Www.uis.unesco.org Innovation data collection: Methodological procedures & basic forms Regional Workshop on Science, Technology and Innovation (STI) Indicators.

Slides:



Advertisements
Similar presentations
WP4: Building Capacities Belgrade, October 2-3, 2008 INNOVATION STATISTICS.
Advertisements

Multiple Indicator Cluster Surveys Survey Design Workshop
Introduction to UIS data collection tools and guidelines TRAINING WORKSHOP ON SCIENCE, TECHNOLOGY AND INNOVATION INDICATORS Cairo, Egypt.
Measuring innovation SUB-REGIONAL HANDS-ON TRAINING ON SCIENCE, TECHNOLOGY AND INNOVATION INDICATORS Damascus, Syria September 2010.
Measuring innovation: Main definitions - Part II South East Asian Regional Workshop on Science, Technology and Innovation Statistics.
Innovation data collection: Advice from the Oslo Manual South East Asian Regional Workshop on Science, Technology and Innovation Statistics.
Measuring innovation CENTRAL ASIAN SUB-REGIONAL CONSULTATION MEETING ON SCIENCE, TECHNOLOGY AND INNOVATION (STI) STATISTICS AND INDICATORS Tashkent, Uzbekistan.
Measuring innovation: Main definitions - Part I
Introduction to UIS data collection tools and guidelines West Africa Regional Science, Technology and Innovation Policy Reviews and.
The UIS strategy for collecting innovation indicators South East Asian Regional Workshop on Science, Technology and Innovation Statistics.
Innovation Surveys: Advice from the Oslo Manual South Asian Regional Workshop on Science, Technology and Innovation Statistics Kathmandu,
Measuring innovation: Main definitions & indicators
The UIS strategy for collecting innovation indicators Regional Workshop on Science, Technology and Innovation (STI) Indicators for Gulf.
Innovation Surveys: Advice from the Oslo Manual National training workshop Amman, Jordan October 2010.
R&D Surveys: Advice from the Frascati Manual National training workshop Amman, Jordan October 2010.
Improving Statistical Systems: Advice from the UIS Technical Guide National training workshop Amman, Jordan October 2010.
How to run an R&D survey and setting up and strengthening R&D statistical systems NATIONAL TRAINING WORKSHOP ON SCIENCE, TECHNOLOGY.
Measuring innovation South Asian Regional Workshop on Science, Technology and Innovation Statistics Kathmandu, Nepal 6-9 December 2010.
The UIS strategy for the collection and development of innovation indicators South Asian Regional Workshop on Science, Technology and.
Introduction to UIS data collection tools and guidelines South East Asian Regional Workshop on Science, Technology and Innovation Statistics.
Measuring innovation CARIBBEAN REGIONAL WORKSHOP ON SCIENCE, TECHNOLOGY AND INNOVATION (STI) INDICATORS St Georges, Grenada 1-3 February.
Survey design. What is a survey?? Asking questions – questionnaires Finding out things about people Simple things – lots of people What things? What people?
ESRC UK Longitudinal Studies Centre A Framework for Quality Profiles Nick Buck and Peter Lynn Institute for Social and Economic Research University of.
Introduction to Sampling : Censuses vs. Sample Surveys
Possibilities of exploiting administrative data in short term statistics in Poland Jacek Kowalewski STATISTICAL OFFICE IN POZNAŃ.
1 Third Workshop on ICP Western Asia Beirut, October 2004 Design of ICP price survey Sultan Ahmad, Consultant Based on Keith.
Introduction Simple Random Sampling Stratified Random Sampling
Unido.org/statistics International workshop on industrial statistics 8 – 10 July, Beijing Non response in industrial surveys Shyam Upadhyaya.
Standardized Scales.
Innovation Survey in Manufacturing Industry Rizka Rahmaida Presented in: Innovation Session 2011 South East Asian Regional Workshop On Science, Technology,
Brian A. Harris-Kojetin, Ph.D. Statistical and Science Policy
Sampling Strategy for Establishment Surveys International Workshop on Industrial Statistics Beijing, China, 8-10 July 2013.
Introduction to the Oslo Manual: main definitions (Part II) Introduction to the Oslo Manual: main definitions (Part II) ECO - UIS Regional.
Documentation and survey quality. Introduction.
Responding driven sampling Principles of Sampling Session 1.
11 Populations and Samples.
Stratified Simple Random Sampling (Chapter 5, Textbook, Barnett, V
FINAL REPORT: OUTLINE & OVERVIEW OF SURVEY ERRORS
The Research Process. Purposes of Research  Exploration gaining some familiarity with a topic, discovering some of its main dimensions, and possibly.
Sample Design.
DRAFT – NOT TO BE QUOTED Measuring Investment in Intangible Asset in the UK: results from a unique survey Presentation by Gaganan Awano, UK Office for.
MATH1342 S08 – 7:00A-8:15A T/R BB218 SPRING 2014 Daryl Rupp.
Sampling : Error and bias. Sampling definitions  Sampling universe  Sampling frame  Sampling unit  Basic sampling unit or elementary unit  Sampling.
MGT-491 QUANTITATIVE ANALYSIS AND RESEARCH FOR MANAGEMENT OSMAN BIN SAIF Session 13.
Giovanna Brancato, Marina Signore Istat Work Session on Statistical Metadata (METIS) Metadata and Quality Indicators Reuse for Quality reporting Geneva,
Rudi Seljak, Metka Zaletel Statistical Office of the Republic of Slovenia TAX DATA AS A MEANS FOR THE ESSENTIAL REDUCTION OF THE SHORT-TERM SURVEYS RESPONSE.
Chap 20-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 20 Sampling: Additional Topics in Sampling Statistics for Business.
1 11 th Joint UNECE/Eurostat/OECD Seminar on Business Registers Valentín Llorente García (INE - Spain) Session 3: Business Register as a source for further.
Evaluating a Research Report
Eurostat Overall design. Presented by Eva Elvers Statistics Sweden.
Scot Exec Course Nov/Dec 04 Survey design overview Gillian Raab Professor of Applied Statistics Napier University.
Copyright 2010, The World Bank Group. All Rights Reserved. Business tendency surveys, part 2 1 Business statistics and registers.
Introduction Osborn. Daubert is a benchmark!!!: Daubert (1993)- Judges are the “gatekeepers” of scientific evidence. Must determine if the science is.
CHAPTER 12 DETERMINING THE SAMPLE PLAN. Important Topics of This Chapter Differences between population and sample. Sampling frame and frame error. Developing.
Valentina Stoevska ILO Department of Statistics Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July
5-4-1 Unit 4: Sampling approaches After completing this unit you should be able to: Outline the purpose of sampling Understand key theoretical.
Data Collection Data Collection Definitions Level of Measurement Time Series and Cross-sectional Data Sampling Concepts Sampling Methods Data Sources Survey.
Academic Research Academic Research Dr Kishor Bhanushali M
United Nations Statistics Division Work Programme on Economic Census Vladimir Markhonko, Chief Trade Statistics Branch, UNSD Youlia Antonova, Senior Statistician,
Question paper 1997.
Overview and challenges in the use of administrative data in official statistics IAOS Conference Shanghai, October 2008 Heli Jeskanen-Sundström Statistics.
1Your reference The Menu of Indicators and the Core Set from the South African Point of View Moses Mnyaka 13/08/2009.
Workshop on MDG, Bangkok, Jan.2009 MDG 3.2: Share of women in wage employment in the non-agricultural sector National and global data.
Slide 7.1 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5 th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009.
R&D statistics in Denmark organization of data collection, and dissemination of R&D statistics.
Sampling and Sampling Distribution
Organization of efficient Economic Surveys
RESEARCH METHODOLOGY ON ENVIRONMENTAL HEALTH PRACTICE IN WEST AFRICA
Sampling and estimation
Presentation transcript:

