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Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Qualitative & Quantitative Data, Data Processing (healthkura.com)
1. Data Collection, Assessment of
Qualitative Data, Data Processing:
Key Issues
Bikash Sapkota
B. Optometry
Institute of Medicine, TU, Nepal
2. • Introduction to data
• Classification of data
• Collection of data
• Methods of data collection
• Assessment of qualitative data
• Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
Presentation Layout
3. What is data?
Data are observations or evidences about the social world
Data, the plural of datum, can be quantitative or qualitative
in nature
‘data is produced, not given’; that is, researchers choose
what to call data, it is not just ‘there’ to be ‘found’. (Marsh
1988)
- The Sage Dictionary of Social Research Methods
4. The terms 'data' and 'information' are used interchangeably
However the terms have distinct meanings
Data
Facts, events, transactions
which have been recorded
Input raw materials from
which information is
processed
Information
Data that have been
produced in such a way as
to be useful to the recipient
Basic data are processed in
some way to form
information
Data & Information
5. The research studies in behavioral science are mainly
concerned with the characteristics or traits
Thus, tools are administered to quantify these characteristics
- but all traits or characteristics can not be quantified
The data can be classified into two broad categories:
Data
Qualitative Data or
Attributes
Quantitative Data or
Variables
Nature of Data
6. Nature of Data
1.Qualitative Data or Attributes
The characteristics or traits for which numerical value
can not be assigned, are called attributes
e.g. gender, motivation, etc.
2. Quantitative Data or Variables
The characteristics or traits for which numerical value
can be assigned, are called variables
e.g. height, weight etc.
7. Constants
A constant is all characteristic or condition that is the same for
all the observed units or sample subjects of a study
Variables
The characteristic or the trait in the behavioral science which
can be quantified is termed as variable
Variables
Continuous variables Discrete variables
8. Variables
1. Continuous variables
A characteristic whose observation can take any values over a
particular range
It can assure either fractional or integral values
E.g. wt. of children in kg, height of pt.
2. Discrete variables
Are those on the other hand, which exist only in units not the
fractional value (usually units of one)
E.g. No. of cataract pts. in a village, WBC count
9. Attribute vs. Variable
Attribute Variable
A category of a characteristic,
to which a subject either
belongs or does not belong or
property that a subject either
possesses or does not possess
The attributes are becoming
sick, describing blood group
etc.
Variable describes a
characteristic in terms of a
numerical value, which is
expressed in units of
measurements
The variables are height,
weight, blood pressure, age of
pts. etc.
10. Qualitative Data
In such data there is no notion of magnitude of size of the
characteristic
They are just categorized
The data are classified by counting the individuals having the
same characteristics or attribute and not by measurement
For examples: Gender: male/female
Disease: present/absent
Smoke: smoking/not smoking
These data can be measured in nominal and ordinal scales
11. Quantitative Data
Anything that can be expressed as a number, or quantity or
magnitude
Describes characteristics in term of a numerical value, which are
expressed in units of measurements
E.g. level of hemoglobin in the blood, no. of glaucoma pts., intra
ocular pressure, weight, etc.
Quantitative observations: as each individual is represented by a
number
These data can be measured in interval and ratio scales
12. Measurement Scale
The choice of appropriate statistical technique depends
upon the type of data in question
Qualitative
Data
• Nominal Scale
• Ordinal Scale
Quantitative
Data
• Interval Scale
• Ratio Scale
13. Nominal Scale
The least precise or crude of the 4 basic scales of
measurement
Implies the classification of an item into 2 or more categories
without any extent or magnitude
There is no particular order assigned to them
The frequency or numbers are used to give a name to
something that may be used for determining per cent, mode
Eg. boys and girls; pass and fail; rural and urban
14. Ordinal Scale
The ordinal scale is more precise scale than the nominal
scale
The variables has been categorized or leveled with
meaningful natural order
But there is no information about the interval
Eg. Pain: none, mild, moderate, severe
15. Interval Scale
The interval scale is more precise and refined scale than
nominal and ordinal scales
This scale has all the characteristics and relationship of the
ordinal scale, besides which distances between any two
numbers on the scale are known
The size of interval between two observations can be
measured
Eg. The temperature of a body
16. Ratio Scale
It has the same properties as an interval scale as well as a
true or absolute zero value
The ratio scale numerals have the qualities of real
numbers, and can be added, subtracted, multiplied or
divided
Eg. Mean systolic BP
17. Process of systematic gathering of data for a particular
purpose from various sources, that has been systematically
observed, recorded, organized
It is the first step of statistical study
There are several ways of collecting data
The choice of procedures usually depends on the objectives
and design of the study and the availability of time, money
and personnel
Collection of Data
18. To obtain information
To keep on record
To make decisions about important issues
To pass information onto others
For research study
Purpose of Data Collection
19. Data collection is an extremely important part of any
research because the conclusions of a study are
based on what the data reveal
How Important it is?
