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Combinatorial meta-analysis (CMA) is the study of the behaviour of statistical properties of combinations of studies from a meta-analytic dataset (typically in social science research). In an article that develops the notion of "gravity" in the context of meta-analysis, Dr. Travis Gee〔 Gee, T. (2005) "Capturing study influence: The concept of 'gravity' in meta-analysis", ''Counselling, Psychotherapy, and Health'', 1(1), 52–75 ()〕 proposed that the jackknife methods applied to meta-analysis in that article could be extended to examine all possible combinations of studies (where practical) or random subsets of studies (where the combinatorics of the situation made it computationally infeasible). ==Concept== In the original article,〔 ''k'' objects (studies) are combined ''k''-1 at a time (jackknife estimation), resulting in ''k'' estimates. It is observed that this is a special case of the more general approach of CMA which computes results for ''k'' studies taken 1, 2, 3 ... ''k'' − 1, ''k'' at a time. Where it is computationally feasible to obtain all possible combinations, the resulting distribution of statistics is termed "exact CMA." Where the number of possible combinations is prohibitively large, it is termed "approximate CMA." CMA makes it possible to study the relative behaviour of different statistics under combinatorial conditions. This differs from the standard approach in meta-analysis of adopting a single method and computing a single result, and allows significant triangulation to occur, by computing different indices for each combination and examining whether they all tell the same story. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Combinatorial meta-analysis」の詳細全文を読む スポンサード リンク
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