Innovation data collection: Methodological procedures & basic forms Regional Workshop on Science, Technology and Innovation (STI) Indicators for Gulf countries Doha, Qatar 15 to 17 October 2012

Ch. 8 OM - Survey procedures Guidelines - collection and analysis of innovation data; Comparable results over time and across countries; Particular circumstances may require other methodology comparability.

The survey approach The subject approach: Innovative behaviour and activities of the firm as a whole; The object approach: Specific innovations (significant innovation of some kind, firms main innovation).

Populations (1) The target population: The target population: Innovation activities in the business enterprise sector (goods-producing and services industries); Minimum: all statistical units with at least ten employees: Small: 10-49; Medium: ; Large: 250 and above.

Populations (2) The frame population: The frame population: Units from which a survey sample or census is drawn; Based on the last year of the observation period for surveys; Ideal frame = up-to-date official business register NSOs; If the register forms the basis for several surveys (innovation, R&D, general business), the information can be restricted to innovation.

Survey methods (1) Mandatory Mandatory surveys increase response rates; Census or sample surveys? Census or sample surveys? Sample surveys - representative of target population (industry, size, region) stratified sample; Census - costly but unavoidable in some cases.

Survey methods (2) Domains (sub-populations): Domains (sub-populations): Subsets of the sampling strata; Potential sub-populations: industry groupings, size classes, regions, units that engage in R&D and innovation-active; Guidelines: » Same statistical units and classifications; » Consistence of the methods for results calculation; » Documentation of deviations in data treatment or differences in the quality of the results from the domains.

