Interpreting results and presenting findings
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Interpreting results and presenting findings. Intermediate Food Security Analysis Training Rome, July 2010. Overview. Determining the question you want to answer Using your analysis plan Interpreting results from SPSS Visualizing findings Writing-up your analysis.

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Interpreting results and presenting findings

Interpreting results and presenting findings

Intermediate Food Security Analysis Training

Rome, July 2010


Overview

Overview

  • Determining the question you want to answer

  • Using your analysis plan

  • Interpreting results from SPSS

  • Visualizing findings

  • Writing-up your analysis


Determining the question you want to answer

Determining the question you want to answer

  • The key questions we typically try to answer in a CFSVA are:

    • Who are the food insecure people?

    • How many are food insecure?

    • Where do they live?

    • Why are they food insecure?

  • For each question, we must first think about the analysis we need


Food and nutrition security conceptual framework

Food and Nutrition Security Conceptual Framework


What is an analysis plan

What is an analysis plan?

  • The link with the conceptual framework that sets out your hypotheses

  • A table detailing data to be collected and how those data will be analyzed

  • A guide to the analytical process


Think back to your analysis plan

Think back to your analysis plan

  • Who are the food insecure people?

    • Cross-tabulate various demographic indicators with food consumption groups

      • Sex of household head

      • Dependency issues

      • Education

      • Etc.

    • Verify differences are significant using hypothesis testing

      • Is the sex of the household head a significant factor different between the food secure and the food insecure?

      • Are households with a high percentage of dependents significantly more food insecure?

      • Does education significantly affect food security?


Thinking about an analysis plan

Thinking about an analysis plan

  • How many are food insecure?

    • Run a frequency on food security groups

  • Where do they live?

    • Cross-tabulate food consumption groups by strata

      • Urban / rural

      • Agro-ecological / livelihood zones (if available)

      • Administrative zones (governorates, provinces, districts, etc.)

    • Always verify differences are significant using hypothesis testing


Thinking about an analysis plan1

Thinking about an analysis plan

  • Why are they food insecure? (a bit out of scope for this training, but good to think about)

    • Keep the conceptual framework for food security analysis in mind and explore the dataset using the tools you have available to you

    • Run hypothesis tests on the various data you have. For example:

      • Exposure to shocks

      • Coping strategies index

      • Ability to cope with shocks

        • Wealth

        • Access to credit

        • Types of livelihoods

      • Access to markets

      • Etc.

    • Use regression analysis (in the next training!)


Presenting results a few pointers

Presenting results: a few pointers

  • A good graph must convey statistical information quickly and efficiently

  • The minimum ink principle

    • Avoid images with 3-D effects or fancy shading. Use the minimum amount of ink to get your point across. 

  • The small table principle

    • A small table is better than a large graph. If you graph contains 20 data points or less, use a table of numbers instead.

  • The rule of seven

    • If a table has seven or more rows or columns, it probably has more information that can be easily interpreted

  • The fault of default principle

    • Don't rely on the default options when creating graphs. Try multiple versions until you get the right information presented


Presenting results using color

Presenting results: using color

  • Danger in the use of color

    • Color should be avoided for ordinal data

    • Shades work better with ordinal data

  • Bright colors can lead to optical illusions

    • For example, areas in bright red sometimes appear larger than areas in bright green

  • Certain color combinations are difficult to distinguish

    • Blue against a black background

    • Yellow against a white background

  • More than 8% of all males and more than 1% of all females are colorblind

    • A red-green deficiency is most common

  • Color is often culturally biased


A few points about tables

A few points about tables

  • Show only two significant digits at most

  • If possible, sort rows with the largest numbers at the top

    • If you’re showing the same rows (strata of analysis) repeatedly, you should consider being consistent in the order of the rows

  •  Use a table anytime you have 20 or fewer numbers.


Types of charts and their use

Types of charts and their use


Example of an area chart fcs food group composition

Example of an area chart – FCS/ Food group composition

Consumption frequency

Food consumption score


Example of a line graph migration by month

Example of a line graph – migration by month


Example of a segmented bar graph food consumption groups by marital status

Example of a segmented bar graph – food consumption groups by marital status


Interpreting results from spss

Interpreting results from SPSS

  • Once you’ve created an analysis plan you can start your work in SPSS

  • Each output of SPSS has a lot of information. Understanding these outputs is critical.

  • What do the ANOVA results below tell you about share of food expenditure between urban and rural populations?

  • What would you share in your findings?


Presenting your results

Presenting your results

  • Never use SPSS outputs for sharing your results!

  • In this case, a very simple table can illustrate that rural populations spend a larger share on food than urban populations

  • In the text that describes the table, we can note the statistical significance (depending on our audience)

Table 1 – Average share of food expenditure by urban / rural

The results from the survey showed that rural populations significantly (p<0.05) spent a larger share on food than urban populations, 47.5% as compared to 39.0% respectively.


Sharing results

Sharing results

  • Consider the table below. Does it clearly illustrate any information?


Sharing results1

Sharing results

  • Generally speaking, ‘the rule of seven’ should be applied during report writing. If a table has more than six rows or columns, it probably has more information that can be easily interpreted. Consider creating a graphic or creating a table with just the pertinent information


Sharing results2

Sharing results

  • Simply sorting data can make a graph much easier to read and can quickly highlight the point you are illustrating

  • What is missing from this table?

Figure 1: Education level of household head by Governorate


Writing up your results

Writing up your results

  • Always remember the question you are trying to answer when writing.

  • Have a solidly defined report structure prepared before you write. The analysis plan can help you with this

  • Don’t make assumptions that you cannot backup

  • Think of the results as telling a story. You need to build your findings over the course of the story and transition from section to section as fluidly as possible. Use the conceptual framework to guide you.

  • Use visual aids to highlight your points, but don’t rely on them to do all the work. Make sure you have meaningful titles

  • Get your colleagues to review your work!


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