has no control over the speed of
presentation. The audience does not have the choice of picking and
choosing from the presentation.
Slide 213
Oral and written report Distinguish between oral and written
report: Written Report Standard format can be adopted This can be
read a number of times and clarification can be sought whenever the
reader chooses. Free from presentation problems. Mistakes if any,
can be pinpointed and corrected. Not applicable The reader can pick
and choose what he thinks is relevant to him. For instance, the
need for information is different for technical and non technical
persons.