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Getting Data for (Business) Statistics: What’s new? What’s next?

Getting Data for (Business) Statistics: What’s new? What’s next?. Ger Snijkers Statistics Netherlands Utrecht University. Getting Data for Business Statistics. Statistical picture of a country. How do we get the data we need for business statistics? Yesterday, today, tomorrow. Data

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Getting Data for (Business) Statistics: What’s new? What’s next?

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  1. Getting Data for (Business) Statistics:What’s new? What’s next? Ger Snijkers Statistics Netherlands Utrecht University NTTS2009, 18-20 February 2009, Brussels

  2. Getting Data for Business Statistics Statistical picture of a country • How do we get the data we needfor business statistics? • Yesterday, today, tomorrow Data • In time • Complete • Correct Respondent Parameters Survey Parameters in and out of control NSI NTTS2009, 18-20 February 2009, Brussels

  3. Getting Data for Business Statistics • Over the years: • Yesterday: ICES-I* 1993 ICES-II 2000 CASM** 1980’s • Today: ICES-III 2007• Challenges and developments• A few examples • Tomorrow• What’s next ? • * International Conference on Establishment Surveys • ** Cognitive Aspects of Survey Methodology NTTS2009, 18-20 February 2009, Brussels

  4. Getting Data for Business StatisticsYesterday • ICES-I (1993): • 1. Surveying various branches of industry:agriculture, energy, health care, trade, finance, education, manufacturing industry • 2. Quality of business frames & sampling • 3. Data analysis & Estimation • Data collection methodology:data quality, registers, non-response, Q-design •  ‘Stove-pipe’ approach •  Single-mode survey designs NTTS2009, 18-20 February 2009, Brussels

  5. Black box External business factors • Econ. climate • Regulatory requirements • Political climate Internal business factors • Policy • Data • Resources • Market position The survey: • Topic • Population and sample • Sponsor / Survey organisation • Resources • Planning • Authority/confidentiality Informant: • Mandate • Data knowledge • Job priority The survey design Single mode Motivation Paper Modes of data collection Contact strategy Letters: Mandatory Response • In time • Complete • Correct Questionnaire Data WE want Respondentburden • De facto • Perception Answering behaviour Decision to participate Survey designs not coordinated: • ‘Stove-pipe’ approach NSI A business NSI NTTS2009, 18-20 February 2009, Brussels

  6. Getting Data for Business StatisticsYesterday • CASM (started in 1980’s; USA, Germany): • Cognitive Aspects of Survey Methodology • From simple stimulus-response model to modelling Question-Answer Process:- comprehension- retrieval- evaluation- response • Pre-testing facilities NTTS2009, 18-20 February 2009, Brussels

  7. Getting Data for Business StatisticsToday • ICES-III (2007): • Survey data collection methodology:• questionnaire design & pre-testing • survey participation: non-response reduction, response burden, bias • mixed-mode designs & e-data collection• understanding the response process in bus’s • Using administrative data • Business frames & Sampling • Weighting, Outlier detection, Estimation & Data analysis NTTS2009, 18-20 February 2009, Brussels

  8. Black box External business factors • Econ. climate • Regulatory requirements • Political climate Internal business factors • Policy • Data • Resources • Market position The survey: • Topic • Population and sample • Sponsor / Survey organisation • Resources • Planning • Authority/confidentiality Statistical picture of a country Informant: • Mandate • Data knowledge • Job priority Image The survey design Motivation Modes of data collection Contact strategy Response • In time • Complete • Correct Questionnaire Respondentburden • De facto • Perception Answering behaviour Decision to participate Registerdata • More than one survey • More than once • In other ways: ○ Registers ○ EDI NSI A business NSI NTTS2009, 18-20 February 2009, Brussels

  9. Getting Data for Business StatisticsOver the years • General picture: • 1993: • 2007: • ‘Stove-pipe’ approach •Single-mode designs • Survey organisation is central 2000: • Transition • Systematisation and standardisation of methods •Towards multi-source/mixed-mode designs •Respondent is central: tailoring NTTS2009, 18-20 February 2009, Brussels

  10. Getting Data for Business StatisticsThe data collection design today • Challenges: • Good statistics:• relevant• more & integrated information• faster • Less money • Less compliance costs:• providing data only once to government • New technologies:• powerful computers, access to the internet • Consequences for the data collection … NTTS2009, 18-20 February 2009, Brussels

  11. Getting Data for Business StatisticsThe data collection design today • Use of administrative data:• Coordination of definitions: - variables - units• Quality of register data:- timeliness • Data collection without questionnaires:• EDI: XBRL• GPS • Surveys:• If other sources are not possible or insufficient • Process measurement and quality control• Getting insight in the data collection process NTTS2009, 18-20 February 2009, Brussels

  12. Getting Data for Business StatisticsThe data collection design today • Surveys: • • Sampling: - controlling for overlap across surveys - controlling for rotation over time (survey holiday) one statistical business register • In order to avoid this: NTTS2009, 18-20 February 2009, Brussels

  13. Getting Data for Business StatisticsThe data collection design today • Surveys: • •Sampling: - controlling for overlap across surveys - controlling for rotation over time (survey holiday) one statistical business register • •Mode: - Mixed-mode designs: paper, internet, CATI - Computer-assisted • •Questionnaires for web data collection: - Customization (tailoring) - Controlling the completion process (routing, checks) NTTS2009, 18-20 February 2009, Brussels

