# calculating the effect size

#### Eleni A

##### New Member
Hello,

In regards to Standard Multiple Regression, to calculate the variance of the IVs on the DV, we just multiple our R by 100?
And, does this percentage account as the "effect size"/ magnitude of our model? Lastly, do we judge our effective size based on Cohen (1988) 0.10 (small), 0.30 (medium), and 0.50 (large)?

Also, when we determine the effect size in a Pearson product-moment correlation, we multiple R by itself and then multiple it by 100?

Waiting for your response, thanks in advance!
E

#### Buckeye

##### Member
I think there are multiple ways to think of effect size. I'm not familiar with what you've mentioned here. But, we can get a sense of the effect size by constructing confidence intervals on the coefficients of interest. I suppose R^2 is a measure of effect size as well

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#### hlsmith

##### Not a robit
@Eleni A,

In the social sciences the tend to use partial R-square as an effect size. So if I have a model:

y = X1 + X2,

I could generate the amount of R-square explained by X1 and X2. I can also slap confidence intervals on these values. They are called something like omega or epsilon partial R-square values - and you use a certain one based on your dataframe characteristics. I think this might be what you are thinking about.

#### Eleni A

##### New Member
@Eleni A,

In the social sciences the tend to use partial R-square as an effect size. So if I have a model:

y = X1 + X2,

I could generate the amount of R-square explained by X1 and X2. I can also slap confidence intervals on these values. They are called something like omega or epsilon partial R-square values - and you use a certain one based on your dataframe characteristics. I think this might be what you are thinking about.
Hello, thanks for the response! So you basically multiple the R-square by 100 to get the percentage of the variance of your IVs on your DV. I'm asking b/c I was confused and I wanted to clarify this. Could you please tell me exactly how you can calculate the confidence intervals or send me any valid source which is step-by-step? I would really appreciate it. The book I use is really good (SPSS Survival Manual) but doesn't mention anything about confidence intervals in the Regression chapter.

#### noetsi

##### Fortran must die
R squared does not get at effect size, its a measure of explained variance of the entire model (only when your dependent variable is interval or ratio in nature). Effect size in regression is usually thought of as the slope coefficients for specific variables. You can generate standardized variants of these to deal with unequal scales (and some question how valid they are for dummy variables).

#### hlsmith

##### Not a robit
@noetsi I agree with you, but I believe some people do deem these partial R-squares as effect "sizes".

#### Eleni A

##### New Member
R squared does not get at effect size, its a measure of explained variance of the entire model (only when your dependent variable is interval or ratio in nature). Effect size in regression is usually thought of as the slope coefficients for specific variables. You can generate standardized variants of these to deal with unequal scales (and some question how valid they are for dummy variables).
Thank you, I will try to figure out this b/c I know the importance of reporting effect sizes and CI.

#### noetsi

##### Fortran must die
I think you will find that the effect sizes that are stressed are those between two variables notably slopes in regression. Or odds ratios perhaps. I have never seen the model effect size emphasized.

#### hlsmith

##### Not a robit
Predicted probabilities and accuracy are two model metrics used regularly.

#### noetsi

##### Fortran must die
Predicted probabilities and accuracy are two model metrics used regularly.
But how often do you see those actually addressed even in the academic literature