1. Medicine & Society II
Collecting & Managing
Data
Dr Azmi Mohd Tamil
Dept. of Community Health,
Faculty of Medicine,
UKM
notes partially based on a lecture by
Assc. Prof. Dr. Roslina Abd. Manap

2. Sampling
 Choosing a relatively small subset such
that it can adequately represent the
entire spectrum of population subjects
 Aim to extrapolate results back to a
substantially larger population
 to save time, money, efficiency and
safety.

3. SAMPLING
PROBABILITY NON-
SAMPLING
 equal chance of being
PROBABILITY
selected SAMPLING
• simple random, • convenience,
• systematic,
•
• quota,
stratified,
• multistage, • purposive.
• cluster

4. SAMPLING &
TYPE OF POPULATION
 Selection representative of population
 ? sampling methods
- simple random sampling (may not be
practical in national study)
- stratified random sampling
(in heterogenous pop./stratum)
- multistage sampling
(national-state-district-sub district-village)
- cluster sampling

5. Data Collection
 Data collection begins after
deciding on design of study and
the sampling strategy

6. Data Collection
 Sample subjects are identified and the
required individual information is
obtained in an item-wise and structured
manner.

7. Data Collection
 Information is collected on certain
characteristics, attributes and the
qualities of interest from the samples
 These data may be quantitative or
qualitative in nature.

8. Types of Variables
 Qualitative - categorised based on
characteristics which differentiate it e.g.
ethnic - Malay, Chinese, Indian etc.
Qualitative variables can be classed into
nominal & ordinal.
 Quantitative - numerical values collected
by observation, by measurement or by
counting. Can either be discrete or
continuous.

9. Variable
Classification
Quantitative
Qualitative
 discrete - from
 Nominal - no rank
counting ie no of
nor specific order
children/wives
e.g. ethnic; M, C, I &
 continuous - can be in
O.
 Ordinal - has
fractions, from
measurement e.g.
rank/order between
blood pressure,
categories but the
haemoglobin level.
difference cannot be
measured.

10. Types of Data
Table 1.1 Exam ples of types of data
Quantitative
Continuous Discrete
Blood pressure, height, w eight, age Number of children
Number of attacks of asthma per w eek
Categorical
Ordinal (Ordered categories) Nom inal (Unordered categories)
Grade of breast cancer Sex (male/female)
Better, same, w orse Alive or dead
Disagree, neutral, agree Blood group O, A, B, AB
http://www.bmj.com/collections/statsbk/

11. SO WHAT!
So what’s the big deal about data
types?

12. Statistical Tests - Qualitative

13. Type of Data Dictates Type of
Analysis - Quantitative

14. Data Collection Techniques
 Use available information
 Observation
 Interviews
 Questionnaires
 Focus group discussion

15. Using Available
Information
 Existing Records
• Hospital records - case notes
• National registry of births & deaths
• Census data
• Data from other surveys

16. Disadvantages of using
existing records
 Incomplete records
 Cause of death may not be verified by a
physician/MD
 Missing vital information
 Difficult to decipher
 May not be representative of the target
group - only severe cases go to hosital

17. Disadvantages of using
existing records
 Delayed publication - obsolete data
 Different method of data recording
between institutions, states, countries,
making comparison & pooling of data
incompatible
 Comparisons across time difficult due to
difference in classification, diagnostic
tools etc

18. Advantages of using
existing records
 Cheap
 convenient
 in some situations, it is the only data
source i.e. accidents & suicides

19. Observation
 Involves systematically selecting,
watching & recording behaviour and
characteristics of living beings, objects
or phenomena
 Done using defined scales
 Participant observation e.g. PEF and
asthma symptom diary
 Non-participant observation e.g.
cholesterol levels

20. Interviews
 Oral questioning of respondents either
individually or as a group.
 Can be done loosely or highly structured
using a questionnaire

21. Administering Written
Questionnaires
 Self-administered
 via mail
 by gathering them in one place and
getting them to fill it up
 hand-delivering and collecting them later
 Large non-response can distort results

22. Questionnaires
 Influenced by education & attitude of
respondent esp. for self-administered
 Interviewers need to be trained
 open ended vs close ended
 the need for pre-testing or pilot study

23. Issues at stake
 Content validity
 Structural validity
 Criterion validity

24. Content Validity

25. Construct Validity

26. Criterion Validity

27. Focus group discussion
 Selecting relevant parties to the
research questions at hand and
discussing with them in focus groups
 examples in your own field of interest?

28. Source of biases during
data collection
 Defective instruments
• close ended questions with poor choice of
options
• open ended questions with no guidelines
• vaguely-phrased questions
• illogical sequences of questions
• weighing scales that are not standardised

29. Source of biases during
data collection
 Observer bias
• reporting of radiographs
 Effect of interview on respondent
 Attitude of respondent
• cough may be ignored by a smoker
• stigmatised diseases may not be disclosed

30. Plan for data collection
 Permission to proceed
 Logistics - who will collect what, when
and with what resources
 Quality control

31. Quality of Data
 How well do the variables designed for
the study represent the phenomena of
interest?
 E.g. How well does FBS represent
control of diabetes

33. Accuracy & Reliability
 Accuracy - the degree which a
measurement actually measures the
measures the characteristic it is
supposed to measure
 Reliability is the consistency of replicate
measures

34. Reliability

35. Reliability & Accuracy

36. Accuracy & Reliability
 Both are reduced by random error and
systematic error from the same sources
of variability;
• the data collectors
• the respondents
• the instrument

37. Strategies to enhance
accuracy & reliability
 Standardise procedures and
measurement methods
 training & certifying the data collectors
 Repetition
 Blinding

38. Data handling
 Check the data gathered
 storing of data - backup, backup &
backup some more!

39. Data Management
 Data processing
• Categorising
• Coding
• Data entry
• Verification/validation

40. Labels & Coding

41. Variable Labels
• Unique
• Not more than 8 characters
• Consists of letters and numbers only
• Begins with a letter instead of a number.
• Try to give a label that means something

42. Coding
• Determine the coding to be used for each
variable.
• For qualitative variables, it is recommended
to use numerical-codes to represent the
groups; eg. 1 = male and 2 = female, this
will also simplify the data entry process.
The “danger” of using string/text is that a
small “male” is different from a big “Male”,
• see Table I.

44. Coding for Dichotomus
Variable
 It is advisable to use
1=present, 0=absent.
 Or 1=higher risk, 0=lower risk

45. Coding for Missing Value
 @ blank responses
 Usually required only for qualitative
variables
 Conventionally coded using a value that
is not part of a valid response. For
example;
• Gender; M=1, F=2, MV=9
• Ethnic in East Malaysia; Codes 1 till 14 for
races, MV=99

46. Advantage of Coding
 Reduce time for “data entry”.
 Make analysis possible e.g. SPSS wont
analyse string responses of more than 8
characters
 Need a proper coding manual
 How to define variables and coding for
application such as SPSS and Excel are
available at the dept website
http://161.142.92.104/spss/
http://161.142.92.104/excel/

47. Data Entry

48. “Data Entry”

49. Data Entry
 Independent operator verification
 Random check of data entered against
the original
 <5% error by convention
 Some checks are built-in by the
software i.e. EpiInfo

50. Thank you!
Gracias!