 So now we will be doing a two way repeated mirror in SPSS. We need to create six variables as we talked about in the previous example, where we have two variables, a drink type of drink and type of imagery. We will be adding data like this as the previous practice. I have entered each slide and each image when I will be doing it in SPSS for you. So you can go back later and consult your notes and see how we entered the variable. So we saw an example where we had six columns of three into two. This is exactly the same example. You can also add data in SPSS. We have already practiced it in one way and two way and one way repeated mirror. We will add the same data and then we will run the analysis. Now let's go to the SPSS file and see how we have added data. I have already added data for you. Our first column is ID, that is participants number 1, 2, 3, you can also add names. We have the first variable is coffee and the second is alcohol. So three levels of coffee, coffee positive, coffee negative, coffee neutral and then we have three levels of alcohol, alcohol positive, alcohol negative and alcohol neutral that we have added data. Now what we will do is we will run the two way repeated mirror and we will go to analyze. And then guess what? Just like we did the first repeated mirror, I will go to general linear model and we will go to repeated mirrors. When we come to this, we will have a catalog that says this kind of box and we will tell it that factor 1, here you can see the name of factor 1 and we can give that type of drink. And the name is also right. Type of drink and how many levels are there now? There are two levels and you will add it. I have given the space in the variable name, SPSS space does not take. And the second level is imagery and we have three levels of imagery. We will add it and we will call it define. So we have one variable drink, two levels, other variable imagery, three levels. We will go define and define that already three into two, six, we should have data in all the six categories. And we have first coffee, positive, coffee, negative and coffee neutral. Now the other three levels of the variable are alcohol positive, alcohol negative and alcohol neutral. After that, the model is full factorial. Let it be by default. For plots, I always encourage because we know a lot about the interaction effect. So usually we do it on horizontal axis where we give it more levels, separate lines where levels come, we give it. So there are three levels of imagery, it will go to horizontal axis. There are two levels of drink, we will define it from separate lines. Add, do it and then continue. There is no need in post hoc because we have two levels of it and it will already come to us in compare means. You can go here and say that it will compare your means. And which LST or Bonferroni, both can be used, Bonferroni is more stringent. I prefer Bonferroni for finding the post hoc. Post hoc means that there is more difference in which two groups. We know that we have to check the descriptive as we have done before. We have to give the effect size definitely because we have talked a lot about it. It is necessary to report size and it is necessary to test our homogeneity. These three things must be left to me because it is a little advanced. For now, at the basic level, this is more than enough for you. I think we are pretty much set. We will hit the OK button and then we will see how to interpret the output of the SPSS.