Thursday, 19 February 2009NTTS2009, February 2009, Brussels1 Getting Data for (Business) Statistics: Whats new? Whats next? Ger Snijkers Statistics Netherlands Utrecht University

Thursday, 19 February 2009NTTS2009, February 2009, Brussels2 Getting Data for Business Statistics How do we get the data we need for business statistics? Yesterday, today, tomorrow Data In time Complete Correct Statistical picture of a country NSI Survey Parameters in and out of control Respondent Parameters

Thursday, 19 February 2009NTTS2009, February 2009, Brussels3 Getting Data for Business Statistics Over the years: 1.Yesterday:ICES-I * 1993 ICES-II2000 CASM**1980s 2.Today:ICES-III2007Challenges and developmentsA few examples 3.TomorrowWhats next ? * International Conference on Establishment Surveys ** Cognitive Aspects of Survey Methodology

Thursday, 19 February 2009NTTS2009, February 2009, Brussels4 Getting Data for Business Statistics Yesterday 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 4.Data collection methodology: data quality, registers, non-response, Q-design Stove-pipe approach Single-mode survey designs

Thursday, 19 February 2009NTTS2009, February 2009, Brussels5 Response In time Complete Correct Decision to participateAnswering behaviourMotivation Respondent burden De facto Perception Internal business factors Policy Data Resources Market position Informant: Mandate Data knowledge Job priority External business factors Econ. climate Regulatory requirements Political climate The survey: Topic Population and sample Sponsor / Survey organisation Resources Planning Authority/confidentiality The survey design Contact strategy Questionnaire Modes of data collection NSI Black box A business Paper Data WE want Letters: Mandatory Survey designs not coordinated: Stove-pipe approach NSI Single mode

Thursday, 19 February 2009NTTS2009, February 2009, Brussels6 Getting Data for Business Statistics Yesterday CASM (started in 1980s; USA, Germany): Cognitive Aspects of Survey Methodology From simple stimulus-response model to modelling Question-Answer Process: - comprehension - retrieval - evaluation - response Pre-testing facilities

Thursday, 19 February 2009NTTS2009, February 2009, Brussels7 Getting Data for Business Statistics Today ICES-III (2007): 1.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 buss 2.Using administrative data 3.Business frames & Sampling 4.Weighting, Outlier detection, Estimation & Data analysis

Thursday, 19 February 2009NTTS2009, February 2009, Brussels8 Response In time Complete Correct Decision to participateAnswering behaviourMotivation Respondent burden De facto Perception More than one survey More than once In other ways: Registers EDI Internal business factors Policy Data Resources Market position Informant: Mandate Data knowledge Job priority External business factors Econ. climate Regulatory requirements Political climate Image The survey: Topic Population and sample Sponsor / Survey organisation Resources Planning Authority/confidentiality The survey design Contact strategy Questionnaire Modes of data collection NSI Register data Statistical picture of a country Black box A business

Thursday, 19 February 2009NTTS2009, February 2009, Brussels9 Getting Data for Business Statistics Over the years General picture: 1993: 2007: Stove-pipe approach Single-mode designs Survey organisation is central Systematisation and standardisation of methods Towards multi-source/mixed-mode designs Respondent is central: tailoring 2000: Transition

Thursday, 19 February 2009NTTS2009, February 2009, Brussels10 Getting Data for Business Statistics The 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 …

Thursday, 19 February 2009NTTS2009, February 2009, Brussels11 Getting Data for Business Statistics The 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 controlGetting insight in the data collection process

Thursday, 19 February 2009NTTS2009, February 2009, Brussels12 Getting Data for Business Statistics The 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:

Thursday, 19 February 2009NTTS2009, February 2009, Brussels13 Getting Data for Business Statistics The 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)

Thursday, 19 February 2009NTTS2009, February 2009, Brussels14 Getting Data for Business Statistics The data collection design today Surveys: Contact strategy: -Mixed-mode:.. paper letters, brochures, telephone,.. s, 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

Thursday, 19 February 2009NTTS2009, February 2009, Brussels15 Getting Data for Business Statistics The 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

Thursday, 19 February 2009NTTS2009, February 2009, Brussels16 Getting Data for Business Statistics Macro paradata Timeliness of response (Monthly Survey) Paper(letter + Q)Online ( + e-Q) Number of responses Days Reminder 1 Reminder 2

Thursday, 19 February 2009NTTS2009, February 2009, Brussels17 Getting Data for Business Statistics Macro paradata R-indicator to monitor fieldwork of business surveysThe representativity of the Monthly Survey for industry and retail trade by number of fieldwork days. Industry Retail

Thursday, 19 February 2009NTTS2009, February 2009, Brussels18 Getting Data for Business Statistics Micro paradata – audit trails Completion process e-SBS: conscientious R

Thursday, 19 February 2009NTTS2009, February 2009, Brussels19 Getting Data for Business Statistics Micro paradata: audit trails Completion process e-SBS: quick n dirty R

Thursday, 19 February 2009NTTS2009, February 2009, Brussels20 Completion Min. Max. Average N times started Completion time00:01:2711:29:5101:07:30 Used functionalities of the Questionnaire Used by % of Rs How many times used mean min.-max. Print button Save button

Thursday, 19 February 2009NTTS2009, February 2009, Brussels21 Getting Data for Business Statistics The data collection design today More complex than yesterday: More data sources Dependent on providers of registersIntegration of sources Mixed-mode surveys Coordinated developments over modesTailoring to mode Tailoring to respondents Tailoring to target populationsCoordination over surveys (samples and Qs) Tomorrow, even more complex

Thursday, 19 February 2009NTTS2009, February 2009, Brussels22 Getting Data for Business Statistics Tomorrow Multi-source/mixed-mode data collection Managing integrated sets of statistics (not stove-pipes)Advanced statistical modelling and estimationCoordinated data collection designs: - not single-purpose, but multi-purpose surveysAdvanced questionnaire design: -images, spoken language, animations, video picturesMethodologists: competent in all modes Opening the survey process Process measurement and quality control: -continuous measurement using paradata -responsive adaptive designsTailoring to the internal businesss processes Improved communication with businesses Opening the businesses Insight in the internal response processes

Thursday, 19 February 2009NTTS2009, February 2009, Brussels23 Getting Data for Business Statistics Whats 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)

Thursday, 19 February 2009NTTS2009, February 2009, Brussels24 Response In time Complete Correct Decision to participateAnswering behaviourMotivation Respondent burden De facto Perception More than one survey More than once In other ways: Registers EDI Internal business factors Policy Data Resources Market position Informant: Mandate Data knowledge Job priority External business factors Econ. climate Regulatory requirements Political climate Image The survey: Topic Population and sample Sponsor / Survey organisation Resources Planning Authority/confidentiality The survey design Contact strategy Questionnaire Modes of data collection Statistical picture of a country NSI Register data Black box A business

Thursday, 19 February 2009NTTS2009, February 2009, Brussels25 Getting Data for Business Statistics Communication model Direct communication Indirect communication Image NSI Response in time, complete, correct Decision to participate One coherent strategy with regard to tone-of-voice, lay-out, and compliance principles Communication we cannot control

Thursday, 19 February 2009NTTS2009, February 2009, Brussels26 References in 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, 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, 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, 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.