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Yes, I do. However, I don't cover anything Python. If you purely want to predict stuff, Python may be better. But SPSS is more useful if you want to explain (rather than predict) what's going on in your data. Use a hammer for nails, a screw driver for screws ;-)

Thanks for the compliments! If you'd like to see more videos like these, perhaps consider taking our full course at bit.ly/spss-beginners-course Happy analyzing!!

I hate to say this but outliers are truly one of the "grey areas" in data analysis. Even highly experienced analysts often disagree on what (not) to exclude. I think the best ways to deal with this are -use your common sense and -be very open and explicit on what you decided and why. Hope that (somewhat) helps! Ruben SPSS tutorials

@@SPSS-tutorials Dank je wel... by the way, this is a good tutorial video indeed. 🤪🤪🤪

@@kwoncey4581 Thanks for the compliment, happy to hear you like it! P.s. if you like this one, you may want to check out our full SPSS course at bit.ly/spss-beginners-course

The SPSS syntax introduction you mean? The video version is at th-cam.com/video/SZqMKZK8ZcE/w-d-xo.html and the written version is at www.spss-tutorials.com/spss-syntax/ Hope that helps!

Hi Hassan! There's no real "right or wrong" here but IMHO, a single analysis with all predictors is preferable over separate analyses because you want the predictors to compete with each other. But if this were my project, I'd first see if I can reduce the number of IV's with PCA ("factor analysis"). Note that you'll usually want at least some 10-15 independent observations (participants) per predictor. Now, if you dummify some of your nominal predictors, you may have more than 40 for 600 observations. Not ideal. Last but not least: perhaps consider doing this hierarchically: enter all subjective IV's in step 1 and the others in step 2 and see if the increase in r-square is statistically significant. Then enter the 2 sets of predictors in the opposite order too. Hope that helps! SPSS tutorials

Hi Vitalis! I had a very close look at this tool and -sadly- SPSS no longer supports it. However, note that recent SPSS versions include CI's for Pearson correlations under Analyze - Correlate - Bivariate (fourth button in right top corner). This requires raw data but if you don't have these, you can use the second sheet in www.spss-tutorials.com/downloads/pearson-correlation.xlsx Just replace r and n and the formula will recalculate the CI for you. Hope that helps! Ruben SPSS tutorials

Yes and no. Strictly, Likert scales are ordinal variables. This implies you can neither compute sums/means over them, nor use them as "normal" (that is, quantitative -note this is slightly different from "continuous") predictors in linear regression. Less strictly, however, many analysts treat Likert scales as if they were quantitative variables under the "assumption of equal intervals". So what you're doing is fairly common practice (especially in non academic research) but strictly not entirely correct. In short, there's no clear yes/no answer to your question. Hope it helps anyway.

Hi Georgios, thanks for the compliment! Your comment makes perfect sense: collinearity is not caused by high correlations among the DV and the IV's. So the short answer is: no, that's not always problematic. However, in the social sciences, super high correlations (say |r| > .80 / .90) are oftentimes suspicious. If we track down where they came from, it's often that 2 variables are basically the same thing such as some sum score computed in 2 different ways (with/without missing values for instance). And that obviously is problematic... So if you see super high correlations, please do a careful data check to see if these truly reflect some linear relation. Hope that helps!

Hi Sonia! This video is not about stepwise regression or any other form of hierarchical regression. For reporting such analyses, there's tons of different APA table formats (most of which are not very useful in my opinion). A useful book for these tables is www.amazon.com/Presenting-Your-Findings-Practical-Creating/dp/143380705X So there's no short or simple answer but we sometimes report -variables entered in each step -R-square change and the associated F- and p-values for (sets of) predictors. Hope that helps! SPSS tutorials

@@soniamanhas4970At your service, Sonia! P.s. do check out the book I linked to, it's super helpful.

@@JonPeck100Hi Jon! I had another look into STATS CORRELATIONS. First off, I think it's a pity that it requires a DATASET NAME. I never use (or recommend) using this. It simply gets in my way without being helpful in any way. Also, the warning may deter SPSS beginners from proceeding with STATS CORRELATIONS if they're unaware that DATASET NAME ... will solve this issue. Why don't you have Python apply and remove a (random) DATASET NAME if not applied by the user? Also, I'm not a big fan of the resulting table layout and the inability to choose which statistics I want to see. Why not have users select r / N / p / CI as they see fit? Finally, as a more general point, I think many extensions are quite inconsistent regarding their naming/titles. I only learned that STATS CORRELATIONS is the same extension as "Bivariate with..." because I stumbled upon some .pdf document with a couple of screenshots. Note that these 2 names literally don't have a single word in common.

Thanks for the compliment. We really appreciate it.

Hi Paul, at your service and thanks for the compliments! All videos thus far were recorded using SPSS version 28.