Wolfgang Bittermann
Directorate Spatial
Statistics
Data sources and data
collection – a first draft
Helsinki
24 October 2012
www.statistik.at
We provide information
The content
• 3 Subchapters
1.
2.
3.
•
Types of data sources
Data collection
Country practices
Open questions
1.
2.
3.
Extension: nr of pages, font type etc.
Level of detail
Links to other manuals e.g. Energy efficiency manual
(IEA), MESH (EUROSTAT) vs independent publication
www.statistik.at
slide 2 | 24 October 2012
Types of data sources - Overview
1. Administrative data sources
2. New Surveys
3. Additional questions to already existing surveys
4. Metering


Smart meters and smart grids
In situ measurements
5. Models
6. (Integrated Approach?)
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slide 3 | 24 October 2012
Types of data sources – Administrative data
1. Registers for public administration
1.
2.
3.
4.
Population register
Housing register
Building register
Car register
2. Private registers
1.
2.
Sales registers
Customers registers
3. Tax information
4. Customs data
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slide 4 | 24 October 2012
Types of data sources - New Surveys
i.
Legal base
a.
b.
ii.
obligatory
voluntary
Type
a.
b.
iii.
Census
Sample survey
Respondents
a.
b.
iv.
Suppliers
Consumers
Format
a.
b.
c.
Paper
Web based (electronic questionnaire)
Interview
o
o
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Personal interview
» Computer assisted (CAPI)
» Non computer assisted
Telephone interview
» Computer assisted (CATI)
» Non computer assisted
slide 5 | 24 October 2012
Types of data sources – General structure
2. New Surveys
Advantages
Disadvantages
Legal base
I.
a.
Obligatory
Advantages
Disadvantages
Text
Text
Summery and conclusions
b. Voluntary
II.
Type
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slide 6 | 24 October 2012
Types of data sources – Example
• Consumer Surveys (households)

Advantages
 Comprehensive information on all fuels used in private
households
 Best achievable data quality if they are well prepared and
combined with a comprehensive data validation process
 Can be used directly and as input for model calculations

Disadvantages




Resource intensive
Expensive
Time consuming
High respondent burden
www.statistik.at
slide 7 | 24 October 2012
Types of data sources – Example
• Consumer Surveys
Conclusions and summery



The 4 main elements to achieve good results are a
careful preparation, a simple questionnaire, well
trained interviewers and comprehensive data
validation.
Grossing up procedures can be improved by using
supplier information, e.g. gas meters attributed to
households.
Model based extrapolation e.g. heating degree days
help decrease survey frequency.
www.statistik.at
slide 8 | 24 October 2012
Data collection - 1
1.
General aspects
 Preparation/Questionnaire
o Complexity
o Length/Completeness
o Structure
o Necessary explanations
o Announcement
 Data validation/imputation/grossing up
o Comprehensiveness
o Documentation
o Use of additional information
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slide 9 | 24 October 2012
Data collection - 2
2.
New surveys/questionnaires
 User and expert implementation into questionnaire
development
 Taking into account respondents knowledge
 Test survey
 Taking into account resource availability (budget, staff) and
data quality aspects (by choosing type and sample size)
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slide 10 | 24 October 2012
Data collections- 3
3.
Metering
 When metering
 What metering
o Electricity
o Natural gas
o District heating
4.
Modeling
 When modeling
 What modeling
 Necessary preconditions
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slide 11 | 24 October 2012
Country practices
• Harmonised format?
• How many examples?
• Results and metadata included?



Directly implemented
As annex
As link
• What level of detail – e.g. different levels already available
for Austria (Metadata reports for UNSD, EUROSTAT, IEA,
national)
• Shall quality aspects be included?



Directly implemented
As annex
As link
www.statistik.at
slide 12 | 24 October 2012
IEA-Examples
www.statistik.at
slide 13 | 24 October 2012
Please address queries to:
Wolfgang Bittermann
Contact information:
Guglgasse 13, 1110 Vienna
phone: +43 (1) 71128-7315
fax: +43 (1) 28-8155
wolfgang.bittermann@statistik.gv
.at
www.statistik.at
Thank you for your
attention
slide 14 | 8 March 2012