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www.statistik.at We provide information
Data sources and data collection – a first draft
Wolfgang BittermannDirectorate Spatial
Statistics
Helsinki24 October 2012
www.statistik.at slide 2 | 24 October 2012
The content
• 3 Subchapters1. Types of data sources 2. Data collection3. Country practices
• Open questions1. Extension: nr of pages, font type etc.2. Level of detail3. Links to other manuals e.g. Energy efficiency manual
(IEA), MESH (EUROSTAT) vs independent publication
www.statistik.at slide 3 | 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?)
www.statistik.at slide 4 | 24 October 2012
Types of data sources – Administrative data
1. Registers for public administration1. Population register2. Housing register3. Building register4. Car register
2. Private registers1. Sales registers2. Customers registers
3. Tax information
4. Customs data
www.statistik.at slide 5 | 24 October 2012
Types of data sources - New Surveys
i. Legal basea. obligatoryb. voluntary
ii. Typea. Censusb. Sample survey
iii. Respondentsa. Suppliersb. Consumers
iv. Formata. Paperb. Web based (electronic questionnaire)c. Interview
o Personal interview» Computer assisted (CAPI)» Non computer assisted
o Telephone interview» Computer assisted (CATI)» Non computer assisted
www.statistik.at slide 6 | 24 October 2012
Types of data sources – General structure
2. New SurveysAdvantagesDisadvantages
I. Legal basea. Obligatory
AdvantagesDisadvantagesTextTextSummery and conclusions
b. VoluntaryII. Type
www.statistik.at slide 7 | 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 8 | 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 9 | 24 October 2012
Data collection - 1
1. General aspects- Preparation/Questionnaire
o Complexityo Length/Completenesso Structureo Necessary explanationso Announcement
- Data validation/imputation/grossing upo Comprehensivenesso Documentationo Use of additional information
www.statistik.at slide 10 | 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)
www.statistik.at slide 11 | 24 October 2012
Data collections- 3
3. Metering- When metering- What metering
o Electricityo Natural gaso District heating
4. Modeling- When modeling- What modeling- Necessary preconditions
www.statistik.at slide 12 | 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
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