Effect size in multiple regression

In summary, the conversation is about using a calculator to determine a sample size for a multiple regression with 20 predictors. The user is unsure about the effect size and asks for clarification on whether a small or large effect size should be used for an accurate model with an R2 > 0.8. The response suggests looking at the Wikipedia page on multiple correlation for more information.
  • #1
bradyj7
122
0
Hello,

I'm using this this calculator to determine a rough sample size for a multiple regression (20 predictors).

http://www.stattools.net/SSizmreg_Pgm.php

I don't really understand the effect size?

Could somebody tell me if you are using multiple regression (with 20 predictors) and you want the regression to have an R2 > 0.8, do you want a small effect size for example 0.2 or a large effect size for example 0.5?

Basically I want a accurate model, what effect size value should I use?

Thank you
 
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Related to Effect size in multiple regression

What is effect size in multiple regression?

Effect size in multiple regression is a statistical measure that quantifies the strength and magnitude of the relationship between two or more variables in a regression model. It is used to determine the practical significance of the relationship between the independent and dependent variables.

How is effect size calculated in multiple regression?

There are various measures of effect size that can be used in multiple regression, including R-squared, partial eta-squared, and Cohen's f-squared. These measures are calculated based on the amount of variance explained by the independent variables in the model.

Why is effect size important in multiple regression?

Effect size is important in multiple regression because it provides information about the strength and practical significance of the relationship between variables. It allows researchers to determine the impact of the independent variables on the dependent variable and assess the overall fit of the regression model.

Can effect size be interpreted as causation in multiple regression?

No, effect size cannot be interpreted as causation in multiple regression. While a large effect size may indicate a strong relationship between variables, it does not necessarily mean that one variable causes the other. Correlation does not imply causation.

How can effect size be used to compare different regression models?

Effect size can be used to compare different regression models by comparing the measures of effect size for each model. The model with a larger effect size indicates a stronger relationship between the variables and a better fit for the data.

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