1. Analysis and Write up Inter-agency Child Protection Working Group & Save the Children Picture by: Lindsay Stark Training material developed by: Hani Mansourian

2. Data Analysis - Definition Data analysis is the process of making sense of the collected data. In other words, it is bringing together individual data points (like an answer to a question) to speak as a collective and tell the ‘story’ of the situation. It is through data analysis that we translate the “raw” data from different sources into meaningful information that enables us to provide informed statements about WWNK.

3. Data Analysis Some Simple Methods Frequency analysis: helps you to determine the frequency of a particular event, issue, or statement within the overall information that has been collected. Example: frequency analysis of the age distribution of separated children under 55 to 1415 to 18 no observable difference don’t know Frequency38323 Percentage16%42%16%11%16%

4. Data Analysis Some Simple Methods Cross tabulation is another method in descriptive statistics. Through cross tabulation, one can separate responses to a specific question based on characteristics of the respondent (e.g. male/female) or the site where the data was collected (e.g. urban/rural/camp/…). Example: increase in incidence of sexual violence in urban versus rural sites Note: see page 2 of handout #9 for the tally sheet and more details on cross tabulation. Increase in SVNo increase in SVTotal Urban527 Rural3811 Total81018

5. Graphic presentation of data Showing the data visually is a very basic but oftentimes effective way of making the data useful. Graphing can take place at different stages of analysis. Note: see page 3 of handout #9 for more details.

6. Interpreting the Data Interpretation is the process through which the data that has been collected and analyzed is then linked back to programmatic objectives (and informing the WWNK) of the assessment. A key step in interpreting the data is to make sure that the data that has been collected is accurate. This will be possible through triangulation.

7. Interpreting the Data Triangulation Triangulation of data is the process of comparing data collected through different methods, by different people and from different sources. This is our main form of a validity check in a Rapid Assessment. Finding similar information across the different sources and methods used in the assessment allows for increased confidence in the results. Triangulation becomes ever more important if we collect our data from a small sample, which is often the case in a rapid assessment setting. Note: see page 4 of handout #9 for more on triangulation.

8. Produce and Disseminate Assessment Data After the analysis phase, it is important to share the results with other actors. Ideally, a mini-workshop should be organized to discuss the main findings and their significance. This will not only enrich the learning from the data, but also ensures buy-in and wider use of the results. You may also want to consider different assessment ‘products’ for different audiences. Note: see page 5 of handout #9 for example of how different assessment products may look like.

9. It is important to acknowledge that the assessment results are NOT representative of the total population. Thank You & Best of Luck