There are many forms of data analysis
used to report on and study survey data. Factor analysis is best when used to
simplify complex data sets with many variables

What is Factor Analysis?

Factor analysis is a way to condense
the data in many variables into just a few variables. For this reason, it is
also sometimes called “dimension reduction.” You can reduce the “dimensions” of
your data into one or more “super-variables''.

” The most common technique is known
as Principal Component Analysis (PCA).

Factor analysis ppt Factor
Analysis-(Concept and analysis)

How Factor Analysis Can Help You?

Factor analysis is useful in:

● Condensing variables

● Uncovering clusters of
responses

Say you ask several questions all driving at different, but closely related, aspects of customer satisfaction:

1. How satisfied are you with our
product?

2. Would you recommend our product to
a friend or family member?

3. How likely are to you purchase our
product in the future?

But you only want one variable to represent a customer satisfaction score. One option would be to average the three question responses. Another option would be to create a factor-dependent variable. This can be done by running PCA and keeping the first Principal Component (also known as a factor). The advantage of PCA over an average is that it automatically weights each of the variables in the calculation.

Say you have a list of questions and you don’t know exactly which responses will move together and which will move differently; for example, purchase barriers of potential customers. The following are possible barriers to purchase:

1. Price is prohibitive

2. Overall implementation costs

3. We can’t reach a consensus in
our organization

4. The product is not consistent
with our business strategy

5. I need to develop an ROI, but
cannot or have not

6. We are locked into a contract
with another product

7. The product benefits don’t
outweigh the cost

8. We have no reason to switch

9. Our IT department cannot
support your product

10. We do not have sufficient technical resources

11.Your product does not have a feature we require

12.Other (please specify)

Factor analysis can uncover the
trends of how these questions will move together. The following are loadings
for 3 factors for each of the variables.

Notice how each of the principal components has high weights for a subset of the variables. The first component heavily weights variables related to cost, the second weights variables related to IT, and the third weights variables related to organizational factors. We can give our new super variables clever names.

If we were to cluster the customers
based on these three components, we can see some trends. Customers tend to be
high in Cost barriers or Org barriers, but not both.

Examples of Factor
Analysis Studies

Factor analysis, including PCA, is
often used in tandem with segmentation studies. Customers or clients might be
segmented using PCA itself or it might be an intermediary step to reduce
variables before using KMeans to make the segments.

Factor analysis provides simplicity
after reducing variables. For long studies with large blocks of Matrix Likert
scale questions, the number of variables can become unwieldy. Simplifying the
data using factor analysis helps analysts focus and clarify the results.

Exactly which questions to perform
factor analysis on is an art and science. Choosing which variables to reduce
takes some experimentation, patience, and creativity. Factor analysis works
well on Likert scale questions and Sum to 100 questions types.

Factor analysis works well on matrix
blocks of the following question genres:

● I believe brand represents
value

● Behavioral (Agree/Disagree):

I purchase the cheapest option

● I am a bargain shopper

● Attitudinal (Agree/Disagree):

The economy is not improving

● I am pleased with the product

● Activity-Based
(Agree/Disagree):

I love sports

● I sometimes shop online
during work hours

● Behavioral and psychographic
questions are especially suited for factor analysis.

Sample Output Reports

Factor analysis simply produces
weights (called loadings) for each respondent. These loadings can be used like
other responses in the survey.

Cost Barrier IT Barrier Org
Barrier

R_3NWlKlhmlRM0Lgb 0.7 1.3 -0.9

R_Wp7FZE1ziZ9czSN 0.2 -0.4 -0.3

R_SJlfo8Lpb6XTHGh -0.1 0.1 0.4

R_1Kegjs7Q3AL49wO -0.1 -0.3 -0.2

R_1IY1urS9bmfIpbW 1.6 0.3 -0.3