How to test whether a regression coefficient is moderated by a grouping variable? I have a regression done on two groups of the sample based on a moderating variable (say gender). I'm doing a simple test for the moderating effect by checking whether the significance of the regression is lost on one set while remains in the other.
Q1: The above method is valid, isn't it?
Q2: The level of confidence of my research is set at 95%. For one group, the regression is significant at .000. For the other, it is significant at 0.038
So, I believe I have to accept both regressions as significant and that there's no moderating effect. By accepting the regression is significant at while it's proven to be not at 0.01 am I causing a Type I error (accepting the falsy argument)?  
 A: Your method does not appear to address the question, assuming that a "moderating effect" is a change in one or more regression coefficients between the two groups.  Significance tests in regression assess whether the coefficients are nonzero.  Comparing p-values in two regressions tells you little (if anything) about differences in those coefficients between the two samples.
Instead, introduce gender as a dummy variable and interact it with all the coefficients of interest.  Then test for significance of the associated coefficients.
For example, in the simplest case (of one independent variable) your data can be expressed as a list of $(x_i, y_i, g_i)$ tuples where $g_i$ are the genders, coded as $0$ and $1$.  The model for gender $0$ is
$$y_i = \alpha_0 + \beta_0 x_i + \varepsilon_i$$
(where $i$ indexes the data for which $g_i = 0$) and the model for gender $1$ is 
$$y_i = \alpha_1 + \beta_1 x_i + \varepsilon_i$$
(where $i$ indexes the data for which $g_i = 1$).  The parameters are $\alpha_0$, $\alpha_1$, $\beta_0$, and $\beta_1$.  The errors are the $\varepsilon_i$.  Let's assume they are independent and identically distributed with zero means.  A combined model to test for a difference in slopes (the $\beta$'s) can be written as 
$$y_i = \alpha + \beta_0 x_i + (\beta_1 - \beta_0) (x_i g_i) + \varepsilon_i$$
(where $i$ ranges over all the data) because when you set $g_i=0$ the last term drops out, giving the first model with $\alpha = \alpha_0$, and when you set $g_i=1$ the two multiples of $x_i$ combine to give $\beta_1$, yielding the second model with $\alpha = \alpha_1$.  Therefore, you can test whether the slopes are the same (the "moderating effect") by fitting the model
$$y_i = \alpha + \beta x_i + \gamma (x_i g_i) + \varepsilon_i$$
and testing whether the estimated moderating effect size, $\hat{\gamma}$, is zero.  If you're not sure the intercepts will be the same, include a fourth term:
$$y_i = \alpha + \delta g_i + \beta x_i + \gamma (x_i g_i) + \varepsilon_i.$$
You don't necessarily have to test whether $\hat{\delta}$ is zero, if that is not of any interest: it's included to allow separate linear fits to the two genders without forcing them to have the same intercept.
The main limitation of this approach is the assumption that the variances of the errors $\varepsilon_i$ are the same for both genders.  If not, you need to incorporate that possibility and that requires a little more work with the software to fit the model and deeper thought about how to test the significance of the coefficients.
A: I guess moderating a grouping variable would work equally well when comparing regression coefficients across independent waves of cross-sectional data (e.g, year1, year2 and year3 as group1 group2 and group3)?  
