 लगा echoes वेल उट़र बदिलिक थपाई लिए ग़ी चो मिचा मए औत उस बी लानठादा तारी वेल ने लगा देटा के लगा गुया ताओ लेकिुय। नाददादा को अच्सेरी यश्प्यसच के लगा तोगग़ोगगा क्या रम।। स्पेसेस को आप जो भी केंगे वो कल्विल्ट कर देगा लिकन आप आपने डीता सही डलाए और खम्पुटर को भताहने है किई फ्रिक्वन्सेस हैं और ये श्खोरस न नहीं हैं तो लेग्स खो तु आप स्पेसेस आप देन भी विल एंटेद डीता 1 is for introvert, 2 is for extrovert. And in colour we have 4 preferences, 1, 2, 3, 4. So red, yellow, 3 is green and 4 is blue for introvert and again 1, 2, 3, 4 extrovert. In this variable view we give value level here and we tell the computer that 1 means introvert and extrovert and here we also tell that 1 means red, 2 means yellow, 3 means green, 4 means blue. And here we go to the mayors and nominalise the scale so that it knows that this is our categorical data. So we are all set with entering the data. Now again as we have done in goodness of fit that we have to tell SPSS that these are like 10, 3, 15, 22 are the frequencies. They are already summed up number of people and not the scores. So in this course we will go to the data. We will go to the cases and we will tell these frequencies that await cases by frequencies. That is we have put anything in the frequency column that is already weighted frequencies. That is summed up frequencies. We will click OK button. Now to calculate chi-square test of independence here we have 2 variables in the data matrix. Rows and columns. You will go in the descriptive and then cross-tab. Now you will not go there like you went in the non-parametric. Now in cross-tab we know that the personality variable was in our rows and column was in our color. So we have entered these 2 variables. Now we will tell it. Let it be the default option for extract. In statistics 5 or Kramer's V value is better. For example, it tells us correlation. But since chi-square does not calculate the effect size like in the parametric we calculate the effect size of strength and magnitude relationship. With the help of fire and Kramer we can have a roughly idea of strength and magnitude. So let the remaining things remain. We will calculate chi-square and cells. I think this is fine. Observed or expected. But it will calculate you by default. So just leave all these options as they are and we will hit OK button. Now you can see that we have K summary. And we do not have any missing value. And we have valid and 200. And this is our table which we showed you in the presentation. We solved manually that 50 people are introvert. 150 are extrovert. And they have given frequencies in the column for everyone. And they have given the total. And now we calculate the value of chi-square. So this is exactly 35.6. This is the Pearson chi-square value which you will be reporting. That's the main value. And we have degrees of freedom which are c-1 and r-1. And our significant value is p i.e. 0.00 because 35.6 is highly significant value which means that null hypothesis is rejected. And this is what we have. Zero cells have expected. This tells us the assumption that our chi-square has one assumption that in any cell or family expected value or expected frequency is less than 5. So you can see that in the observed we have 3 values here. But we have to check that expected count is less than 5 in any cell. And we have to calculate that. And this we have given the value of phi and kremor. 0.42 0.42 is if we have to take out the effect size if we have to take out the effect size then we can mainly divide the value of phi on n and we can do rough estimation. Now I will calculate and tell you that I have given the whole tutorial in your slides. Whatever we have done I have mentioned it one by one in your power point. So this is we have added the first step variables of personality and color. After that we have added the value in the label and after that when we have added the frequencies we have set all the variables as we have done in the previous slide. After that we have done the weight cases and we have told the SPSS that the values are already summed up and these are the frequencies. This is how we awaited them and then we run the chi-square where we went in the descriptive and cross-tab. So go to analyze descriptive and send the one variable to rows and one to column and then we just checked phi or cramers and then chi-square value and we hit ok button and this is the output target which is the value of chi-square calculated 35.0 significant LFO 0.05 and we checked that we did not void any assumption because no expected frequency in any cell is less than 5. So the main value which we are interested in is in the PSN chi-square mainly 35.6 that is the main value in which we are interested and that is how we report and this is how we report as per APA results from the chi-square so the chi-square value is the symbol of chi-square 3 degrees of freedom 35.6 with an associated significance level of P is less than 0.01 this indicates that our result is significant and there appears to be an association between personality type and color preference this is basically what I have told you that we do not have an effect size but as you can see correlation coefficient can range from 0 to 1 phi or cramers it ranges from 0 to 1 higher value means stronger association between the two variables and for the tables larger than 2 by 2 the value of report is cramers value or if we have table is 4 into 2 so in this example the cramers value is shown in the table smittica mayors point 4 to indicating a larger effect size so if we have table of 2 into 2 we will report the value of phi coefficient but if it is larger than 2 then we will give the value of cramers value and we have 0.422 which is a larger effect size as you can see 0.422 is a greater correlation so chi-square test for greater significant association between personality type and color preference and similarly chi-square value after that degrees of freedom and is 200 reported value p is less than 0.001 and cramers v is 0.422 so this is how you will be reporting chi-square value as per APA format so you see it's simple and easy but remember that you are a scientist when you have to choose parametric and when you have to choose non-parametric you should know how to tell computer what these numbers are is this nominal data is this ordinal data is this a frequency data and then SPSS will work for you and do all the calculations for you within no time and calculate it