 तुवे नोवा अज़े सेद के हमें SPSS के औग़्र करना इत से उझी अग़्ोंगत लगती सेज़ायं। तुवे नोवा अग़्ोंगत लेक्शंदर करत आपको में फिल्य तुवे नोवा का मतबाए के हमारे पस दो इंटपन्नेच वेरियवाल है। तो दोनो के दोनो केटागोरिकल हैं, नोमिनल डेटा के अपर हैं, और एक हमारा दीपनन वेर्यबल नजोके कान्तिनूस हैं. इसके अंदर हम भिट्वीन ग्रुप इंटिकेट्स देफ्रन पीपल आर इन इच अप दे ग्रुप्स दिस टिक्निक अलावास to look at the individual and joint effect of two independent variables on one dependent variable. उनके में इप्ट्ट्स भी हमें बताता है, अप भी हमें उनके inter-action effects भी बताता है. 2-way NOVA allows you to simultaneously test for the effect of each of your independent variable on the dependent variable and also identifies any interaction effect which we have already talked about. To run the 2-way NOVA, we need three variables minimum. Two independent variables and one dependent variable. 2-way NOVA's data to be put into SPSS is quite tricky. There are a lot of students who are given data in four matrices. They mess up while entering the data into SPSS. So, I will definitely enter the data for you in the SPSS. This is an example we will be solving in SPSS. So, the data are from our two-factor study examining depression scores across gender and two treatment conditions. So, we will use NOVA with the alpha 0.05 for all tests to evaluate the significance of the main effects and the interaction effects. So, there are two types of treatments used for the depression treatment. Treatment A and treatment B. So, first, our factor or independent variable is treatment 1 and 2. And second, our variable is gender. There are two levels of that, which are male and female. So, it is 2-way NOVA which will be our 2-way NOVA. And we will put it in SPSS right now. So, for ease, I have made this tutorial in step-by-step which we will do in SPSS. So, you can go back and check this PowerPoint presentation where I have added every detail that how we will do it in SPSS. And you can, you know, it is for your record and for your preparation. Now, let's start with the SPSS. And we will be entering this data and running a 2-way analysis and making sense of the output. So, we have this SPSS file when we open it. Now, we have the same matrix you remember which we have said that there are two treatments and gender variable. And on that, we are looking at the depression, how the score is decreasing or increasing. So, we had two-by-two matrix but when we will put data in SPSS, we will be using three columns. So, one for the dependent variable and two for the independent variable. So, in that matrix, the top left corner of the first one was treatment 1 and male. So, we will first put data for treatment 1. 3, 8, 9, 4. These are the scores of the male along with treatment 1. And we have to put the male scores along with treatment 2 because this is a treatment variable. We will do this in 1, 2, later. So, for treatment 2, the male scores are 2, 8, 7 and 7. Now, we have the female scores for treatment 1, 0, 0, 2 and 6. And for treatment 2, the female scores are 12, 6, 9 and 13. So, now you have given them the name of the variable. I had told you that the first 4 are our treatment 1. So, we will call them 1, 1, 1, 1. This is our treatment 1. And if these 4 scores were called treatment 2, 2, 2, 2. So, this is treatment 1 and treatment 2. And the next 4 scores are for treatment 1 and females, which are our bottom left matrix. And on the right matrix, for females, treatment 2 is the scores for the females. So, this is our treatment column. Treatment 2 and treatment 1 for females. And this is treatment 2 and treatment 1 for males. So, we have to add the gender variable. So, the gender is our first 8 people, we have added the male data. And in the next, we have added the female data of the 8. So, we have already told you that we do 1, 2, 1, 2 for the categorical variable. So, these are our names. This is our dependent variable, which is the depression score. So, we will give it a name. And the second is our variable treatment, in which we have added it, we will give it the name of the treatment. And the third is our gender variable, we will give it the name of the gender. Now, you can see that our variable names have come. Depression, treatment and gender. Now, the data we had was the matrix of 2 by 2. When we have added it, we have identified our variables that we have a treatment variable, in which we have used 1, 2, 1, 2, 2 treatments. And this is our gender variable. And this is our dependent variable, the score on the depression, the running continuous score, which we have added. Now, after going into the variable view, we have to tell the computer what is 1 and what is 2. So, we will go into the values column. And in this, we will tell the treatment 1 or treatment A and 2 means treatment B. That is, we have given it treatment from the second method. In this, you can also suppose that these are the two therapies that we have used. And similarly, in the gender, we will go and tell the computer that 1 means that it is male and 2 means that it is female. We will add it and okay it. So, we have defined these variables and defined these values as well. The rest of the default, you know, is the same as we have. We have one more thing that we have to tell in the measures. I have told that our independent variable, our level of measurement is nominal. Whereas, this is our continuous dependent variable. So, we will also tell them. Now, we will go into the step-by-step menu and in the step-by-step menu, I have given you the power point, but listen very carefully that we have to run two-way analysis of variance. We will go to analyze. We will go to general linear model and we will go to univariate. When we go to univariate, then we will tell it that dependent variable is our depression and fixed factors are our independent variables. These are the two. Model contrast, plots are necessary. As I have told you, plots will tell us about interaction. Usually, we do this that we do more than level 2 on horizontal and separate lines on the two variables. But in this case, there are two levels of these two variables. Treatment is also two. Gender is also two. Male and female doesn't matter. We can do this on anyone. We will add. One variable here. And then you will put add button and then continue. Post-hoc, we do that when our groups are more than two. But since we have two groups, we don't need to go to post-hoc. Because post-hoc, if there are more than two groups, like first current treatment, we have used five. Treatment 1, 2, 3, 4, 5. And we want to know which two are significantly different or better than the other. But here, we don't need it. We don't need to save. Usually, when we save the variable regression weights, we do it. Options are descriptive, estimate of effect size is important and your homogeneity is very important. You can definitely check these three things. The rest are of a slightly higher level. We will skip it till now. We don't do it. So, check these three things. Second, when we want to display the means to display, usually, when we do more than two groups, because it has the main effect and then it does post-hoc analysis too. So, I think if we do it, it doesn't matter but usually, when we do more than two groups, we do it. We continue it. I think there is nothing left. We will hit the OK button and this will give the output of the two-way analysis of variance.