Guidelines for Interpreting Correlations

I discuss Cohen's guidelines for interpreting the magnitude of correlations, as well as newer, empirically derived guidelines for interpreting correlations.
Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and individual Differences, 102, 74-78.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.

[@Farinata2]

Are you Ok? You haven't posted since 3 years.

[@deepthy8155]

Can you please do a video on mixed effect models with interaction...ALso generalized mixed effect models using SPSS

[@jbeebe2]

Very cool

[@SergeantRooster]

Thanks for freshening up my statistics

[@getit2932]

Thank you sir.
Your videos helped me alot during COVID19

[@christinabts9327]

Hi! I have to perform correlation for my master thesis. My research is about word associations and my data are multiple response sets. I need to examine whether there is a correlation between the part of speech of stimuli items ( 38 nouns, 3 verbs and 4 adjectives) and the response category (paradigmatic, syntagmatic, phonological). What should I do?

[@scienceandtechnology9228]

if we want to make a correlation between ordinal and nominal data, what type of test i should use?

[@Cuezaltzin]

Thank you this is great

[@mdaltinisik]

Dear how2stats. I bother you for a SPSS related issue. I will be very grateful if you can help.
A = categorical (2 categories, independent) variable
B = numerical (dependent variable)
C = numeric (covariant variable)
Both B and C appeared significantly different between the two groups according to T-test. How do we analyze the effect of A on B, free from C?
1. ANCOVA (B dependent variable, C covariant). But because C is significantly different between the two groups, does it cause bias?
2. If we assign A (dummy variable) and C independent and B dependent variable and perform regression analysis but B and C show colinearity and regression assumptions cannot be provided?

3. Need to randomise the groups again in terms of C and then perform t test?
4. None :)?

[@how2stats]

I discuss the misconception that C must be unrelated to A to perform an ANCOVA in my free textbook (www.how2statsbook.com; the chapter on ANCOVA). In my opinion, you could do an ANCOVA, assuming you want to control for the effects of C on both B and A; if you only want to control for the effects of C on only B or only A, then you could do a semi-partial correlation (I discuss two types of semi-partial correlations in my free textbook).

[@mdaltinisik]

@@how2stats thank you very much :)

[@mdaltinisik]

@@how2stats the last question is if the ancova assumptions are not met what can we do?

[@heybyrd]

Woww This is very useful. Thank you so much

[@rommeliheozor-ejiofor2949]

Thanks for the video. Why does the linear regression in SPSS have a correlation matrix that calculates the pearson correlation. I understand from previous videos, that normality of the independent variables isn't important for linear regression. The problem is I am trying to do a regression and one of the independent variables is not normally distributed (I tried to transform it, but couldn't get it normal) but I still put it in a regression and the correlation matrix calculated pearsons rather than spellmans coefficient respectively. Do you have any advice or recommendations?

[@how2stats]

SPSS just reports the Pearson correlations by default. It doesn't first check normality. If you're data are insufficiently normally distributed for a "regular" linear regression analysis, you should try to conduct the analysis with bootstrapping as the estimation procedure (to estimate the standard errors and p-values). I don't know if you have the bootstrapping module in SPSS or not.

[@rommeliheozor-ejiofor2949]

@@how2stats I have just one non-normally distributed variable (I tried to transform but unsuccessfully). I have also tried bootstrapping (it is present on my system) but I'm not sure how to interpret it. Besides, the dataset has quite a few missing records. It is patients data from 2012 and for some of them certain variables were not taken. I had used the ''exclude cases pairwise" for the missing values so as not to miss out useful correlations in the matrix and I think that somehow messes up bootstrapping process and stops the output at the correlation stage.

[@svalbard01]

Do you know if any similar guidelines exist for Spearman's rho?

[@how2stats]

I haven't come across any for Spearman's r, however, my hunch is that essentially the same guidelines would apply to Spearman (if the analysis were ever undertaken like Gignac & Szodorai (2016).

[@yulinliu850]

Thank you!

[@mudassirasworld1651]

wow

[@pratapchauhan4798]

Dear Sir, Kindly Guide us on logistical regression too, when these are going to be released. waiting eagerly