Week 6

Question 1

What is the simplest way to describe categorical variables?

Answer 1

Labels

• no order
• no value

Question 2

What are levels in categorical variables

Answer 2

They have categories with different levels (e.g eye colour - blue)

Question 3

What kind of variables do many psychological and health-related research questions often use?

Answer 3

Categorical Variables

Question 4

drugs, experimental groups, sex, medicines are what kind of variables?

Answer 4

Categorical

Question 5

What makes ordinal variables different to categorical variables?

Answer 5

They have order and levels.

Question 6

What type of variable are those in a Likert Scale?

Answer 6

Ordinal

Question 7

When using categorical data, what kind of questions are usually asked?

Answer 7

Questions about proportions. (e.g what genre was the most popular in the hottest 100?)

Question 8

Give an example of factorial design using two or more variables (categorical data, stairs)

Answer 8

Does using the lift or stair depend at all on what direction they’re going (up or down)?

Question 9

Define the degrees of freedom

Answer 9

In statistics, the number of degrees of freedom is the number of values in the calculation of a final statistic that are free to vary.

Question 10

Give an example of a degrees of freedom

Answer 10

Three numbers sum to 30. How many degrees of freedom are there? First step can be random, second step can be a random amount of the remaining length but the last step HAS to cover the remaining length. Therefore it is 2. 2 degrees of random choice.

Question 11

What are inferential statistics?

Answer 11

Statistics that answer questions about whether statistics from a sample generalise to a population.

Question 12

Inferential statistics: what are p-values?

Answer 12

Used to make probabilistic decisions

Question 13

Inferential statistics: how are p-values used to make probabilistic decisions?

Answer 13

Using null hypothesis statistical testing. If the p-value is small enough, reject the null hypothesis (p < .05). If p-value is bigger than criterion, fail to reject the null hypothesis.

Question 14

Why do we say “fail to reject the null hypothesis” instead of keep the null hypothesis?

Answer 14

For clarification

Question 15

What is effect size?

Answer 15

A (usually standardised) measure of the strength of relationship or magnitude of difference among a set of statistics.

Question 16

What does effect size allow?

Answer 16

An assessment of practical significance (p-values only assess statistical significance).

Question 17

What is a univariate test?

Answer 17

A chi-square goodness of fit test is one. Tests one variable

Question 18

What does a chi square goodness of fit test test?

Answer 18

whether proportions within levels of one categorical variables are inconsistent with hypothesised portions.

Question 19

What is the default assumption for null hypothesis in chi-square goodness of fit testing?

Answer 19

That the population is uniformly distributed across categories. Proportions are equal for all levels of the variable.

Question 20

If zodiac sign is NOT associated with business uses, the number of CEOs in each birth sign should be what?

Answer 20

equal (uniformly distributed)

Question 21

What are three chi-square data assumptions

Answer 21

1. Data must be counts (categorical or ordinal variable)
2. Counts in each category (cell) must be independent
3. Sample size must be large enough (expected frequencies, or counts, in all cells should be at least 5

Question 22

Discuss the things we would need to deem something having no effect in terms of the zodiac example, chi-square goodness test with 256 participants
:

Answer 22

N= 256 participants across all categories (example)

• uniform distribution of counts: 256/12= 21.3 per birth sign
• uniform distribution of proportions: (%) 21.3/256 = 8.35 % per birth sign

Question 23

What formula is the chi-square similar to?

Answer 23

The formula for variance

Question 24

What does chi-square measure?

Answer 24

How well the data match (fit) the expected distribution.

Question 25

What does a small chi-square indicate?

Answer 25

A good fit. 0= perfect fit

Question 26

What is the problem with chi-squares?

Answer 26

• magnitude of chi-square statistic is dependent on
• sample size
• degree of freedom

Question 27

How do you calculate the degree of freedom in a goodness of fit test?

Answer 27

df= cells -1

Question 28

In regards to Cramer’s V, what does a score of >5 equal?

Answer 28

A large effect

Question 29

In regards to Cramer’s V, what does a score of .3-.5 equal

Answer 29

medium effect

Question 30

In regards to Cramer’s V, what does a score of .1-.3 equal

Answer 30

small

Question 31

In regards to Cramer’s V, what does a score of 0-.1 equal

Answer 31

trivial

Question 32

What range is the Cramer’s V?

Answer 32

between 0 and 1 (always positive because to find it, you have to square the number)

Question 33

What do you click in Jamovi to begin a goodness-of-fit test?

Answer 33

Frequencies-one sample proportion tests- N outcomes

Question 34

Can you do a chi-square test if the expected count is lower than 5?

Answer 34

NO. Collect more data, and if you can’t then check data cautiously

Question 35

What should we do if in a chi-square goodness of fit test, the p value is greater then .5?

Answer 35

Fail to reject the null, effect size is not statistically significant

Question 36

When is the chi-square test of independence used?

Answer 36

To test whether two categorical variables are independent. (e.g is choice of major independent of biological sex?)

Question 37

What is an example of a chi-square test of independence using the titanic as an example?

Answer 37

Was survival on the titanic independent of ‘class’ of passenger? IF proportions are independent, all levels of the other variable will equal the total proportion.

Question 38

How do we calculate expected frequencies?

Answer 38

Expected Frequency = (row total X column total)/N total

Question 39

For chi-square test of independence, how do we calculate the degrees of freedom?

Answer 39

df=(#rows-1) X (#columns - 1)

df= (R-1)X(C-1)

Question 40

In test of independence, what makes the test robust in relation to cell frequency?

Answer 40

Robust if fewer than 20% of cells have small expected frequencies. If expected frequencies are small (<5), observed frequencies cannot be normally distributed about expected frequencies.
COLLECT MORE DATA.

Question 41

What are the steps to do a test-of independence with Jamovi?

Answer 41

Frequencies, independent samples, x^2 test of association,