The two-way ANOVA compares the mean differences between groups that have been split on two independent variables (called factors). The primary purpose of a two-way ANOVA… is to understand if there is an interaction between the two independent variables on the dependent variable. For example, you may want to determine whether there is an interaction between physical activity level(IV) and gender(IV) on blood cholesterol concentration(DV) in children.
The interaction term in a two-way ANOVA informs you whether the effect of one of your independent variables on the dependent variable is the same for all values of your other independent variable (and vice versa).
There some assumptions to do Two way ANOVA or we can say that these are the conditions for Two way ANOVA
- Assumption #1: Your dependent variable should be measured at the continuous level (i.e., they are interval or ratio variables).
- Assumption #2: Your two independent variables should each consist of two or more categorical, independent groups.
- Assumption #3: You should have independence of observations, which means that there is no relationship between the observations in each group or between the groups themselves.
- Assumption #4: There should be no significant outliers. Outliers are data points within your data that do not follow the usual pattern
- Assumption #5: Your dependent variable should be approximately normally distributed for each combination of the groups of the two independent variables.
- Assumption #6: There needs to be homogeneity of variances for each combination of the groups of the two independent variables.
Two way ANOVA calculation by hand:
We will do two way ANOVA with example, lets start the calculation
Example: Suppose you want to determine whether the brand of laundry detergent used and the temperature affects the amount of dirt removed from your laundry. To this end you buy two detergents with different brand (“Super” and “Best”) and choose three different temperature levels (“cold”, “warm” and “hot”). Then you divide your laundry randomly into “6*r” pile of equal size and assign each ‘r’ piles into the combination of (“super” and “Best”) and (“cold”, “warm” and “hot”). In this example, we are interested in testing Null Hypothesis.
H(οD) = The amount of dirt removed does not depend on the type of detergent.
H(οT) = The amount of dirt removed does not depend on the temperature.
The example has two factors(factor detergent, factor temperature) at a=2(Super and Best) and b=3(cold, warm and hot) levels. Thus, there are a*b = 3*2=6 different combination of detergent and temperature with each combination. There are r=4 loads. (r is called the number of replicates). This sums up to “n=a*b*r”=24=2*3*4 loads in total.
The amounts of Y(ijk) of dirt removed when washing sub pile k(k=1,2,3,4) with detergent i(i=1,2) at temperaturej(j=1,2,3) are recorded in table below:-
| cold | warm | hot | |
| 4 | 7 | 10 | |
| 5 | 9 | 12 | |
| 6 | 8 | 11 | |
| Super | 5 | 12 | 9 |
| 6 | 13 | 12 | |
| 6 | 15 | 13 | |
| 4 | 12 | 10 | |
| Best | 4 | 12 | 13 |
Solution:
| cold | warm | hot | M(d) [Y(i)] | |
| 4 | 7 | 10 | ||
| 5 | 9 | 12 | ||
| 6 | 8 | 11 | ||
| Super | 5 | 12 | 9 | |
| mean(Yij)=5 | mean(Yij)=9 | mean(Yij)=10.5 ~10 | 8 | |
| 6 | 13 | 12 | ||
| 6 | 15 | 13 | ||
| 4 | 12 | 10 | ||
| Best | 4 | 12 | 13 | |
| mean(Yij)=5 | mean(Yij)=13 | mean(Yij)=12 | 10 | |
| M(t)[Y(j)] | 5 | 11 | 11 | 9 |
We have calculated all the means like detergent mean(Md), temperature mean(Mt) and mean of every group combination.
Now what we only have to do is calculate the sum of squares(ss) and degree of freedom(df) for temperature, detergent and interaction between factor and levels.
First calculate the SS(within)/df(within) we have already know how to calculate SS(within)/df(within) in one way ANOVA we calculated this but in two way anova the formula is different 🙂
STEP 1) Formula for calculation of SS(within) is:
Yijk are the elements in the groups.
Y‾(ij) is mean of combinations
When we put the values and do calculations with this formula we will get SS(within) is
= (4 − 5)² + (5 − 5)² + (6 − 5)² + (5 − 5)²
+(7 − 9)² + (9 − 9)² + (8 − 9)² + (12 − 9)²
· · · · · ·
+(12 − 12)² + (13 − 12)² + (10 − 12)² + (13 − 12
= 38
Calculate the df(within):
df(within) = (r-1)*a*b = 3*2*3 = 18
Calculate MS(within):
MS(within) = SS(within)/df(within) = 38/18 = 2.1111
STEP 2) Calculate SS(detergent) and df(detergent) and MS(detergent)
Y¯(i) is the mean of detergent
Y¯ is the total mean detergent and temperature
= 4*3[(8-9)²+(10-9)²]
= 24
Calculate df(detergent):
df(detergent) = a-1= 2-1 = 1
Calculate MS(detergent):
MS(detergent) = SS(detergent)/df(detergent)
= 24/1= 24
STEP 3) Calculate the SS(temperature), df(temperature) and MS(temperature)
Y¯(i) is the mean of detergent
Y¯ is the total mean detergent and temperature
= 4*2*[(5 − 9)² + (11 − 9)² + (11 − 9)²]
= 192
Calculate df(temperature):
df(temperature) = b-1 = 3-1 = 2
Calculate MS(temperature):
MS(temperature) = SS(temperature)/df(temperature)
= 192/2 = 81
STEP 4: Calculate SS(interaction), df(interaction) and MS(interaction)
Y‾(ij) is mean of combinations
Y¯(i) is the mean of detergent
Y¯(j) is the mean of temperature
Y¯ is the total mean detergent and temperature
Calculate SS(interaction):
= 4 ×(5 − 8 − 5 + 9)² + (9 − 8 − 11 + 9)² +
(110 − 8 − 11 + 9)² + · · · + (12 − 11 − 10 + 9)²
= 12
Calculate df(interaction):
df(interaction) = (a-1)*(b-1) = (2-1)*(3-1) = 2
Calculate MS(interaction):
MS(interaction) = SS(interaction)/df(interaction)
= 12/2
= 6
Its time to calculate the F-test: Calculate critical F-value
MS(detergent)/MS(within) ~ F(df(detergent), df(within))
MS(temperature)/MS(within) ~ F(df(temperature), df(within))
MS(interaction)/MS(within) ~ F(df(interaction), df(within))
If you found the F-value less than the critical F-value then you will not be able to reject the null hypothesis I explained and how to and from where to calculate the critical F-value.
Analytics Buddhu 
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