Page 215 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

1. Kaiser rule– It is one of the most common component
retaining procedure. Kaiser (1960) rule defines that retain
only those components whose Eigen values are greater than
1. Although in certain situation, one can also include
component that has Eigen value close to 1 (around .95).

2. Scree Test – This is graphical method that has been propose
by Cattel (1966). In Scree Test graph the Eigen Values are
places in vertical axis and components are places in
horizontal axis. The magnitude of successive Eigen values
drops off sharply that makes descent steep curve. “The
recommendation is to retain all Eigen values (and hence
components) in the sharp descent before the first one on
the line where they start to level off” (Stevens, 2012, p.329)

3. Total Variance – In this method, the researcher extracted
number of components based on the specific total variance.
Commonly, Stevens’s (2012) suggest that at least 70% of the
total variance should be retained.

Extraction Method
Principal Axis Factoring (PAF) is not a model fit procedure however
it does not require multivariate normality. PAF is an iterative
process to get communalities. The goal of PAF is to maximize the
variance extracted. Maximum Likelihood (ML) procedure required
large sample size, at least 20 observations for each variable or
(300+). It is try and error procedure. This procedure is mostly use in
CFA (Confirmatory Factor Analysis).

The Theory and Applications of Statistical Inferences 199