 We are doing independent sample t-test and previously we have learned how to do calculate t-value manually and test the hypothesis through t-test, putting the values in the formula and then interpreting the values. Now moving forward we will do the same in SPSS. It takes few seconds but yes there are few certain things we need to know and learn especially how to enter the data in SPSS. So let's start and do independent sample t-test in SPSS. So first of all we will enter the data in SPSS when we open SPSS it opens a blank data sheet like this. So you remember the previous example that was like soccer player and swimmers and we wanted to see kya unki neurological test ke upa unki responses aite test scores thay and we want to compare ke kya unki performance on that test is different. So let's enter the data. So pehla aise pe data dalne ke liye you remember ke amare pas doh groups dekin un doh no groups ka data hain ek hi column mai dalne ke. So pehle amare pas swimmers the aur unka amare pas jo score tha wo tha 10, 8, 7, 8, 13, 7, 6 and 12. So these were swimmers. So hain unki aage group 1 karlinge. So swimmers are identified as 1 aur hain hain pas dosa group tha soccer players ka. So again soccer players ka b data aise mai aega because this variable will represent scores on that neurological test. So dosa group ka hain hain pas jo scores tha wo tha 7, 4, 9, 3 and 7. So amare pas 2, 3, 4, 5, 6, 7, 8 lokh jo tha wo swimmer group me tha aur 5 lokh amare pas soccer group me tha. So hain ne soccer groups ko hain ne coding karke 2 de di value. So you remember ke hain ne shuru mai baat ki thi ke SPSS will understand numbers only. It will not take any word, any letter, pal case mein aapne level of measurement batana hain ke numbers ka kya hain. So if this variables are group and one is a representing swimmers and two is representing soccer players ka matlab hain ki yeh humara categorical variable hain aur yeh humara nominal scale ke opa data. So you will go to the variable view aur yaha pe hain hain SPSS ko batayenge ke kis variable ka level of measurement kya hain. So pehle variable me hain hain hain hain hain sko naam deinge, hain hain hain scores aat ki hain to scores hain hain hain. So yeh numeric variable hain, leave it like this. So yeh by default width uti yeh. Width ka matlab hain ke aap kitne letters type kr sakta hain, you can increase it aur you can decrease it as py or default mein art hain to usko art heeran ne dein. Decimals by default 2 values tak leta hain. Sometimes yaha pe hum variable ka naam bohot lamba nahin add kr sakte. Dursra yeh ke isme yeh spaces nahin leta. Agar aap scores on karenge to wo nahin kareka. Lekin label ke andar aap jitna lamba chahin uska title de sakte hain. Dursra aap yeh kash sakta hain scores on neurological tests. Values nisha jaa ke hum deta hain sko aur yeh humara level of measurement hain usko humne computer ko batana hain ki yeh humara numeric variable hain, yeh running score it's a continuous variable. So we will tell SPSS ke consider them as a scale, which mean this interval scale, which mean this numerical data, which mean it's a continuous score. Dursra variable humara jo hain wo groups hain, right? So groups ka matlab hain ki humara pas 2 groups te aur hume se ek soccer tha humne usko numbers yeh dee hain pe bhi aap jitna lamba chahin naam add kr sakte hain, lekin yeh values ke column ke andar yeh have to go and tell SPSS ke 1 ka matlab hain swimmers, add karein aur 2 ka matlab hain soccer players, add karenge. Ok karenge now computer knows ke 1 o 2 ka matlab kya hain. Yehni 1 o 2 numerical data nahin hain jis me 2 is greater than 1, rada hum jaha jake batayin ke uska level of measurement jo hain, wo nominal hain. Yehni numbers are just to identify the categories, right? So this is the first step, putting data and then labelling it properly, telling SPSS which variable is what. Iske andar yeh hume swimmers aur soccer players ka deh rahe, lekin agar aap isko karenge yeh hume numbers dekhahin to aap isme view me jaa ke aap iske value labels ko aan chek karenge to wo 1 2 1 2 aajayega, dekin if you want to see ke wo kya hain actually to wo exactly aapko uske labels baidega. So now running independent sample t test, you will go to analyze menu, then you will go to compare means and then you will hit independent sample t test. Jha aap aap independent sample t test karenge to aap test variable ke andar aapna dependent variable bejainge aur dependent variable hameesa t test ke liye humaara continuous running score interval scaled variable hoega jo ke humaare scores hain. Dursah humaara hai grouping variable jo ke humeesa humaara independent variable hota hain. Independent variable ke humaara jaana chaat hain ke soccer aur swimmers ke andar kya fark hain performance ke andar test ke upa. So hum grouping variable, bohi jo humaar groups ke naam diye hain, we will send it here. Dekin remember ke again you have to define the groups here. Isme aapne batana hai ki group one hain aur phir wo swimmers