Module 5

Question 1

Cross - sectional study design

Answer 1

• observational
• descriptive
• collects data from a population at one specific point in time
• groups determined by existing differences

Question 2

Pearson’s correlation coefficient

Answer 2

measures linear relationship between 2 variables - p = 0 suggests no linear relationship

Question 3

Pearson’s product-moment coefficient

Answer 3

examines whether continuous outcome variables are associated with a set of independent variables

Question 4

Regression modelling

Answer 4

measure strength and direction of an association between variables

• continuous outcome - linear or non-linear regression models
• categorical outcome - logistic regression

Question 5

Linear regression data considerations

Answer 5

Outcome variable (DV) must be continuous, independent variable (IV) can be categorical or continuous

Question 6

Assumptions of linear regression

Answer 6

• relationship between DV and IV is linear
• observations are independent and randomly selected
• homogeneity of variances - constant variance
• residuals are independent and normally distributed
• absence of outliers and multicollinearity

Question 7

Descriptives of outcomes

Answer 7

If normally distributed, skewness and kurtosis = 0

Mean, median and mode should be equal

Question 8

Tests of Normality - MVPA

Answer 8

eg. Kolmogorov - Smirnov and Shapiro - Wilk
- non-significant test (P>0.05) suggests that the distribution is not significantly different from a normal distribution
- a significant test (P<0.05) suggests that the distribution is significantly different from a normal distribution

Question 9

Multi-collinearity

Answer 9

refers to IV’s that are correlated with other IV’s

Question 10

Variance Inflation Factor (VIF)

Answer 10

measure of how much variance of estimated regression coefficient is ‘inflated’ by the existence of correlation among IVs in the model
VIF 1 = no correlation
VIF >4 = more investigation
VIF >10 = serious multicollinearity

Question 11

Constant Variance

Answer 11

Plot of fitted values against residuals - if scattered randomly around 0 - supports homoscedasticity
Can also use statistical analysis - if P>0.05 for homoscedasticity test - supports constant variance assumption

Question 12

Breusch-Pagan test and Koenker test

Answer 12

• tests of heteroscedasticity
• if insignificant - can assume constant variances
• if significant - cannot assume constant variances

Question 13

R^2

Answer 13

% of variability that is explained by fitted model