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Youth Outcome Questionnaire (YOU) Name of instrument Youth Outcome Questionnaire (YOU) Summary overview The Youth Outcome Questionnaire (YOU) is a brief 64-item parent report measure of treatment

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Hi everyone in this video I'm going to show you how to do correlation analysis in SPSS what you're going to find SPSS is that I provide you with more than one choice of correlation, so there is a question there about which one do I use and when okay, and then I'm going to move on to some examples an example when you use Pearson correlation an example where we use what's called a Spearman Kendal's tell B I'm going to wrap up things by looking at correlation matrix looking at the first data set we've got two variables here height and weight okay so the question here we might want to know is like if one of the variables go up what happens the other one does it tend to go up or down in this context if someone is taller does that mean they tend to be heavier or lighter and would expect that tall some players probably the weight will tend to go up as well and that's what we say that there's a positive correlation if it's a negative correlation it means as one thing goes up the other one tends to go the opposite way it goes down so in this case is if one of them say weight goes up the other one say well that's height goes down alternative can we can say that as height goes up weight goes down or weight goes up high it goes down same thing alright so how to do correlation you go to analyze correlate got see this is choice of three things here what we want is this clinical bivariate means two variables so correlation basic correlation here will connect correlate will get relation between two variables we click on it, I want to draw your eyes down to here where it says correlation coefficient you see this check box three things Pearson Kendall's tell Spearman by default spearmint Pearson is chosen if you've done the first course in stats Pearson's correlation coefficient is the one you're most likely to be introduced to okay, but there are another two so whichever one we use it will produce a number for our two variables which lies between minus 1 to plus 1 inclusive now if your number is closer to either extreme minus 1 or plus 1 it means stronger the relationship if it's closer to negative 1 it means the strong negative relationship Y for that means relationship means if it's closer to positive 1, and it's a stronger the positive relationship positive correlation between the two variables if it's 0 I midway between minus 1 plus 1 means there is no correlation between these two variables ok, so that's like the interpretation now what we want to do is to just determine whether you use Pearson's candles or Spearman is look at the nature of the variables here we've got height and weight they're both continuous we're going to also assume that they are normal be distributed or approximately normal we're also going to assume there are no outliers in this data set now if we consume these two things which I am doing Pearson is the right one to use okay if these conditions do not satisfy this when we look at these other two guys so what I just taught you that...

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