Transcript anova
S519: Evaluation of Information Systems
Social Statistics Inferential Statistics Chapter 11: ANOVA
This week
When to use F statstic How to compute and interpret Using FTEST and FDIST functions How to use the ANOVA
The problem with t tests…
We could compare three groups with multiple ttests: M1 vs. M2, M1 vs. M3, M2 vs. M3
What is ANOVA?
“
An
alysis
o
f
Va
riance” A hypothesis-testing procedure used to evaluate mean differences between two or more treatments (or populations).
Related to: t-tests using independent-measures or repeated- measures design.
Advantages: 1) Can work with more than two samples.
2) Can work with more than one independent variable
What is ANOVA?
In ANOVA an independent or quasi independent variable is called a factor.
Factor = independent (or quasi-independent) variable.
Levels = number of values used for the independent variable.
One factor → “single-factor design” More than one factor → “factorial design”
What is ANOVA?
An example of a single-factor design A example of a two-factor design
F value
Variance between treatments can have two interpretations: Variance is due to differences between treatments.
Variance is due to chance alone. This may be due to individual differences or experimental error.
Three Types of ANOVA
Independent measures design: Groups are samples of independent measurements (different people) Dependent measures design: Groups are samples of dependent measurements (usually same people at different times; also matched samples) “Repeated measures” Factorial ANOVA (more than one factor)
Excel: ANOVA
Three different ANOVA: Anova: single factor - independent Anova: two factors with replication - factorial Anova: two factors without replication - dependent
Example (independent)
Three groups of preschoolers and their language scores, whether they are overall different?
Group 1 Scores 87 Group 2 Scores 86 76 56 78 98 77 66 75 67 87 Group 3 Scores 89 85 99 91 96 85 79 81 82 87 89 90 89 78 85 91 96 96 93
F test steps
Step1: a statement of the null and research hypothesis One-tailed or two-tailed (there is no such thing in ANOVA)
H
0 : 1 2 3
H
1
: at least one
is different
F test steps
Step2: Setting the level of risk (or the level of significance or Type I error) associated with the null hypothesis 0.05
F test steps
Step3: Selection of the appropriate test statistics See Figure 11.1 (S-p227) Simple ANOVA (independent)
F test steps
Between-group degree of freedom=k-1 k: number of groups Within-group degree of freedom=N-k N: total sample size
F test steps
Step4: determination of the value needed for rejection of the null hypothesis using the appropriate table of critical values for the particular statistic Table B3 (S-p363) df for the denominator = n-k=30-3=27 df for the numerator = k-1=3-1=2
F test steps
Step5: comparison of the obtained value and the critical value If obtained value > the critical value, reject the null hypothesis If obtained value < the critical value, accept the null hypothesis 8.80 and 3.36
F test steps
Step6 and 7: decision time What is your conclusion? Why?
How do you interpret F (2, 27) =8.80, p<0.05
Example (dependent)
Five participants took a series of test on a new drug
P1 P2 P3 P4 P5 2 0 0 T1 3 0 1 1 1 T2 4 3 4 3 4 T3 6 3 5 4 3 T4 7 6
F test steps
Step1: a statement of the null and research hypothesis One-tailed or two-tailed (there is no such thing in ANOVA)
H
0 : 1 2 3 4
H
1
: at least one
is different
F test steps
Step2: Setting the level of risk (or the level of significance or Type I error) associated with the null hypothesis 0.05
F test steps
Step3: Selection of the appropriate test statistics See Figure 11.1 (S-p227) Simple ANOVA (independent)
F test steps
Between-group degree of freedom=k-1 k: number of groups Within-group degree of freedom=N-k N: total sample size Between-subject degree of freedom=n-1 n: number of subjects Error degree of freedom=(N-k)-(n-1)
F test steps
Step4: determination of the value needed for rejection of the null hypothesis using the appropriate table of critical values for the particular statistic Table B3 (S-p363) df for the denominator = (N-k)-(n-1)=16-4=12 df for the numerator = k-1=4-1=3
F test steps
Step5: comparison of the obtained value and the critical value If obtained value > the critical value, reject the null hypothesis If obtained value < the critical value, accept the null hypothesis 24.88 and 3.49
F test steps
Step6 and 7: decision time What is your conclusion? Why?
How do you interpret F (3, 12) =24.88, p<0.05
Factorial ANOAVA
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