1
Q
Repeated Measures
A
- Within- Subjects
- When each participant is exposed to all the treatments
2
Q
One-way ANOVA
A
- Tells whether there are differences in mean scores on the DV across 3 or more groups
- Invented by Sir Ronadl Fisher - F statistic
- Post-hoc tests can be used to find out where the differences are
3
Q
Null Hypothesis
A
- Usually denoted by letter H with subscript ‘0’
- There is no significant difference between the means of various groups
4
Q
Alternative Hypothesis
A
At least one of the means is different from the rest
5
Q
Factor
A
The Independent Variable
One-way = single-factor = One independent variable
e.g. the type of treatment
6
Q
Between-Subjects
A
- Independent groups
- Each group is different to the other groups
- e.g. comparing male and female & intersex
7
Q
Within Subjects Group
A
- Dependent Groups
- One group of participants exposed to all levels of Individual Variable
8
Q
Examine and Compare
A
- Indicates there will be a t-test or an ANOVA
- Two groups = t-test
- 3 or more groups = ANOVA
9
Q
Familywise Error
A
- The more t-tests we do the greater the risk of error
- ANOVAs guard against familywise errors
10
Q
Repeated-measures ANOVA
10:49 Part 1
A
- Can analyse differences between means from same group of participants
- If overall F is significant then run post-hoc analyses
11
Q
Statistical Question
A
- Is there a statistically significant difference among the averages of the means
- Different treatments completed by the same group of subjects
12
Q
Benefits of Repeated Measures
A
- Sensitivity
- Economy
13
Q
Repeated Measure - Sensitivity
A
- A source of error is removed
- No individual differences when same subjects are in each group
- By removing variance data becomes more powerful in identifying experimental effects
14
Q
Repeated Measure - Economy
A
- Research often constrained by time and budget
- Fewer subjects required to get the same data
15
Q
Problems with Repeated Measures
A
- Drop-out
- Practice/Order/Carry-over Effects
16
Q
Repeated Measures Drop Out
A
- Participants may withdraw for many differnt reasons
- If we miss even one score all data for that subject has to be removed
16
Q
Repeated Measures Drop Out
A
- Participants may withdraw for many differnt reasons
- If we miss even one score all data for that subject has to be removed
- Researchers should state what the drop out rate is
17
Q
Repeated Measures Practice/Order/Carry-Over Effects
A
- Receiving one type of treatment can make subsequent treatments easier
- May cause varied performance
- What happens at beginning might affect what happens at the end of research
- We can use counterbalancing to get around this
18
Q
Assumptions - Tests for Sphericity
A
- With t-tests and Between-subjects ANOVAs we look for homogeneity of variance
- With paired samples t-tests and Within-subjects ANOVAs we want Differences to be equal
- Mauchly’s Test for sphericity
- Equality in variances of differences
19
Q
Mauchly’s Test for Sphericity
A
- Equality in variances of differences
- Tested using Mauchly’s Test
- p < .05, assumption of sphericity has been violated
- p > .05, assumption of sphericity has been met
20
Q
Looking at Dataset
A
Each row is a participant and each column is the IV Condition
21
Q
SPSS - Repeated Measures Within ANOVA
A
1. Analyse
2. General Linear Model
3. Repeated Measures
4. Type the factor (IV) name (Recovery_Methods) and number of levels (3)
5. Add
6. Define
22
Q
SPSS Within-subjects ANOVA
A
- Replace ? marks!
* One at a time drag each group to Within-Subjects Variables window
* Place them in order
* Click EM Means
23
Q
EM Means
A
8. EM Means
* Drag IV (recovery method) into “Display Means For” Window(recovery Method)
9. Continue
10. OK
24
Descriptive Statistics - Within Subjects Anova
* Sample Sizes for each of the conditions
* Standard Deviations
* Means
25
Tests for Sphericity
| Part 4 - 5:46
* Mauchly's Test of Sphericity
* don't need df or others
* Only state the test used and p-value
* Say whether assumption was violated or not
* *p* < .05 Assumption has been met
26
Interpret Within-Subjects Effects
* If spericity has been met we use first row
* If sphericity is violated we can use Greenhous-Geisser or Huynh-Feldt Corrections
27
Within-Subjects ANOVA Degrees of Freedom
* k = the number of conditions (3)
* N = the sample size (40)
Formula = dfwithinfactor = k - 1 = 2
dferror = (k-1)(N-1) = 2 x 39 x = 78
28
Multivariate Test
* This is to get around the Sphericity Assumption
* Wilks Lambda
* Non Parametric Test
* is less powerful but is another option
29
Post-Hoc Tests
**1. Analyse
2. General Linear Model
3. Repeated Measures
4. EM Means**
5. Tick **compare main effects**
6. Select **Bonferroni** under "CI adjustment"
7. **Continue
8. Ok**
30
Pairwise Comparisons - Bonferroni
* Inspect the *p*-values to see which treatments are significantly different
*
31
APA Write up for Within-subjects ANOVA
* A one-way within-subjects ANOVA revealed that ther was an overall sidnificant difference between the ream recover rates of athletes across the three recovery methods, *F*(2, 78 = 12.38, *p* < .001. The assumpthion of sphericity was met, *p* = .75. Post-hoc analyses using Bonferroni adjustment showed the combined method resulted in significantly higher recovery rates than both the carbohydrates (*p* = .03) and the rehydration (*p* < .001) methods. There was however, no significant difference between the carbohydrates and rehydration techniques (*p* = .09)
32
Write up APA Format if Sphericity is violated
Use Wilks Lambda instead and the df are different, and the *F* value is different
33
Part 4 - 15:20
Activity and Non-Parametric Tests
PSYC3102 - HSA3
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