 as I previously talked about that in your PowerPoint lectures, I captured every step of the SPSS screen, kept it here so that you don't forget the idea, but we won't do it, in fact, we will actually go into the SPSS and put the data. By putting the data, you must have mastered it so far, remember that putting the data in ANOVA is very tricky, because its variables are given to us in the matrix, but we have to put the rows and columns in further variables. If you just look at how many variables we have for each one, then putting the data becomes easy, because then we put one variable in the same rows, in the SPSS, one variable is equal to one row. Now let's go to SPSS and enter data for the same example that we have discussed. This is the file of SPSS, and I have already put the data in it because it will save time. So our first variable is ID, remember that in the first column, you will put the name of the participants or any ID that you have. So we have a second variable gender, which is between group, we had 6 male data and 6 female data. So for those 6 male, we have low, medium, high scores, which is repeated mayor. Repeted mayor, remember that the data is always, the levels of your columns will be the same. If repeated mayor, like in the last example, with 2 independent variables, we have 2 into 3 repeated mayor, then we have 6 columns of data in SPSS, it is necessary. Here we have one repeated mayor, so for repeated mayor, we have 3 levels, so we have 3 columns of data, it is necessary, whereas our gender is between group, it will come in the same column. So we have put the data in it, and in it, we have put one more thing, like in the variable view, we will assign values in the gender, that 1 means male, and you have to tell your computer so that it will know what 1 and 2 means, and this is our second variable, which is between group variable, we will add it, rest by default, these things are left, but you let it stay like this, like by default. So for mixed and over, as I told you, in the repeated mayor family, we will go in the general linear model, and then we will go in the repeated mayors. In the repeated mayor, as we did the first variable for repeated mayor, we will tell it a factor, which is a caffeine consumption, low, medium, high, and we will tell it its three levels, it will add it and we will define it. Now you have to tell your low, medium, high, that what it is, as we did in the repeated mayor, so one, the first level is low, second level of the within variable is medium and third level for the within variable is high. Now our gender is between subject factor, so we will be sending this gender here in the between subject factor. After adding this, now you have to go in the model contrast or plots, plots because we have to take out the interaction effect, it mostly makes sense through graph. And for graph, because there are three levels of caffeine, so we will send it horizontally, there are two levels of gender, so we will add it on separate lines, we will continue. For post hoc, again, because when we compare the means in options, it already does post hoc analysis for us, but here you have to pick three options, homogeneity test, estimate of effect size and then descriptive statistics. After these three, we will not do the rest, we will continue and hit OK button, it will give us its mixed inova output file.