Week 10 Flashcards
why Analysis of Variance (ANOVA) is needed to examine differences between multiple groups of means
Understand the philosophy underlying ANOVA
Including terminology such as between-subjects and familywise error rate
Know the assumptions that underpin ANOVA
Be able to interpret one-way between-subjects ANOVA in SPSS
Be able to interpret post-hoc testing in SPSS
Be able to write up an ANOVA in correct APA style
t-tests
- Try to answer questions comparing two dependent variables
- Is there a significant differenct between two mean variables
- independent samples test
e.g. rehydration vs carbohydrates
or any combination of rehydration, carbohydrates, physio or combination
Problem with multiple comparisons (t-tests)
- Error rate per comparison increases Type 1 Error
- Or rejecting the null hypothesis when it is true
- Familywise error rate:
- Probability of making more Type 1 errors
Familywise Error Rate
The probability of making one or more Type 1 errors in a set of comparisons
Type 1 error
- Rejecting the null hypothesis when it is true
- Or we say something significant is happening but actually the null happening is true.
Alpha Value
= 0.05
* An acceptable level of error
* There is a 5% chance you have calculated a Type 1 error
* Connected to p value
* probability of finding a 5% magnitude difference if null is true
Analysis of Variance
- ANOVA
- Statistical procedure in psychology
- guards against familywise error
- Can analyse differences between more that one mean
- t-tests only does two
One way Between-Groups ANOVA
- Will tell you if there are significant differences in DV means across 3 or more groups
*** Invented by Sir Ronald Fisher - F Statistic**
- Post-hoc tests can be used to find where the differences are
When to use One-Way Between-Groups ANOVA
- 1 Dependent variable is continuous
- 1 Independent variable has three or more levels
- Different participants in each level of the IV
e.g. different football players in different groups
Dependent variable is continuous
Aa variable with many possible values.
Benefits of Between Groups Anova
Its possible to reduce the practice effect
Its possible to reduce the carry over effect.
e.g. physio could have carry over effects or other DVs could produce results when subjects practice doing the same test
Advantages of ANOVA
Can be used in wide range of experimental design
* Independent groups
* Repeated Measures
* Matched Samples
* Designs involving mixtures of independent groups and repeated measures
* More than on IV can be evaluated at once
Independent Groups Design
Between-Groups Design
Repeated Measures
Within Groups Design
Same people in each level of the IV
e.g. Each person does physio, carbs and rehydrate
Matched Samples
- Control for confounding variables
- Matching people to outcomes that might be important
e.g. age and experience could influence recovery time from sport activities, so match fit people and older people for true results
Mixed Design ANOVA
- Involves mixed IV Groups
- Involves Repeated Measure samples
- VCombination of a between-unit ANOVA and a within-unit ANOVA
- Requires a minimum of two IVs called factors
- At least one variables has to vary between-units and at least one of them has to vary within-units
Adjusted Factorial ANOVA
- More than one IV evaluated at a time
- Much more sophisticated
e.g. measuring recovery time and heart rate at the same time
4 Assumptions of Between-Groups ANOVA
- DV must be continuous
- Independence: each participant must not influence the other
- Normality: Each group of scores should have normal distribution (No Outliers?)
- Homogeneity of Variance: approximatley equal variablity in each group
How to Check for Normality
- Kolmogorov-Smirnov/Shapiro-Wilds: p > 0.05
- Skewness & Kurtosis
- Histograms
- Detrended Q-Q Plots
- Normal QQ Plots