20. Nature, scope & objective of the enquiry
Sources of information
Availability of fund
Techniques of data collection
Availability of trained persons
Factors to be considered before data
collection
22. Internal sources of Data
o Many institutions and
departments have information
about their regular functions ,
for their own internal
purposes
o When those information are
used in any survey is called
internal sources of data
o Eg. social welfare society
External sources of data
o When information is collected
from outside agencies is called
external sources of data
o Such types of data are either
primary or secondary
o This type of information can
be collected by census or
sampling method by
conducting survey
Internal & External Sources of Data
23. Data collected by investigator from personal experimental
studies for a specific research goal is called primary data
The data are collected specially for a research project
Used when secondary data are unavailable and inappropriate
Data are to be unique, original, reliable and accurate in nature
Primary data hahe not been changed or altered by human
beings, therefore its validity is greater than secondary data
Primary Data
24. Demerits
Evaluated cost
Time consuming
More number of resources
are required
Inaccurate feedback
Required lot of skill with
labor
Targeted issues are
addressed
Data interpretation is better
Merits
High accuracy of data
Greater control
Address as specific research
issues
Primary Data
26. The data is collected by the investigator personally, he/she
must be a keen observer
He/she asks or cross-examines the informant and collects
necessary information
It is original in character
Direct personal observation
27. Direct personal observation is adopted in the following cases
Where greater accuracy is needed
Where the field of enquiry is not large
Where confidential data are to be collected
Where sufficient time is available
Suitability of direct personal observation
28. Merits
Original data
True and reliable data
Encouraging response
because of personal
approach
A high degree of accuracy
Direct personal observation
Demerits
Unsuitable in large area
Expensive & time-consuming
Untrained investigator brings
worst results
Collection of information
according to the ease of the
informant
29. The investigator approaches the witness or third parties,
who are in touch with the informant
The enumerator interviews the people, who are directly or
indirectly connected with the problem under the study
Generally this method is employed by different enquiry
committees and commissions
The police department generally adopts this method to
get clues of thefts, riots , murders, etc.
Indirect oral interview
30. It is more suitable when the area to be studied is large
It is used when direct information cannot be obtained
This system is generally adopted by governments
Suitability of indirect oral interview
31. Merits
Simple and convenient
Saves time, money and labor
Useful in investigation of a large area
Adequate information can be had
Demerits
Information can’t be relied as absence of direct contact
Interview with an improper man will spoil the results
To get real data, a sufficient no. of people are to be interviewed
Careless attitude of informant affects the degree of accuracy
Indirect oral interview
32. The local agents or correspondents will be appointed, they
collect the information and transmit it to the office or person
They do according to their own ways and tastes
Adopted by newspapers, agencies, etc.
