Oh my gosh I needed this so much. I had to redo all my results and I'm trying to relearn how to use spss so I can run a multiple regression analysis. This was so helpful.

Best video I’ve seen explaining multiple regression analysis. I have watched so many but this is the best this far. Thank you

@mikecrowson2462

3 жыл бұрын

Glad it was helpful, Racquel! Best wishes!

Thank you, this is great. Clear, concise and yet detailed.

This is great. Your channel is my go-to for any statistics problems. Thanks for your work :)

@mikecrowson2462

5 жыл бұрын

Hi Britt, thanks for watching and for your kind feedback! Best wishes!

Excellent work with detailed explanation. Keep it up! Thank you!

Thank you sooo much for such a helpful video and your consideration to upload the slides. They are very helpful!

I love the way you teach. You make things very easy. thanks for your kindness

@mikecrowson2462

2 жыл бұрын

You are so welcome, Kingley! Cheers!

Excellent video, presentation, PowerPoint presentation, and SPSS data file. I am highly benefited. Thanks a lot.

Great work! It's really informative and clear. Thanks for sharing.

Your efforts are really commendable. Thank you

Excellent presentation and explanation.

thank you so much for this VERY helpful video.

Thank you Dr Crowson!

Woow..Thanks for a clear presentation sir..!

@mikecrowson2462

3 жыл бұрын

Hi there. Thanks for visiting! Best wishes.

In my case: the standardized coefficients beta and the square of the part correlations give me 2 quite different rankings of the dependent variables

great work and awesome contribution.

@mikecrowson2462

5 жыл бұрын

Hi Nasir, thank you so much for your feedback! Thanks for visiting!

Thank you so much. God bless you! Thank you

It's very helpful. Thank you

Hello Sir, What if the variables are measured on different point likert scale. For example 5 point and 6 point. How to run a multiple linear regression in such a case.?

Hello 👋🏻 I’ve 1question to ask. As a beginner I currently run the spss. My hypothesis is to find “effect. From my knowledge regression test can do it. But, my data not normally distributed. Can i use regression to find my hypothesis with data not normally distributed?

thank you this has been very helpfull

How do you obtain the values of the independent variable.. I mean those numbers under interest and the other independent variable

very useful indeed. thanks for sharing. GS Bawa

Thank you so much. it is very benefical.

@mikecrowson2462

3 жыл бұрын

You are very welcome, Mehmet!

How about there are more than one dependent variables? Shall I run Analyze>Regression>Linear for each dependent variables as much as number of DVs?

@romentong9274

4 жыл бұрын

Same question

Hi everyone, I have just uploaded a new video (Sept 2021) on multiple linear regression using SPSS. I hope you consider visiting it at kzread.info/dash/bejne/YoJos5qziszQk6w.html .

VERY gOOD PRESENTATION

Great lecture!

@mikecrowson2462

5 жыл бұрын

Hey, thanks Yulin! I appreciate the feedback!

@yulinliu850

5 жыл бұрын

@@mikecrowson2462 You're very welcome! Thank you for the generous sharing of knowledge!

very good

thanks a lot

Good job and best wishes... How possible to get the power point that u mentioned in the video?

@mikecrowson2462

3 жыл бұрын

Hi there. Thanks for visiting! There is a link to the PowerPoint underneath the video description. Cheers!

Thank you so much

@mikecrowson2462

2 жыл бұрын

You are very welcome!

Hey Mike nicely explained Can we use regression for scale data for dependent and independent Variables are in scale... if Yes what will be the way to do so

@mikecrowson2462

3 жыл бұрын

Hi there. When you say 'scale ' data are you referring to the measurement setting in SPSS? That setting is sort of a catch-all for interval and ratio-level variables. You would use this setting if you are assuming your variable is continuous. Least squares regression assumes that your dependent variable is continuous. And generally your IV's are continuous. [It is possible to model categorical independent variables via the use of some type of dummy or effect coding system.] So running the analysis in SPSS with your variable set to 'scale' is the same as what I demonstrated in the video. Cheers!

@swatioza26

3 жыл бұрын

@@mikecrowson2462 Thank you so much Mike

So as far as I understood you can just mark a categorical variable as a continuous one in order to include in the model. What's the point of logistic regression then?

@mikecrowson2462

2 жыл бұрын

Hello, thanks for visiting and for your question. First off, logistic regression is used when the dependent variable is binary and your goal is to predict the probability of a case falling into a target group (as opposed to a reference group) as a function of the predictors in your model. [there are actually other forms such as multinomial and ordinal, but I'm sticking to the most commonly used logistic regression model in this discussion]. OLS regression is used to predict a continuous dependent variable. With respect to your statement about 'marking a categorical variable as continuous for inclusion in the model', I would point out that the decision to do this does not convert your previously 'categorical' variable into a continuous one. SPSS allows you to change how you indicate the scaling of your variable under the Variable View tab, but whether you decide to have the indicator set to nominal, ordinal, or scale will not impact the mathematics behind the computation when running your regression. The program will give you the same OLS regression output when you perform your analysis with the variables listed in either way. I think of that option in SPSS as largely something that encourages folks to think about the scaling of their variables. Now, if you are asking about the decision by a researcher to treat an ordered categorical variable "as if" it was continuous (such as those cases where a researcher might treat a Likert type item, ranging from 1=strongly disagree to 7=strongly agree) in a model, then that is something different. Generally, the decision to do something like that is based on a variety of theoretical and pragmatic considerations, as well as the possible effects of doing this when it comes to meeting model assumptions. I'm not going to go into all the finer points here; but I will say that OLS regression technically makes no assumptions regarding the distributional characteristics of the predictors in the model. The only distributional assumptions pertain to the residuals (prediction errors). I hope this is helpful to you. Cheers! p.s., I do have a newer video on OLS regression here: kzread.info/dash/bejne/YoJos5qziszQk6w.html

thumbs up

I sure wish you were my neighbor

thanks a lot, can I have your email plz