  14. Getting Data for Business StatisticsThe data collection design today • Surveys: • • Contact strategy: • - Mixed-mode: .. paper letters, brochures, telephone, .. e-mails, website information • - Message: .. Cooperation = mandatory! .. What, how, who, when? • - Cooperation no longer taken for granted: .. Motivating and stimulating respondents: . Cialdini: Compliance (persuasion) principles . Dillman: Social Exchange Theory • - Two-way communication via the internet NTTS2009, 18-20 February 2009, Brussels

  15. Getting Data for Business StatisticsThe data collection design today • Process measurement and quality control: • • Paradata – process data: • - Macro paradata (survey process data): .. Process summaries: response rates, timeliness of response, quality of response over time • - Micro paradata (process data at R level): .. Completion process: audit trails NTTS2009, 18-20 February 2009, Brussels

  16. Getting Data for Business StatisticsMacro paradata • Timeliness of response (Monthly Survey) Paper (letter + Q) Online (e-mail + e-Q) Reminder 1 Reminder 1 Reminder 2 Reminder 2 Number of responses NTTS2009, 18-20 February 2009, Brussels Days Days

  17. Getting Data for Business StatisticsMacro paradata • R-indicator to monitor fieldwork of business surveys• The representativity of the Monthly Survey for industry and retail trade by number of fieldwork days. Industry Retail Retail Industry NTTS2009, 18-20 February 2009, Brussels

  18. Getting Data for Business StatisticsMicro paradata – audit trails • Completion process e-SBS: conscientious R NTTS2009, 18-20 February 2009, Brussels

  19. Getting Data for Business StatisticsMicro paradata: audit trails • Completion process e-SBS: quick ‘n’ dirty R NTTS2009, 18-20 February 2009, Brussels

  20. NTTS2009, 18-20 February 2009, Brussels

  21. Getting Data for Business Statistics The data collection design today • More complex than yesterday: • • More data sources • • Dependent on providers of registers• Integration of sources • • Mixed-mode surveys • • Coordinated developments over modes• Tailoring to mode • • Tailoring to respondents • • Tailoring to target populations • Coordination over surveys (samples and Q’s) • Tomorrow, even more complex NTTS2009, 18-20 February 2009, Brussels

  22. Getting Data for Business StatisticsTomorrow • Multi-source/mixed-mode data collection • • Managing integrated sets of statistics (not stove-pipes)• Advanced statistical modelling and estimation• Coordinated data collection designs: - not single-purpose, but multi-purpose surveys• Advanced questionnaire design:- images, spoken language, animations, video pictures• Methodologists: competent in all modes • Opening the survey process • • Process measurement and quality control: - continuous measurement using paradata - responsive adaptive designs• Tailoring to the internal business’s processes • Improved communication with businesses • Opening the businesses • • Insight in the internal response processes NTTS2009, 18-20 February 2009, Brussels

  23. Getting Data for Business StatisticsWhat’s next? • Opening the businesses • • Insight in the response processes • A Business CASM movement: • Communicative Aspects of Business Survey Methodology • Communication sciences • Administrative sciences • Organisational sciences • Psychology (organisational, work and social, cognitive) NTTS2009, 18-20 February 2009, Brussels

  24. Black box External business factors • Econ. climate • Regulatory requirements • Political climate Internal business factors • Policy • Data • Resources • Market position The survey: • Topic • Population and sample • Sponsor / Survey organisation • Resources • Planning • Authority/confidentiality Statistical picture of a country Informant: • Mandate • Data knowledge • Job priority Image The survey design Motivation Modes of data collection Contact strategy Response • In time • Complete • Correct Questionnaire Respondentburden • De facto • Perception Answering behaviour Decision to participate Registerdata • More than one survey • More than once • In other ways: ○ Registers ○ EDI NSI A business NSI NTTS2009, 18-20 February 2009, Brussels

  25. Direct communication One coherent strategy with regard to tone-of-voice, lay-out, and compliance principles Decision to participate Response • in time, • complete, • correct Indirect communication Image NSI Getting Data for Business StatisticsCommunication model Communication we cannot control NTTS2009, 18-20 February 2009, Brussels

  26. Referencesin addition to proceedings paper • Bethlehem, J., F. Cobben, and B. Schouten (2008), Indicators for the Represen-tativity of Survey Response. Presentation at the 24th International Methodology Symposium of Statistics Canada: “Data Collection: Challenges, Achievements and New Directions”, 28-31 October 2008, Gatineau, Canada. • De Nooij, G. (2008), Representativity of Short Term Statistics. Statistics Netherlands, The Hague. • Groves, R.M. (2008), Dynamic Survey Design managed by modelled Paradata. Presentation at the 24th International Methodology Symposium of Statistics Canada: “Data Collection: Challenges, Achievements and New Directions”, 28-31 October 2008, Gatineau, Canada. • Scheuren, F. (2001), Macro and Micro Paradata for Survey Assessment. Urban Institute: unpublished paper, Washington D.C., USA. • Snijkers, G. (2007), Collecting Data for Business Statistics: Yesterday, Today, Tomorrow. Presentation at 56th Meeting of the ISI, 22-29 August 2007, Lisbon, Portugal. • Snijkers, G. (2008), Getting Data for Business Statistics: A Response Model for Business Surveys. Presentation at the 4th European Conference on Quality in Official Statistics, 8-11 July 2008, Rome, Italy. NTTS2009, 18-20 February 2009, Brussels

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