The Empty Model for Computing Average Effect Size and HeterogeneityĪn empty model in regression is one in which the dependent variable is regressed against no predictors, but only a constant (i.e., the value of 1 for all cases). I write this section with the assumption that you have a solid grounding in multiple regression if not, you can read this section trying to obtain the “gist” of the ideas (for a thorough instruction of multiple regression, see Cohen et al., 2003). I will then draw general conclusions about this framework and suggest some more complex possibilities.
In this section, I describe how an empty (intercept-only) model accomplishes basic tests of mean effect size and heterogeneity (9.3.1), how you can evaluate categorical moderators in this framework through the use of dummy codes (9.3.2), and how this flexible approach can be used to consider unique moderation of a wide range of coded study characteristics (9.3.3). However, before considering inclusion of multiple moderators, I think it is useful to take a step back to consider how a regression approach can serve as a general approach to evaluating moderators in meta-analysis (in this context, the analyses are sometimes referred to as meta-regression).
After considering the regression approach to analyzing continuous moderators (previous section), you are probably wondering whether this approach allows for evaluation of multiple moderators-it does.
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