# SPSS Factor Analysis

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Factor Analysis
Carrying out a principle component analysis using varimax rotation
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Descriptive Statistics
The case to variable ratio is _/ _:1, greater/less than our min of 10:1, have sufficient/too few cases for the analysis
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Correlation matrix
The determinant of _ is GREATER than 0.00001, therefore there is no evidence of multicollinearity or singularity (less than is evidence)
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KMO
The KMO test of sampling adequacy (_) is less than 0.7 therefore factor analysis may be appropriately undertaken on the data
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Bartlett's test of sphericity
... is significant (p_) therefore the data correlation matrix is sufficiently different to the identity matrix so we have sufficient significant correlations to proceed with the analysis
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Anti-image matrices
Only needed in KMO is below 0.7, drop variables with the lowest value until it raises
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Communalities
The commonalities are all greater than our minimum requirement of 0.2 therefore the factor solution account for an acceptable amount of the variance of each variable
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Total variance explained
Using the kaiser (K1) criteria suggests we retain _ factors, accounting for _% of the total variance
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Scree Plot
The scree plot suggest an elbow at _, in indicating a (one less) factor solution, dis/agreeing with the K1 criteria
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Monte Carlo
(Table: random and data eigenvalue) Using parallel analysis suggests a _ factor solution, dis/agreeing with the K1 criteria and scree plot
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Component matrix
ignore
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Rotated component matrix
Factor one can be called _. Write each variable and its value. Repeat for each factor.
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Component transformation matrix
ignore
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T-test
(to interpret factor scores in relation to another variable) report levene's and compare the means
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## Other cards in this set

### Card 2

#### Front

Descriptive Statistics

#### Back

The case to variable ratio is _/ _:1, greater/less than our min of 10:1, have sufficient/too few cases for the analysis

### Card 3

#### Front

Correlation matrix

KMO

### Card 5

#### Front

Bartlett's test of sphericity