Factor analysis is a statistical method used to identify underlying factors that explain relationships among interrelated variables, with applications in data reduction and exploratory research. It was historically conceptualized by Charles Spearman to understand general intelligence through a single underlying factor. Key steps include computing a correlation matrix, determining necessary factors, and interpreting results through tests such as Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin measure.

1. FACTOR ANALYSIS

2. OUTLINE
1. WHAT IS FACTOR ANALYSIS?
2. BASIC ASSUMPTION
3. HISTORY AND GOAL OF FACTOR ANALYSIS
4. OBJECTIVE,LIMITATION AND APPLICATION
5. STEPS
6. UNDERSTANDING OF FACTOR ANALSIS THROUGH DATA
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3. Factor Analysis
• Factor analysis is a statistical procedure used
to identify a small number of factors that can
be used to represent relationships among sets
of interrelated variables
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4. COMPUTER USE BY
TEACHERS is a
broad construct that
can have a number
of FACTORS
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5. • Data reduction and summarization.
• Exploratory research
• Reducing the number of variables
• Explorative technique.
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6. BASIC ASSUMPTION
• Underlying dimensions – or factors
– can be used to explain complex
events or trends.
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7. HISTORY OF FACTOR ANALYSIS
• Psychologist Charles Spearman
• Mathematical Skill, Vocabulary,
Other Verbal Skills, Artistic Skills,
Logical Reasoning Ability
• Explained by one underlying
"factor" of general intelligence that
he called “g”
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8. GOAL
• To identify otherwise not-directly-
observable factors on the basis of a
set of observable variables.
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9. OBJECTIVE OF FACTOR ANALYSIS
• SIMPLIFYING THE DATA
• ANALYZING THE
INTERDEPENDENCE
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10. BENEFITS OF FACTOR ANALYSIS
1.It brings out the hidden dimensions
2. find out relationships
3. when the data is large
4. simplified and condensed.
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11. LIMITATION OF FACTOR ANALYSIS
• Complicated tool.
• Reliability of the results
• Suitability
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12. FACTOR ANALYSIS IN MARKETING RESEARCH
• EXPLORATORY
• CONFIRMATORY

13. Factor analysis in market research
• Customer satisfaction studies.
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14. FOUR STEPS:
1. Compute a correlation matrix for all
variables.
2. Determine the number of factors
necessary to represent the data and the method
of calculating them (factor extraction):.
3. Transform the factors to make them
interpretable (rotation)
4. Compute scores for each factor
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15. STEP-I COMPUTE A CORRELATION MATRIX
FOR ALL VARIABLES.
BARTLETT’S TEST OF SPHERICITY -
used to test the hypothesis that the
correlation matrix is an identity matrix
• Level of SIGNIFICANCE has to be less
than .05
• All items are perfectly correlated with
themselves (one), and have some level of
correlation with the other items
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16. STEP 1-COMPUTE A CORRELATION
MATRIX FOR ALL VARIABLES
KAISER-MEYER-OLKIN
• Measure of sampling adequacy
• Large KMO values are good
• KMO is below .5, don’t do a factor
analysis.
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17. Step 2 Determine the number of factors necessary to
represent the data and the method of calculating
them (factor extraction):.
• The ‘Eigenvalue’ is the total variance
explained by each factor. Any ‘factor’
that has an Eigenvalue of less than
one does not have enough total
variance explained to represent a
unique factor, and is therefore
disregarded.
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18. Screen Plot
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19. Interpretation through Factor Analysis
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20. Should Factor Analysis be Used
Variable Value Given Particular Rule/Action
KAISER-
MEYER-OLKIN
0.698 Large KMO values
are good because
correlations
between pairs of
variables
Rule: If the KMO is below .5, don’t do
a factor analysis
Action: As the Value is 0.698 i.e
above 0.5 hence Factor Analysis can
be done
Barlet's Test of
Sphericity
Look for
Significance Value
Value=0.000
is used to test the
hypothesis that
the correlation
matrix is an
identity matrix
Rule: Looking for SIGNIFICANCE (less
than .05) because if less than 0.05
the variables are correlated
. This value, 0.000 is less than 0.05,
and is an indication you can continue
with the Factor Analysis.
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21. Calculate the Eigen Value
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22. Total Variance Calculation and Communalities
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24. You Tube Link
• https://youtu.be/FqTZrH0KRs0
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