factor analysis

Question 1

factor analysis is an analysis of ___

Answer 1

interdependence

Question 2

what is analysis of interdependence

Answer 2

There is no dependent variable in an analysis of interdependence

Question 3

what is factor analysis?

Answer 3

Is a method of sorting a large amount of variables into FACTORS. Clumping together a large amount of variables into factors.

Question 4

what is a factor?

Answer 4

A factor is a super-variable that contains variables that have high intercorrelations between each other but low correlations with other groups

Question 5

what is the ‘loading’ factor

Answer 5

The relative connection of each of the original variables to a factor is called the variable’s factoring loading on that factor.

Question 6

what is the purpose of factor analysis? (3 points)

Answer 6

 To assess the degree to which items are ‘tapping’ the same concept (or different ones)
 If we have a large number of variables, factor analysis can determine the degree to which they can be reduced to a smaller set.
 help make sense of complex phenomena by reducing them to a more limited number of factors

Question 7

factor analysis can also help to reduce ___

Answer 7

multicollinearity

Question 8

what are the 3 main factor analysis steps

Answer 8

1. Correlation matrix
2. Extraction of initial orthogonal factors
3. Rotation to final factors

Question 9

2 methods for extraction of initial orthogonal factors

Answer 9

principal components analysis (PCA)

principal axis factoring (PAF)

Question 10

what is PCA

Answer 10

exploratory

Question 11

what does exploratory factor analysis mean

Answer 11

means you have very little knowledge of the variables in question, assume there is only common variance and nothing unique

Question 12

what is PAF

Answer 12

confirmatory - realistic assumptions from theory

Question 13

what does confirmatory mean

Answer 13

know more about the variables, use the analysis to confirm hypotheses

Question 14

what is variance

Answer 14

how much much a variable loads onto a particular factor

Question 15

what are the 3 types of variance

Answer 15

common variance, specific variance (unique to that variable), error variance (fluctuations from measuring something)

Question 16

what type of variance does PCA assume

Answer 16

common (assumes that all circles overlap)

Question 17

what variance does PAF look at

Answer 17

common and unique (what they dont have in common)

Question 18

when should you use PCA (eg what would the data have to be to use it?)

Answer 18

have no big assumptions in data, high correlations, large number of variables, just want to reduce the data set to smaller variables

Question 19

in principal component analysis what is the number in the commonalities table and why

Answer 19

initial will be 1 because all variance is held in common

Question 20

what is the extraction score in PAF

Answer 20

extraction score indicates the degree of common variance that is attributed to each variable once the analysis is complete.

Question 21

do you want a high extraction score in PAF

Answer 21

YES the higher the better - means it is more significant

Question 22

what is an eigenvalue

Answer 22

the eigenvalue is how much variance that particular variable accounts for. the bigger value is more significant

Question 23

in SPSS any eigenvalue less than __ gets cut out of the table

Answer 23

1

Question 24

what are the 2 processes for deciding which factors to retain?

Answer 24

kaisers criterion

scree test

Question 25

what is kaisers criterion

Answer 25

if the eigenvalue is more than 1 then the value should be retained

Question 26

what is the scree test

Answer 26

a graph plot of the component number and eigenvalue, look for the flattening of the slope (POINT OF INFLEXION) which is where you should stop adding factors.

Question 27

the higher the number in the component matrix the more it ___ onto that factor

Answer 27

loads

Question 28

why do you use rotation as a step

Answer 28

if there are 2 numbers close together on the component matrix then you rotate to make the distinction clearer

Question 29

orthogonal rotation rotates the data at __ degrees

Answer 29

90 degrees

Question 30

how does oblique rotation work

Answer 30

allows the factors to correlate (ie lets x and y axes to assume a different angle than 90 degrees)

Question 31

in principle axis factoring what will the communalities table show

Answer 31

that all the variables do not have a value of 1 (like PCA). the extraction column indicates the degree of common variance after analysis is complete - will be lower