Description
SAS
Exploratory Factor Analysis HW
Perform an Exploratory Factor Analysis paralleling the HBAT analysis performed in the textbook and in class, but now use the HATCO dataset, variables, X1 –X7.
Include the following analyses:
Exploratory Factor Analysis seeking the best parsimonious set of variables and factors, based on deleting variables per their MSA score and cross-dual loadings, and factors based on contribution to variance contribution and explanation
Provide a brief management summary including key selected results
along with the SAS program and output as an appendix.
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Explanation & Answer
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Running head: EXPLORATORY FACTOR ANALYSIS
Exploratory Factor Analysis
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Running head: EXPLORATORY FACTOR ANALYSIS
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Exploratory Factor Analysis
Introduction
The expansive reason for factor analysis is to condense information so
connections and examples can be effectively translated and comprehended. As a matter
of first importance, we complete a few tests to decide whether our dataset is appropriate
for EFA. We first take a look at Bartlett's test of sphericity to affirm that our information
has designed connections. The aftereffects of these two test were introduced in the table
underneath. The p-value for Bartlett's test of sphericity is under 0.05 while the KMO
value is 0.842, which is over the cut-off estimation of 0.50. To be sure, these tests
demonstrate that we do have designed connections among the factors. At that point, we
take a gander at the askew components of the Anti-picture Correlation grid that has the
superscript. By the aftereffects of every one of these tests, our information is reasonable
for EFA as the KMO seems to be 0.84 and the individual askew components of the Antipicture Correlation network were > 0.65.
Problem statement
The fluctuation clarified by the underlying arrangement extricated parts, and
pivoted segments are shown and are given in the table. The principal segment of the table
demonstrates the initial eigenvalues. The total section gives the eigenvalue, or me...