Sampling Adequacy
1
Sampling Adequacy
Tests if an EFA is useful
with your data: if
significant, it indicates that
the data is appropriate for
a factor analysis.
Communalities
•
Indicate the proportion of common variance in an
item
•
Sum of squared factor loadings for that variable (e.g.,
item) across all the factors
•
Keeps track of how much of the original variance that
was contained in a particular variable is still
accounted for by all retained factors
2
Communalities
3
Number of factors
Determining the number of factors can be done
by two methods:
•
Scree plot (visual examination)
•
Kaiser’s rule (eigenvalue of greater than 1)
4
5
Scree Plot
Eigenvalue
•
The number of factors with an eigenvalue of
greater than 1 is two, which explains 58.0% of
the variance of the data.
6
This content covers Sampling Adequacy Tests to determine if factor analysis is suitable for your data, communalities to assess common variance in items, methods for determining the number of factors, and factors' eigenvalue explaining variance. The visuals provided include Sampling Adequacy, Communalities, Number of Factors, Scree Plot, and Eigenvalue.
Uploaded on Feb 16, 2025 | 0 Views
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Presentation Transcript
Sampling Adequacy Tests if an EFA is useful with your data: if significant, it indicates that the data is appropriate for a factor analysis. 1
Communalities Indicate the proportion of common variance in an item Sum of squared factor loadings for that variable (e.g., item) across all the factors Keeps track of how much of the original variance that was contained in a particular variable is still accounted for by all retained factors 2
Number of factors Determining the number of factors can be done by two methods: Scree plot (visual examination) Kaiser s rule (eigenvalue of greater than 1) 4
Eigenvalue The number of factors with an eigenvalue of greater than 1 is two, which explains 58.0% of the variance of the data. 6
