3.1 Data preparation in Excel

3.1.1 Setting the columns of the excel spreadsheet

To conduct Meta-Analyses in R, you need to have your study data prepared. For a standard meta-analysis, the following information is needed for every study.

  • The names of the individual studies, so that they can be easily identified later on. Usually, the first author and publication year of a study is used for this (e.g. “Ebert et al., 2018”)
  • The Mean of both the Intervention and the Control group at the same assessment point
  • The Standard Deviation of both the Intervention and the Control group at the same assessment point
  • The number of participants (N) in each group of the trial
  • If you want to have a look at differences between various study subgroups later on, you also need a subgroup code for each study which signifies to which subgroup it belongs. For example, if a study was conducted in children, you might give it the subgroup code “children”.

As per usual, such data is stored in EXCEL spreadsheets. We recommend to store your data there, because this makes it very easy to import data into RStudio.

However, it is very important how you name the columns of your spreadsheet. If you name the columns of your sheet adequately in EXCEL already, you can save a lot of time because your data doesn’t have to be transformed in RStudio later on.

Here is how you should name the data columns in your EXCEL spreadheet containing your Meta-Analysis data

Column Description
Author This signifies the column for the study label (i.e., the first author)
Me The Mean of the experimental/intervention group
Se The Standard Deviation of the experimental/intervention group
Mc The Mean of the control group
Sc The Standard Deviation of the control group
Ne The number of participants in the experimental/intervention group
Nc The number of participats in the control group
Subgroup This is the label for one of your Subgroup codes. It’s not that important how you name it, so you can give it a more informative name (e.g. population). In this column, each study should then be given an subgroup code, which should be exactly the same for each subgroup, including upper/lowercase letters. Of course, you can also include more than one subgroup column with different subgroup codings, but the column name has to be unique

Note that it doesn’t matter how these columns are ordered in your EXCEL spreadsheet. They just have to be labeled correctly.

There’s also no need to format the columns in any way. If you type the column name in the first line of you spreadsheet, R will automatically detect it as a column name.

It’s also important to know that the import will distort letters like ä,ü,ö,á,é,ê, etc. So be sure to transform them to “normal” letters before you proceed.

3.1.2 Setting the columns of your sheet if you have calculated the effect sizes of each study already

If you have already calculated the effect sizes for each study on your own, for example using Comprehensive Meta-Analysis or RevMan, there’s another way to prepare your data which makes things a little easier. In this case, you only have to include the following columns:

Column Description
Author This signifies the column for the study label (i.e., the first author)
TE The calculated effect size of the study (either Cohen’s d or Hedges’ g, or some other form of effect size
seTE The Standard Error (SE) of the calculated effect
Subgroup This is the label for one of your Subgroup codes. It’s not that important how you name it, so you can give it a more informative name (e.g. population). In this column, each study should then be given an subgroup code, which should be exactly the same for each subgroup, including upper/lowercase letters. Of course, you can also include more than one subgroup column with different subgroup codings, but the column name has to be unique




banner