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Is precision-based weighting central to meta-analysis? Borenstein et al. (2009) write that for meta-analysis to be possible all that is necessary is that:

1. Studies report a point estimate which can be expressed as a single number
2. Variance can be computed for that point estimate

It's not immediately clear to me why (2) is strictly necessary. But, indeed, all of the widely accepted methods of meta-analysis rely on precision-based (i.e. inverse variance) weighting schemes, which do require an estimate of variance for each study's effect size. Note that while Hedges' Method (Hedges & Olkin, 1985; Hedges & Vevea, 1998) and Hunter and Schmidt's Method (Hunter & Schmidt, 2004) both basically use sample size weighting, these methods apply only to normalized mean differences, and thus require a standard deviation elsewhere. It makes sense that weights inversely proportional to the variance in each study will minimize the variance in the overall effect size estimator, so is this weighting scheme a requisite feature of all methods?

Is it possible to conduct a systematic review without access to the variance for each effect size and still call the result a meta-analysis? Sample size would seem to have potential as a proxy for precision when variance is unavailable. Could one, for instance, use sample-size weighting in a study where effect size was defined as raw mean difference? How would that effect the consistency and efficiency of the resulting mean effect size?

Is precision-based weighting central to meta-analysis? Borenstein et al. (2009) write that for meta-analysis to be possible all that is necessary is that:

1. Studies report a point estimate which can be expressed as a single number.
2. Variance can be computed for that point estimate.

It's not immediately clear to me why (2) is strictly necessary. But, indeed, all of the widely accepted methods of meta-analysis rely on precision-based (i.e. inverse variance) weighting schemes, which do require an estimate of variance for each study's effect size. Note that while Hedges' Method (Hedges & Olkin, 1985; Hedges & Vevea, 1998) and Hunter and Schmidt's Method (Hunter & Schmidt, 2004) both basically use sample size weighting, these methods apply only to normalized mean differences, and thus require a standard deviation elsewhere. It makes sense that weights inversely proportional to the variance in each study will minimize the variance in the overall effect size estimator, so is this weighting scheme a requisite feature of all methods?

Is it possible to conduct a systematic review without access to the variance for each effect size and still call the result a meta-analysis? Sample size would seem to have potential as a proxy for precision when variance is unavailable. Could one, for instance, use sample-size weighting in a study where effect size was defined as raw mean difference? How would that effect the consistency and efficiency of the resulting mean effect size?

Is precision-based weighting central to meta-analysis? Borenstein et al. (2009) write that for meta-analysis to be possible all that is necessary is that:

1. Studies report a point estimate which can be expressed as a single number
2. Variance can be computed for that point estimate

It's not immediately clear to me why (2) is true. But, indeed, all of the widely accepted methods of meta-analysis rely on precision-based (i.e. inverse variance) weighting schemes, which do require an estimate of variance for each study's effect size. Note that while Hedges' Method (Hedges & Olkin, 1985; Hedges & Vevea, 1998) and Hunter and Schmidt's Method (Hunter & Schmidt, 2004) both basically use sample size weighting, these methods apply only to normalized mean differences, and thus require a standard deviation elsewhere.

This strikes me as odd, since meta-analysis is typically defined much more broadly, as "a set of statistical methods for systematically combining the results of multiple studies," or something similar. Is there a more specific description for meta-analysis which explains why inverse-variance weighting is unavoidable? Or alternately, is it possible to conduct a systematic review without access to the variance for each effect size and still call the result a meta-analysis? Could one, for instance, use sample-size weighting in a study where effect size was defined as raw mean difference? How would that effect the consistency and efficiency of the resulting mean effect size?

Is precision-based weighting central to meta-analysis? Borenstein et al. (2009) write that for meta-analysis to be possible all that is necessary is that:

1. Studies report a point estimate which can be expressed as a single number
2. Variance can be computed for that point estimate

It's not immediately clear to me why (2) is strictly necessary. But, indeed, all of the widely accepted methods of meta-analysis rely on precision-based (i.e. inverse variance) weighting schemes, which do require an estimate of variance for each study's effect size. Note that while Hedges' Method (Hedges & Olkin, 1985; Hedges & Vevea, 1998) and Hunter and Schmidt's Method (Hunter & Schmidt, 2004) both basically use sample size weighting, these methods apply only to normalized mean differences, and thus require a standard deviation elsewhere. It makes sense that weights inversely proportional to the variance in each study will minimize the variance in the overall effect size estimator, so is this weighting scheme a requisite feature of all methods?

Is it possible to conduct a systematic review without access to the variance for each effect size and still call the result a meta-analysis? Sample size would seem to have potential as a proxy for precision when variance is unavailable. Could one, for instance, use sample-size weighting in a study where effect size was defined as raw mean difference? How would that effect the consistency and efficiency of the resulting mean effect size?