Testing the Assumptions for Correlation in SPSS

[@zurghamriaz2246]

Passed my MBA with distinction late last year. This guy played a huge role in my success!

[@blakem4331]

Doctor, I can't stress this enough, you've saved my academic hide. Thank you very, very much! Most of my classes give so much waffle in explaining what to do, but you made it simple and to the point while also explaining why you do it.

[@butterdee59]

Thank you so much Dr. Grande! This video is on point for me. I have watched several videos on correlation and testing assumptions and by far, none compare to opening my understanding. Thank you again and God bless you!

[@DrGrande]

Thank you so much for your kind words -

[@sannyuharris2827]

Thank you for the step-by-step instructions on how to test the assumptions.

[@keithwilkerson9074]

Excellent demonstration of testing for assumptions for correlations.

[@rachaelmccormick4093]

This was great! Our book did not go into this level of detail on testing assumptions. I would have missed some of these points if I had only read the assignment. Thank you for the clarifications.

[@bradleyfairchild1208]

Very informative and helpful. Dr. Grande explained well how to test for all the assumptions, thanks.

[@vanessagraham6473]

This was helpful in walking through the steps to take in SPSS.

[@celumusandlangamandla1704]

Thank you Dr!!! That was very helpful

[@jasminepandit9861]

Excellent video, thank you so much!

[@SPORTSCIENCEps]

Thank you for the explanation!

[@timstauffer1351]

Thank you for this very informative and helpful lesson.

[@bradleyfairchild1208]

Dr. Grande, this was good, I'm wondering why you choose Shapiro-Wilks over other tests for normality? Thanks for the video.

[@yashikaneaves3818]

This makes it clear cut on how to identify the assumptions in correlation.

[@arjunrijal]

Thank you! explained really well!

[@teresaedwards8026]

Very easy to follow! Thank you!

[@linamateeva2826]

Brilliant, thank you for posting this video!!!!You just saved my grade :)

[@williamhaffelder7032]

Does it mean that the kurtosis and skewness falling outside the limits mean that the 'satisfaction' data is not normally distributed? If so, isn't the assumption then violated and you will need to transform DV and/or IV and coax the residuals to normality?

[@corieschoeneberg9295]

This was extremely helpful.

[@yvonne2904]

Dr. Grande, what does it mean when you test for assumptions, and the Shapiro Wilks test is significant at .00? Plus the data has outliers?
Thanks

[@NidhiSinha4U]

It means your data is not meeting the assumption of normality

[@caglayanozdemir348]

Thank you! This is great.

[@couragee1]

thank you!

[@fizzazaidi117]

So when do I bootstrap my correlation?

[@andeggbreaks]

Thank you!!

[@90210spa]

So basically if all the points together for the linearity assumption run in a straight line then the assumption is met. How about if some of the points run in a straight line but some of the other points run differently, that would indicate a curve which would mean no linear relationship so the assumption was not met?
Also when testing for the multicollinearity assumption, if there is multicollinearity then the assumption was met but if there is none then the assumption was not met or is it the other way around?

[@elenaw713]

What do you do then if your assumptions have been violated?

[@terihourihan6896]

This is great! Thank you.

[@echolou923]

just a quick question in this case the correlation is 0.88. these is note in my spss saying correlation is signinificant at the 0.01 level. So what does 0.88 indicate?? thanks

[@joellachica8715]

sir what if, one variable is normally distributed, and the other variable is not normally distributed..can i still use pearsons r?

[@anurajms]

great videos

[@kimeeshareedwalker3263]

The data input to obtain the scatter plot seemed complicated to verify homoscedasticity

[@saraspong6698]

Thank you. This was helpful!

[@Ritu-ey6nv]

Hello, thank you for your video.
I am trying to find the relationship between Happiness (Ordinal variable with more than 5 categories) and Income (continuous variable). In this case, I am having trouble deciding if I should run Pearson correlation (by treating the happiness variable as continuous since it has more than 5 categories) or whether run a spearman's correlation?
Also, while I tried to check the assumption for Pearson correlation (treating happiness as continuous), I found that Income is not normally distributed. And when I generated a scatterplot for these two variables, the result is not clear and somehow visually indicates no linear relationship. What should I Do???

[@pearlr.2411]

for ordinal variables by default we use spearmans rho, probably late

[@briannagray8151]

Thank you, this helped me so much!

[@DrGrande]

You are quite welcome!

[@AnnessiaAMLewis]

Hi this was so very helpful. but i'd like to test the assumptions for a spearman's correlation. Can you help me?

[@DrGrande]

+Anne Go Thank you for the idea - here you go: th-cam.com/video/VM8iLkCiHXY/w-d-xo.html

[@AnnessiaAMLewis]

Todd Grande thank you so much, you dont know how much this means to me. I'm actually doing my thesis due on Sunday night. You just saved me.

[@lakshmishankar5873]

CAN YOU PLEASE ELABORATE CANONICAL CORRELATION TEST FOR SURVEY QUESTIONAIRE'S RESPONSES?
THANKS IN ADVANCE.

[@jcartz739]

can i ask what software spreadsheet are you using?

[@pratimawenju7263]

while testing for normality if one variable is normally distributed and another is not, at this point can we use Pearson's correlation ?

[@nargisali7298]

Did you find the solution?

[@Gustavo-uc6ci]

Try to use data transformation (square root, log10, Box-Cox) and then test for normality again.

[@Gustavo-uc6ci]

If it isn't normality even after data transformation, you should use a nonparametric correlation test, like Kendall or Spearman correlation.

[@serawiteshetu3294]

thx great help!

[@DrGrande]

You're welcome!