exam 3 Flashcards
Define ANOVA
Analysis of variance between groups
Why use ANOVA?
To test more than two groups as opposed to T-test which test only for two groups. The difference between the two groups is that ANOVA compares the difference of more than 2 means while T test compares only the difference between 2 means.
.
•
Objective of ANOVA
Test for the difference between the means of two or more groups on one factor/dimension/variable. Each group is only tested once.
Question to ask when using ANOVA
is there a difference between the means of the different groups? And is this difference more than we would expect by chance?
Hypothesis and ANOVA
Null Hypothesis: There is no difference between groups on variable/measure X except that which is expected by chance.
H0: u1 = u2 = u3
Research Hypothesis: There is a difference between groups on variableX that is more than what is expected by chance; There is at least one difference that is significant. H1: x1 ≠ x2 ≠ x3
Hypotheses and ANOVA notes
–(this does not tell us which one of the three differences is responsible for the rejection of the null. It could be one of the three, it could be all of the three)
–“More than what is expected by chance” – we interpret this to mean that it is due to the grouping variable.
Non-directional. All ANOVAs are non-directional.
Types of ANOVA we will use
1) Simple Analysis of Variance/one-way analysis of variance
2) Factorial design
Simple analysis variance-ANOVA
Where there is one factor or one treatment variable i.e. group membership, this is also called one-way analysis of variance because there is only one grouping dimension.
Factorial design-ANOVA
Factorial ANOVA (e.g. 3x2): Effect of exercise (high, medium, low impact) on weight loss by gender.
–Factor 1 (Independent Variable)Treatment (high/medium/low impact exercise)
–Factor 2 (Independent Variable)Gender (male/female)
–Weight Loss Outcome (Dependent Variable)
–Questions to be answered: Main effect of exercise? Main effect of gender? Interaction between exercise & gender?
COMPUTING ANOVA
•F Test Statistic
F = MSBetween / MS Within
Logic behind this ratio: (test statistics)
- Mean squares are estimates of variance
- Within Group Variance is Due to Chance (individual difference)
- Between Group Variance is due to the grouping category (Independent Variable)
- An increasing F value…
STEPS to compute ANOVA
1)A statement of Null and Research Hypotheses Null H0: u1 = u2 = u3 Research H1: x1 ≠ x2 ≠ x3 2)Level of risk .05 (always) 3)Test statistic ANOVA F = MSBetween / MS Within 4)Compute the test statistics value
•The F ratio definition:
Ratio of variability between groups to variability within groups. To get this we compute the sum of squares for each group of variability between groups; within groups; and the total
explain between Groups:
The sum of the differences between the mean of all scores and the mean of each group’s score, squared. (How different is each group’s mean from the overall mean?)
explain Within Groups:
The sum of the differences between each individual score in a group and the mean of each group, squared. (How different is each score in a group from the group mean?)
Total in ANOVA means:
The sum of the between-group and within-group sum of squares
Total sum of squares in ANOVA means:
Sum between –group and within-group sum of square
Degrees of freedom definition
approximation of the sample or the group size