Multiple Regression  reporting results

 Posts: 5
 Joined: Thu Dec 18, 2008 7:30 pm
Multiple Regression  reporting results
Hi,
Is there anyone out there who can help clarify what exactly should be reported in the results section of a report after running a multiple regression test?
I think I need to report these values:
• adjusted r square value
• standard error
• F value
• Significance
• Beta coefficient
But whether they should be within a table or not.. and whether a graph or descriptives are required, I haven't a clue!
Could you offer me some tips on what exactly to report? I'd be extremely grateful.
Many Thanks
Is there anyone out there who can help clarify what exactly should be reported in the results section of a report after running a multiple regression test?
I think I need to report these values:
• adjusted r square value
• standard error
• F value
• Significance
• Beta coefficient
But whether they should be within a table or not.. and whether a graph or descriptives are required, I haven't a clue!
Could you offer me some tips on what exactly to report? I'd be extremely grateful.
Many Thanks
I reported the multiple regression like this in my undergrad dissertation:
In order to discover what might explain the significant overlap between the dissociation and schizotypy measures, multiple regression analysis was applied. In two separate analyses using the enter method , nonpathological dissociation (DES) and modified schizotypy (OLIFE) scores were entered as dependent variables with the everyday memory failures (EMQ) and sleeprelated experiences (ISES) scores entered as a block of predictor variables. They were entered this way due to hypothesis 5.
3.5.1. The relationship between dissociation and the independent variables
Table 5. reveals a significant model for the predictor variables with a multiple correlation of .52, [F(2,119) = 21.720, P < .001; adjusted R² = .258]. However, Table 5. also shows that the ISES scores were not a significant predictor, but EMQ scores were. This indicates that the everyday memory failures scores were important for a significantly better prediction of nonpathological dissociative tendencies.
Figure 1.
A scattergram showing the relationship between schizotypy (OLIFE) and dissociation (DES) measure scores and their significant predictor, everyday memory failures (EMQ) measure scores
In order to discover what might explain the significant overlap between the dissociation and schizotypy measures, multiple regression analysis was applied. In two separate analyses using the enter method , nonpathological dissociation (DES) and modified schizotypy (OLIFE) scores were entered as dependent variables with the everyday memory failures (EMQ) and sleeprelated experiences (ISES) scores entered as a block of predictor variables. They were entered this way due to hypothesis 5.
3.5.1. The relationship between dissociation and the independent variables
Table 5. reveals a significant model for the predictor variables with a multiple correlation of .52, [F(2,119) = 21.720, P < .001; adjusted R² = .258]. However, Table 5. also shows that the ISES scores were not a significant predictor, but EMQ scores were. This indicates that the everyday memory failures scores were important for a significantly better prediction of nonpathological dissociative tendencies.
Figure 1.
A scattergram showing the relationship between schizotypy (OLIFE) and dissociation (DES) measure scores and their significant predictor, everyday memory failures (EMQ) measure scores

