Flashcards in Non-Parametric Alternatives to ANOVA & Statistical Power Deck (24)

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1

## What are the parametric test assumptions?

###
Interval/ratio data.

Independence.

Normally distributed data.

Homogeneity of variance (for between-subjects).

2

## What are 3 advantages of non-parametric tests?

###
Fewer assumptions.

Can use small datasets.

Easy to calculate + interpret by hand.

3

## What is a disadvantage of non-parametric tests?

### They have a lower power than parametric tests (increase in Type II error).

4

## When is Friedman's ANOVA used?

### For repeated measures where the IV has 3 or more levels.

5

## When is Kruskal-Wallis used?

### For independent measures where the IV has 3 or more levels.

6

## What is the main statistic for Friedman's ANOVA named in SPSS?

### Chi-squared statistic.

7

## What is the asymptotic sig./adj.sig. in SPSS?

### The p-value.

8

## What is the z-value named in SPSS?

### The std. test statistic.

9

## What does R stand for in both tests' formula?

### R is the sum of ranks for each condition.

10

## How do you calculate H in Kruskal-Wallis?

###
You rank all scores ignoring the group they belong too (as it's independent conditions).

Add up ranks for each condition and put these into the main formula.

11

## How do you calculate Fr in Friedman's ANOVA?

###
You rank scores within each participant (as it's repeated conditions).

Calculate the sum and mean of ranks in each condition and put these into the main formula.

12

## What are the two options when normality assumptions aren't met (therefore a mixed ANOVA can't be used)?

###
Transforming data.

Using several non-parametric tests.

13

## Why do we transform data?

### If we have skewness or kurtosis then transforming data could potentially stop this (e.g. log transformation) and we can then use a mixed ANOVA.

14

## What non-parametric test is used for within-subject conditions?

### Wilcoxon.

15

## What non-parametric test is used for between-subject conditions?

### Mann-Whitney.

16

## What are the two choices when looking at the interaction of variables in several non-parametric tests?

###
1. Calculate the change of score and compare changes across groups using an Independent T-Test or Mann-Whitney.

2. Calculate difference between groups' scores using a Dependent T-Test or Wilcoxon.

17

## How do you complete a Bonferroni adjustment for the number of tests used?

### Bonferroni alpha = alpha / number of comparisons.

18

## What is a Type I error?

### Incorrectly rejecting the null hypothesis. Probability of this is alpha (.05).

19

## What is a Type II error?

### Incorrectly accepting the null hypothesis. Probability of this is beta.

20

## What is the definition of power?

### The ability to detect an effect if there is one.

21

## What value should power usually be?

### .80.

22

## What are the three influences on power?

###
Effect size (the larger the effect, the easier it is to detect).

Alpha level (usually p < .05).

Sample size (larger samples are more representative = less error, more power).

23

## Power, effect size, alpha level and sample size are all linked. Which one is useful to be able to calculate?

### The estimated sample size needed to achieve adequate power.

24