fix(script): Split validity figures in two
Since otherwise the docx file did not contain correct representations of the discussion section validity robustness, I split them in two separate figures (no sub-figures)instead. Now they are like any other figure.
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@ -1285,12 +1285,11 @@ Using the validity ranking separated into internal and external validity for eac
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it is possible to identify the general make-up of the overall sample,
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the relationship between both dimensions and the distribution of studies within.
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As can be seen in @fig-validity-relation, the relationship between the internal dimension and the external dimension of validity for the study pool follows a normal distribution.
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@fig-validity-relation shows the relation between each study's validity on the internal dimension and the external dimension,
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with experimental studies additionally distinguished.
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Generally, studies that have a lower internal validity, between 2.0 and 3.5, rank higher on their external validity,
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while studies with higher internal validity in turn do not reach as high on the external validity ranking.
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::: {layout-ncol=2 .column-body-outset}
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```{python}
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#| label: fig-validity-relation
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#| fig-cap: "Relation between internal and external validity"
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@ -1318,23 +1317,6 @@ sns.swarmplot(
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)
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```
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```{python}
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#| label: fig-validity-distribution
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#| fig-cap: "Distribution of internal validities"
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sns.displot(
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data=validities,
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x="external_validity", hue="internal_validity",
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kind="kde",
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multiple="fill", clip=(0, None),
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palette="ch:rot=-0.5,hue=1.5,light=0.9",
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bw_adjust=.65, cut=0,
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warn_singular = False
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)
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```
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:::
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Studies with an internal validity ranking of of 3.0 (primarily made up of difference-in-difference approaches) and an internal ranking of 5.0 (randomized control trials) have the same tight clustering around an external validity between 4.0 (national) and 5.0 (census-based), and 2.0 (local) and 3.0 (subnational), respectively.
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This clearly shows the expected overall relationship of studies with high internal validity generally ranking lower on their external validity.
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@ -1351,10 +1333,25 @@ It additionally shows that studies with low internal validity make up the domina
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while locally or non-representative samples are almost solely made up of internally highly valid (ranking 4.0 or above) analyses,
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again with the exception of @Thoresen2021 already mentioned.
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```{python}
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#| label: fig-validity-distribution
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#| fig-cap: "Distribution of internal validities"
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sns.displot(
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data=validities,
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x="external_validity", hue="internal_validity",
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kind="kde",
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multiple="fill", clip=(0, None),
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palette="ch:rot=-0.5,hue=1.5,light=0.9",
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bw_adjust=.65, cut=0,
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warn_singular = False
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)
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```
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Looking at the data per region, census-based studies are primarily spread between Latin America and the Caribbean, as well as Europe and Central Asia.
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Meanwhile, studies using nationally, subnationally or non-representative data then to have a larger focus on North America, as well as East Asia and the Pacific.
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A slight trend towards studies focusing on evidence-based research in developing countries is visible, though with an overall rising output, as seen in @fig-publications-per-year,
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and the possibly a reliance on more recent datasets, this would be expected.
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A slight trend towards studies focusing on evidence-based research in developing countries is visible,
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though with an overall rising output as could be seen in @fig-publications-per-year, and the possibly a reliance on more recent datasets, this would be expected.
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### Inequality types analysed
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