Flashcards in 2-way ANOVA & Beyond Deck (29)

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1

## What 4 things are required for establishing causality?

### Theoretical credibility, co-variation, elimination of confounds and temporal precedence.

2

## Describe necessary causation.

### If you see Effect(B), you must be at level B.

3

## Describe sufficient causation.

### If you see Effect(B), you may be at level B.

4

## Describe Simpson’s paradox.

### Where a correlation seems to go one direction, but adding a factor means there are many correlations, possibly in a different direction.

5

## What is observed in 2-way ANOVA?

### Grand mean + F1 effect + F2 effect + Interaction effect + error.

6

## What does adding a factor that matters do?

### Decrease SS, df and error term.

7

## What does adding a factor that doesn’t matter do?

### No decrease in SS, decrease in df, increase in error term and power, possible loss of significance.

8

## What is used to control for multiple t tests?

### The overall effect size given by R squared.

9

## What is eta-squared?

### The proportion of all variability explained the effects/interactions.

10

## What is partial eta-squared?

### The proportion f the variance that could be explained, that the effect/interaction explains.

11

## What does including participants as a random factor allow us to do?

### Account for variability in a dataset due to individual differences.

12

## Why do we calculate the average across participant means rather than the average across trials?

### We usually want to talk about ”people on average” rather than the average trial.

13

## What makes up a nesting effect?

### The mean for one level of nested factor 2 - (grand mean + one level of factor 1 effect).

14

## What is observed in a nested ANOVA?

### Grand mean + F1 effect + F2(F1) effect + error.

15

## How can bivariate data be analysed?

### Pearson’s correlation and simple linear regression/1-way ANOVA.

16

## How can multivariate data be analysed?

### Partial and multiple correlation and multiple linear regression/n-way ANOVA/ANCOVA.

17

## What does partial correlation ask about?

### The partial variance of X3.

18

## What does semi-partial correlation ask about?

### The total variance of X3.

19

## Give 2 key aspects of partial correlation.

### It is a “pure” measure uncontaminated by other variables and its uniqueness of variance makes it theoretically simpler.

20

## Give 2 key aspects of semi-partial correlation.

### It has a more intuitive baselines and it allows easy comparison of coefficients because X3 is constant.

21

## How can ANCOVA be used to control for a variable?

### You can add an extra variable, and use a regression line to estimate when X is the same.

22

## What is observed in an ANCOVA?

### Grand mean + factor effect + participant effect + individual differences + noise.

23

## How can a covariant remove unwanted variability?

### If some DV variability is attributed to the covariate it is removed from the error sum of squares.

24

## When can adding a covariate increase degrees of freedom?

### When the covariate is not related to the DV.

25

## When can adding a covariate reduce power?

### When it is co-linear with the first.

26

## What assumptions does ANCOVA share with ANOVA?

### Normality of error distributions, homogeneity of error variance and independence of “error term.”

27

## What assumptions does ANCOVA share with regression?

### Interval/ratio scale data and linear relationship.

28

## Give 3 reasons to use ANCOVA compared to ANOVA.

### Accounts for more variability, “controls for” other variables and quantifies which subject characteristics are important.

29