Indiscriminate data aggregations in meta-analysis. A cause for concern among policy makers and social scientists.
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.
Lopez-Lee
David
D
University of Southern California, USA.
D015982:Bias; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine / Q000379:methods / Q000592:standards; D006801:Humans; D015201:Meta-Analysis as Topic; D011050:Policy Making; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards*