Week 8

Question 1

within group design

Answer 1

compare conditions in same group of participants

Question 2

between group design

Answer 2

compare across different groups of participants

Question 3

independent measures

Answer 3

different people in each condition

Question 4

issues with independent measures

Answer 4

participant variation - individual differences
need to be controlled for e.g. match participants of characteristics
but this is time consuming and we cannot control for all characteristics

Question 5

repeated measures

Answer 5

same group of people in all conditions

Question 6

issues with repeated measures

Answer 6

order effects - one condition impacts the behaviour of the other conditions
boredom effects
guessing the purpose of the study - demand characteristics

Question 7

washout period

Answer 7

period between conditions
removes order effects

Question 8

when would we use between group design?

Answer 8

distinctive groups e.g gender
quick study

Question 9

when would we use within subject design?

Answer 9

participant variation
effects of different conditions on behaviour

Question 10

counterbalancing

Answer 10

avoids order effects
systematically varying the order to remove systematic bias
half participants complete condition a -> b
half participants complete condition b -> a

Question 11

problems with counterbalancing

Answer 11

more conditions make conterbalancing complicated

Question 12

latin square counterbalancing

Answer 12

incomplete counterbalancing
randomising the order of conditions

Question 13

control conditions

Answer 13

act as a comparison
absence of manipulation
e.g. in drug trails, the control is known as the placebo

Question 14

parametric tests

Answer 14

independent t test
make assumptions about the data shape, if our data doesn’t meet these assumptions then our p value will be misleading
increases the risk of type 1 or type 2 error

Question 15

non-parametric test

Answer 15

mann whitney u
no assumptions about data shape, applied to any data set
reduces the chance of detecting a true difference

Question 16

assumptions of a independent t test

Answer 16

data is normally distributed
data shows homogeneity in variance
data measured on interval or ratio scale

Question 17

homogeneity of variance

Answer 17

dispersion is similar in both groups
if assumption is wrong then stats tests and the p value will be bias

Question 18

levene test

Answer 18

test for equality of variances
tests the null hypothesis are the same
if the test is significant, then homogeneity of variance Is violated
parametric test shouldn’t be used

Question 19

independent t test

Answer 19

examines difference in sample means in relation to the standard deviation
underestimating the p value increases the chance of type 1 error
overestimating the p value increases the risk of type 2 error

Question 20

nominal data

Answer 20

observations placed into categories
categories cannot be ranked in any way
e.g. political party

Question 21

ordinal data

Answer 21

can be ranked/ordered but there isn’t even spacing
e.g. postions in a race

Question 22

interval/ratio data

Answer 22

data can be ranked and evenly spaced e.g. weight

Question 23

difference between interval and ratio data?

Answer 23

ratio data has a true zero point

Question 24

further up in the hierarchy

Answer 24

more options for analysing data
interval data for example, can be converted into ordinal or nominal but not the other way around

Question 25

independent t test

Answer 25

differences between the means, in relation to the variability looks at difference between the groups and differences within the groups null hypothesis of no differences, t forms dispersion with known shape and can map the positions of p values

Question 26

mann whitney u test

Answer 26

compares values of each score in a group with each score in other group how often is group 1s score later than that of group 2s null hypothesis U forms a distribution to allow a map of p value