Variables, designs and hypotheses

Question 1

Define an experiment

Answer 1

Changing the IV whilst measuring the DV

Question 2

How can we infer causality?

Answer 2

Changes in DV must be caused by changes in the IV

Question 3

What is a Quasi-experiment?

Answer 3

IV can’t be manipulated

Question 4

What is one issue with a Quasi-experiment?

Answer 4

Confounding variables

Question 5

What is a Correlational design?

Answer 5

There’s no manipulation, and you measure 2 variables and determine whether they’re related

Question 6

What is one issue with Correlational design?

Answer 6

Can’t infer causality

Question 7

What is an IV?

Answer 7

Variable that is changed

Question 8

How many levels can an IV have?

Answer 8

2 or more

Question 9

What is a DV?

Answer 9

Variable that is measured

Question 10

What are the 4 measurement scales for a DV?

Answer 10

nominal, ordinal, interval and ratio

Question 11

What is a nominal scale?

Answer 11

Categorical- numbers refer to different class

Question 12

What is an ordinal scale?

Answer 12

Ranking- numbers indicate a rank in a list

Question 13

What is an interval scale?

Answer 13

Equal steps are meaningful

Question 14

What is a ratio scale?

Answer 14

Equal steps are meaningful and theres a meaningful zero point

Question 15

What is a confounding variable?

Answer 15

Variable that confuses the interpretation of the results

Question 16

When would a confounding variable occur?

Answer 16

When some aspect of the experiment varies systematically with the IV

Question 17

What is the aim of an experimental design?

Answer 17

To eliminate any potentially confounding variables from the experiment

Question 18

What is a between-subjects design?

Answer 18

Each condition is applied to a different group of participants

Question 19

How can you balance individual differences?

Answer 19

By assigning participants randomly

Question 20

What is a within-subjects design?

Answer 20

Same participant performs at all levels of the IV

Question 21

What is within-subjects design also known as?

Answer 21

Repeated measures design

Question 22

What is one disadvantage of within-subjects design?

Answer 22

Order effects

Question 23

How can you combat order effects?

Answer 23

Randomly or use counterbalancing

Question 24

What can you do when you can’t run a within-subject design?

Answer 24

Use a matched design

Question 25

What can we do when we can't actively manipulate the variables we want to test?

Answer 25

Consider pre-existing variables and measure the extent to which they are co-related

Question 26

What is an experimental hypothesis?

Answer 26

Questions we wish to address in experiments, based on our theories

Question 27

What is a statistical hypothesis?

Answer 27

Precise statements about collected data

Question 28

What is a null hypothesis?

Answer 28

states the different sample we look at come from the same population

Question 29

For parametric stats, what does the null hypothesis state?

Answer 29

all the means are equal

Question 30

For non-parametric stats, what does the null hypothesis state?

Answer 30

All the distributions are the same

Question 31

What is an alternative hypothesis?

Answer 31

The logical opposite of the null hypothesis

Question 32

When do we reject the null hypothesis?

Answer 32

When the probability of null hypothesis being true is less than the criterion

Question 33

What do we set the criterion as?

Answer 33

a=0.05

Question 34

What does p<0.05 mean?

Answer 34

There a less than 1 in 20 probability of it happening by chance

Question 35

How do we determine p?

Answer 35

Calculate a test statistic

Question 36

What is p?

Answer 36

The probability of collecting this data assuming the Null hypothesis to be true (the probability the effect we measured is simply due to chance)

Question 37

When can we think the events we measured are unusual?

Answer 37

When p is less than a