2. Variables & Causal Logic

Question 1

A feature that varies across cases/observational units/units of analysis

Answer 1

Variable

Question 2

an individual person/object whose features you are measuring and evaluating.

Answer 2

Case

Question 3

What are the two tupes of variables?

Answer 3

1. Numerical Variables (how much? how many?)
2. Categorical Variables (no numerical meaning, describes qualities)

Question 4

What are the two types of numerical variables?

Answer 4

1. Continuous (infinite possible values within a range)
2. Discrete (Variables that represent countable, whole numbers - only certain numerical values)

Question 5

What are the two types of categorical variables?

Answer 5

1. Nominal (cannot be ordered in a meaningful way)
2. Ordinal (can be ordered in a meaningful way)

Question 6

In R, a table with cases and variables will be called a

Answer 6

dataframe

Question 7

What are the three criteria of causality?

Answer 7

1. Plausibility (There must be a credible link or explanation for why X causes Y)
2. Time Order (X must occur before Y for X to be the cause of Y)
3. Non-Spuriousness (The relationship between X and Y must not be explained by another variable (Z))

Question 8

You control for other variables to rule out what?

Answer 8

spuriousness

Question 9

Mediation/Indirect Effects

Answer 9

When X influences Z and Z influences Y. This does not mean X causes Y but there is an indirect pathway between X and Y

Question 10

Mediator/Mechanism

Answer 10

A mediator is a variable that explains how or why two other variables are related in a cause-and-effect relationship. It serves as a middle step or pathway through which the independent variable (X) influences the dependent variable (Y).

Question 11

Explain the difference between an indirect effect and a spurious relationship

Answer 11

Indirect: When X influences Z and Z influences Y

Spurious: When Z influences both X and Y making it seem like there is a relationship between X and Y.