Principal Component Analysis Using the Factor Procedure

2010 ◽  
pp. 171-193
Author(s):  
Sean Eom
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Techniques ◽  
Data Input ◽  
Number Of Factors ◽  
Rotation Method ◽  
Definition Of

This chapter describes the factor procedure. The first section of the chapter begins with the definition of factor analysis. This is the statistical techniques whose common objective is to represent a set of variables in terms of a smaller number of hypothetical variables (factor). ACA uses principal component analysis to group authors into several catagories with similar lines of research. We also present many different approaches of preparing datasets including manual data inputs, in-file statement, and permanent datasets. We discuss each of the key SAS statements including DATA, INPUT, CARDS, PROC, and RUN. In addition, we examine several options statements to specify the followings: method for extracting factors; number of factors, rotation method, and displaying output options.

Impact of Outliers Arising From Unintended and Unknowingly Included Subpopulations on the Decisions About the Number of Factors in Exploratory Factor Analysis

2012 ◽  
Vol 72 (3) ◽  
pp. 388-414 ◽  
Author(s):  
Yan Liu ◽  
Bruno D. Zumbo
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Sample Size ◽  
Exploratory Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Chi Square ◽  
Number Of Factors ◽  
Decision Methods ◽  
Sequential Tests

There is a lack of research on the effects of outliers on the decisions about the number of factors to retain in an exploratory factor analysis, especially for outliers arising from unintended and unknowingly included subpopulations. The purpose of the present research was to investigate how outliers from an unintended and unknowingly included subpopulation affected the decisions about the number of factors to retain using four commonly used methods separately. The results showed that all the decision methods could provide biased results and the number of factors could be inflated, deflated, or remain the same depending on the decision methods used and outlier conditions. The findings also revealed that symmetric outliers did not affect the three principal component analysis–based methods but affected chi-square (ML) sequential tests. Finally, sample size did not play a role in the effect of outliers.

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