Page 208 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

Factor Analysis
Factor Analysis (FA) is the analysis of the correlations between items
(or subtests) in order to determine the minimal number of factors
accounting for the variation in test scores. Note that factor analysis
assumes that there is an underlying causal structure of latent
variables that impact participants' responses on the observed
variables (e.g., items in our situation).

Exploratory versus Confirmatory

a. Exploratory – searching for the optimal factor
structure

b. Confirmatory – testing a pre-specified factor
structure with estimated parameters

Exploratory Factor Analysis (EFA)
Exploratory Factor Analysis determines the number of latent
(unobservable) variables that account for observed variation and
covariation among set of observed indicators. EFA summarize
patterns of correlation among indicators to establish the construct.
EFA can be simply defined when “the researcher is attempting to
determine how many factors are present and whether the factors
are correlated, and wishes to name the factors” Stevens, 2012,
p.326)

The Theory and Applications of Statistical Inferences 192