Stats

Question 1

Multiple regression assumptions:

Answer 1

OV - Continuous, PV = Continuous/dichotomous.

Question 2

What are the 2 types of variables?

Answer 2

Qualitative (categorical):

• Data occur when we assign objects into labelled categories.
• No natural ordering.
• Measured on ordinal/nominal scale.

Quantitative (Measurement):
- Measured on interval/ratio, ordinal scale.
Numerical

Question 3

Types of kurtosis and describe their shape:

Answer 3

Leptokurtic - heavy tails, score centred in the middle.

Platykurtic - Light tails, score spread across the distribution.

Question 4

What are variables?

Answer 4

Measured constructs that vary across entities in the sample.

Question 5

What are parameters?

Answer 5

These are estimated from the data and are constructs believed to represent some fundamental truth about the relations between variables in the model.

Question 6

When would you use a point biserial correlation?

Answer 6

Used when one variable is a discrete dichotomy (i.e gender).

Question 7

Are directional hypothesis possible with chi square tests?

Answer 7

Yes, but only when you have 2x2 design. If its larger than this the chi square will be testing a compound hypothesis.

Question 8

T-test assumptions:

Answer 8

Both between and within:

• Normal distribution
• Interval level data at least.

Between:

• Homogeneity of variance.
• Independence of scores.

Question 9

Pearson’s correlation assumption:

What to do when they are violated?

Answer 9

• Continuous data (interval/ratio).
• Independence of scores.
• Linear relationship between variables.
• Observations are from random samples with normal
  distribution.

When violated - Boostrap CI, or use non-parametric alternatives such as Spearman’s R or Kendall’s Tau

Question 10

Simple Liner regression assumptions:

Answer 10

OV = Continuous, PV = Dichotomous or continuous. 
Independence of scores. 
Independence of errors. 
Normal distribution. 
Non-zero variance. 
Linearity
Homoscedasticity.

Question 11

One-way ANOVA assumptions.

Answer 11

Normal distribution.
Homogeneity of variance.
Independence of scores (for between groups design).

Question 12

ANCOVA assumptions:

Answer 12

Same as normal ANOVA +

• Independence of the covariate and the IV (can’t be highly correlated).
• Homogeneity of regression slope (regression line fits to the entire data set regardless of groups).

Question 13

Different measures of error:

Answer 13

Standard deviation. 
Sum of squares. 
Deviance. 
Variance. 
Standard error.

Question 14

What does a 95% confidence interval tell you?

Answer 14

Confidence interval is the likelihood that they will contain the population parameter.
- 95 out of 100 samples will contain the population parameter.

Question 15

What are the different types of hypotheses?

Answer 15

One tailed (directional hypothesis).

• Only focuses on one tail of the distribution (5% in the direction that the hypothesis states).
• Reject the Ho for scores that fall within this 5% region.

Two tailed (non - directional).

• Considers both end of the distribution (2.5% either end).
• Reject the Ho for extreme sores in both directions (in the 2.5% region).

Question 16

Types of error:

Answer 16

Type 1 error - state that there is a significant effect when in reality there isn’t (falsely reject the Ho).
- Acceptable level for this error is Alpha level .05.

Type 2 error - When you state there is no effect in the population when in fact there is (too quick to accept the Ho).
- Acceptable level for this is p = level .2 (Beta is the probability level).

Question 17

Effect size

Answer 17

How close the predictions of the model are to the observed outcomes.

• These are standardised measures which are comparable across measures (this is why they are reported in meta analyses).
• Not as reliant on the sample size at significance level are.
• Larger effect size = lower type 1 error rate.

Question 18

Power:

Formula for power:

Answer 18

Ability for a test to successfully find an effect and correctly reject the H.
1 - Beta (beta = .2).
- 1 - probability of making a type 2 error.
1 - .2 = .8 - this would indicate 80% chance of detecting an effect if it exists.

Question 19

Parametric/normal distribution assumptions:

Answer 19

Linearity/additivity.
Normality.
Homogeneity of variance.
Independence of errors.

Question 20

How to reduce bias when assumptions are met before resorting to non-parametric tests.

Answer 20

• Boostrap confidence intervals.
• Transform data (apply mathematical function to the scores i.e. log transform to get data in a form that can be modelled.
• Trim data (delete certain amount of extreme scores).
• Windsorzing (substituting outliers with the highest value that isn’t an outlier)