data collection

1. Data presentation &
interpretation

2. Learning Objectives
 Understand different ways to best
summarize data
 Choose the right table/graph for the right
data
 Interpret data to consider the programmatic
relevance

3. Summarizing data
 Tables
 Simplest way to summarize data
 Data are presented as absolute numbers or
percentages
 Charts and graphs
 Visual representation of data
 Data are presented as absolute numbers or
percentages

4. Basic guidance when
summarizing data
 Ensure graphic has a title
 Label the components of your graphic
 Indicate source of data with date
 Provide number of observations (n=xx) as a
reference point
 Add footnote if more information is needed

5. Tables: Frequency distribution
Year Number of births
1900 61
1901 58
1902 75
Set of categories with numerical counts

6. Tables: Relative frequency
number of values within an interval
total number of values in the table
Year # births (n) Relative frequency (%)
1900–1909 35 27
1910–1919 46 34
1920–1929 51 39
Total 132 100.0
x 100

7. Tables
Year Number of births
(n)
Relative frequency
(%)
1900–1909 35 27
1910–1919 46 34
1920–1929 51 39
Total 132 100.0
Percentage of births by decade between 1900 and 1929
Source: KENYA. Census data, 1900–1929.

8. Charts and graphs
 Charts and graphs are used to portray:
 Trends, relationships, and comparisons
 The most informative are simple and self-
explanatory

9. Use the right type of graphic
 Charts and graphs
 Bar chart: comparisons, categories of data
 Line graph: display trends over time
 Pie chart: show percentages or proportional
share

10. Bar chart
Comparing categories
0
1
2
3
4
5
6
Quarter 1 Quarter 2 Quarter 3 Quarter 4
Site 1
Site 2
Site 3

11. Percentage of new enrollees tested for HIV at each
site, by quarter
0
1
2
3
4
5
6
Quarter 1 Quarter 2 Quarter 3 Quarter 4
%
o
f
new
enrollees
tested
for
HIV
Months
Site 1
Site 2
Site 3
Q1 Jan–Mar Q2 Apr–June Q3 July–Sept Q4 Oct–Dec
Data Source: Program records, AIDS Relief, January 2009 – December 2009.rce:
Quarterly Country Summary: Nigeria, 2008

12. Has the program met its goal?
0%
10%
20%
30%
40%
50%
60%
Quarter 1 Quarter 2 Quarter 3 Quarter 4
%
of
new
enrollees
tested
for
HIV
Site 1
Site 2
Site 3
Percentage of new enrollees tested for HIV at each site, by
quarter
Data Source: Program records, AIDS Relief, January 2009 – December 2009..
quarterly Country Summary: Nigeria, 2008
Target

13. Stacked bar chart
Represent components of whole & compare wholes
3
4
6
10
0 5 10 15
Males
Females
0-14 years
15+ years
Number of months patients have been enrolled in HIV care
Number of Months Female and Male Patients Have Been
Enrolled in HIV Care, by Age Group
Data source: AIDSRelief program records January 2009 - 20011

14. Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year 3 Year 4
Number
of
clinicians
Clinic 1
Clinic 2
Clinic 3
Number of Clinicians Working in Each Clinic During Years 1–4*
*Includes doctors and nurses
Displays trends over time

15. Line graph
0
1
2
3
4
5
6
Year1 Year2 Year3 Year4
Number
of
clinicians
Clinic1
Clinic2
Clinic3
Number of Clinicians Working in Each Clinic During Years 1-4*
*Includes doctors and nurses
Y1 1995 Y2 1996 Y3 1997 Y4 1998
Zambia Service Provision Assessment, 2007.

16. Pie chart
Contribution to the total = 100%
59%
23%
10%
8%
Percentage of All Patients Enrolled by Quarter
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
N=150

17. Interpreting data

19. Interpreting data
 Adding meaning to information by making
connections and comparisons and exploring
causes and consequences

20. Interpretation – relevance of finding
 Adding meaning to information by making
connections and comparisons and exploring
causes and consequences

21. Interpretation – relevance of finding
 Does the indicator meet the target?
 How far from the target is it?
 How does it compare (to other time periods,
other facilities)?
 Are there any extreme highs and lows in the
data?

22. Interpretation – possible causes?
• Supplement with expert opinion
• Others with knowledge of the program or target
population

23. Interpretation – consider other data
Use routine service data to clarify questions
• Calculate nurse-to-client ratio, review
commodities data against client load, etc.
Use other data sources

24. Interpretation – other data sources
 Situation analyses
 Demographic and health surveys
 Performance improvement data

25. Interpretation – conduct further
research
 Data gap conduct further research
 Methodology depends on questions being asked
and resources available

26. Key messages
 Use the right graph for the right data
 Tables – can display a large amount of data
 Graphs/charts – visual, easier to detect patterns
 Label the components of your graphic
 Interpreting data adds meaning by making
connections and comparisons to program
 Service data are good at tracking progress &
identifying concerns – do not show causality

27. Activity: Calculating coverage
and retention

28. Learning Objectives
 Use basic statistics to measure coverage and
retention
 Develop graphs that display performance
measures (utilization, trends)
 Interpret performance measures for
programmatic decision making

29. Small group activity
 Form groups of 4–6
 Each group reviews 2 worksheets from Excel file
and answers the questions (1 hr 45 min)
 Each group presents 2 findings from each
worksheet, focusing on the programmatic
relevance of the findings (10 min per group)
 Audience provides feedback on analysis and
interpretation (notes errors, additional
interpretation) (10 min per group)

30. Spreadsheet – simple to use,
basic graphs
Statistical packages, e.g.
SPSS
Qualitative data analysis tools
Categorization and theme-
based analysis
TOOLS TO SUPPORT DATA ANALYSIS
•Spreadsheet – simple to use, basic graphs
•Statistical packages, e.g. SPSS
•Qualitative data analysis tools
•Categorization and theme-based analysis, e.g. N6

31. DATA SUMMARY
• The data analysis that can be done
depends on the data gathering that was
done

32. • Qualitative and quantitative data may be
gathered from any of the three main data
gathering approaches
• Percentages and averages are commonly
used in Interaction Design
• Mean, median and mode are different
kinds of ‘average’ and can have very
different answers for the same set of data

33. • Grounded Theory, Distributed Cognition
and Activity Theory are theoretical
frameworks to support data analysis
• Presentation of the findings should not
overstate the evidence