hain, usko already computer ko pata hain aur phir humaar usko batayenge group two. Idhar ek cut point bhi diya hoa hai, sometimes aap ka data running score me hota hain, dekin aap usme t test run karna wasar mainin income livi hai, sare logon se puchhi vi hai, ki unki income kitni hai. So kisi ne mujhe bataye viye 60,000, kisi ne 35,000, kisi ne 70,000. So agar me yaha pe cut point dedoon ki jit 50,000 se, niche wala ko one group karde aur 50,000 se upar wala ko two karde aur compare kare ki kya income group ka koi effect hain, mere dependent variable ke upar, to eat it automatically can code any continuous variable into the categorical variable to fit the data for independent sample T test. Isko baat me hum mazid bhi baat karenge, but still abhi humaar apas vari bus clear hain, hum ne enter kardi. Yaha pe options me agar aap jayenge, to ye 95% confidence interval calculate karta hai, isko by default aise hi renne de. Exclude cases analysis by analysis, by default option checked dhoti hai, which is right, kyunki agar aap exclude cases list wise karenge, to jitni bhi missing values hoti hain, wo puri ki puri masan agar first karenge, humaara ek subject hain yaha pe. Aur wo missing data, wo puri ki puri roh joh hain wo skip kar de. Asya humaara apas data size bohot kam raya jata hai, agar aap exclude cases list wise karenge. So exclude cases analysis by analysis karke, it's default isko aise hi renne de. Bootstrap karne ke zroth nahin hain, that's I think enough, we will hit the OK button and here is the SPSS, Output. So output ke andar ye me khasa analysis pehle ki hu ba main, let me delete them. So T test ke andar wo aapko ye doh tables dehta basically, in doh tables me pehle table group statistics ka hai, jisme wo scores on numerical test ke upar aapka deh rahe ke swimmers ka kithna score hai, average 8.87 hai, with the standard deviation 2.53, and with the standard error of mean is 0.89518, right. So hume hi aapata hai ki humne standard error sigma over n under root karke calculate ki thi, aur yaa peer humne pooled variance karke independent sample ke li ki thi, lekin we don't need to calculate it here, it automatically with just one click, give you the standard error, give you the standard deviation, give you the mean and the end for both the groups. And here it has given the T test value jo humne manually calculate ki thi, jo aapko ye aada ke mean 1 minus mean 2 divided by standard error of mean difference humne kiya tha. So yaa pe usne T ki value, acha ek humare pass jo hai, abhi hum baat karenge thodi der mein ki T test ke liye assumptions kya hai, aur un assumptions ko kaise hum chek karte hain SPSS ke andar. So T test independent sample ke liye one most important assumption, homogeneity of variance hai, jo mehle pehle baat ki thi, ki agar hum doh groups ko compare kar rahe hai, usme already bohar sari variability nahi ho nahi chahi, yani wo doh groups already, unka andar variance drive bohar different nahi ho nahi ho nahi chahi hai. So hum equal variances yung karne ke liye Levin's test idar run karte hain. So Levin's test hume baatata hai ki kya, homogeneity of variance ki assumption meet ho rahi hai ki nahi ho rahi. So yaa pe ye test value hai, aur yaa pe uska significant level hai. Always remember ki jab bhi koi cheez humare significant nahi hai, toh iska matlab hai ki humara nal hypothesis accept ho jata hai. So humara yaha pe nal hypothesis yeh ho gha ke variance one is equal to variance two, right? Toh ye humare value non-significant ho nahi chahiye. Agar humare value non-significant hai toh iska matlab hai ki hum nal hypothesis ko reject nahi kar sakta. Yani results non-significant hai, aur nal hypothesis hume sha ye state katta hai ke variance one is equal to variance two, and that's actually what we want ke bohth groups ke andar variability aur variance joh hai, that is equal. Toh humare assumption meet ho rahi ye homogeneity of variance ki through Leven's test because the test value is non-significant indicating that bohth groups are equal variance. Sometimes agar humare pass yeh value significant aajatiya toh pheer hum Tee ki value neechhe wali report karte hain jaan pe likhawa hai ke equal variance not assumed. Pheer hum yeh wali Tee ki value report karte hain. So far aap dekhlein ke humare Leven's test ki value non-significant hai. So we meet the assumption of homogeneity of variance Tee ki value 2.015 hai, degrees of freedom 11 hai, yani 8 plus 5 minus 2, which is 11, aur ye humare 0.069 hai. Agar ye value significant value 0.05 se less hoti toh humara results significant hai, aur humara joh hai nal hypothesis reject ho jaanata. Lekin iss surat mein because it is slightly greater than 0.05. So we fail to reject the nal hypothesis because the value is not smaller than 0.05. Diska humata bhai ki significant differences nahi hai skanda hai. So abhi hum aage batata hai aapko ki in values ko aapne apne thesis kanda yeh paper kanda report kya se karna hai. Lekin this is how we run independent sample Tee test in SPSS.