Week 3: Customer Segmentation and Targeting I (Factor Analysis)

Question 1

Sources of Customer Heterogeneity

Answer 1

Individual differences
Life experiences
Functional needs
Self identity/image
Marketing activities

Question 2

Advantages of Market Segmentation

Answer 2

• Identification of customer groups
• Target group specific product design
• Target group specific communication
• Price differentiation
• Identify opportunities and threats
• Differentiate from competitors

Question 3

Data Sources for Segmentation

Answer 3

Primary data
Exisiting data
Third party data

Question 4

The Segmentation Process: Data Analytics

Answer 4

Data preparation - Factor analysis
Cluster analysis
Review & Refinement

Question 5

Factor Analysis

Answer 5

Factor Analysis is a class of analytical procedures primarily used for data reduction and summarization

Question 6

Applications of Factor Analysis in Marketing

Answer 6

Pricing
Advertising
Product Development
Market Segmentation

Question 7

Types of Factor Analyses

Answer 7

Exploratory Factor Analysis
Principal Component Analysis
Confirmatory Factor Analysis
Common Factor Analysis

Question 8

Eigenvalue

Answer 8

Total variance of all variables accounted for by one factor

Question 9

Factor loadings

Answer 9

Correlations between the variables and factors (ranges from -1 to +1)

Question 10

Factor scores

Answer 10

Relation between observations and factors

DO NOT SEE FACTOR SCORE IN THE OUTPUT

Question 11

Communality

Answer 11

Proportion of one variable’s variance explained by all factors extracted

Question 12

Factor analysis process

Answer 12

1. Check assumptions and check if it makes sense to conduct an EFA
2. Determine the number of factors
3. Interpret the factor solution
4. Evaluate the goodness-of-fit
   of the factor solution

Question 13

Assumptions

Answer 13

• Measurement level: Interval or ratio scales
• Standardised data (mean = 0 and standard deviation = 1), especially if variables were measured on different measurement
  units or ranges
• Sample size: rule of thumb >100 observations

Question 14

Does it make
sense to conduct
an EFA?

Answer 14

1. There should be high correlations among sets of variables (>0.5: moderately high; >0.6: high; >0.7: very high)
2. Sample adequacy: Kaiser-Meyer-Olkin (KMO) evaluates all
   correlations among all variables
3. Bartlett’s test of sphericity:
   Significance indicates sufficient
   correlations.

Question 15

A multivariate statistical technique for studying interrelationships among variables, usually for discovering underlying constructs or data reduction is known as:

a)Multiple regression
b)Factor analysis
c)Discriminant analysis
d)Canonical correlation analysis

Answer 15

b)Factor analysis

Question 16

To determine which variables relate to which factors, a researcher would use:

a)Factor loadings
b)Communalities
c)Eigen values
d)Beta coefficients

Answer 16

a)Factor loadings

Question 17

If a researchers wants to determine the amount of variance in the original variables that is associated with a factor, they would use:

a) factor loadings
b) communalities
c) eigen values
d) beta coefficients
e) none of the above

Answer 17

c) eigen values

Question 18

Which of the following can be used to determine how many factors to extract from a factor analysis:

a) Eigen values and percentage of variance explained by each factor
b) Scree plots
c) Factor loadings
d) All of the above
e) None of the above

Answer 18

d) All of the above

Question 19

In exploratory factor analysis, how much variance would a good model be likely to explain?

a) 0 to 25%
b) 25% to 50%
c) 50% to 75%
d) 75% to 100%

Answer 19

c) 50% to 75%

Question 20

When a factor loading matrix is rotated, what will be the likely outcome:

a) The pattern of factor loadings changes and the total variance explained by the factors remains the same.
b) The pattern of factor loadings stays the same and the total variance explained by the factors remains the same.
c) The pattern of loadings changes and the total variance explained by the factors changes too.
d) The pattern of loadings stays the same and the total variance explained by the factors changes

Answer 20

a) The pattern of factor loadings changes and the total variance explained by the factors remains the same.

Question 21

In SPSS, orthogonal rotation in factor analysis is called:

a) Oblimin
b) Oblimax
c) Oblique
d) Varimax
e) None of the above

Answer 21

d) Varimax

Question 22

The total of all eigen values will equal:

A) 1
B) 50
C) 100
D) The number of variables in the analysis
E) Impossible to tell without further information

Answer 22

D) The number of variables in the analysis

Question 23

1. Which of the following is not the part of the exploratory factor analysis process?

a) Extracting factors
b) Determining the number of factors before the analysis
c) Rotating the factors
d) Refining and interpreting the factors

Answer 23

b) Determining the number of factors before the analysis

Question 24

Which of the following criteria cannot be used to determine the number of factors in an EFA?

a) Asking a group of researchers before the analysis
b) Eigenvalue rule
c) Scree test
d) Parallel analysis

Answer 24

a) Asking a group of researchers before the analysis

Question 25

Which of the following is not an oblique rotation technique?

a) Promax
b) Oblimax
c) Varimax
d) Quartimin

Answer 25

c) Varimax

Question 26

(T/F) Factor analysis works best if each variable has a high correlation with every other variable.

Answer 26

False

Question 27

Each new variable created in a factor analysis is called a _______.

a) factor
b) indicator
c) vector

Answer 27

a) factor

Question 28

In which kind of factor analysis does the researcher have a priori knowledge of the number and nature of the factors?

a) Exploratory factor analysis
b) Confirmatory factor analysis

Answer 28

b) Confirmatory Factor Analysis

Question 29

When checking to see if a study’s data are “suitable” for a factor analysis, most researchers check their sample size and then examine 3 things: multicollinearity, the determinant, and the _______.

a) sample means on each variable
b) KMO measure of sampling adequacy
c) factor structure
d) standardized interquartile range

Answer 29

b) KMO measure of sampling adequacy

Question 30

The number of eigenvalues is equal to the _______ whereas the sum of the eigenvalues quantities is equal to the _______.

a) sample size; number of variables

b) sample size; number of factors

c) number of variables; number of factors

d) number of factors; number of variables

Answer 30

d) number of factors; number of variables

Question 31

Communalities primarily help indicate the usefulness of _______ (variables/factors).
variables

Answer 31

Variables

Question 32

A factor loading is:

a) a correlation coefficient between a variable and a factor (cluster of variables).
b) the correlation between a binomial variable and a variable which has a continuous distribution of scores.
c) empirically based hypothetical variable consisting of items which are strongly associated with each other.
d) the correlation of a variable with a whole score.

Answer 32

a correlation coefficient between a variable and a factor (cluster of variables).

Question 33

A factor loading of 0.80 means, generally speaking, that:

a) there is no relationship between that variable and the factor.
b) the item correlates well with the factor, though not perfectly.
c) the variable is moderately related with the factor.
d) the item is poorly related to the factor

Answer 33

b) the item correlates well with the factor, though not perfectly

Question 34

Rotation usually involves _____ high correlations and _____ low ones.

Answer 34

maximising; minimising

Question 35

The amount of variance ‘explained’ by a factor is shown by:

a) the factor loadings.
b) the eigenvalue.
c) the name of the factor.
d) the correlation matrix.

Answer 35

b) the eigenvalue.

Question 36

The scree plot is…..

Answer 36

the number of factors plotted agains varaince accounted for.