Interpreting results and presenting findings

Intermediate Food Security Analysis TrainingRome, July 2010

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 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

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 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 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 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 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 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 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 useChart Type Typical Use CommentsArea Cumulated totals (numbers

or percentages)Percentage, Cumulative

Column/Bar Observations over time or under different conditions; data sets must be small

Vertical (columns), horizontal (bars); multiple columns/bars, columns/bars centered at zero

Segmented Column/Bar Proportional relationships Total100%

Histogram Discrete frequency distribution

Columns/bars without gaps

Line, Curve Trends, functional relations Data point connected by lines or higher order curves

Pie Proportional relationships Segments may be pulled out of the the pie for emphasis (exploded pie chart)

Scatterplot Distribution of data points along one or two dimensions

One-dimensional, two-dimensional

Example of an area chart – FCS/ Food group composition

Food consumption score

Cons

umpt

ion

frequ

ency

Example of a line graph – migration by month

Oct

-08

Nov

-08

Dec

-08

Jan-

09

Feb-

09

Mar

-09

Apr-

09

May

-09

Jun-

09

Jul-0

9

Aug-

09

Sep-

09

Oct

-09

Nov

-09

50%

55%

60%

65%

70%

75%

80%

85%

90%

migration by month (households who has a migrant)

Urban Rural Total food insecure food secure

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

married (several spouses)

single

married (one

spouse)

divorced/separated

widowed

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

8.8%

16.1%

11.8%

11.4%

18.8%

18.3%

14.6%

20.5%

26.7%

26.5%

72.9%

69.3%

67.7%

61.9%

54.8%

poor borderlineacceptablePercent Households

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?

Descriptives

share (%) food expenditure (out of the total)

N Mean Std. Deviation Std. Error

95% Confidence Interval for Mean

Minimum Maximum Lower Bound Upper Bound

Urban 1939 39.0292 16.10120 .36565 38.3121 39.7463 .00 88.11

Rural 4623 47.5104 20.58760 .30280 46.9167 48.1040 .00 100.00

Total 6562 45.0042 19.75194 .24384 44.5262 45.4822 .00 100.00

ANOVA

share (%) food expenditure (out of the total)

Sum of Squares df Mean Square F Sig.

Between Groups 98258.038 1 98258.038 261.841 .000

Within Groups 2461320.979 6559 375.259

Total 2559579.017 6560

Presenting your results

Urban / RuralMean share of food

expenditure

Urban 39.0%

Rural 47.5%

Total 45.0%

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 Consider the table below. Does it clearly illustrate any

information?

Illiterate

no formal schooling

or incomplete but can read and

writePrimary

completedSecondary completed

higher completed

‘Ibb’ 42.1% 26.7% 12.4% 9.0% 9.7%

‘Abyan’ 34.3% 28.4% 14.2% 16.2% 6.9%

‘Sana'a City’ 18.3% 24.2% 12.8% 16.4% 28.3%

‘Al Bayda’ 46.8% 32.2% 9.8% 7.2% 4.1%

‘Taiz’ 44.8% 19.5% 8.7% 13.2% 13.7%

‘Hajja’ 51.8% 23.5% 11.3% 7.8% 5.6%

‘Hodeidah’ 61.8% 19.4% 9.9% 6.0% 2.9%

‘Hadramout’ 30.8% 27.8% 20.6% 12.8% 8.1%

‘Dhamar’ 57.3% 23.0% 8.3% 7.0% 4.4%

‘Shabwa’ 32.2% 31.5% 16.4% 13.2% 6.8%

‘Sana'a’ 41.9% 32.5% 10.3% 8.3% 6.9%

‘Aden’ 21.0% 19.6% 15.1% 24.7% 19.6%

‘Lahej’ 35.2% 28.8% 10.5% 18.0% 7.5%

‘Mareb’ 38.2% 26.6% 13.3% 14.0% 7.9%

‘Al Mahweet’ 59.5% 20.8% 8.1% 6.7% 4.9%

‘Al Mahra’ 42.3% 26.7% 15.6% 11.4% 4.0%

‘Amran’ 49.0% 24.6% 9.1% 9.4% 7.8%

‘Ad Daleh’ 38.0% 21.4% 16.2% 15.5% 9.0%

‘Rayma’ 57.2% 27.5% 5.1% 7.1% 3.1%

Total 43.8% 24.0% 11.3% 11.1% 9.8%

What is the highest educational level completed by Household head?

Governorate

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 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 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!