Module 4: Data presentation & interpretation
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Transcript of Module 4: Data presentation & interpretation
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Module 4: Data presentation & interpretation
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Module 4: 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
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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
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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
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Tables: Frequency distribution
Year Number of births
1900 61
1901 58
1902 75
Set of categories with numerical counts
Data source: Pagano, M. Vital Events Survey in Northern Niger. Studies in Demographics, 1993. Vol. 27, pp. 32–37.
Number of Births in Ouallam, Niger, 1900–1902
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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
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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: U.S. Census data; 1900-1929.
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Charts and graphs
Charts and graphs are used to portray:
Trends, relationships, and comparisons
The most informative are simple and self-explanatory
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Use the right type of graphic
Charts and graphs
Bar charts: comparisons, categories of data
Line graphs: display trends over time
Pie charts: show percentages or proportional share
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Bar chartComparing categories
0
1
2
3
4
5
6
Quarter 1 Quarter 2 Quarter 3 Quarter 4
Site 1
Site 2
Site 3
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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 ne
w e
nrol
lees
te
sted
fo
r HI
V
Months
Site 1Site 2Site 3
Q1 Jan-Mar Q2 Apr-June Q3 July-Sept Q4 Oct-Dec
Data Source: Program records, AIDSRelief, January 2009 – December 2009.rce: Quarterly Country Summary: Nigeria, 2008
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Has the program met its goal?
0%
10%
20%
30%
40%
50%
60%
Quarter 1 Quarter 2 Quarter 3 Quarter 4
% o
f new
enr
olle
es t
este
d fo
r HIV
Site 1Site 2Site 3
Percentage of new enrollees tested for HIV at each site, by quarter
Data Source - Program records, AIDS Relief January 2009 – December 2009rce: Quarterly Country Summary: Nigeria, 2008
Target
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Stacked bar chartRepresents components of whole & compares 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
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Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year 3 Year 4
Num
ber o
f clin
icia
ns
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
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Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year 3 Year 4
Num
ber o
f clin
icia
ns
Clinic 1
Clinic 2
Clinic 3
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.
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Pie chartContribution 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
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Interpreting data
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Interpreting data
Adding meaning to information by making connections and comparisons and by exploring causes and consequences
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Interpretation – relevance of finding
Adding meaning to information by making connections and comparisons and by exploring causes and consequences
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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?
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Interpretation – Possible causes?
• Supplement with expert opinion
• Others with knowledge of the program or target population
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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
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Interpretation – Other data sources
Situation analyses
Demographic and health surveys
Surveillance data
Performance improvement data
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Interpretation – conduct further research Data gap conduct further research
Methodology depends on questions being asked and resources available
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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
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Group Work 2: Calculating coverage and efficiency
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Learning objectives
Use basic statistics to measure coverage and efficiency
Develop graphs that display performance measures (utilization, trends)
Interpret performance measures for programmatic decision making
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Small group activity
Form groups of 4–6
Each group reviews 2 worksheets from Excel file and answers the questions (1 hour 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 interpretations) (10 min per group)
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Group Work 2: Report back
Each group will have 10 minutes to present its completed Framework
Group discussion – Are there other data sources that might have been used in this decision? Were there other stakeholders that should have been considered? (10 min)