DATA COLLECTION
What is Data?
Data is a collection of facts, such as numbers, words, measurements,
observations or even just descriptions of things
Data is all around us. But what exactly is it?
Data is a value assigned to a thing.
Color, Shape, Number,
Condition, Size
QUALITATIVE DATA : is everything that refers to the
quality of something: A description of colours,
texture and feel of an object, a description of
experiences, and interviews are all qualitative data.
QUANTITATIVE DATA : is data that refers to a
number. E.g. the number of golf balls, the size,
the price, a score on a test etc
Categorical Data : puts the item you are describing into a
category: For example, the condition “used” would be
categorical and also categories such as “new”, “used”,
”broken” etc
Discrete Data : is numerical data that has gaps in it: e.g.
the count of golf balls. There can only be whole numbers
of golf ball (there is no such thing as 0.3 golf balls).
Continuous Data : is numerical data with a continuous
range: e.g. size of the golf balls can be any value (e.q.
10.53mm or 10.54mm but also 10.536mm). In
continuous data, all values are possible with no gaps in
between.
Primary Data
Secondary Data
From Data to Information to Knowledge
Data, when collected and structured becomes more useful and meaningful.
Color White
Category Sport - golf
Condition Used
Diameter 43 mm
Price (per ball) RM2.00
PhD
WISDOM
MSc
KNOWLEDGE
BSc
INFORMATION
DATA
LINEAR MODEL FOR RESEARCH PROCESS
Testing validity
Intrepetation
Data Collection
Sampling
Operationalization
Hypothesis
Theory
Data Collection Techniques
Observations,
Tests,
Surveys,
Document analysis
Experiments
Basic Issues
• Main questions:
* Study objectives?
* What is being investigated?
* Attributes & variables involved?
* Variable measurement?
* Method of data collection?
• Main problems with data:
* Sufficient amount but irrelevant
* Relevant but insufficient
* Too much data, some useless
• Implications:
* Waste of resources
* Loose analysis
* Non-fulfilling objectives
* Unresolved research questions
* Lack of quality thesis
Determining Data
• Research issue, objectives, hypotheses?
* Maintaining the “thesis”
* What are involved in maintaining your idea, testing
hypotheses, etc.
* What “answers” to be required
• In what way research issue is to be operationalised?
* What cause-and-effect phenomenon to be explained?
* What variables are involved?
* What data required for the variables?
* How to measure them?
• Types of data
* Primary
* Secondary
* others
Principles of Data Collection
• Understanding and knowing what types of data required
• Collect only relevant data
• Determine methods of data collection
* Survey/questionnaire
* Observation, participatory
* Standard instruments
* Content analysis, etc
• Where, who, how, and when to collect
* Research design
* Sampling procedure
* Prepare field work schedule/data plan
* Conduct preliminary investigation
• Assess situation and prepare further strategies
Data plan
Scope of data Type Method Source
1. Geography Secondary MPJBT, UTM
2. Jemaah Primary Sample Mosque in
Skudai
3. Tenants Primary Sample Residential area
in Skudai
4. Patrons Primary Sample Shops in Skudai
5. Public users Primary Sample Public facilities in
Skudai
Instrumentation
• A process of selecting and developing research tool for the
purpose of data collection
• Examples of instrument:
* Questionnaire
* Interview checklist
* Observational form
* Attitude/view scale
* Content analysis form
* Researcher-designed achievement test
* Field Tools and equipments
• Depends on method of study
Important aspects of instrumentation
• Reliability: can it produce consistent results?
• Validity: can it fulfill the required function?
• Feasibility: can it fulfill the need of the
researcher→ Reliable? Valid?
• Calibration needed
Questionnaire
• Crystallise research issue and objectives
• List specific questions, the issue & objectives
• Identify & list key words and their relationships
• Identify cause-and-effect explanation by relating all the
keywords
• Identify how to operationalise the issue
* define concepts
* identify variables
* variable measurement
• Construct questionnaire table
* Related questions that address issue & objectives
Primary and Secondary Data
Secondary Data
• Secondary data – data someone else has
collected
Secondary Data – Examples of Sources
• District health departments
• Vital Statistics – birth, death certificates
• Hospital, clinic, school nurse records
• Private and foundation databases
• Federal and State governments
• Surveillance data from state government programs
• Federal agency statistics
• Dept of Environment
Secondary Data – Limitations
• When was it collected? For how long?
– May be out of date for what you want to analyze.
– May not have been collected long enough for
detecting trends.
Secondary Data – Limitations
• Is the data set complete?
– There may be missing information on some
observations
– Unless such missing information is caught and
corrected for, analysis will be biased.
Secondary Data – Limitations
• Are there confounding problems?
– Sample selection bias?
– Source choice bias?
– In time series, did some observations drop out
over time?
Secondary Data – Limitations
• Are the data consistent/reliable?
– Did variables drop out over time?
– Did variables change in definition over time?
Secondary Data – Limitations
• Is the information exactly what you need?
– In some cases, may have to make certain adjustments to
suit your data requirements. Are they reliable? Is there
correlation to what you actually want to measure?
Secondary Data – Advantages
• No need to reinvent the wheel.
– If someone has already found the data, take
advantage of it.
Secondary Data – Advantages
• It will save you money.
– Even if you have to pay for access, often it is
cheaper in terms of money than collecting your
own data.
Secondary Data – Advantages
• It will save you time.
– Primary data collection is very time consuming.
Secondary Data – Advantages
• It may be very accurate.
– When especially a government agency has
collected the data, incredible amounts of time and
money went into it. It’s probably highly accurate.
Secondary Data – Advantages
• It has great exploratory value
– Exploring research questions and formulating
hypothesis to test.
Primary Data
• Primary data – data you collect directly from
source
Primary Data - Examples
• Surveys
• Focus groups
• Questionnaires
• Personal interviews
• Experiments and field study
Primary Data - Limitations
• Do you have the time and money for:
– Designing your collection instrument?
– Selecting your population or sample?
– Pre-testing/piloting the instrument to work out
sources of bias?
– Administration of the instrument?
– Entry/collation of data?
Primary Data - Limitations
• Uniqueness
– May not be able to compare to other populations
Primary Data - Limitations
• Researcher error
– Sample bias
– Other confounding factors
Precision versus accuracy
"Precise" means sharply defined or measured.
"Accurate" means truthful or correct.
Both Accurate Accurate
and Precise Not precise
Not accurate
But precise
Neither accurate
nor precise
PhD
WISDOM
MSc
KNOWLEDGE
BSc
INFORMATION
DATA
The Data Equation
Oceans of
Data
Ocean Biodiversity
Informatics, Hamburg
29 Nov
The Data Equation
Rivers of
Information
Ocean Biodiversity
Informatics, Hamburg
29 Nov
The Data Equation
Streams of
Knowledge
Ocean Biodiversity
Informatics, Hamburg
29 Nov
The Data Equation
Drops of
Ocean Biodiversity
Informatics, Hamburg
Wisdom & Understanding
29 Nov
