We need to account for the correlation between pre- and post treatment ‘performance’, because we end up with a wrong estimate of the precision of the combined effect size ( Morris & DeShon, 2002). The problem is that the groups in the experimental design are not independent of each other and we need some method to account for this dependence. Why do I need the correlation between pre- and post treatment in an experimental design with dependent groups? For more information, see the text on effect sizes of the r-family in the User Manual. The files can be used to calculate special types of correlation coefficients based on the information from regression models.
Meta-Essentials Semi-Partial Correlational data. Meta-Essentials Partial Correlational data and 7.
The results of a regression model can be used to calculate so-called partial or semi-partial correlations ( Aloë & Becker, 2012 Aloë, 2014). Therefore, two separate workbooks are included in the Meta-Essentials package: 6. This way, the regression coefficient of the first study doesn’t represent the same effect as the coefficient from the second study. For example, a study on the effect of innovation on financial performance includes a control for firm size, but another study of the same relationship includes a control for prior financial performance. The problem that often occurs is that the variables included as controls differ between the studies.
Specifically, a regression coefficient represents the effect of X on Y, controlled for other variables ( Z). Several problems emerge with meta-analyses of regression coefficients, mostly caused by differences between the regression models. How can I meta-analyze regression coefficients?
0 kommentar(er)
