analyses:morris2008

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analyses:morris2008 [2021/01/16 09:55] Wolfgang Viechtbauer |
analyses:morris2008 [2021/11/08 13:17] 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 $$g_T = c(n_T-1) \frac{\bar{x}_{post,(1988) who uses $d$ to denote this).)) | + | As described by Becker (1988), we can compute the standardized mean change (with raw score standardization) for a treatment and control group with $$g_T = c(n_T-1) \frac{\bar{x}_{post,, 1988, who uses $d$ to denote this). There was (and sometimes still is) some inconsistency in notation when referring to the biased and the bias-corrected version of standardized mean difference / change measures, but I would say the general trend has been to use $d$ for the biased version and $g$ for the bias-corrected version and this is the notation I am also using here.)) |

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: | ||

Line 99: | Line 99: | ||

==== The Actual Meta-Analysis ==== | ==== The Actual Meta-Analysis ==== | ||

- | For the actual meta-analysis part, we simply pass the ''a fixed-effects model can be fitted with: | + | For the actual meta-analysis part, we simply pass the ''an equal-effects model can be fitted with: |

<code rsplus> | <code rsplus> | ||

- | rma(yi, vi, data=dat, method="FE", digits=2) | + | rma(yi, vi, data=dat, method="EE", digits=2) |

</ | </ | ||

<code output> | <code output> | ||

- | Fixed-Effects Model (k = 5) | + | Equal-Effects Model (k = 5) |

- | Test for Heterogeneity: | + | I^2 (total heterogeneity / total variability): |

+ | H^2 (total variability / sampling variability): | ||

+ | | ||

+ | Test for Heterogeneity: | ||

Q(df = 4) = 4.43, p-val = 0.35 | Q(df = 4) = 4.43, p-val = 0.35 | ||

Model Results: | Model Results: | ||

- | estimate se zval pval ci.lb ci.ub | + | estimate se zval pval ci.lb ci.ub |

- | 0.95 0.14 6.62 < 0.67 1.23 *** | + | 0.95 0.14 6.62 < 0.67 1.23 *** |

--- | --- | ||

- | Signif. codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | + | Signif. codes: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 |

</ | </ | ||

Note that these results are slightly different than the ones in Table 5 due to the different ways of estimating the sampling variances. | Note that these results are slightly different than the ones in Table 5 due to the different ways of estimating the sampling variances. |

analyses/morris2008.txt · Last modified: 2021/11/08 13:17 by Wolfgang Viechtbauer

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