ANOVA stands for analysis of variance. It is commonly used to compare the means of independent groups of measurement data.
1. One-Way ANOVA
Compares the means of the measurement variable in each group; one measurement variable and one nominal variable
ex: Do Whites, African Americans, and Latinos students perform differently on test anxiety levels?
Assumptions: 1. normal distribution - the observations within each group are normally distributed
2. homoscedasticity - the standard deviations are equal in the groups
2. Two-Way ANOVA
Compares the mean of the measurement variable in each group and tests if there is interaction between the two nominal variables; one measurement variable and two nominal variables
ex: Do Whites, African Americans, and Latinos students perform differently on test anxiety levels? Do low-income, medium-income, and high-income family students perform differently on test anxiety levels? Is there an interaction between race and income level on students' performance on test anxiety levels?
3. Repeated Measures ANOVA
AKA within-subjects ANOVA or ANOVA for correlated samples. Compares means across one or more variables that are based on repeated observations.
ex: Is the mean blood pressure the same at t1, t2, and t3?
