Step 1. Protocol stage

If you have not yet registered your protocol, you can benefit of our tools to select a priori the type of input data that could be extracted to estimate an effect size.

start by determining the effect size measure (SMD, OR, RR, etc.) you plan to estimate
then, identify all types of input data that can be used to estimate this effect size measure

see_input_data(measure = "or")

last, generate a data extraction sheet for this effect size measure

data_extraction_sheet(measure = "or")

Step 2. Dataset comparison

When data extraction has been performed in duplicate, our tools offer the possibility to flag the differences between the two datasets. For this example, we will use two datasets (df.compare1 and df.compare2) distributed with metaConvert.

compare_df(
    df_extractor_1 = df.compare1,
    df_extractor_2 = df.compare2,
    output = "html")

rowname	chng_type	study_id	author	year	n_exp	n_nexp	prop_cases_exp	prop_cases_nexp
1	df_extractor_1	1	Michellini	2000	20	40	0.2	0.2
1	df_extractor_2	1	Smith	2000	20	40	0.2	0.2
2	df_extractor_1	2	Jones	2019	52	32	0.44	0.34
2	df_extractor_2	2	Vietillini	2019	52	32	0.44	0.34
3	df_extractor_1	3	Raymond	2022	198	238	0.32	0.22
3	df_extractor_2	3	Raymond	2020	188	238	0.32	0.22
4	df_extractor_1	4	El-Jiher	2017	2010	1991	0.1	0.21
4	df_extractor_2	4	El-Jiher	2017	2010	1991	0.1	0.31
5	df_extractor_1	5	Tortolinni	2005	111	181	0.5	0.45
5	df_extractor_2	5	Tortolinni	2005	111	181	0.5	0.4
6	df_extractor_2	3	Raymond	2022	198	10	0.32	0.22

Only rows with differences between the two datasets are identified, and you can easily retrieve the row number by looking at the ID in the rowname column. In grey, values that are consistent between the two data extractors. In green/red, the values that differ.

Step 3. Effect size computation

Basic usage

To generate an effect size from a dataset that contains approriate column names and information, you simply need to :

pass this dataset to the convert_df() function
indicate the effect size measure that should be estimated

For this example, we will generate effect sizes from the df.short dataset, and we will estimate Hedges’ g.

res = convert_df(x = df.short, measure = "g")
#> Warning in convert_df(x = df.short, measure = "g"): When you enter input data
#> that cannot be negative (F-test, eta-squared, p-value, or chi-square values),
#> do not forget to properly set up the direction of the generated effect size
#> using corresponding reverse_* argument!

summary(res)

To know more about the information stored in each column, refer to documentation of the summary.metaConvert function, available in the R manual of this package.

More advanced usage

A tutorial on a more advanced usage will be proposed in the companion paper of this tool; the link will be inserted as soon as the paper will be published.

For now, you can refer to the documentation of the convert_df function in the R manual of this package, in which all options are described.

I do not like R

If you prefer having a graphical user interface (GUI) when performing data analysis, we are please to introduce you to our web-app that enables to perform ALL calculations of this package using an interactive GUI https://metaconvert.org/