Factor analysis is a statistical method used to reduce the number of variables in a dataset by grouping correlated variables together into factors. It requires a minimum sample size of 10 observations per variable to avoid computational issues, and the Kaiser-Meyer-Olkin test and Bartlett's test are used to assess the factorability of the correlation matrix. The rotated factor matrix displays the factor loadings and shows which variables group together under each factor.

1. EFA (EXPLORATORY FACTORY ANALYSIS)

2. FACTOR ANALYSIS
 Factor analysis is a method of data
reduction.
 In other words, if your data contains many
variables, you can use factor analysis to
reduce the number of variables.
 Factor analysis groups variables with similar
characteristics together.

3. ADVICE REGARDING SAMPLE SIZE
 100 is Poor
 200 is Fair
 300 is Good
 500 is Very Good
 1000 or more Excellent
 As a rule of thumb, a bare minimum of 10
observations per variable is necessary to
avoid computational difficulties.

4. THE CORRELATION MATRIX

5. KAISER-MEYER-OLKIN (KMO) TEST
 The KMO measures the sampling adequacy
which should be greater than 0.5 for a
satisfactory factor analysis to proceed
 Kaiser (1974) recommend 0.5 as minimum
(barely accepted), values between 0.7-0.8
acceptable, and values above 0.9 are superb

6. BARTLETT'S TEST
 Bartlett's test is another indication of the
strength of the relationship among variables.
This tests the null hypothesis that the
correlation matrix is an identity matrix.
 You want to reject this null hypothesis
 It’s associated probability should be less than
0.05 (significant value, p<0.05)

7. TOTAL VARIANCE EXPLAINED
 As a rule of thumb, cumulative frequency
should be 70%
 Otherwise increase number of groups or
number of observations

8. SCREE PLOT
 The graph is useful for determining how
many factors to retain. High zig zag will have
large variation.

9. ROTATED FACTOR MATRIX
 Factor loading less
then 0.3 have not been
Displayed because we
asked for suppressed
lesser values
Highest value lead the
group and also
important.