 Posts: 5
 Joined: Thu Dec 18, 2008 7:30 pm
Hi thank you for your inputs, Yes I do understand what the values mean  I'm just not confident on how to display results in an appropriate format.
My model was not significant and only 1 variable was found to be a significant predictor (eek!). Is it worth displaying a graph?
Also :
 do I have to report a predictive regression equation? I'm not sure if this is considered superfluous.
 are there any descriptives I need to report e.g. mean, SD?
My model was not significant and only 1 variable was found to be a significant predictor (eek!). Is it worth displaying a graph?
Also :
 do I have to report a predictive regression equation? I'm not sure if this is considered superfluous.
 are there any descriptives I need to report e.g. mean, SD?
I would do a scatterplot displaying the correlation between that variable and the outcome variable.Frozengiblets wrote:My model was not significant and only 1 variable was found to be a significant predictor (eek!). Is it worth displaying a graph?
Most people don't, especially in psychology as any regression will only account for a small amount of variance so prediction equations aren't all that helpful.Also :
 do I have to report a predictive regression equation? I'm not sure if this is considered superfluous.
Always report descriptives for any stats. are there any descriptives I need to report e.g. mean, SD?
Ruthie
3d scattergrams/ multiple regression
Could anybody help me with my multiple regression at all? Any help appreciated.
I have selected cases and got a random sample of 100 cases (from 300). "research has indicated that intention is the strongest predictor of behaviour followed by perceived behavioural control"
DV  Behaviour
IV  Intention
IV  Perceive behavioural control
I need to run an appropriate statistical analysis to determine the predictive value of both variables on the data.
I also need to predict a score from it too, i have the equation for this i just need to run the statistical test to find out the numbers.
In my opinion (which may be wrong as this is my frist try at multiple regression) is to run a linear regression using my DV and two IV's then to do a scattergram?
Due to using both IV's I thought the 3d scattergram Bubbly used was the most appropriate? However my values appear wrong? and I can't work out how to input the plane of best fit?
If anyone could shed some light on this for me it would make me very very happy! Reading too many stats books has left me baffled and confused
I have selected cases and got a random sample of 100 cases (from 300). "research has indicated that intention is the strongest predictor of behaviour followed by perceived behavioural control"
DV  Behaviour
IV  Intention
IV  Perceive behavioural control
I need to run an appropriate statistical analysis to determine the predictive value of both variables on the data.
I also need to predict a score from it too, i have the equation for this i just need to run the statistical test to find out the numbers.
In my opinion (which may be wrong as this is my frist try at multiple regression) is to run a linear regression using my DV and two IV's then to do a scattergram?
Due to using both IV's I thought the 3d scattergram Bubbly used was the most appropriate? However my values appear wrong? and I can't work out how to input the plane of best fit?
If anyone could shed some light on this for me it would make me very very happy! Reading too many stats books has left me baffled and confused
There may not always be a solution to the problem, but there is always another perspective...

 Posts: 122
 Joined: Mon Apr 21, 2008 12:49 pm
I won't comment about the appropriate kind of chart because I really don't know (I have just been using line graphs), but you need to be a bit more specific about your question as "the predictive value of both variables" can be taken a few ways. You can...
(1) Look at Control's effect on Behaviour, and Intention's effect on Behaviour, separately. This is a simple linear regression of IV=Control DV = Behaviour, rinse and repeat for your other IV.
(2) Look at Control's effect on Behaviour controlling for Intention, and Intention's effect on Behaviour controlling for Control. For this you enter in Step 1 both IVs. The output will show you e.g. Control's effect on Behaviour once Intention has been accounted for.
(3) Look at your IVs in interaction. First create an interaction variable using SPSS of Control X Intention. In Step 1, enter your IVs separately as in (2) but in Step 2 enter your interaction variable. The output shows you the main effects of your IVs controlling for the other variables, as well as the interaction effect. Usually we are very interested in interactions.
This is it at its most simplistic (though it's definitely not simple! I've so been there, with the battle scars to show!).
(1) Look at Control's effect on Behaviour, and Intention's effect on Behaviour, separately. This is a simple linear regression of IV=Control DV = Behaviour, rinse and repeat for your other IV.
(2) Look at Control's effect on Behaviour controlling for Intention, and Intention's effect on Behaviour controlling for Control. For this you enter in Step 1 both IVs. The output will show you e.g. Control's effect on Behaviour once Intention has been accounted for.
(3) Look at your IVs in interaction. First create an interaction variable using SPSS of Control X Intention. In Step 1, enter your IVs separately as in (2) but in Step 2 enter your interaction variable. The output shows you the main effects of your IVs controlling for the other variables, as well as the interaction effect. Usually we are very interested in interactions.
This is it at its most simplistic (though it's definitely not simple! I've so been there, with the battle scars to show!).
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