Week 3 & 4

Question 1

Parametric

Answer 1

assess group means
normal distribution
can deal with unequal variances across groups
generally more powerful

Question 2

non-parametric

Answer 2

assesses group medians
don’t require normal distribution
can handle small sample size

Question 3

parametric test assumptions

Answer 3

additivity and linearity
normality
homogeneity of variance
independence of observations

Question 4

additivity and linearity

Answer 4

outcome is a linear function of the predictors X1 and X2, and the predictors are added together
outcome y is an additive combination of the effects of X1 and X2

Question 5

Assessing linearity

Answer 5

observed vs predicted values (symmetrically distributed around diagonal line)
residuals vs predicted values (symmetrically distributed around horizontal line)

Question 6

fixing non-linearity

Answer 6

apply non linear transformation to variables
add another regressor that is a nonlinear function (polynomial curve)
examine moderators

Question 7

central limit theory

Answer 7

as the sample size increases towards infinity, the sample distribution (NOT DATA) approaches normal distribution

Question 8

skewness

Answer 8

how symmetrical the data is
positive: scores bunched at low values, tail pointing to high values
negative: scores bunched at high values, tail pointing to low values

Question 9

kurtosis

Answer 9

how much the data clusters either at the tails/ends or peak of the distribution

Question 10

leptokurtic

Answer 10

heavy tails

Question 11

platykurtic

Answer 11

light tails

Question 12

normality checks

Answer 12

Q-Q plot compares sample quantiles to quantiles of normal distribution; normal= forms straight line
Shapiro wilkes test: tests if data differs from normal distribution; normal=p>.05, data does not vary significantly from a normal distribution
histogram

Question 13

homogeneity of variance

Answer 13

all groups or data points have same or similar variance
equal distribution above and below horizontal line on residual vs predicted plot= homoscedasticity
heteroscedasticity would be cone shapes

Question 14

Independence

Answer 14

residuals unrelated
if non-independent: downwardly biased SE (too small) and incorrect statistical inference (p values <.05 when they should be >.05)

Question 15

Univariate outlier

Answer 15

outlier when considering only the distribution of the variable it belongs to
bias mean and inflate SD

Question 16

Bivariate outlier

Answer 16

outlier when considering the joint distribution of two variables

Question 17

Multivariate outlier

Answer 17

outliers when simultaneously considering multiple variables, difficult to assess using numbers or graphs
bias relationship between two variables e.g. change strength

Question 18

changing the data= winsorizing

Answer 18

next highest value plus some small number
z score of +/- 3.29
mean plus 2 or 3 SDs
percentile of distributions

Question 19

winsorizing

Answer 19

a predefined quantum of the smaller and/or largest values are replaced by less extreme values

Question 20

linear transformations

Answer 20

adding constant to each value
converting to z score (x-m)/SD
mean centering (x-m)

Question 21

non linear transformations

Answer 21

log, log (x) or In (x)
square root of x
reciprocal, 1/x

Question 22

log (x)

Answer 22

reduce positive skew and stabilise variance
positive values >0

Question 23

square root of x

Answer 23

reduce positive skew and stabilise variance
zero and positive values

Question 24

1/x

Answer 24

reduce impact of large scores and stabilise variance
score reversal can be avoided by reversing before transforming: 1/(xhighest-x)

Question 25

variance

Answer 25

average squared distance from mean
linked to sum of squares

Question 26

covariance

Answer 26

how much two variables differ from their means
linked to sum of cross products

Question 27

correlation coefficient

Answer 27

standardised version of covariance
divide covariance by SD of both variables

Question 28

pearsons correlation assumptions

Answer 28

interval/ratio variables
normality
linearity

Question 29

coefficient of determination

Answer 29

r^2

Question 30

r^2 in spearmans correlation

Answer 30

proportion of variance in the ranks that the two variables share

Question 31

partial correlations

Answer 31

measure the association between two variables, controlling for the effects that a third variable has on them both

Question 32

semi-partial correlations

Answer 32

part correlation
measures the relationship between two variables, controlling for the effect that a third variable has on one of the others

Question 33

zero order correlations

Answer 33

measuring correlation between two variables when not controlling for anything

Question 34

excluding cases pairwise

Answer 34

for each correlation, exclude particiapnts who do not have a score for both vairables

Question 35

excluding cases listwise

Answer 35

across all correlations, exclude particiapnts who do not have a score for each variable

Question 36

linear regression looks at

Answer 36

direction (unstandardised B)
magnitude (standardised beta)
significance (p<.05)

Question 37

Standardised coefficients (beta)

Answer 37

show the expected SD change in DV for a 1SD change in IV