Prism Lab Stats

Dr. Roger Newport

&

Laura Condon

Room B47. Drop-In Sessions: Tuesdays 12-2pm.roger.newport@nottingham.ac.ukhttp://www.psychology.nottingham.ac.uk/staff/rwn/Teaching/C82MPR.html

How a standard experiment might look

Condition 1PreExp (e-prime?)Post

Condition 2PreExp (e-prime?)Post

We only need to record the last few pre-exposure and the first few post-exposure in each manipulation

132

-6-5-3

} 2.0

} -4.7

Man1

Pre

Man1

Post

Man2

Pre

Man2

Post

2.0 -4.7 1.8 -3.2S1

S2

Sn

Manipulation 1

Table of means for ANOVA

Two-factor ANOVA

2-way repeated measures Analysis of Variance.

Each subject participates in ALL conditions

We could stick it all into a….

What does the 2-factor ANOVA test for?

• Differences between levels of Factor A(“main effect of A”)

• Differences between levels of Factor B(“main effect of B”)

• Other differences(“interaction”)

• So the 2-factor ANOVA involves computing three separate F ratios - three independent hypothesis tests!

What would we do if the interaction was significant?

Unplanned comparisons

Accuracy

-20-15-10

-505

101520

Pre Post

Exposure

mm YM1

YM2All possible comparisons

M1pr v M1poM1pr v M2prM1pr v M2poM1po v M2prM1po v M2poM2pre v M2po

What would we do if the interaction was significant?

Unplanned comparisons

Accuracy

-20-15-10-505

101520

Pre Post

Exposure

mm YM1

YM2All possible comparisons

M1pr v M1poM1pr v M2prM1pr v M2poM1po v M2prM1po v M2poM2pre v M2po

What would we do if the interaction was significant?

Unplanned comparisons

Accuracy

-20-15-10-505

101520

Pre Post

Exposure

mm YM1

YM2All possible comparisons

M1pr v M1poM1pr v M2prM1pr v M2poM1po v M2prM1po v M2poM2pre v M2po

alpha = .0083

Planned comparisons

Accuracy

-20-15-10-505

101520

Pre Post

Exposure

mm YM1

YM2

What do we really want to know?What were our hypotheses?Why did we do the experiment?

All possible comparisons

M1pr v M1poM1pr v M2prM1pr v M2poM1po v M2prM1po v M2poM2pre v M2po

alpha = .05

Simplifies analysis

Reduces risk of type I errors (& don’t have to adjust alpha level)

Interpretation is easy as each comparison is derived from a specific hypothesis

No tricky interactions to interpret

Only have results that you are interested in

Advantages of planned comparisons:

Disadvantages: SPSS does not (easily) do the comparisons we need

Accuracy

-20-15-10-505

101520

Pre Post

Exposure

mm YM1

YM2

The outcome we predicted

Show there is no difference here

But that there is a difference here

How do we perform a planned comparison?

Not just a t-test because we must take into account the variability of the whole model

How do we do it in SPSS when SPSS doesn’t do it?

Testing the planned comparisons

F ratios need to be calculated for each comparison and for that we need the mean square

the mean square is calculated by:

FAcomp =MSAcomp

MSS/A

Do a 2x2 ANOVA…

…to get the residual mean square from the whole model

Then do a one way ANOVA on the comparison in question…

…to get the mean square for the comparison

FAcomp =MSAcomp

MSS/A

Divide one by the other to get the F ratio

FAcomp =MSAcomp

MSS/A

Do the same for the other comparison

Critical F's for comparisons use the degrees of freedom for the numerator and the denominator of the F-ratio.

In my example there are 1 and 7 degrees of freedom for this comparison.F(1, 7) = 10.51, p= 0.0142

Bung these values into an online F ratio calculatore.g. http://faculty.vassar.edu/lowry/tabs.html#f

Or use a book of tables

You can’t trust anything you find online, so test the calculator with these values: F = 5.99; Numerator = 1; Denominator = 6. You should get a p value of 0.05

Note that this is different to both the 2 way interaction and the 1 way significance values

Evaluating the planned comparison’s null hypothesis

Now then, although you have run three ANOVAs you do not have to report this.

If you specify planned comparisons there is no need to describe or report the omnibus.

You do, however, have to state that you are performing planned comparisons and give the rationale/hypothesis for them early doors (i.e. at the end of the introduction)

General

Why

Specific

Specific

Why

General

What/How

Outcome

Prism adaptation

Other variable

Hypotheses and predictions

Method - replicability

Results F[1,5] = 2.4, p<0.05SE bars on graphs

Describe outcomes

Why you got those outcomes

What it all meansFuture research

Refer to literature

Refer to literatureFollow the guidelines on the prism web page

What is known about PA(that is relevant to the study)

What is known about our manipulation (that is relevant)

How one should affect the other

Hypothesis / brief method of testing

ResponseTarget

Measurement

Make good use of pictures to describe your experimental setup

time

If people were right handed (for example) say how you know

Make sure it is prelicable

F[1,5] = 2.4, p<0.05SE bars on graphs

Collation of means etc.

Type of analysis (not package used)

0

5

10

15

20

Pre Post

Phase

erro

r (m

m)

two legsone leg

Figure 1: blah de blah de blah

Give direction of effects

Give means in table or graph, not both

No need to report the omnibus

Come to a conclusion

If suggesting further research give concrete examples of how to go about it

and how it would have a bearing on your results?

What are the implications of your results?How do they add to the literature?

What do they mean?Do they fit previous research - why?

What were the main results?

There are further instructions about how to present your reports on the course web page. Ignore them at your peril (by which I mean: ignore them and lose marks)

DoNot Use TextThatIsTooSmall

Do not put too much information on one page as this will make it really really difficult for your audience to take in both what is on the screen and what you are saying. They won’t have time to read it and they will start to lose interest. At the same time do not simply read out exactly what is on the page because your audience will be able to do that for themselves. You should try to give the impression that you know what you are talking about and above all do not mumble

And they can remind you what to say next, but they can also look rubbish if not done very well

Bullet points are good

They emphasise important points

If you choose a fancy backgroundMake sure you can still read the text

If you choose a fancy backgroundMake sure you can still read the text

If you choose a fancy backgroundMake sure you can still read the text

If you choose a fancy backgroundMake sure you can still read the text

If you choose a fancy backgroundMake sure you can still read the text