analyses:morris2008
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analyses:morris2008 [2017/03/19 13:09] – external edit 127.0.0.1 | analyses:morris2008 [2021/08/27 07:27] – Wolfgang Viechtbauer | ||
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Morris (2008) discusses various ways for computing a (standardized) effect size measure for pretest posttest control group designs, where the characteristic, | Morris (2008) discusses various ways for computing a (standardized) effect size measure for pretest posttest control group designs, where the characteristic, | ||
- | As described by Becker (1988), we can compute the standardized mean change (with raw score standardization) for a treatment and control group with $$g_T = c(n_T-1) \frac{\bar{x}_{post, | + | As described by Becker (1988), we can compute the standardized mean change (with raw score standardization) for a treatment and control group with $$g_T = c(n_T-1) \frac{\bar{x}_{post, |
Morris (2008) uses five studies from a meta-analysis on training effectiveness by Carlson and Schmidt (1999) to illustrate these computations. We can create the same dataset with: | Morris (2008) uses five studies from a meta-analysis on training effectiveness by Carlson and Schmidt (1999) to illustrate these computations. We can create the same dataset with: | ||
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==== Alternative Estimates ==== | ==== Alternative Estimates ==== | ||
- | In his article, Morris (2008) discusses two other ways of computing an effect size measure for pretest posttest control group designs. The second approach that pools the two pretest SDs actually can be more efficient under certain conditions. However, that approach assumes that the true pretest SDs are equal for the two groups. That may not be the case. The approach given above does not make that assumption and therefore is more broadly applicable (but may be less efficient). | + | In his article, Morris (2008) discusses two other ways of computing an effect size measure for pretest posttest control group designs. The second approach that pools the two pretest SDs actually can be more efficient under certain conditions. However, that approach assumes that the true pretest SDs are equal for the two groups. That may not be the case. The approach given above does not make that assumption and therefore is more broadly applicable (but may be slightly |
If you really want to use the approach with pooled pretest SDs, then this can be done as follows: | If you really want to use the approach with pooled pretest SDs, then this can be done as follows: | ||
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5 0.44 0.16 | 5 0.44 0.16 | ||
</ | </ | ||
- | The '' | + | The '' |
==== Pretest Posttest Correlations ==== | ==== Pretest Posttest Correlations ==== |
analyses/morris2008.txt · Last modified: 2022/08/03 17:05 by Wolfgang Viechtbauer