comparing 3 means- anova

Comparing 3 Means- ANOVA Evaluation Methods & Statistics- Lecture 7

Dr Benjamin Cowan

Research Example- Theory of Planned Behaviour  Ajzen & Fishbein (1981)

 One of the most prominent models of behaviour

 Used a lot in behaviour change research

 Has 3 core components:

 Beliefs (right)

  Intentions (middle)

 Behavior (left)

Research Example- Theory of Planned Behaviour  Beliefs:

 Attitude- Persons attitude towards an action

  Subjective norms- what people perceive others to believe about the action

 Perceived behavioural control- peoples perceptions on how able they are to do a certain behaviour

Research Example- Theory of Planned Behaviour

  Intentions: A person’s internal declaration to act

 Behavior: The action

TPB and Pro Environmental Behaviour  Perceived Behavioural Control

  Self Efficacy concept

 We want to see whether 2 interventions we design impact pro environmental behaviour self efficacy compared to a control group with no intervention.

 We therefore have 3 conditions in our experiment

How would we design this experiment?

How would we design this experiment?   IV- Intervention   Level 1- Control Group (No Intervention)

  Level 2- Intervention 1 (Generic Information Condition)

  Level 3- Intervention 2 (Tailored Information condition)

 DV- Self Efficacy/Perceived Behavioural Control  Measured by questionnaire

One Way ANOVA- The Idea  Compare more than 2 means to identify whether

they are significantly different   i.e. whether they come from different populations

 We could do 3 t-tests  Compare control to group 1

 Compare control to group 2

 Compare group 2 to group 3

Familywise error rate   If we have 3 tests in a family of tests and assume each is

independent

  If we use Fishers level of 0.05 as our level of significance…

  The probability of no Type I error in all of these tests   0.95 x 0.95 X 0.95 = 0.857   This is because we would expect to get a chance significant

results 5% of the time.   => Probability of Type 1 error is 1- 0.857=0.143

  That is far greater than the Type I error for each test separately (0.05)

  We therefore use ANOVA rather than lots of t-tests

ANOVA- The Idea  Compare 3 (or more) means

to identify whether they are significantly different   i.e. whether they come

from different populations

 Or more accurately…..we are testing the null hypothesis that the samples come from the same population.

  It is what we call an omnibus test   It tells us there is a

significant difference, not where it is.

ANOVA- The Idea

What this means in our example  We are testing whether:

  The scores on perceived behavioural control in each condition come from the same population of scores (i.e. our interventions had no effect - H0)

  If they don’t (i.e. they come from different populations) then we can reject the null hypothesis

The Key: ANOVA & F Ratio  We found a significant effect of condition on

perceived behavioural control [F(2, 56)= 11.78, p<0.001]

 F ratio is the ratio of explained (that accounted for by the model we are proposing) to unexplained variation

 This is calculated using the Mean Squares

The mindset of “models” Remember the mean is a statistical model, just sometimes not a very good one…….

We want to see whether the statistical model we have proposed explains the variation in our data better

Step 1- Total Sum of Squares  The total amount of variation in our data

 This should look familiar (see Lecture 3)

€

SST = xi − xgrand( )∑2

Step 1- Graphically What the equation is doing

€

xgrand

€

xi

Step 2- Model Sum of Squares  We now need to know how much variation our

model can explain

 How much the total variation can be explained due to data points coming from different groups in “the perfect model”

  is the amount of people in that condition

€

SSM = nk (xk − xgrand∑ )2€

nk

Step 2- Graphically

€

xk

€

xgrand

What the equation is doing

Step 3- Residual Sum of Squares  How much of the variation cannot be explained

by the model i.e. what error is there in the model prediction?

 Easy way to calculate: SSR= SST – SSM

 But here is the real formula

€

SSR = xik − xk( )2

∑

Step 3- Graphically

€

xik

€

xk

What the equation is doing

Great ….. So what next?  These are summed values   Therefore impacted by the number of scores in the

sum (remember variance in Lecture 3)

 We can get around this by dividing by the respective degrees of freedom for each SS

Degrees of Freedom for each SS  Degrees of Freedom for SST (dfT):

 N-1

 Degrees of Freedom for SSM(dfM):  Amount of Conditions (k) -1

 Degrees of Freedom for SSR(dfR):  N-k

F Ratio  The F Ratio is calculated using the:

 Mean Squares model (MSM):   SSM/dfM

 Mean Squares residual (error) (MSR):   SSR/dfR

F Ratio

Variation explained by our model

Variation unexplained by our model

F Ratio

Mean Square Model (MSM)

