## 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 |