One-way between -subjects analysis ANOVA
3 important questions on One-way between -subjects analysis ANOVA
When is a one-way between-subjects analysis of variance (ANOVA) used?
It is used in situations where researches compare means on a quantitative Y outcome variable across two or more groups.
The main idea behind an ANOVA is to compare the variances between groups and variances within groups to see whether the results are best explained by the group differences or by individual differences. If there’s higher between-group variance relative to within-group variance, then the groups are likely to be different as a result of your treatment. If not, then the results may come from individual differences of sample members instead.
The main idea behind an ANOVA is to compare the variances between groups and variances within groups to see whether the results are best explained by the group differences or by individual differences. If there’s higher between-group variance relative to within-group variance, then the groups are likely to be different as a result of your treatment. If not, then the results may come from individual differences of sample members instead.
Why is it called variance? Variance tells you the degree of spread in your data set. The more spread the data, the larger the variance is in relation to the mean.
Because the goal is to partition (seperation) or divide the variance of scores on the Y outcome into vairances ttha can be predicted from group membership and variance tha can be prediced from group membership and variance ha canno be prediced from group membership.
Which assumptions are there for one-way anova?
- Observations must be independent
- Scores should be normally distributed within each group
- Variance should be approximately equal across groups
- It's robust against violation of normality and equal variance assumptions if within-groups n are large
- no extreme outliers.
- the independent t test uses the same procedure
- Scores should be normally distributed within each group
- Variance should be approximately equal across groups
- It's robust against violation of normality and equal variance assumptions if within-groups n are large
- no extreme outliers.
- the independent t test uses the same procedure
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