Thanks a lot for the tutorial. I would like to ask a question, i have 4 control variables (ordinal and nominal categorical variables), and 4 independent variables ((ordinal and nominal categorical variables). Can i put all of them at the same time as you explained on the video? Like in the first block put all the control vaariables, and in the second put all the independent variables. Can you please make also a video to teach us how to interpret and report the results about the effect of the controls variables according to the statistical results. Thanks in advance
In a previous video, you stated to report adjusted R square because its smaller and it's wise to be conservative when reporting statistics. However, here you used the normal R square. Is there a reason for this? Did you use it to make data look better, or coz of analysis reasons or coz you had a change of opinion. Either way, great video, very helpful. Thanks for sharing.
Adjusted R2 change is rarely done and hard to compute as the correction for variables present is difficult to do in the context of additional variance explained
So just to check my knowledge, I am currently considering the impact of REBT on coaches levels of motivation and well-being, but in gender I have three categories (Male, Female & Transgender) am I right in saying that I couldn't include gender in my regression as there is more than 2 categories? Also I found this video excellent, made easy to understand and from a personal note I found the examples of the tables very useful. Garth
You'd have to create dummy variables, here's a useful guide statistics.laerd.com/spss-tutorials/creating-dummy-variables-in-spss-statistics.php. How many are in each of the three conditions, if there are very few in the transgender condition then you would not be able to produce reliable estimates from it. I'm glad that you found it helpful
This is the best explanation of regression output. Thank you
Thank you for this very comprehensive explanation.
Thank you for this very clear presentation.
This was incredibly helpful. Many thanks!
very informative
Thank you
Thanks a lot for the tutorial. I would like to ask a question, i have 4 control variables (ordinal and nominal categorical variables), and 4 independent variables ((ordinal and nominal categorical variables). Can i put all of them at the same time as you explained on the video? Like in the first block put all the control vaariables, and in the second put all the independent variables. Can you please make also a video to teach us how to interpret and report the results about the effect of the controls variables according to the statistical results. Thanks in advance
In a previous video, you stated to report adjusted R square because its smaller and it's wise to be conservative when reporting statistics. However, here you used the normal R square. Is there a reason for this? Did you use it to make data look better, or coz of analysis reasons or coz you had a change of opinion. Either way, great video, very helpful. Thanks for sharing.
Adjusted R2 change is rarely done and hard to compute as the correction for variables present is difficult to do in the context of additional variance explained
so do i need to generate once again with male:0 female:1?
So just to check my knowledge, I am currently considering the impact of REBT on coaches levels of motivation and well-being, but in gender I have three categories (Male, Female & Transgender) am I right in saying that I couldn't include gender in my regression as there is more than 2 categories?
Also I found this video excellent, made easy to understand and from a personal note I found the examples of the tables very useful.
Garth
You'd have to create dummy variables, here's a useful guide statistics.laerd.com/spss-tutorials/creating-dummy-variables-in-spss-statistics.php.
How many are in each of the three conditions, if there are very few in the transgender condition then you would not be able to produce reliable estimates from it.
I'm glad that you found it helpful
Hi, for what reason would you use Adjusted R Square over R square during a write up of a reggression?
It's more conservative, ie always lower than the R2 statistic so you don't overstate your model performance. Either are acceptable though!
thanks!
thanks