QDA2

Question 1

Variance

Answer 1

statistical measurement (1) of the spread (2) between numbers in a data set (3)

Question 2

Why (2) do we use PCA

Answer 2

1. go from large number of variables to smaller number of variables
2. helps to show which variables are strongly related/seperated

Question 3

Data requirements PCA

Answer 3

1) quantitative variables (normally distributed, based on correlation)
2) large numbers of observations
3) strong correlation amongst variables (>0.3)

Question 4

Where does the b stand for in the equation for PCA

Answer 4

component loadings (= how much they relate to each other)

Question 5

difference between common and unique variance

Answer 5

common variance is variance that is shared among other variables in the data set and unique variance is variance that is specific to a variable.

Question 6

communality

Answer 6

proportion of common variance once that is present in a variable, the higher, the more common variance

Question 7

what does EV > 1 mean

Answer 7

component explains more variance that individual variable

Question 8

what does kaiser-meyer Olkin measure?

Answer 8

compares observed correlations with partial correlations.

Question 9

whats the conclusion for Kaiser-meyer Olin

Answer 9

P = x and x > 0.5 so there is more shared variance than unique variance.

Question 10

whats the conclusion for Bartletss test

Answer 10

P = x so significant, this means that the correlations between items differs from zero. H0 = all variables are uncorrelated so we can reject H0.

Question 11

what is component rotation

Answer 11

redistributes explained variance over components and makes components loadings more extreme. makes it easier to assign and interpret.

Question 12

what is the conclusion of R2

Answer 12

the model explains x% of the variance in the OV

Question 13

when it comes to the follow up tests, if the variances are not homogeneous, what do we do use then?

Answer 13

Welch’s test

Question 14

whats the disadvantage of POST HOC TEST

Answer 14

artificial inflation of alpha

Question 15

What do you do it a component loading loads on an unexpected component

Answer 15

you do nothing, apparently the items correlate more strongly with a latent factor that is not the one that they were expected to load on. this doesn’t make the analysis unreliable, it basically indicated that it is good that you did the analysis.

Question 16

What does Tom have to do after component rotation, before he goes further with his analysis?

Answer 16

Recode the negative loaded factors

Question 17

5 criteria ANOVA

Answer 17

1) categorical PV, quantitative OV
2) residuals are equally distributed
3) homogeneity of variances
4) mutually exclusive (overlap)
5) EQUAL SAMPLE SIZES

Question 18

whats the H0 of ANOVA

Answer 18

H0: µx = µx2 = µx3

Question 19

categorical vs quantitative

Answer 19

Categorical: nominal, ordinal
Quantitative: ratio, discrete, interval (temperature), continuous (weight, age)

Question 20

make table and show criteria

Answer 20

are they equal to zero horizontally? are they vertically equal to zero (orthonogality) and why is there orthogonality: because they are used together in one part so you cannot use them again, inflation of alpha

Question 21

is they ask about which pv has strongest effect on OV, we use

Answer 21

partial eta squared

Question 22

main effect on direction and position

Answer 22

direction: core pv, position: legenda

Question 23

when you dont have the p value for the t statistic nor the CI and you need to check if it wil have an impact on OV what do you do?

Answer 23

=2 or > 2 then it has a significant impact, otherwise its just too low to have an impact

Question 24

use two statistics to conclude if the second model is better than the first

Answer 24

F change + p value and the R square/Rsquare adjusted value

Question 25

multicollinearity and whats the problem with that?

Answer 25

two or more PVs in a multiple regression model are highly correlated, which affects the testing for the coefficient testing

Question 26

why do we use dummy variables

Answer 26

If you enter Education as PV, SPSS it will treat the nominal categories as numeric, meaning it will estimate a linear
effect for this PV, which makes no sense for nominal PVS.

Question 27

F test why used

Answer 27

The additional explanatory power of model 2
compared to model 1 is formally tested in the F-change test.