*MetaWin* 2.0 will allow one to summarize the results of multiple
independent studies using meta-analytic procedures. Version 2.0 is much more
general than its predecessor, and allows for greater flexibility in both the
effect sizes that can be used as well as the statistical models for summarizing
meta-analytic data. This version can calculate both fixed effects models and
random effects models, and can be used for a variety of meta-analytic data
structures, including no data structure, categorical (grouped) data, and
continuous (regression) data. *MetaWin* now comes with its own
spreadsheet; data can be entered directly into the spreadsheet or can be read
from a text file, a Microsoft Excel file, or a Lotus 1-2-3 file. A variety of
commonly used meta-analysis effect sizes can be calculated, including Hedges'
*d*, response ratio, odds ratio, risk difference, relative risk, and
Fisher's *z*- transform. From these (or other) effect sizes, cumulative
mean effects, their confidence intervals, and various heterogeneity statistics
can be calculated. The total heterogeneity, *QT*, is
calculated for each analysis, and, for categorical and continuous data models,
the heterogeneity explained by the model, *QM*, and the
residual error heterogeneity, *QE*, are calculated as
well.

*MetaWin* 2.0 also allows one to refine the analysis by removing
certain studies or groups of studies from the analysis without having to alter
the data file. A variety of exploratory data analyses can be performed,
including various tests to evaluate potential publication bias. Cumulative
meta-analyses can also be performed, in order to investigate changes in the
cumulative mean effect size as new studies are added to the model. In addition,
one can graphically explore the data through histograms, normal quantile plots
and funnel plots. Scatter plots, regression plots, radial plots, and plots of
cumulative mean effect sizes can also be generated.

Finally, because data may violate the underlying assumptions of
meta-analysis, it may be useful to evaluate the significance of meta-analytic
statistics using resampling methods (Adams et al., 1997). Therefore, this
program will allow one to incorporate resampling tests into the meta-analysis.
In particular, confidence intervals for cumulative mean effect sizes can be
generated using two different bootstrap procedures (bootstrap confidence
intervals and bias-corrected bootstrap confidence intervals). MetaWin also
allows one to test the significance of the heterogeneity explained by the model,
*QM*, using a randomization test.

Revised by Dean C. Adams, dcadams@iastate.edu, 7 December, 1999.