Stats

Question 1

What type of test is mixed factorial ANOVA?

Answer 1

Parametric

Question 2

What is the purpose of mixed factorial ANOVA?

Answer 2

To show the effects of each IV and also the main interactions

Question 3

What two assumptions are made by mixed factorial ANOVA and how can they be tested?

Answer 3

Homogeneity of variance - Lavene’s test

Sphericity of covariance - Maulchys test

Question 4

What types of formulas are used for a) BS b) WS and c) interactions?

Answer 4

a) BS
b) WS
c) WS

Question 5

How are the F values for mixed factorial ANOVA reported?

Answer 5

F(between groups df, within/error df) = F-value, p= p-value

Question 6

What is the purpose of a correlation?

Answer 6

To investigate relationships between variables (NO CAUSATION)

Question 7

Is there a non-parametric alternative to mixed factorial ANOVA?

Answer 7

No

Question 8

What are the parametric and non-parametric forms of a correlation test?

Answer 8

Non-parametric - Spearman’s

Parametric - Pearson’s

Question 9

What are the conditions that must be present in order to carry out a Pearson’s correlation?

Answer 9

• Must have linear relationship
• Data interval/ratio and normally distributed (as it involves means and SDs)
• Must be free of outliers

Question 10

What are the conditions that must be present in order to carry out a Spearman’s correlation?

Answer 10

• Monotonic relationship
• Data that is ordinal/interval/ration
• Outliers can be accepted

Question 11

What is the unit of a correlation coefficient?

Answer 11

none

Question 12

How can a graph be z-transformed?

Answer 12

z= (score-mean)/SD

Question 13

What does having a z-transformed graph allow?

Answer 13

Direct comparison even if measured on different scales

Question 14

What type of statistics is regression?

Answer 14

Inferential

Question 15

What is the purpose of regression?

Answer 15

Test of association which allows us to make predictions/estimate how much to intervene, used when causal relationships are likely

Question 16

What are the assumptions of regression?

Answer 16

• Linear, interval/ratio data which is normally distributed and free of outliers
• Homoscedasticity, the same degree of variation across all predictor variable scores
• That predictors are not highly correlated to one another

Question 17

What are residuals?

Answer 17

The difference between the actual outcome score and the predicted score outcome

Question 18

How is a simple regression reported?

Answer 18

R^2 = R(squared), F(df regression, df residual) = F-value p= p-value

Question 19

What are the 3 ways in which the different variables in a multiple regression can be entered?

Answer 19

Simultaneous - all predictors entered at the same time
Hierarchical - predictors entered in a pre-defined order, used when informed by well defined theory
Stepwise - Entered in order of how well they correlate with the outcome, rarely used as is unstable