Thanks so much, I have a lab report due and needed to check and found this. It has helped me very much. I am sure this info is still relevant for today.
Mam, you really explain in a very simple and effective manner. Just have 1 question that how to decide which is controlling and normal variable in partial correlation?
Hello! Thank you for this tutorial, it was sooooo helpful! However, I am not sure how you calulated the coefficient of determination (minute 13.20) as you said "square the r value and multiply it by 100. So, your r is 0.581 and if I square it I get 0.7622 and multiplying it by 100 would give me 76.22%. Can you please explain to me what I am doing wrong? By the way, my r is so similar to yours so it is crucial for me to get this right! Thank you so much in advance.
Marco V Calculating the Coefficient of Determination@2009 Step 1: Find the correlation coefficient, r. In the tutorial example, r = 0.581.Step 2: Square the correlation coefficient. 0.5812 = .581 x.581 = .337Step 3:Convert the correlation coefficient to a percentage. .337 x 100 = 33.7%
Hi. Do you have a tutorial video on how to conduct correlation of two variables both having numerous items? the independent variable has 50 items while the dependent has 29 items.
Big thnx to it really helpful video..... but i got problem with me data after did correlate i will find no factors support what can i do to solve this problem plz??
Thank you so much for the post. it was much more helpful for me. i have one question that, can we conduct correlation between nominal scale and continuous scale?
+Santosh Koirala For a bivariate correlation analysis, the following are the variables needed: two variables, ratio or interval or one variable (i.e. test score), ratio or interval, and one variable, ordinal or nominal (e.g. gender). So, yes.
+Marshmallows77 The researcher who chooses to conduct correlational research is simply examining whether a relationship between or among variables exists. The researcher cannot make statements about any cause and effect relationships because he or she does not know the direction of the cause and cannot guarantee that another variable is not influencing the relationship between variables. This is why statisticians often emphasize, “Correlation does not equal causation!” If you want to examine the difference between male and females, you may consider a t-test. For a bivariate correlation analysis, the following are the variables needed: two variables, ratio or interval or one variable (i.e. test score), ratio or interval, and one variable, ordinal or nominal (e.g. gender).
Thanx SO much for postings. Prior to watching this I was clueless despite attending lectures. Feeling confident about my exam now.
+La luna Glad to hear this was able to provide you with the confidence you needed for your exam.
Is anyone else here because their university's resources are so dire that the only usable education is here?
Ok
I am so sorry to hear this. I started this a resource for my students. More can be found at: thedoctoraljourney.com/resourcesandmore/ as needed.
Thanks so much, I have a lab report due and needed to check and found this. It has helped me very much. I am sure this info is still relevant for today.
Glad it was helpful!
Thanks! You have explained a complex topic very lucidly!
I will be contacting you Dr. R-S!
Mam, you really explain in a very simple and effective manner. Just have 1 question that how to decide which is controlling and normal variable in partial correlation?
good morning sir
Hello! Thank you for this tutorial, it was sooooo helpful! However, I am not sure how you calulated the coefficient of determination (minute 13.20) as you said "square the r value and multiply it by 100. So, your r is 0.581 and if I square it I get 0.7622 and multiplying it by 100 would give me 76.22%. Can you please explain to me what I am doing wrong? By the way, my r is so similar to yours so it is crucial for me to get this right! Thank you so much in advance.
Marco V Calculating the Coefficient of Determination@2009
Step 1: Find the correlation coefficient, r. In the tutorial example, r = 0.581.Step 2: Square the correlation coefficient.
0.5812 = .581 x.581 = .337Step 3:Convert the correlation coefficient to a percentage.
.337 x 100 = 33.7%
GREAT explanation!!! Thank you very much!
So clear. Thank you very much.
Outstanding! Thank you, thank you!
Hi. Do you have a tutorial video on how to conduct correlation of two variables both having numerous items? the independent variable has 50 items while the dependent has 29 items.
In my analysis i dont get the bottom text that says the level of significant correlation....?
Where did you get r(425) = -.58 from? Racking my brain trying to figure it out
Please use the cursor to direct your teaching.
Big thnx to it really helpful video..... but i got problem with me data after did correlate i will find no factors support what can i do to solve this problem plz??
+Thamer Alamery Thamer, it is hard to respond to specific analyses without seeing the data.
Thank you so much for the post. it was much more helpful for me. i have one question that, can we conduct correlation between nominal scale and continuous scale?
+Santosh Koirala
For a bivariate correlation analysis, the following are the variables needed: two variables, ratio or interval or one variable (i.e. test score), ratio or interval, and one variable, ordinal or nominal (e.g. gender). So, yes.
nice very informative thanks!
Is there a way to do correlation with a nominal variable, say gender, to look at the differences between the correlations?
+Marshmallows77
The
researcher who chooses to conduct correlational research is simply examining
whether a relationship between or among variables exists. The researcher cannot
make statements about any cause and effect relationships because he or she does
not know the direction of the cause and cannot guarantee that another variable
is not influencing the relationship between variables. This is why
statisticians often emphasize, “Correlation does not equal causation!” If you
want to examine the difference between male and females, you may consider a
t-test. For a bivariate correlation analysis, the following are the variables
needed: two
variables, ratio or interval or one variable (i.e. test score), ratio or interval, and one
variable, ordinal or nominal (e.g. gender).
Sorry, so silly, just ignore my comment. It was obviously right! Thanks again! :)
thanks so much~
is the df not 424? N-k-1= df?
--> 436-1-1=424
Robot