Factor analysis

Question 1

What is factor analysis?

Answer 1

A method where the variations in the scores are expressed in the smallest amount of data possible. It creates a set of variables for similar items that are called dimensions to

Question 2

What are dimensions in factor analysis?

Answer 2

When factor analysis creates a set of variables for similar items in a set. The set is called a dimension

Question 3

What does factor analysis try to determine?

Answer 3

Whether the variation in the variables can be explained by a smaller number of variables we can’t see

Question 4

What are the assumptions of factor analysis? (Hunt: there are 5)

Answer 4

1) no outliers
2) adequate sample size
3) minimal multi collinearity
4) interval data
5) linearity of data

Question 5

When would you use the exploratory factor analysis?

Answer 5

When you do not know how many dimensions are in your set of variables; used when you don’t know what your answer will be

Question 6

What does Confirmatory factor analysis allow us to do?

Answer 6

It allows us to find out if there is a relationship between our set of variables and to see r there is any underlying constructs

Question 7

What does multivariate correlational statistics do?

Answer 7

Correlates everything; it correlates one participants scores with all other participants scores

Question 8

What can the variance in each question in factor analysis be explained by?

Answer 8

It can be explained by a common factor

Question 9

What is the Beck Depression inventory?

Answer 9

Where you read a group of statements that belong to a question and you pick the question that most relates to you. When you get a high score this can contribute to your overall depression mark

Question 10

What is a potential issue with the Beck Depression inventory?

Answer 10

Some questions such as “I’m more tired than usual” may be due to working long hours and not depression

Question 11

What is explanatory factor analysis? What does it reduce?

Answer 11

It reduces data into smaller sets of summary variables.

Question 12

How does factor analysis removes redundacy of variables?

Answer 12

It does this by removing duplication variables; it represents correlated variables with a set of variables called factors. These factors are relatively seperate from one another. They are independent

Question 13

How do you check to correlations in factor analysis?

Answer 13

By looking at the correlation matrix or the determinant of the R-matrix

Question 14

Why is Singularity of variables a problem?

Answer 14

When the variables are perfectly correlated it is impossible to determine which variable had what effect on the criterion variable

Question 15

In SPSS, what is a useful way to establish how many factors should be kept in your factor analysis?

Answer 15

Looking at the scree plots.

Question 16

What does rotation do in factor analysis?

Answer 16

Rotation increases the loading of each variable on the extracted factors while decreasing the loading of all other factors

Question 17

When would you use a orthogonal rotation in factor analysis?

Answer 17

We would use it if we expect our factors to be independent

Question 18

What type of rotation do we use for factor analysis if we expect our factors to not be independent?

Answer 18

We would use oblimin

Question 19

What does it mean if a variable has high communality in factor analysis?

Answer 19

It means the variable shares more in common with the rest of the variables

Question 20

How can you tell how much variability in the criterion variable is explained by the model in factor analysis? (Using spss)

Answer 20

You will look at the model summary table and look at the R square and adjusted r square

Question 21

How to work out the critical value of sample size in factor analysis, what is the equation?

Answer 21

50+8x. (X is the number of predictors you had)

E.g. If you had 4 predictors then the equation is

50+8x4= 36+50= 86

Question 22

When deciding whether to use R2 or adjusted r2 to see what value explains variability in criterion value, what helps us decide that?

Answer 22

If the sample size is GREATER or EQUAL to the critical value then you use r2

If it is LESS than the critical value then you use the adjusted r2

Question 23

Who introduced factor analysis? How did he define it

Answer 23

Thurstone: to minimise an excess of individual measure into fewer and more meaningful dimensions

Question 24

Why does factor analysis identify correlations between predictor variables?

Answer 24

It does this to make them into one Factor that drives their values

Question 25

How do you check if variables are linear to each other in factor analysis?

Answer 25

Looking at scatter plots, check if they are significant

Question 26

What two methods are used to see if there is multicolinarity? What value should it be to indicate multiconlineraity or signualirty?

Answer 26

Method 1: correlation matrix. Correlations above 0.8 show milticolinesrity/singularity

Method 2: looking at the determinant. The value should be more than 0.00001 for there to be no multicolinesrtiy

Question 27

What should you consider if there is lots of variables with correlations below 0.3?

Answer 27

Removing them

Question 28

What test can you use to determine sufficient correlations? What value I dictates that the variables are correlated to some degree?

Answer 28

Using Barrett’s test of sphericity. The value needs to be less than 0.5

Question 29

What do we look at to check for an adequate sample size?

Answer 29

We look at the kaiser myer olkin. It needs to be more than 0.5 for factor analysis to be suitable

Question 30

What is Eigenvalues? What is it used for and what does it indicate?

Answer 30

Used to condense/minimisale the variance in the correlation matrix. It indicates the amount of variance in the correlation matrix that is produced by a factor

Question 31

What is the minimum eigenvalue used for extracting factors? What is it known as?

Answer 31

The minimum value is 1. It is called the Kaiser-Guttman rule

Question 32

Why might you use PCA?

Answer 32

1) if you think you have too many variables and you think some are measuring the same underlying concept
You

Question 33

What table do you check in SPSS output to see if the data is suitable for reduction?

Answer 33

Barrett’s test of spbericity