BASICS/ pre-knowledge

Question 1

Discrete VS Continuous?

Answer 1

Discrete = counts / whole numbers (0, 1, 2, …)

Continuous = can take any value on a scale (1.73, 1.731, …)

Question 2

What are the 4 measurement levels?

Answer 2

Nominal (names),
Ordinal (ranked),
Interval (equal gaps, no true zero),
Ratio (equal gaps + true zero).

Question 3

Difference: Nominal vs Ordinal?

Answer 3

Nominal = no order (e.g., gender categories).

Ordinal = ordered (e.g., low/med/high).

Question 4

Difference: Interval vs Ratio?

Answer 4

Interval = no true zero (°C).

Ratio = true zero (kg, time, income).

Question 5

Categorical vs Continuous?

Answer 5

Categorical = groups/labels (nominal/ordinal).

Continuous = numeric scale (interval/ratio).

Question 6

Independent vs Dependent variable?

Answer 6

IV/predictor = input/explanatory.

DV/outcome = what you predict/explain.

Question 7

What is the standard error (SE)?

Answer 7

Estimated SD of a statistic (often the mean); smaller SE = more precise estimate.

Question 8

Type I vs Type II error?

Answer 8

Type I: false positive (reject true H₀).

Type II: false negative (fail to reject false H₀).

Question 9

Linear regression DV measurement level?

Answer 9

DV should be continuous (interval/ratio).

Question 10

Linear regression IVs: what types can they be?

Answer 10

Continuous or categorical (dummy-coded)

Question 11

Standardised vs unstandardised coefficients?

Answer 11

Unstandardised β: change in Y units per 1-unit X.

Standardised β: change in SD units per 1 SD X (compare predictors).

Question 12

Logistic regression DV measurement level?

Answer 12

Categorical, typically binary nominal (0/1).

Question 13

What does categorical mean?

Answer 13

A variable is categorical when each value is a label/group you belong to.

eg.
Gender category: male / female / nonbinary
Outcome: pass / fail

Question 14

Difference between nominal and categorical?

Answer 14

Categorical = the big umbrella: variables made of categories/labels (not amounts).

Nominal = a specific measurement level under categorical: categories with no natural order.

Examples:
Gender → categorical, nominal
Yes/No outcome → categorical, nominal
Low/Medium/High pain → categorical, ordinal (still categorical, but ordered)