Beyond the standard analysis, what other types of analysis is available?
The LNOB analysis (CART & D-Index) is recalculated when additional relevant circumstances are available. To date, ethnicity and religion are used as default and when available. When ethnicity is not available, we use language as a substitute. If both ethnicity & language are available, we prefer to use ethnicity.
Why is the standard analysis tree different from the "incl. Ethnicity etc." tree, even though no new circumstances appear in the tree?
For the “including Religion, Ethnicity and/or Language” analysis, the minimum size of the end nodes has been lowered from 9% to 5% (of the population), to be able to capture subgroups that are smaller in size. Therefore, variation in tree formation may be seen, even if the additional variables "ethnicity", "religion" and "language" or "caste" do not actually show up in the tree. These trees which allows smaller subgroups often are more complex and therefore perhaps more difficult to interpret, but they do provide more granular information.
How are the splits in the trees determined?
There are several splitting methods for trees. For our data and desired output, we use analysis of variance (ANOVA), because we are not interested in prediction (at the end nodes), but rather a proportion of "1" (share of the end node group that has access to our indicator of interest). ANOVA calculates the average in each node (in our case, the proportion of "1"). To determine how many splits are made, we use the Complexity Parameter (cp), a threshold that tells our the algorithm when to stop splitting. You can read more about the statistics here: https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf.
What is the difference between indicators and circumstances?
This question is at the core of the LNOB analysis! Indicators are the "response variables" we are called to observe and monitor, such as access to electricity (an opportunity) or stunting in children (a barrier). Circumstances are the determinant factors or "independent variables" that we assume should NOT matter in shaping access to indicators. In reality, we see that they DO matter, and therefore the LNOB trees create subgroups, based on various interactions of these circumstances, that shape the groups that are furthest behind, or ahead.
What is the difference between opportunities and barriers?
Our indicators are split into two types: opportunities, that everyone should have access to (such as access to electricity), and barriers, that no one should experience (such as stunting in children). Our analysis, colour schemes and data insights are customized to reflect this fundamental difference in these two types of indicators.