Your SlideShare is downloading. ×
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Eduworks kick-off presentation: USAL
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Eduworks kick-off presentation: USAL

294

Published on

Dr. Pablo de Pedraza's presentation about University of Salamanca, Wageindicator Foundation and Webdatanet at the Eduworks project's kick-off meeting.

Dr. Pablo de Pedraza's presentation about University of Salamanca, Wageindicator Foundation and Webdatanet at the Eduworks project's kick-off meeting.

Published in: Education, Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
294
On Slideshare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Wage Indicator, WEBDATANET & eduworks Eduworks kick off meeting, Amsterdam December 11th, 2013. Pablo de Pedraza
  • 2. 1.- Wage Indicator: Quick, reliable and internationally comparable data 1.1.- Continuous Voluntary Web Surveys (CVWS): Drawbacks 1.2.- Methodological approaches and research examples 2.- Webdatanet
  • 3. 1.-Wage Indicator: Quick, reliable and internationally comparable data The current economic crisis requires fast information for quick reaction to predict economic behavior early difficult at times of structural changes.
  • 4. 1.-Quick, reliable and internationally comparable data Web vs traditional Labour Surveys CURRENT CONTEXT Global Economy Quick changes
  • 5. 1.-Quick, reliable and internationally comparable data Web vs traditional Labour Surveys CURRENT CONTEXT Global Economy Quick changes Traditional Surveys Slow National/regional coverage International comparisons
  • 6. 1.-Quick, reliable and internationally comparable data Web vs traditional Labour Surveys CURRENT CONTEX Global Economy Quick changes Traditional Surveys Slow National/regional coverage Web surveys Fast (collecting & processing) Multi-country/Multi-lingual homogenized surveys (75 countries) International comparisons International comparisons HOWEVER…
  • 7. 1.- Wage Indicator: Quick, reliable and internationally comparable data 1.1.- Continuous Voluntary Web Surveys (CVWS): Drawbacks 1.2.- Methodological approaches and research examples 2.- Webdatanet
  • 8. 2.- CVWS drawbacks CVWS process
  • 9. 2.- Advantages and drawbacks CVWS process TRADITIONAL CONCEPTS OF SURVEY METHODOLOGY: -Coverage -Non-response… Total Survey Error APPROACH And other surveys…
  • 10. 1.- Wage Indicator: Quick, reliable and internationally comparable data 1.1.- Continuous Voluntary Web Surveys (CVWS): Drawbacks 1.2.- Methodological approaches and research examples 2.- Webdatanet
  • 11. 1.2.- Methodological approaches and research examples 1.2.a.- Bias description 1.2.b.- Design base approach 1.2.c.- Model base approach: Calculate and test weights 1.2.d.- Test innovations and use paradata 1.2.e.- Wage Indicator Research examples
  • 12. 1.2.- Methodological approaches: present & future 1.2.a.- Bias description – National (Labour Force Survey & Structures of Earnings S.) Bias description: National Reference Surveys vs Wage Indicator sample Reference Survey proportions vs Wage Indicator proportions (using demographic variables)
  • 13. 3.- Methodological approaches: present & future 1.2.a- Bias description MARKETING MEASSURES - Attract large masses of visitors 1.2.b.- Design based approach - Address underrepresented groups 1.2.c- Model base approach: Calculate and test weights Able to correct socio-demographic bias Ex. Spain, Germany 1.2.d.- Test innovations and use paradata Provide internet access Mixed modes Offline questionnaires 1.2.e.- Wage Indicator Research examples  Costs (LISS PANEL) But less and less Low Income Countries & Middle-Low Income Countries
  • 14. 1.2.- Methodological approaches and research examples 1.2.a.- Bias description 1.2.b.- Design base approach 1.2.c.- Model base approach: Calculate and test weights 1.2.d.- Test innovations and use paradata 1.2.e.- Wage Indicator Research examples
  • 15. 1.2.- Methodological approaches: present & future 1.2.c- Model base approach: Calculate and test weights -Post-stratification: weight= npopulation / nsample