The informants are generally called correspondents
Suitable in those cases where the information is to be
obtained at regular intervals from a wide area
Information through agencies
33. Merits
Demerits
Extensive information can be had
It is the most cheap and economical method
Speedy information is possible
It is useful where information is needed regularly
The information may be biased
Degree of accuracy cannot be maintained
Uniformity cannot be maintained
Data may not be original
Information through agencies
34. The questionnaires is sent to the respondents, there are blank
spaces for answers
A covering letter is also sent along with the questionnaire,
requesting the respondent to extend their full cooperation
Adopted by research workers, private individuals, non-officials
agencies and government
Appropriate in cases where informants are spread over a wide
area
Mailed questionnaires
35. Merits
Of all the methods, the mailed questionnaire is the most
economical
It can be widely used, when the area of investigation is large
It saves money, labor and time
Demerits
Cannot be sure about the accuracy and reliability of the data
There is long delay in receiving questionnaires duly filled in
Mailed questionnaires
36. Very similar to the questionnaire method
The main difference is that a schedule is filled by the
enumerator who is specially appointed for the purpose
Enumerator goes to the respondents, asks them the
questions from the Performa in the order listed, and records
the responses in the space provided
Enumerators must be trained in administering the schedule
Data Collection Through Schedules
37. A detailed study of geographical area to gather data,
attitudes, impressions, opinions, satisfaction level etc., by
polling a section of the population
Census Survey
• Conducted
regularly at large
interval of time
Continuous
Survey
• Conducted
regularly and
frequently
Ad-hoc Survey
• Conducted at
specific times for
specific need
• ‘as and when’
required
Survey
Types
38. Merits
Cover large population
Less expensive
Information is accurate
Demerits
On small scale survey
avoided
Time consuming
Information does not
penetrate deeply
Researcher must have
good knowledge
Survey
39. It is the method of comprehensive study of social unit which
may be a person, a family, an institution, an organization or a
community
Merits
Direct behavioral study
Real & personal
experience record
Make possible the
study of social change
Increase analysis
ability & skills
Demerits
One case almost different
from another case
Personal bias
Use only in limit sphere
More time & money
consuming
Case Study
40. Useful to further explore a topic, providing a broader
understanding of why the target group may behave or
think in a particular way
And assist in determining the reason for attitudes and
beliefs
Conducted with a small sample of the target group and
Used to stimulate discussion and gain greater insights
Focus Group Discussion
41. Merits
Useful when exploring cultural values and health beliefs
Can be used to explore complex issues
Can be used to develop hypothesis for further research
Do not require participants to be literate
Demerits
Lack of privacy/anonymity
Potential for the risk of ‘group think’
Potential for group to be dominated by one or two people
Group leader needs to be skilled at conducting focus groups, dealing
with conflict, drawing out passive participants
Time consuming to conduct and analyse
Focus Group Discussion
42. Application and combination of several research methods in the
study of the same phenomenon
Researchers can hope to overcome the weakness or intrinsic biases
and the problems that come from single method, single-observer
and single-theory studies
The purpose of triangulation in qualitative research is to increase
the credibility and validity of the results
Triangulation
Types (Denzin
1978)
Data
Triangulation
Investigator
Triangulation
Theory
Triangulation
Methodological
Triangulation
Beating the Bias
43. Secondary data are those data which have been already
collected and analysed by some earlier agency for its own
use and later the same data are used by a different agency
Published Sources Unpublished
Sources
Sources of
Secondary Data
Secondary Data
44. Various governmental, international and local agencies
publish statistical data, and chief among them are:
International publications: They are UNO, WHO, Nature, etc.
Official publications of Government: Department of Drug
Administration, Central Bureau of Statistics
Semi-Official publications: Semi-Govt. institutions like
Municipal Corporation, District Board, etc. publish reports
Published Sources
45. Publications of Research Institutions: Nepal Development
Research Institute, Nepalese Journal of Ophthalmology etc.
publish the finding of their research program
Journals and Newspapers: Current and important materials
on statistics and socio-economic problems can be obtained
from journals and newspapers like, Swasthya Khabar Patrika,
Health Today Magazine, The Sight, etc.
Published Sources
46. Records maintained by various government and private
offices
Researches carried out by individual research scholars in
the universities or research institutes
According to Prof. Bowley “It is never safe to take published statistics
at their face value without knowing their meaning and limitations and
it is always necessary to criticize arguments that can be based on
them.”
Unpublished Sources
47. Before using the secondary data, the investigators should
consider the following factors:
Precautions in the use of Secondary Data
Suitability of data
Adequacy of data
Reliability of data
48. Reliability of data – may be tested by checking:
Who collected the data?
What were the sources of the data?
Was the data collected properly?
Suitability of data
Data that are suitable for one enquiry may not be necessarily
suitable in another enquiry
Objective, scope and nature of the original enquiry must be studied
Adequacy of data – data is considered inadequate, if they are related
to area which may be either narrower or wider than the area of the
present enquiry
Secondary Data must possess the following
characteristics
49. Primary data
o Real time data
o Sure about sources of data
o Help to give results/ finding
o Costly and time consuming
process
o Avoid biasness of response
data
o More flexible
Secondary data
o Past data
o Not sure about of sources of
data
o Refining the problem
o Cheap and no time
consuming process
o Can not know in data
biasness or not
o Less flexible
50. The characteristics or traits for which numerical value can
not be assigned, are called qualitative data (attributes)
e.g. gender, color, honesty etc.
