data management

1. DATA MANAGEMENT AND
PRESENTATION.
Presented By:
Mian Muhammad Syed Hassan Iqbal.
Naveed Iqbal.
Yaser Ud-din.
Faisal Zahir.
Mehboob Ali.
Aftab Ahmad.
Royal College Of Nursing (SSSS).

2. OBJECTIVES:
 By the end of presentation the students will be able to:
 Define the term data ?? ??
 Discuss the types of data and various methods of data
collection ?? ??
 Discuss the different means and interpretation of data
presentation through:
Tables ?? ??
Graphs ?? ??
Charts ?? ??
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3. “DATA”
 Data is a collection of facts, such as values or measurements.
 It can Be:
o Numbers, words, measurements, observations or even just
descriptions of things.
 Simply data is information.
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4. TYPES OF DATA :
DATA
Numerical.
(Quantitative)
DISCRETE CONTINOUS
Categorically.
(Qualitative)
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5. QUALITATIVE vs. QUANTITATIVE:
 Data can be qualitative or quantitative.
• Qualitative data is descriptive information.
(It describes something).
• Quantitative data is numerical information.
(Numbers).
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6. CONT. . .
1. Qualitative Data. 2. Quantitative Data
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 OVERVIEW:
 Deals with descriptions.
 Data can be observed but
measured.
 Colors, textures, smells, tastes,
Appearance, beauty etc.
 Qualitative Quality
 OVERVIEW:
 Deals with numbers.
 Data which can be measured.
 Length, height, area, weight,
temperature, speed, time, ages,
members, etc.
 Quantitative. Quantity

7. QUANTITATIVE DATA CAN ALSO BE:
 Discrete or Continuous.
 Discrete data can only takes certain values.
(Like whole numbers).
 Continuous data can take any value.
(Within a range).
a) Discrete data is counted.
b) Continuous data is measured.
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8. EXAMPLE:
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 What do we know about the tiger?? ??
1. Qualitative:
• He is brown & black.
• He has long hairs.
• He has lot of energy.

9. EXAMPLE:
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2) Quantitative:
a) Discrete:
 He has 4 legs.
 He has 2 brothers.
b) Continuous:
 He weighs 400 pound.
 He is 3.5 feet tall.

10. ANOTHER TYPES OF DATA :
SPATIAL DATA. DEFINITIVE DATA.
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 Which is normally presented
on maps and plans and
provides an indication of
where things are.
 Usually provide in the form
of a drawing, which define a
particular item and the way
in which it has been or is to
be build.

11. METHODS OF DATA COLLECTION:
OBSTRUCTIVE UNOBSTRUCTIVE
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 Obtrusive data collection
methods that directly obtain
information from those
being evaluated e.g.
interviews, surveys, focus
groups, observation, case
study, questionnaires.
Unobtrusive data
collection methods that do
not collect information
directly from evaluees.
e.g. : Document analysis,
observation at a distance.

12. DATA COLLECTION TOOLS:
 Participatory Methods.
 Records and Secondary Data.
 Observation.
 Surveys and Interviews.
 Focus Groups.
 Diaries, Journals, Self-reported Checklists.
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13. CONT. . .
 Expert Judgment.
 Delphi Technique.
 Other Tools.
-scales (weight).
- tape measure.
- stop watches.
- chemical tests :
i.e. quality of water.
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- health testing tools:
i.e. blood pressure.
-citizen report cards.

14. DATA PRESENTATIONS:
1. TABLES.
2. GRAPH.
3. CHART.
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15. DATA PRESENTATIONS:
 Tables:
◦ Simplest way to summarize data.
◦ Data are presented as absolute numbers or percentages.
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16.  Adults and children with HIV/AIDS by region in Country Y,
end year X:
EXAMPLE: TABLE.
Region Adults and adolescents ≥ 15 years Children <15 years Total
1 14 800 200 15 000
2 400 000 20 000 420 000
3 997 000 3 000 1 000 000
4 985 000 15 000 1 000 000
5 1 460 000 40 000 1 500 000
6 465 000 35 000 500 000
7 940 000 10 000 950 000
8 380 000 220 000 600 000
9 900 000 600 000 1 500 000
10 545 000 5 000 550 000
Total 7 086 800 948 200 8 035 000
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17. CHARTS AND GRAPS:
 Charts and graphs:
◦ Visual representation of data.
◦ Data are presented as absolute numbers or percentages
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18. CONT. . .
 GRAPHS:
 Graphs Make Data Easier to Understand.
 Below is data without a graph. World wide disease ratio.
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ANIMALS. QUANTITY.
HIV/AIDS 65
CERVICAL CANCER. 28
DIABETES MELLITUS. 70
LUNG CANCER. 33

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0
10
20
30
40
50
60
70
HIV/AIDS CERVICAL CANCER DIABETES
MELLITUS
LUNG CANCER
LUNG CANCER
DIABETES MELLITUS
CERVICAL CANCER
HIV/AIDS
COLOUMN GRAPH:

20. BAR GRAPH:
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0 10 20 30 40 50 60 70
HIV/AIDS
CERVICAL CANCER
DIABETES MELLITUS
LUNG CANCER
LUNG CANCER
DIABETES MELLITUS
CERVICAL CANCER
HIV/AIDS

21. LINE GRAPH:
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0
10
20
30
40
50
60
70
80
HIV/AIDS CERVICAL CANCER DIABETES MELLITUS LUNG CANCER
LUNG CANCER
DIABETES MELLITUS
CERVICAL CANCER
HIV/AIDS

22. PIE CHARTS:
 A circular (360 degree) graphic representation
 Compares subclasses or categories to the whole class or
category using differently coloured or patterned segments.
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24. DATA INTERPRETATION:
1. It involves 2 terms:
• ‘Results’ – presentation of data/findings
(statistics).
• ‘Discussion’ – interpretation of data/findings.
2. Things to consider when interpreting your data:
• Interpret findings based on the purpose and
objectives of your study.
• Relate the findings to real life context.
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25. CONT. . .
• Use persuasive language to convince your readers.
• To see the research from your point of view.
• Order your interpretation to highlight the most
important findings.
• Include limitations to your research.
• Use simple, clear language.
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