Mean Square Residual (MSR)

F Distribution   F Distribution for specific pair

of degrees of freedom   Table of Critical Values

One Way Independent ANOVA- Assumptions

 Normally distributed data (Shapiro-Wilkes test)

 Equality of Variance (Levene’s test)

  Interval or ratio data

  Independent data

Reporting ANOVA  F ratio

 Degrees of Freedom (dofM, dofR)

 P value

 Back to the example we saw earlier:   F(2, 56)= 11.78, p<0.001

 We can therefore state that there is a significant effect of our independent variable on perceived behavioural control

One Way Repeated Measures ANOVA  Experiment measuring self efficacy at:

1.  Time 1 (before Intervention)

2.  Time 2 (directly after intervention)

3.  Time 3 (1 month after the intervention)

  This is a Repeated Measures design (Within Subjects- see last week)

  Reduces unsystematic variability due to individual differences

Repeated Measures ANOVA- Sphericity  The assumption of independence of data

doesn’t hold as the data is from the same participants

  Instead we look for sphericity   The assumption that the variance of the differences

between scores in each treatment are equal

 Calculate the difference between pairs of scores in all possible combination of treatment levels ,then calculate the variance of these differences

Mauchly’s test of sphericity   It tests the hypothesis that the variances of the

differences are equal (H0)

  If I got p<0.05 for this test would it be good or bad?

Mauchly’s test of sphericity   It tests the hypothesis that the variances of the

differences are equal (H0)

  If I got p<0.05 for this test would it be good or bad?

  It would be bad as it states there is a significant difference between variance of differences

 Corrections exist if this is the case, usually Greenhouse-Geisser correction is used

One Way Repeated Measures ANOVA-Theory

 Within-Participant Variance

 This includes   Experiment effect (as all people have taken part in

all conditions)

  Error variance- that not explained by the model

Step 1- Total Sum of Squares  The total amount of variation in our data

 dfT = N-1

€

SST = xi − xgrand( )∑2

Step 2- Within Participant Sum of Squares  How much of the variation is within participant

variance

  is a person (i)’s score on a condition (k) and is the person’s mean across those conditions

 dfW= Number of participants X (Number of conditions-1)

€

SSw = xik − x i _ across_ conditions( )2

∑

€

x i _ across_ conditions

€

xik

So far we know…..

 The total amount of variation in our data (SSt)

 How much this is caused by individual’s performance under the different experiment conditions (SSw)

Step 3- Model Sum of Squares  We now need to know how much variation our

model can explain

  In other words how much variation is attributed to our experiment and how much isn’t

 This calculation is the same as in Independent ANOVA

€

SSM = nk (xk − xgrand∑ )2

Step 4- Residual Sum of Squares  We now need to know how much of this variation

is noise or error variation

 Simplest way to calculate this is: SSR= SSW-SSM

 Degrees of freedom are also calculated using: dfR = dfW-dfM

One Way Repeated Measures ANOVA- Assumptions

 Normally distributed data for each condition (Shapiro-Wilkes test)

 Sphericity

  Interval or ratio data

Omnibus test & Post Hoc   If we had a significant main effect of intervention

on self efficacy [F(2, 56)= 11.78, p<0.001]

 This tells us there is a significant effect of our experiment conditions on self efficacy

  i.e. that the means of the conditions are not equal

 But how does this break down?   Information >Control condition?

  Tailored Information > Information & Control conditions?

 We then need post hoc tests

Post Hoc Tests  Used when no specific a priori predictions about

the data we have

 They are used for exploratory data analysis

 Pairwise comparisons   Like performing t-tests on all the pairs of mean in our

data

 But they control the Type I error rate by correcting the significance level across all tests

 Using the Bonferroni correction (0.05/Ntests)

  There are many to choose from……..

A selection of common post hoc tests  LSD (Least Significant Difference)  Analogous to multiple t-tests

 Bonferroni  Uses Bonferroni correction to control for Type I

 With multiple comparisons this may be too conservative (increase chance of Type II error)

 Tukey’s test  Control Type I and better when testing large

amounts of means

Which one to choose?  Trade off between:

  Type I error rate likelihood

  Statistical power (ability to find an effect if there is one)

 Whether assumptions of ANOVA have been violated, although most are robust to minor variations

Lecture Readings and Further concepts to consider  Core:- Field (2009) Chapters 8 & 11 (pages

427-454)

 Other concepts to consider:   Statistical Power: Cohen (1992). A power primer.

Psychological Bulletin

 Planned Contrasts: Field (2009), Chapter 8, p.325-339