Topics in this article
- When does an indicator have a baseline?
- How do I enter baselines?
- How are baselines used?
- Why is there no separate field for baselines?
Some indicators have baseline values. Generally, indicators with baselines are observations, while indicators without baselines are measurements of work done.
For example, "number of people with access to clean water" is an observation. You could do a survey to establish how many people have access (the baseline), then implement a program to improve access, and then repeat the survey to see whether more people have access to clean water.
In contrast, "number of people trained" is a measurement of work done. There is no baseline for this indicator (or the baseline is zero) because before you start, no one has been trained.
You can define whether or not your indicator has baselines on the indicator definition page.
Enter baselines like any other data. For example, if you do a baseline survey for an indicator in 2018 Q1, enter the baseline for that indicator in 2018 Q1.
You can view/download a table of your site's baseline values by going to administration > custom queries, then selecting "Complete Baselines Export". The report includes all indicators defined as "indicator has baseline values". The report shows the date, activity, geographic place, and disaggregations for each baseline value.
These baselines are included in IATI exports.
When you create a MultiMatrix report of indicator results over time, baseline values are the fist values displayed per activity, per place, per disaggregation (for indicators with baselines).
Baselines can be too complex to capture in a single field.
- Different activities might begin reporting on an indicator at different times, in which case each activity would have its own baseline at a separate time.
- A single activity might begin reporting on an indicator in different places at different times, so each place would also have its own baseline, per activity.
- It might even be the case that different disaggregations have different baselines. For example, you could roll out a program to girls one year and boys the following year, so the baselines would be recorded in separate years for each gender.
To capture this complexity, baselines must be entered like every other observation for that indicator so that comparisons are valid and contextualized between activities, time periods, places, and disaggregations.
Here's an example that demonstrates how this complexity arises. In this example:
- Activity 1 measured their baseline for both regions and both genders in 2015.
- Activity 2 measured their baseline for Region A in 2016 and Region C in 2017
- Activity 3 measured their baseline for Females in 2015 and Males in 2017.
The baselines are shaded in green. As you can see, a single field for defining an indicator's baseline isn't enough -- we have to capture baselines at different times for different activities, different time periods, and different disaggregations.
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