Survey methods (3) Sampling techniques: Sampling techniques: Stratified sample surveys: size and principal activity; Sampling fractions should not be the same for all strata; Cross-sections: Cross-sections: standard approach - new random sample for each innovation survey; Panel data: Panel data: alternative/supplementary approach.

Survey methods (4) Suitable respondents: Suitable respondents: Methods: e.g., postal surveys, web-based questionnaires, personal interviews; Units most suitable respondent - very specialised questions that can be answered by only a few people; Try to identify respondents by name before data collection starts. Try to identify respondents by name before data collection starts.

Survey methods (5) The questionnaire: The questionnaire: Pre-test; Simple and short; Order of the questions; Questions on qualitative indicators - binary or ordinal scale; International innovation surveys - attention to translation and design; Short-form questionnaires - units with little/no innovation activity previously reported.

Survey methods (6) Combination of Innovation and R&D surveys: Combination of Innovation and R&D surveys: Reduction in the overall response burden; Scope for analysing the relations between R&D and innovation activities; Increase in the frequency of innovation surveys; Country experiences - it is possible to obtain reliable results for R&D expenditures; Longer questionnaire; Units not familiar with the concepts of R&D and innovation may confuse them; Different frames for the two surveys.

Survey methods (7) Guidelines for conducting combined surveys: Guidelines for conducting combined surveys: Questionnaire: two distinct sections; Smaller individual sections; Comparison of results from combined and stand-alone surveys should be done with care - surveying methods should be reported; Samples extraction from a common business register.

Estimation of results (1) Weighting methods: Weighting methods: Weighting by the inverse of the sampling fractions of the sampling units, corrected by the unit non- response; If a stratified sampling technique with different sampling fractions is used, weights should be calculated individually for each; Based on the number of enterprises in a stratum; International and other comparisons: same weighting method.

Estimation of results (2) Non-response: Non-response: Unit non-response: reporting unit does not reply at all; Item non-response: response rate to a specific question - % of blank or missing answers; biased results » Disregarding missing values + applying simple weighting procedures based on the responses received assumes that respondents and non-respondents are distributed in the same way biased results; imputation methods » Possibility: imputation methods.

Presentation of results Descriptive analysis: no generalisation of results; Inferential analysis: conclusions about target population; Variance for the results: (average) values for innovation indicators and their coefficients of variation and/or confidence intervals; Results presentation: metadata (data collection procedure, sampling methods, procedures for dealing with non-response, quality indicators).

Frequency of data collection Every 2 years; If not economically feasible frequency of 3 or 4 years; Specify an observation period; The length of the observation period for innovation surveys should not exceed 3 years nor be less than 1 year.

Annex A - 5. Methodological issues for developing country contexts (1) Information system specificities: Relative weakness of statistical systems: » Absence of linkages between surveys and data sets; » Lack of official business registers; Involvement of NSOs; When lacking, basic variables about firms performance can be included in the innovation survey.

Annex A - 5. Methodological issues for developing country contexts (2) General methodological considerations: Survey application: » In-person; » Trained personnel; Questionnaire design: » Sections can be separated to allow different persons in the firm to reply them; » Guidance/definitions; » Language and translation of technical terms.

Annex A - 5. Methodological issues for developing country contexts (3) General methodological considerations: Frequency: CIS CIS » Every 3 to 4 years (e.g., timed to CIS rounds);CIS » Update a minimum set of variables every year; Purpose of surveys; Clear questions; Adequate legislative base; The results should be published and distributed widely. The results should be published and distributed widely.

Example - product innovation/degree of novelty

Example - innovation activities and expenditures for product and process innovations

Example - co-operation

Example - hampering factors

Example - organisational innovation

Basic forms - CIS The Community Innovation Survey (2010) (CIS 2010): The Community Innovation Survey (2010) (CIS 2010): 1. General information about the enterprise 2. Product innovation 3. Process innovation 4. Ongoing or abandoned innovation activities for pdt/pcs innovation 5. Innovation activities and expenditure s for pdt/pcs innovation 6. Sources of information and co-operation for pdt/pcs innovation 7. Objectives for pdt/pcs innovation 8. Factors hampering pdt/pcs innovation activities 9. Organisational innovation 10. Marketing innovation 11. Creativity and skills 12. Basic economic information on your enterprise

Basic forms - AU/NEPAD AU/NEPAD Standard Innovation Questionnaire: AU/NEPAD Standard Innovation Questionnaire: 1. General information about the enterprise 2. Product innovation 3. Process innovation 4. Ongoing or abandoned innovation activities for pdt/pcs innovation 5. Innovation activities and expenditure s for pdt/pcs innovation 6. Sources of information and co-operation for pdt/pcs innovation 7. Effects/Objectives of pdt/pcs innovation 8. Factors hampering (pdt/pcs) innovation activities 9. IPRs 10. Organisational and m arketing innovation 11. Specific innovations by your enterprise

Thank you!