Methods of collecting qualitative data
Methods of Qualitative
Data Collection
Direct
Observation
In-depth
Interview
Case Study Triangulation
Use of
Secondary
Data
Assessment of Qualitative Data
51. Classification of Qualitative data
Qualitative
Data
Geographical
Classification
Chronological
Classification
Qualitative
Classification
Assessment of Qualitative Data
52. Tabulation of Qualitative Data
Qualitative data values can be organized by a frequency
distribution
A frequency distribution lists
– Each of the categories
– The frequency/counts for each category
Assessment of Qualitative Data
53. Frequency Table
A simple data set is: cataract, cataract, keratoconus, glaucoma,
glaucoma, cataract, glaucoma, cataract
A frequency table for this qualitative data is
The most commonly occurring eye condition is cataract
Eye condition Frequency
Cataract 4
Keratoconus 1
Glaucoma 3
Assessment of Qualitative Data
54. What Is A Relative Frequency?
The relative frequencies are the proportions (or percents)
of the observations out of the total
A relative frequency distribution lists
– Each of the categories
– The relative frequency for each category
Relative frequency = Frequency/Total
Assessment of Qualitative Data
55. Relative Frequency Table
A relative frequency table for this qualitative data is
A relative frequency table can also be constructed with
percents (50%, 12.5% and 37.5% for the above table)
Refractive Error Relative Frequency
Cataract .500 (=4/8)
Keratoconus .125 (=1/8)
Glaucoma .375 (=3/8)
Assessment of Qualitative Data
56. Graphical representation Of Qualitative Data
Bar Diagram
Pie or Sector
Diagram
Line Diagram
Pictogram
Map Diagram or
Cartogram
Assessment of Qualitative Data
58. The data, after collection, has to be prepared for analysis
Collected data is raw and it must undergo some processing
before analysis
The result of the analysis are affected a lot by the form of
the data
So, proper data processing is must to get reliable result
Data Processing
59. Checking the questionnaires and schedules
Reduction of mass data to manageable proportion
Sum up the materials so as to prepare tables, charts,
graphs and various groupings and breakdowns for
presenting the result
Minimizing the errors which may creep in at various stage
of the survey
Objectives of Data Processing
60. 1. Manual Data Processing
Involves human intervention
Implies many chances for errors, such as delays in data
capture, high amount of operator misprints
Implies higher labor expenses in regards to spending for
equipment and supplies, rent, etc.
Types of Data Processing
61. 2. Mechanical Data Processing
Different calculations and processing are performed
using mechanical machines like calculators etc.
The use of mechanical machines makes data processing
easier and less time- consuming
The chances of errors also become far less than manual
data processing
Types of Data Processing
62. 3. Electronic Data Processing
Processing of data by use of computer and its programs
Types of Data Processing
63. 4. Real Time Processing
There is a continual input, process and output of data
Data has to be processed in a small stipulated time period
(real time)
Eg, when a bank customer withdraws a sum of money from
his or her account it is vital that the transaction be processed
and the account balance updated as soon as possible
Types of Data Processing
64. 5. Batch Processing
In a batch processing group of transactions collected over a
period of time is collected, entered, processed and then the
batch results are produced
Batch processing requires separate programs for input, process
and output
It is an efficient way of processing high volume of data
Eg, Payroll system, examination system and billing system
Types of Data Processing
65. QUESTIONNAIRE
CHECKING EDITING CODING CLASSIFICATION
TABULATION
GRAPHICAL
REPRESENTATION
DATA CLEANINGDATA ADJUSTING
The processing of data involves activities such as
Important Steps in Data Processing
66. When the data is collected through questionnaires, the first
steps of data process is to check the questionnaires if they
are accepted or not
Not accepted if:
Gives the impression that respondent could not
understand the questions
Incomplete partially or fully
Answered by a person who
has inadequate knowledge
Questionnaire Checking
67. Process of examining the data collected in
questionnaires/schedules
to detect errors and omissions
to correct these when possible
to make sure the schedules are ready for tabulation
Data Editing
68. Editor is responsible for seeing that the data are;
Accurate as possible
Consistent with other facts secured
Uniformly entered
As complete as possible
Acceptable for tabulation and arranged to facilitate
coding tabulation
Data Editing
69. • Data form complete
• Free of bias, errors,
inconsistency and dishonesty
Editing for quality
• Modification to facilitate
tabulation,
• Ignoring extremely high/low
Editing for
tabulation
• Translating or rewriting
Field editing
• Wrong and replacement
Central editing
Types of Editing
70. To gather information
