What is Exploratory Factor Analysis? Description
The Exploratory Factor Analysis method (EFA) is a technique that can be
used for uncovering the underlying structure (dimensions) of a large set of
variables. Therefore, EFA reduces a large set of variables to a couple of
underlying factors.
Example: You have set up a questionnaire about customer satisfaction
in the civil aviation industry (United Airlines, Delta, Lufthansa). You have
identified 30 items to describe and evaluate customer satisfaction (e.g. "convenience
of buying tickets", "convenience of checkingin", "environment of the lounges",
"friendliness of the flight attendants", "fulfilling special desires", "quality
of food on board", "comfort of the seats", "special offers such as inflight
movies", "accuracy of arrival"). By using EFA you can reduce the set of 30
items within your analyzing process to a couple of central factors which underlay
your set of items. You can consider for example that the items "convenience
of buying tickets", "convenience of checkingin", "environment of the lounges",
"quality of food on board", "comfort of the seats" and "special offers such
as inflight movies" are part of a potential dimension. The hard things which
airlines can perform to drive their business. However, "friendliness of the
flight attendants", "fulfilling special desires" and "accuracy of arrival"
are more part of a process dimension.
That means EFA is finding out exactly these structures  in our example the
factor "potential" and "process". Managers now can get a deeper insight for
developing marketing activities to improve the satisfaction of their customers
by focusing more on the "potential"part or on the "process"part.
Origin of Exploratory Factor Analysis. History
Factor analytic methodologies may be conceived on a continuum. This continuum
ranges from confirmatory techniques towards pure exploratory procedures. Charles
Spearman (1904 onward) was interested in confirming the idea of a general
intelligence. With extended experimental evidence, developed through years
of studies involving larger test batteries given to larger samples of individuals,
Spearman's theory of a single intellectual factor proved to be inadequate.
A possibility had to be created to deal with group factors. In the early 1930s,
Thurstone broke with a common presumption based on prior assumptions as to
the nature of factors and developed a general theory of multiple factor analysis.
Thurstone's book "Vectors of Mind" (1935) presented the mathematical and logical
basis for this theory.
Calculation of Exploratory Factor Analysis. Formula
To analyze data by using EFA you can use statistical packages such as SPSS
or SAS.
Usage of Exploratory Factor Analysis. Applications
 Customer satisfaction surveys.
 Measuring service quality.
 Personality tests.
 Image surveys.
 Identifying market segments.
 Typing customers or products or behavior.
Steps in Exploratory Factor Analysis. Process
A typical EFA process is as follows:
 Identify the indicators/items which go in the EFA.
 Calculate a correlation matrix (coefficient of correlation from BravaisPearson).
 Examine the correlation matrix to be used for a EFA (level of significance,
inverse of the correlation matrix, BartlettTest, antiimagecovariancematrix,
KaiserMeyerOlkinCriteria KMO)
 Choose a factor extraction method (principal components analysis, principal
factor analysis).
 Discover the factors and of the factor loadings. Factor loadings are
the correlation coefficients between the variables (rows in the table) and
factors (columns in the table).
 Fix the number of factors to be extracted (for this step it is useful
to take the KaiserCriteria and the ScreeTest with the elbowcriteria).
 Interpret the factors extracted (e.g. "potential" and "process" in the
given example above)
Strengths of Exploratory Factor Analysis. Benefits
 Easy to use
 Useful for lots of survey questions,
 Basis of other instruments (e.g. regression analysis with factor scores),
easy to combine with other instruments (e.g. confirmatory analysis)
Limitations of Exploratory Factor Analysis. Disadvantages
 Variables have to be intervalscaled.
 Falling number should be larger than three times of the amount of variables.
Assumptions of Exploratory Factor Analysis. Conditions
 No outliers, interval data, linearity, multivariate normality, orthogonality
for principal factor analysis
Book: Klaus Backhaus,
Bernd Erichson, Wulff Plinke  Multivariate Analysemethoden
Book: Joseph F
Hair, Ronald L Tatham, Rolph E. Anderson, William Black  Multivariate Data
Analysis
Book: John C. Loehlin
 Latent Variable Models
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