Multiple Linear Regression in SPSS - Complete Tutorial

DESCRIPTION

Multiple linear regression is perhaps the most popular analysis in a wide variety of fields.

In this video I’ll slowly walk you through each step for running a correct and complete analysis in SPSS.

Some main points I’ll cover are

-basic data screening with histograms
-more data screening with scatterplots for which I’ll show a nice tool
-a quick inspection of Pearson correlations
-interpreting the actual regression output and finally
-the four main regression assumptions.

I’ll also explain some basic theory -such as the b and beta coefficients- but I’ll do so with simple language and examples instead of mathematical formulas.

Hope it helps!

TIMESTAMPS:

03:34 overview main steps
04:39 data screening I - histograms
11:28 data screening II - scatterplots
15:02 data screening III - Pearson correlations
20:23 SPSS regression dialogs
24:59 regression output I - coefficients table
26:10 b-coefficients (unstandardized)
36:55 beta coefficients (standardized)
46:49 regression output II - model summary
49:07 r-square
50:15 Cohen's rules of thumb for small, medium, large r-square
51:00 adjusted r-square
54:22 APA style multiple regression table
55:50 evaluating multiple regression assumptions

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[alialsuheelasseri]

Thank you so much!!! amazing explanation.

[revisionandthewritingproce]

This is incredibly useful. Thank you so much.

[SaadKhan-ydss]

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?

[soniamanhas]

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.

[georgiost]

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 :)

[jkmwxkn]

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

[hassanalrabbaie]

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?

[kwoncey]

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. 🤔🤔🤔

[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?

[paulinbill]

Thank you for sharing, is it SPSS 29 ?