Flashcards in Factor Analysis Deck (16)

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1

## How is Factor Analysis most commonly used

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an exploratory approach:

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

-good to explain the nature of their relationships

2

## How is Factor Analysis used in a confirmatory manner

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-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

3

## Exploratory Factor Analysis is used to:

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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

4

## Goals to EFA

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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

5

## Assumptions of EFA

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continuous level of measurement with normal distribution

sample size should be large enough

correlation >0.3 between the variables

6

## Limitations of EFA

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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

7

## Confirmatory Factor Analysis is used to:

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

8

## Procedure of CFA

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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

9

## Limitations of CFA

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sample size must be large

multivariate normality

outliers

missing data

10

##
Exploratory Factor Analysis:

Factor Loadings

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

11

##
Exploratory Factor Analysis:

Criteria for significance of Factor Loadings

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FL>0.3: minimum consideration

FL>0.4: more important

FL>0.5: practically significant

12

##
Exploratory Factor Analysis:

Extraction of Factors

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

13

##
Exploratory Factor Analysis:

Rotation of Factors

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

14

##
Exploratory Factor Analysis:

Naming Factors

### Naming the factors assigned in Rotation of Factors

15

## Applications of Factor Analysis

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Exploratory Analysis

Reduction of data

Factor scores

Construct Validity

Hypothesis Testing

16