Skip to end of metadata
Go to start of metadata

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 5 Next »

Overview

Text should include: Overview, purpose, when it happens in the HPC or IM cycle

Analysis Spectrum and the HPC 

(provide details on the process for each phase/product of the HPC)

    • Exploratory
      • Aggregation,  disaggregation
      • Chronologies, timelines
      • SDR
    • Describe
      • Prioritization (sudden onset)
      • Severity (sudden onset)
    • Explain
      • Cause and effect
    • Interpret
      • Ratings, rankings and uncertainty
    • Anticipate
      • Prediction, forecasting
      • scenario building


Exploratory Analysis

Focus:  To identify if data required is available (credible, reliable, timely) and structure it in a way that best suit the requirement and identifies information gaps. 

Main activities and questions

  1. Familiarise yourself with the data and check its characteristics - How relevant, sufficient and reliable is the data?
  2. Clean and enrich your data to ensure it is as good as it gets - How clean and ready for analysis is the data? Do I have enough data? 
  3. Are potential signals hidden in the data?
  4. Sort, aggregate and disaggregate and define suitable taxonomy of categories. Code & refine your data – Can the data be better prepared for queries?
  5. What are the main results so far?

Examples of Exploratory analysis findings

  • There is a variety of information sources on food insecurity in country X, primarily from IPC, Fewsnet, WFP and FAO.
  • Some are purely observational, some are quantitative.
  • Recent figures on food security are available after a comprehensive national survey by WFP
  • Findings are mostly aligned.
  • No recent information is available from the southern region where accessibility is limited
  • There seem to be higher levels of food insecurity in rural areas.


Descriptive Analysis

Focus: Summarise and describe the data, to reduce the amount of data and make it easier to compare. Comparison is key to analysis. 

Main activities and questions

  • What is written in these sources? What does the data tell us about a given situation? Who is affected, where, how many people? 
  • Group similar observations and reduce your data - What meaningful comparisons reveal differences?
  • Select the metric that best describes the situation – How can I summarise my data in a way that best describes it?
  • Compare and contrast between and within groups of data to identify meaningful and significant differences and similarities - What consistent patterns, trends, or anomalies emerge from the data? Compare to what?
    • Humanitarian standards (e.g. humanitarian conditions vs SPHERE standards)
    • Time (e.g. Pre- vs in-crisis)
    • Geographic (e.g. Governorate A. vs Governorate B.)
    • Population group (e.g. Refugees vs IDPs

Examples of Descriptive analysis findings

  • There are 15 million people in country X who are food insecure.
  • The large majority is in rural areas.
  • Conflict-affected areas such as district A, B and C have the highest proportion of food insecure people about the total population.
  • The proportion of food insecure people has more than doubled in the last five years.

 Explanatory




Interpretive: 




Anticipatory: 




Prescriptive:


Outputs/Resources

Text should include: Essential Reading, Additional Readings, Templates. Examples, Tutorials


  • No labels