  • 16. 1.2.- Methodological approaches: present & future 1.2.c.- Model base approach: Calculate and test weights -Post-stratification: weight= -Probability functions npopulation/nsample predicted probability weight=1/calc.prob.
  • 17. 1.2.- Methodological approaches: present & future 1.2.c.- Model base approach: Calculate and test weights -Post-stratification: weight= -Probability functions npopulation/nsample predicted probability example weight=1/calc.prob.
  • 18. 1.2.- Methodological approaches: present & future SES WI Proportional PSW Structures of Wage Indicator Wage Indicator Wage Indicator 18 888.18€ 22 902.81€ 21 903.06€ 21 288.67€ (33.46) (212.63) (251.95) (351.81) 0.3687 0.3596 0.3593 0.3645 Earnings Survey Mean salary (standard error) Wage-Gini-index Theoretical model of Subjective Job Insecurity - WI Wages > SES Wages → Education Corroborated for five EU countries - Same (EJIR, determinants salary Pedraza & Bustillo 2009) - Good special campaigns - Good performance of Propensity Score Weights - Corroborate Life Satisfaction literature2010)DP) (REIS, Pedraza et al. (IZA - New findings regarding - Employment status - Crisis impact on Life Satisfaction determinants
  • 19. 1.2.- Methodological approaches and research examples 1.2.a.- Bias description 1.2.b.- Design base approach 1.2.c.- Model base approach: Calculate and test weights 1.2.d.- Test innovations and use paradata 1.2.e.- Wage Indicator Research examples
  • 20. 1.2.- Methodological approaches: present & future 1.2.d.- Test innovations and use paradata Dynamic testing for Occupational questions (Ulf D. Reips) Study of paradata to improve quality Ex. study drop out (AIAS Working Paper, K.Tijdens, 2011) Other web based data collection methods
  • 21. 1.2.- Methodological approaches and research examples 1.2.a.- Bias description 1.2.b.- Design base approach 1.2.c.- Model base approach: Calculate and test weights 1.2.d.- Test innovations and use paradata 1.2.e.- Wage Indicator Research examples
  • 22. 1.2.- Wage Indicator content research examples and opportunities 1.2.e.-Bias study→ weights→ efficiency of w.→ content research Spain: Job Insecurity, Life Satisfaction Brazil: Life satisfaction International comparisons (BRICS) - National: LFS - International: ILO LFS, World Values Survey; European Social Survey.
  • 23. 1.- Wage Indicator: Quick, reliable and internationally comparable data 2.- Continuous Voluntary Web Surveys (CVWS): Drawbacks 3.- Methodological approaches and research examples 4.- Webdatanet
  • 24. 4.- Webdatanet: Who we are? What are our goals? How? Why? Webdatanet is a Multidisciplinary Network of web-based data collection experts funded by the European Commission Who Sociologists, Psychologists, Economists, Media researchers, Computer scientists… -Universities -Data collection Institutes -Research Institutes -Companies -Statistical Institutes We are researchers from EU but also outside the EU (80 members, 30 countries)
  • 25. 4.- Webdatanet: Who we are? What are our goals? How? Why? Webdatanet is a Multidisciplinary Network of web-based data collection experts funded by the European Commission Scientific goal - Foster scientific usage of web-based data: Surveys, Experiments, Tests, Non-reactive data collection, Mobile Internet research. -Benefit society giving behavioral and social scientist high quality web data
  • 26. 4.- Webdatanet: Who we are? What are our goals? How? Why? Webdatanet is a Multidisciplinary Network of web-based data collection experts funded by the European Commission How - Enhancing quality, integrity and legitimacy of these new forms of data collection, - Methodological issues: Theoretical and empirical foundations, - Stimulating its integration into the entire research process (i-science), - Increasing interaction and communication across disciplines,