To make data relevant and appropriate for analysis
To find errors and modify them
To ensures that the information provided is accurate
To establish the consistency of data
To determine whether or not the data are complete
To obtain the best possible data available
Necessity of Editing
71. Process of assigning numerals or other symbols to answers so
that responses can be put into limited number of categories
or classes
Translating answers into numerical values or assigning
numbers to the various categories of a variable to be used in
data analysis
Coding is done by using a code book, code sheet, and a
computer card
Coding is done on the basis of the instructions given in the
codebook
The codebook gives a numerical code for each variable
Coding of Data
72. 72
• A codebook contains coding instructions and the necessary
information about variables in the data set
• A codebook generally contains the following information:
- column number
- record number
- variable number
- variable name
- question number
- instructions for coding
Codebook
73. To organize data code
To form structure for coding
For interpretation of data
For conclusions of data coded
To translating answers into numerical values
To assign no. to the various categories for data analysis
It is necessary for efficient analysis
Necessity of Coding
74. The process of arranging the primary data in a definite
pattern and presenting it in a systematic way
The crude data obtained from experiment or survey is
classified according to their properties
Classification cab be done by qualitatively or quantitatively
Classification of Data
75. The classified data is more easily understood
It presents the facts into a simpler form
It facilitates quick comparison
It helps for further statistical treatment such as
average, dispersion etc.
It detects the error easily
Objectives of classification
77. Geographical Classification
Data are classified by location of occurrence (i.e. area, region)
eg cataract pts. district wise
Chronological classification
Data are classified by time of occurrence of the observations,
events
The categories are arranged in chronological order
eg, no. of trachoma pts. recorded from 2000 to 2010
Qualitative Classification
78. Qualitative classification (Classification according to attributes)
Data are classified according to some quality such as religion,
literacy, sex, occupation etc.
Simple classification
Classification is made into 2 classes, such as classification by
male or female
Manifold classification
2 or more than 2 attributes are studied simultaneously
Eg. Classification according to sex, again marital status and
again literacy
Qualitative Classification
79. Process of systematic organization and recording of
long series of data for further analysis and
interpretation into rows and columns
It is concise, logical & orderly arrangement of data in a
columns & rows
Tabulation
80. It presents an overall view of findings in a simpler way
To identify trends
It displays relationships in a comparable way between parts
of the findings
It conserves space and reduces explanatory and descriptive
statement to a minimum
It facilitates the process of comparison
It provides a basis for various statistical computations
Usefulness of Tabulation
81. Graphical Representation
Graphs help to understand the data easily
A single picture is worth a thousand words-so goes a
common saying
The non statistical minded people also easily understands
the data and compares them
Most common graphs are bar charts and pie charts in
qualitative study and histogram in quantitative study
82. Graphical Representation
Advantages
It is easier to read
Can show relationship between 2 or more sets of
observations in one look
Universally applicable
Has high communication power
Simplifies complex data
Has more lasting effect on brain
83. Presentation of Qualitative data
1. Bar Diagram
• Consists of equally spaced vertical (or horizontal)
rectangular bars of equal width placed on a common
horizontal (or vertical) base line
• The categories are placed on X-axis and their frequencies
on Y-axis
Graphical Representation
87. Graphical Representation
Presentation of Quantitative Data
1.Histogram
• Graphical representation of a set of contiguously drawn
bars
• Most popular graph for continuous variable
90. Includes consistency checks and treatment of missing
responses
Although preliminary consistency checks have been made
during editing, the checks at this stage are more thorough
and extensive, because they are made by computer
Computer packages like SPSS, SAS, EXCEL and MINITAB can
be programmed to identify out-of-range values for each
variable
Data Cleaning
91. If any correction needs to be done for the statistical
analysis, the data is adjusted accordingly
Data Adjusting
Data adjusting is not always necessary but it may
improve the quality of analysis sometimes
Data Analysis
92. • Biostatistics by Prem P. Panta
• Fundamentals of Research Methodology and
Statistics by Yogesh k. Singh
• Research Design by J. W. Creswell
• Internet
References
Thank