Flashcards in Factor analysis Deck (13)

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1

## What is factor analysis used for?

### Although factor analysis is typically associated with questionnaire data, it can be used on any data set with lots of variables

2

## How do you reduce all of your questions into a smaller number of more meaningful variables?

###
-Understand distinct aspects of a questionnaire

-Form a smaller dataset that is easier to analyse

3

## What are manifest variables?

### Your individual questions

4

## What are latent variables?

### Your broad factors

5

## What does Kaiser-Meyer-Olkin (KMO) measure?

###
•Sampling adequacy

•Are there enough to have a reliable solution?

6

## What does a determinant measure?

###
• Tests for singularity in the data

• Variables should be correlated, but not too much

7

## What does Bartlett's test of sphericity measure?

###
• Correlations between clusters of variables

• FA is only appropriate if variables correlate

8

## What is a determinant? (vs sphericity)

###
-Shouldn’t have all variables correlated.

-If all questions represent the same thing, what is there to factor together?

9

## What is sphericity? (vs determinant)

###
Must be some correlations between variables.

If each question represents a different thing, how can we make factors?

10

## In a rotation, where do you want the factors to be positioned?

### The two factors are far from each other: clearly distinct factors Likely to be a “good” solution

11

## In a rotation, where you you want your items to be located?

### Items tightly clustered: Likely to be a “good” factor

12

## What is factor loading?

###
-Tells you how good the item is within the factor

-Correlation between an individual item and factor it is placed in

-High loadings are good

-Greater than .40 - good enough to be included within a factor

13