Here are the steps I would take to conduct an exploratory factor analysis on SERVQUAL data: 1. Examine the correlation matrix to identify highly correlated variables and check for suitability of factor analysis using KMO and Bartlett's test. 2. Extract initial factors using principal axis factoring or maximum likelihood. 3. Examine scree plot and eigenvalues to determine number of factors to retain. 4. Rotate the factors using varimax to achieve a simple structure and more interpretable factors. 5. Interpret the rotated factor solution by examining which variables load highly on each factor. Name the factors based on the groupings of variables. Previous research on SERVQUAL has found a 5 factor solution

1. FACTOR ANALYSIS AND
MARKETING
RESEARCH I
Mark Ng
2014

2. Outline
• Use of Factor analysis in Marketing Research
• Exploratory factor analysis
• Confirmatory factor analysis
• Basic ideas of Structural Equation Modeling

3. What is Factor Analysis?
• Statistical technique that serves to combine
variables to create new factors
• Purpose
• To reduce number of variables to enhance
manageability
• To identify the underlying constructs (latent variables)
A
A1
A3
A2

4. Why Factor Analysis?
• Many constructs/variables are latent (derived
from other observable variables) and multi-
dimensional
• E.g. Service quality, consumer innovativeness,
language abilities, stress level
• Identification of underlying dimensions (factors) to
understand and describe complex constructs

5. Mechanism of Factor Analysis
• Assumption of factor analysis:
• underlying dimensions (factors) can be used to explain
complex phenomena.
• Observed correlations between variables result from
their sharing of factors.

6. Good Factor Analysis Solution…
• Major goal: to represent relationships among sets
of variables parsimoniously yet keeping factors
meaningful.
• A good factor solution is both simple and
interpretable (with new insights).
© Dr. Maher Khelifa 6

7. Applications of Factor Analysis
• Product
• to identify brand attributes and selection criteria of
consumers
• Advertising
• to understand attitudes, media habits of customers
• Pricing
• to identify the characteristics of different groups of
consumers
• Distribution
• to better understand channel selection criteria
• Develop marketing research

8. Two types of Factor Analysis
• Exploratory Factor Analysis: to explore the
underlying structure of a large set of data
• Confirmatory Factor Analysis: to test whether the
data fit a hypothesized measurement model

9. Procedure of Exploratory Factor Analysis
1. To examine correlation matrix
2. To extract factors
3. To select rotation methods
4. To interpret and name the factors

10. Step 1: Examine Correlation Matrix
• Identify variables not related to other variables
• Small correlation (r<0.3): unlikely that the two
variables share common factors
• Examine visually
• Bartlett Test of Sphericity: sphericity should be large and
significant (p<0.05)
• Kaiser-Meyer-Olkin (KMO) measure of sampling
adequacy (.8 and higher are great, .7 is acceptable, .6
is mediocre, less than .5 is unacceptable ).

11. Step 2 To extract factors
• Two most commonly
used methods:
• Principal Component
Analysis: Aim to
summarize data into
smaller set of factors
• Common Factor Analysis:
Aims to uncover the
underlying dimensions

12.  Other factor extraction methods include:
Maximum likelihood method
Alpha method
Unweighted lease squares method
Generalized least square method
Image factoring

13. • After selecting method
of extraction, determine
the number of factors
extracted by:
• Statistical criteria:
• Eigenvalues >1 (explained
variance > 1 single variable)
• Scree Plot
• Theoretical consideration

14. • Results of factor extraction:

15. • Scree Plot

16. • Component Matrix

17. Step 3 Factor rotation
• To interpret the components, the researcher must
group X-variables to which component they load
most on.
• Un-rotated factors are typically not very interpretable
• Rotated factors – more meaningful and easy to interpret
• Different rotation methods may result in the
identification of somewhat different factors.

18. • Most commonly used
method: Varimax
• More interpretable since:
• Develop uncorrelated
factors/components.
• Minimize number of
variables that have high
loadings on a factor

19. • Other rotation methods
• Next common rotational method: Oblique Rotations
• Factors are correlated
• More difficult to interpret
• Other rotational methods include:
• Quartimax (Orthogonal)
• Equamax (Orthogonal)
• Promax (oblique)

20. • Initial results for comparison
VARIMAX OBLIQUE

21. Step 4 Interpretation of factors
• Select the most interpretable solution: number of
factors and rotated solution
• Consider: statistical criteria, a priori conceptual
beliefs (past research), interpretability of solutions
• SERVQUAL – 5 components
• Tangibility
• Reliability
• Responsiveness
• Communication, credibility and security
• Understanding/ knowing consumers

22. Practice
• Make use of the data to see how many factors
could you establish from the SERVQUAL data.