  • 27. 4.-Webdatanet: Scientific Structure (WGs & TFs). WG1 Quality WG2 Innovation WG3 Implementation TF1 Measuring wages via web surveys (S. Steinmetz) TF6 New types of measurement (U. Reips) TF10 TSE Categorization (F. Thorsdottir & S. Biffignandi) TF2 Evaluating questionnaire quality (A. Slavec) TF7 Webdatametrics Workshops (U. Reips & K. Kissau) TF 11 How web change empirical world (S. Steinmetz & K. Manfreda) TF3 Mixed modes & representativ. (A.Jonsdottir & K. Kalgraff) TF8 Dissemination WG2 (U. Reips & A. Selkala) TF16 Selecting surveys (M. Revilla) TF4 Internet Panels Europe (A. Scherpenzeel) TF9 iScience portals (U. Reips) TF17 Web data & Official Statistics (S. Biffignandi) TF15 Non-reactive data (N. Fornara) TF21 GenPopWeb (G.Nicolas) TF19 Mobile research (R. Pinter & A. Wijnant) TF25 Applied Economics and web data (P. Pedraza) TF20 Paradata (I. Andreadis) TF26 Web data journal (Konstantinos T.) TF24 Experiments on students samples (K. L. Manfreda) TF22 German Elections, Facebook & Twitter (R. Vatrapu, L. Kaczmirek) TF14 Development & supervision of the web (F. Serrano & C. Zimmerman) TF12 Master in webdatametrics (Alberto Villacampa) TFs for Meetings, training schools, workshops, WebSM (TF18, TF13...) SGs (Small Group meetings)
  • 28. 2.-Scientific Structure (WGs & TFs). WG1 Quality WG2 Innovation WG3 Implementation TF1 Measuring wages via web surveys TF6 New types of measurement (S.WGs & TFs: www.webdatanet.eu Steinmetz) (U. Reips) TF10 TSE Categorization (F. Thorsdottir & S. Biffignandi) TF2 Evaluating questionnaire quality TF7 Webdatametrics Workshops (A.-Slavec) Conferences & Meetings (U. Reips & K. Kissau) TF 11 How web change empirical world (S. Steinmetz & K. Manfreda) TF3 Mixed modes & representativ. (A.Jonsdottir & K. Kalgraff) - STSMs (2500€) TF8 Dissemination WG2 (U. Reips & A. Selkala) TF16 Selecting surveys (M. Revilla) TF4 Internet Panels Europe (A.-Scherpenzeel) Schools Training TF9 iScience portals (U. Reips) TF17 Web data & Official Statistics (S. Biffignandi) (TS) (Ljubljana April 2013) TF15 Non-reactive data (N. Fornara) TF24 Experiments on students samples (K.-L. Manfreda) TF19 (WW) Webdatametrics Workshops Mobile research (R. Pinter & A. Wijnant) Bergamo, January 2013 TF21 GenPopWeb (G.Nicolas) TF23 Applied Economics and web data (P. Pedraza) TF20 Paradata (I. Andreadis) - Involvement of ESR & PhD students (STSM, TS,&WW, TFs ...) TF22 German Elections, Facebook Twitter (R. Vatrapu, L. Kaczmirek) - AIAS-WEBDATANET Working papers & C. Zimmerman) TF14 Development & supervision of the web (F. Serrano (IJIS) TF12 Master in webdatametrics (Alberto Villacampa) TFs for Meetings, training schools, workshops, WebSM (TF18, TF13...) SGs (Small Group meetings)
  • 29. 4.- Webdatanet: Some Examples of TFs: - TF1.- Measuring wages in web surveys - TF17.- Web data & official statistics - TF23.- Web data and Applied Economics - TF12.- Master in Webdatametrics
  • 30. 4.- Some Examples of TFs: TF 1.- Measuring wages in web surveys www.wageindicator.org Measurement & comparability 70 countries ILO and Decent Work Projects Also labor conditions and satisfaction variables Paradata (Quality of data)
  • 31. 2.- Webdatanet scientific structure (WGs & TFs). TF 17.- Integrating web data with Official Statistics ESSNet Eurostat & Statistical Institutes Contribute web data to expansion to: ILO UN www.unglobalpulse.org World Bank
  • 32. 4.- Some Examples of TFs: TF 12.- Master in webdatametrics WEBDATAMETRICS Multidisciplinary Academic Board “General concept that emerges from the existing diverse variety of disciplines September 2014 related to web data collection methods and analyses. Putting this knowledge together webdatametricsOnline & F2F teachings aim to generate new knowledge to take advance of ICT to collect data for scientific proposes” Core: 5 types of web base data TF12 Master in webdatametrics (Alberto Villacampa) Elective: implementation to specific disciplines
  • 33. 4.- Some Examples of TFs: TF 25.- Web data & Applied Economics - Systematically explore all the possibilities web data Applied Economic research; - identify & classify limits of any kind -scientific, ethical, legal, institutional, related to data access... - work overcame those limits and open new research opportunities aiming to benefit society; - foster the Webdatanet international multidisciplinary networking process with leading academics, companies and national and international institutions; -Apply for the necessary institutional and private support for all the above.
  • 34. THANK YOU Amsterdam, December 11th, 2013.

×