Whether you are a policy maker of social scientist, you are slowly being drowned in a sea of often inconsistent research data. Proponents of meta-analysis claim that such data can be objectively and usefully summarized for you. The author notes how the assumptions of the meta-analytic model preclude the synthesis of experimental data (which has a clear cause-and-effect logic) with quasi-experimental and/or nonexperimental data (both of which lack such clarity). Yet in the author's review of 64 recent meta-analytic articles, 11 were found to improperly make such aggregations. Why? The author shows how the guidance provided by the leading proponents of meta-analysis either blurs the distinction or is misleading.

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