Hi Everyone,

I am using the General Social Survey from NORC. I have an N of ~1200 people. I am using linear regression to determine if atheists and theists differ in terms of nihilism (nih_ath). As you can see, the results for the variable negath (0=theists; 1=atheists) are non-significant, but I want to discuss the observed effect size. There was a group imbalance (approximately 9 theists for every 1 atheist), which has implications for power that I would like to address preemptively.

Prior to running the analyses, I standardized the outcome variable; the coefficients of the model can be interpreted as relating to changes in SD. My decision to standardize the outcome variable was because I wanted the coefficients to be interpretable as approximations of Cohen's d. The approximations appear to be pretty decent, but I'd rather use a precise approach.

I am using the General Social Survey from NORC. I have an N of ~1200 people. I am using linear regression to determine if atheists and theists differ in terms of nihilism (nih_ath). As you can see, the results for the variable negath (0=theists; 1=atheists) are non-significant, but I want to discuss the observed effect size. There was a group imbalance (approximately 9 theists for every 1 atheist), which has implications for power that I would like to address preemptively.

Code:

------------------------------------------------------------------------------ | Linearized nih_ath | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- negath | .1877569 .1159446 1.62 0.108 -.0419951 .4175089 sex | -.1471211 .0613612 -2.40 0.018 -.2687124 -.0255297 age | -.0012937 .0024676 -0.52 0.601 -.0061835 .003596 drac2 | -.0386779 .0971669 -0.40 0.691 -.2312206 .1538648 drac3 | .4211109 .171517 2.46 0.016 .0812384 .7609833 dmar2 | -.007733 .1130517 -0.07 0.946 -.2317524 .2162864 dmar3 | -.1266924 .0924548 -1.37 0.173 -.3098977 .0565129 dmar4 | .092398 .1612822 0.57 0.568 -.2271935 .4119895 dmar5 | .0825658 .0852277 0.97 0.335 -.0863185 .2514501 ded2 | -.5666068 .1159979 -4.88 0.000 -.7964643 -.3367493 ded3 | -.5428579 .1286772 -4.22 0.000 -.7978404 -.2878754 ded4 | -.5687932 .1207139 -4.71 0.000 -.8079958 -.3295906 ded5 | -.6803206 .1411244 -4.82 0.000 -.9599679 -.4006732 inc | -.0288876 .0074534 -3.88 0.000 -.0436571 -.0141181 _cons | .8739862 .2451671 3.56 0.001 .3881712 1.359801 ------------------------------------------------------------------------------

Prior to running the analyses, I standardized the outcome variable; the coefficients of the model can be interpreted as relating to changes in SD. My decision to standardize the outcome variable was because I wanted the coefficients to be interpretable as approximations of Cohen's d. The approximations appear to be pretty decent, but I'd rather use a precise approach.

**How do you calculate the effect size for group differences, within a multiple linear regression?**
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