# Lecture 7 - Within Subjects Anova Flashcards

What is a within subjects design?

It is the same thing as a repeated measures design

It involves comparing mean scores between the participants, each of which takes part in all IV conditions

What are two advantages to within subjects/repeated measures design?

controls for individual differences

fewer participants are required in total as each takes part in all IV conditions

What are five disadvantages to within subjects/repeated measures design?

Practice effects

Boredom effects

Differential carry-over effects

Sometimes the data isn’t completely independent (and this is an assumption of ANOVA)

It isn’t always possible for all designs - e.g. if the IV is gender you cant really take part in both conditions

What are differential carry-over effects?

the treatment condition affects participants’ performance in a later condition in one way, and in another way when followed by a different condition

How many f-ratios do we calculate?

two

one for the main effect

one for the subject variables

How many components can we split the deviations into?

two

1 - between treatments component (measures effect plus error)

2 - within treatment component (measures error alone)

How do we interpret a Tukey HSD test?

It involves calculating a critical difference and any mean difference (called q in the table) greater than this critical difference value is significant

However, we may just be given stars next to the table which represent the level of significance

Then we go on to describe the results - e.g. ‘A post-hoc Tukey HSD test (p

How do we write a table of means and standard errors?

This should be the FIRST thing you do in your interpretation section for ANY kind of ANOVA

Make a table with the means and standard errors written like so ‘20(1.581)’ (the standard error is in brackets

Above this should be the condition name

Label this table something like ‘Table 1’ and underneath write ‘Table 1 (above) shows the means (and standard errors) for ……………………………………….’

What happens if you find a significant main effect of the subject variable

It is very common and not usually a problem

It is only a problem if…..

1 - specific predictions have been made in advance about performance

2 - there is a hidden aptitude treatment interaction (e.g. skill at crosswords pre-test wasn’t taken into account as they only looked at the effect of practice)

How do we construct an error term in a two way repeated measures design?

There are two options

1 - we may construct an overall error term for all effects (but we may well overestimate the error for individual effects, so this isn’t very good)

2 - we may construct an error term for each area of interest (this is good as we are unlikely to overestimate the error for individual areas of interest)

In a within subjects/repeated measures design, each effect has its OWN error term

what are the assumptions of a within subjects/repeated measures ANOVA?

Same as all ANOVA: 1 - continuous level data 2 - normal distribution 3 - independence of scores 4 - homogeneity of variance

Specific to within subjects/repeated measures

1 - Sphericity/compound symmetry (this means homogeneity of different treatment variances.)

How and when do we test for sphericity

We don’t test for sphericity if each IV only has one or two levels

SPSS does ‘Mauchly’s test of sphericity’

If it is significant = we can assume homogeneity of variances

If it isn’t significant = we cannot

Why do we test for sphercity?

So that we can correct any violations or do an alternate test - if we do not then we are at an increased risk of making type 1 errors

What alternative tests can SPSS do when sphericity has not been met?

Greenhouse-Geisser (liberal so an increased chance of type 1 errors)

Huynh-Feldt (conservative so an increased chance of type 2 errors)