Analysis of Variance (ANOVA)

Question 1

When would ANOVA be used?

Answer 1

• In situations where we want to compare more than two
conditions (instead of T-tests)
•and / or more than one independent variable (factor)

Question 2

What are the key functions of using an ANOVA test?

Answer 2

• it allows you to investigate the effect of multiple factors on your dependent variable at the same time (in combination)
• It tries to determine whether we have a true
effect of the IV rather than an effect of individual difference (variance between conditions greater than variance within conditions)

Question 3

Why not use t-tests instead of ANOVA?

Answer 3

• If we just used t‐tests on each pair of groups we would carry out three separate tests:
• By running multiple tests you increase the chance of making a Type 1 error (not finished)
• AKA experiment wise error rate

Question 4

How does ANOVA control for experiment wise error rate?

Answer 4

ANOVA controls for these errors so that the Type 1 error
remains at 5% and you can be more confident that any
significant result you find is not just down to chance

Question 5

When are t-tests more efficient than ANOVA and vice versa?

Answer 5

• ANOVA and t‐test are similar
• Compare means between‐groups
• With 2 groups both work but: 
  -t‐test more efficient
  -ANOVA inefficient
• With more than 2 groups:
  - t‐test not efficient
  - ANOVA more efficient

Question 6

What are the assumptions of ANOVA?

Answer 6

1. DV is ratio/interval
2. Normally distributed population
3. Homogeneity of variance
4. independent random samples taken from each population for independent groups

Question 7

What is homogeneity of variance?

Answer 7

The samples being compared are drawn from populations with the same variance
-shape of distribution the same between groups

Question 8

How does ANOVA work?

Answer 8

• Analyses the different sources from which variations in scores arise
• It looks at the variability between conditions (between‐
groups variance) and within conditions (within‐group
variance)
• It tries to determine whether we have a true effect of the IV rather than an effect of individual differences (variance between conditions greater than variance within conditions)

Question 9

What is between-group variance?

Answer 9

The variation (difference) between mean scores in each condition

Question 10

Explain between-group variance

Answer 10

• When the means are very different there is what is called a greater degree of variation between the conditions
• If there were no differences between the means, there would be no variation

Question 11

What sources does between-group variance arise from?

Answer 11

• Individual differences
• Treatment effects
• Random errors

Question 12

Explain the source of the treatment effects of between-group variance

Answer 12

• This is the effect of the IV(s) – what we are actually
trying to measure
• We want a difference between experimental conditions
– the scores of participants in one group are different to scores of participants in another group
• We are measuring whether the variable we are looking at is actually having an effect

Question 13

Explain the source of the individual difference of between-group variance

Answer 13

• People naturally vary
• We don’t want a high amount of individual differences as this may suggest the IV is having an effect when it is actually due to differences between participants ability in different groups

Question 14

Explain the source of the random errors of between-group variance

Answer 14

• Errors of measurement can arise from a variety of
sources such as:
-Varying external conditions – differences in time of
day at testing
- State of the participant – current focus of attention,
motivation
- Experimenter’s, or computer’s, ability to measure
and score accurately

Question 15

What is within-groups variance?

Answer 15

Within‐groups variance is the variation (difference)

between people within the same group

Question 16

Explain within-group variance

Answer 16

•Within‐groups variance can be called error variance

-The variability within the groups is not produced by the experiment and that is why it is considered error variance

Question 17

What sources can within-group variance arise from?

Answer 17

• Individual differences

* Random errors

Question 18

Explain the source of individual differnces of within-groups variance

Answer 18

• People within the same group differ even though they are treated the same
-It is highly likely to have some level of within‐groups
variance because people naturally differ e.g.,
knowledge, IQ, personality etc

Question 19

Explain the source of the random errors of within-groups variance

Answer 19

• Errors of measurement can arise from a variety of
sources such as:
-Varying external conditions – differences in time of
day at testing
- State of the participant – current focus of attention,
motivation
- Experimenter’s, or computer’s, ability to measure
and score accurately

Question 20

What is the logic of ANOVA?

Answer 20

• Subjects in different groups should have different scores because they have been treated differently (i.e. given different experimental conditions)
• but subjects within the same group should have the same score

Question 21

What is ANOVA trying to find out in terms of the logic of ANOVA?

Answer 21

• Trying to find out if the variance or spread of
scores is larger between the groups (i.e. in the different
conditions) than the spread within the groups
• If variance between groups is much > within-group variance =IV having larger effect than individual differences

Question 22

What is the partitioning of the variance?

Answer 22

The comparison of variance due to nuisance factors (error variance, individual differences) compared to variance due to our experimental manipulation

Question 23

What is the f-ratio and formula?

Answer 23

• Calculation of the ratio of the variance due to our manipulation of the IV and the error variance
• F = between‐groups variance/within‐groups variance

Question 24

What are the meaning’s of different f-ratios?

Answer 24

• An F ratio of less than 1 indicates that the effect of the
IV is not significant
• The greater the F‐ratio the better

Question 25

How do you find out if the F‐ratio is significant?

Answer 25

• SPSS can calculate whether the effect of the IV is
sufficiently larger than the effect of the errors
• SPSS will report the exact p‐level for a given F‐ratio
-It takes into account the number of observations
(degrees of freedom)
. The p value needs to be equal to or less than 0.05 for the F ratio to be regarded as significant

Question 26

What is the p-value when calculating the f-ratio?

Answer 26

• The p value is the probability of getting this F ratio by

chance alone

Question 27

What are factors in ANOVA?

Answer 27

• These are the independent variable(s) e.g. music type

Question 28

What are the level of factors in ANOVA?

Answer 28

• These are like conditions. e.g. three levels of the factor: constant low level of music, no music and intermittent music

Question 29

What are between-subjects factors?

Answer 29

• Factors that vary between participants. e.g. each participant will only experience one level of a factor
• Like an independent samples t-test

Question 30

What are within-subjects variance?

Answer 30

• factors that vary within a participant e.g. administering all levels of factors to participants
• like a repeated-measures t-test

Question 31

What are mixed ANOVA designs?

Answer 31

• when a study design includes one or more within subjects factors and one or more between-subjects factors.
• e.g we want to look at male and female test scores (between‐subjects) for each of the facial expressions (within‐subjects)

Question 32

What needs to be done when describing an ANOVA design?

Answer 32

We need to specify:

1. How many factors are involved in the design
2. How many levels there are in each factor
3. Whether the factor(s) are within or between subjects

Question 33

What are the different types of ANOVA test in terms of the number of factors?

Answer 33

• One‐way ANOVA (one factor, e.g. facial
expression)
• Two‐way ANOVA (two factors, e.g. facial
expression and gender)
• Three‐way ANOVA (three factors, e.g. facial
expression, gender and age group)
• and so on

Question 34

What are the different types of ANOVA test in terms of the number of levels in each factor?

Answer 34

e.g. We could describe our 2-way ANOVA example
(two factors, e.g. gender and facial expression)
as a 2 x 4 ANOVA
• Gender has 2 levels (male or female)
• Facial expression has 4 levels (neutral, angry,
happy, and sad)