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Flashcards in Factor Analysis Deck (16)
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How is Factor Analysis most commonly used

an exploratory approach:

-good to examine the structure within a large number of variables

-good to explain the nature of their relationships


How is Factor Analysis used in a confirmatory manner

-used to verify the factor structure of a set of observed variables

-used to test the hypothesis that a relationship between observed variables and their underlying latent constructs exists


Exploratory Factor Analysis is used to:

1. Explore possible underlying factor structure of a set of observed variables without imposing a predefined structure to the outcome

2. Identify the underlying factor structure

3. Describe and ID the # of factors


Goals to EFA

determine the # of latent constructs underlying a set of variables

provide a means of explaining variation among variables using few new factors

define the content of meaning of factors


Assumptions of EFA

continuous level of measurement with normal distribution

sample size should be large enough

correlation >0.3 between the variables


Limitations of EFA

variables could be specific

non-normal distribution of data

sample size larger than required is desirable to accommodate for possible missing data

No casual interferences can be made from correlations alone


Confirmatory Factor Analysis is used to:

Test the hypothesis that there exists a relationship between the observed variables and their underlying latent constructs


Procedure of CFA

1. review the relevant theory and research literature to support model specification

2. specify a model

3. collect data

4. assess model fit by
- hypothesis testing
- fit indices look up


Limitations of CFA

sample size must be large

multivariate normality


missing data


Exploratory Factor Analysis:
Factor Loadings

the coefficient as a measure of the correlation between the individual variable and the overall factor


Exploratory Factor Analysis:
Criteria for significance of Factor Loadings

FL>0.3: minimum consideration

FL>0.4: more important

FL>0.5: practically significant


Exploratory Factor Analysis:
Extraction of Factors

Pull out only the components using a cutoff point where eigenvalue at least 1


Exploratory Factor Analysis:
Rotation of Factors

Process of developing a unique statistical solution so that each variable relates highly to only one factor


Exploratory Factor Analysis:
Naming Factors

Naming the factors assigned in Rotation of Factors


Applications of Factor Analysis

Exploratory Analysis
Reduction of data
Factor scores
Construct Validity
Hypothesis Testing


Limitations of Factor Analysis

Factors are hypothetical statistical concepts

Data may be organized differently by using different extraction or rotation methods that alter the factor's meaning

Generated factors may be totally uninterpretable within the framework of the research question