Multiple Linear Regression in SPSS - Complete Tutorial

[@revisionandthewritingproce846]

This is incredibly useful. Thank you so much.

[@SPSS-tutorials]

Thanks for the compliment. We really appreciate it.

[@soniamanhas4970]

Thank you for such a detailed video. And could you please tell in the table of Step-wise multiple regression, what should be added. And what should be discussed in the interpretation of the table.

[@SPSS-tutorials]

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

[@soniamanhas4970]

@@SPSS-tutorials thank you so much sir. Your answer is helpful.

[@SPSS-tutorials]

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

[@alialsuheelasseri9486]

Thank you so much!!! amazing explanation.

[@georgiost6189]

Excellent video! I wish it existed all those years ago when I was learning about regression analysis. I have a small comment/question: At 19:50 you say that we don't want to see high corelations in the table, but you bring a corelation between the dependent and an independent variable as an example. Would you say it's actually problematic to have a strong corelation there? I can understand that multicollinearity among the independent variables can be a problem, but what about between the DV and an IV? Thanks again for the great tutorial :)

[@SPSS-tutorials]

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!

[@yingyao-d5o]

cool, I like it very much!!!! like the details in the video

[@V-2BN]

Thank you very much!

[@SaadKhan-yd7ss]

Sir, this is marvellous. However, I am looking to study regression model as a data scientist and hardly we use SPSS in office for our predictive modelling. Python has been used for almost all the time. Do you have any experience of Python to conduct such regression?

[@SPSS-tutorials]

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 ;-)

[@hassanalrabbaie8560]

thank you so much. I have a dataset collected from 600 participants, with the dependent variable continuous and the independent variables (about 40 variables) mixed (continuous and nominal ), my research question is about the factors associated with the dependent variable. i believe multiple regression is the best choice for this purpose. the independent variables were collected subjectively and objectively (standardized tools). my questions: can I run regression two times (first with subjective variables alone the objective variables ) is this right in statistics?

[@SPSS-tutorials]

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

[@chefberrypassionateresearcher]

Can I directly use individual 5 point Likert items, as independent variables in Multiple regression, as these items are formative in nature, not reflective, so I can't transform them to a single mean score. I have read that Likert items on 5 or more point scale can be treated as continuous variable...Further, my dependent variable has 4 items which are again measured on 5 point likert scale, but are reflective in nature, so i will transform them to a single mean value (to be taken as dependent variable) in multiple regression. Is that OK?

[@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.

[@kwoncey4581]

Sometimes, I am so confused about utliers. I know they are all originally from my data collection. And they are all authentic. But, when I run frequency and see them in the histogram, some extreme values always make me think whether should I delete them for the better regression output. 🤔🤔🤔

[@SPSS-tutorials]

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

[@kwoncey4581]

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

[@SPSS-tutorials]

@@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

[@paulinbill1568]

Thank you for sharing , is it SPSS 29 ?

[@SPSS-tutorials]

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