Dtable has the tests option, which delivers statistical tests for differences in the specified varlist by group category (see below for a code example).
By default, dtable uses linear regression when testing for differences in continuous variables and posts a single p-value for the difference between groups. As the documentation indicates, this is equivalent to pooled t tests when the grouping variable has two categories. But what about when it does not?
I'm using dtable when comparing across the groups, and it still delivers a single p-value. It's unclear what this is in reference to, because a linear regression for the difference in the var across three categories should produce two coefficients and two p-values. What exactly is dtable doing in this instance? How would it be appropriate to describe in a paper?
Code:
dtable varlist, by(group, tests)
I'm using dtable when comparing across the groups, and it still delivers a single p-value. It's unclear what this is in reference to, because a linear regression for the difference in the var across three categories should produce two coefficients and two p-values. What exactly is dtable doing in this instance? How would it be appropriate to describe in a paper?
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