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Internet Surveys & Future Works In Official Statistics

The 4th International Workshop on Internet Survey Methods, 12-13 Sept. Statistical Center, Statistics Korea, Daejeon. Internet Surveys & Future Works In Official Statistics . Yeonok Choi. Statistical Research Institute, Statistics Korea, Daejeon , Korea . The Rise of Internet Surveys.

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Internet Surveys & Future Works In Official Statistics

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  1. The 4th International Workshop on Internet Survey Methods, 12-13 Sept. Statistical Center, Statistics Korea, Daejeon Internet Surveys & Future Works In Official Statistics YeonokChoi Statistical Research Institute, Statistics Korea, Daejeon, Korea

  2. The Rise of Internet Surveys • The optimum properties of a survey • High response rates • Low costs • Quick response and return • High quality data • F to F interview can not be a predominant method • People are busier • Privacy is highly valued and sensitive

  3. The Rise of Internet Surveys (cont.) • With the advancement of science and technology • Internet emerged as a data collection option • Needs for innovation of data collection modes in Official Statistics(OS) • Budgetary pressures • Growing difficulty in access to respondents Internet == good solution candidate

  4. Expectations from the Workshop • The 1st workshop was held in 2009 • To promote Internet surveys(IS) • To discuss latest research on web survey methods • To explore its potential in official statistics • The past three workshops dealt with major issues relating to Internet surveys • E-census experiences, Estimation for Volunteer Panel, Response Rate problem • The use of mixed mode data collection, Propensity score weighting, the statistical use of the administrative data • The focus of this workshop • How to ensure data quality from Internet Surveys

  5. Expectations from the Workshop • Key Questions - Does IS mode contribute to the improvement of data quality? - How to effectively implement Internet surveys in OS to ensure data quality?

  6. Summary of Sessions [Session 1] Advantages and disadvantages of utilizing IS methods for OS

  7. Summary of Sessions [Session 2] Design and implementation issues in web surveys

  8. Summary of Sessions [Session 3] Cases of statistics utilizing Internet survey methods

  9. Summary of Sessions [Session 4] Data Quality issues in web surveys

  10. Lessons Learned - using IS in OS • Does IS mode contribute to the improvement data quality? • IS mode is not so promising in RR - However, RR can vary on the survey content, disposition of the persons, and process of survey i.g) The survey on Private Education Expenses in Korea: high RR in IS • Potential to improve data quality by appropriate web survey design was found • There seems no common criterion for measuring the data quality • MM becomes more appropriate for OS as survey circumstances are changing • Strength in survey cost, response speed, and access to hard-to-contact group

  11. Lessons Learned - using IS in OS • How to effectively implement Internet surveys in OS to ensure data quality? • Sequential MM design with the Internet mode as the first step • Analyzing the dispositions of respondents by mode • Considering enumerator and incentive effects in MM • Trying to minimize the measurement error differences

  12. Review of Internet Surveysin Statistics Korea • Utilizing the internet survey as MM design • Total of 43 Survey • Uses of 26 survey : 16 on establishment, 10 on households • About 48% internet uptake rate in 2010 census  Offering convenience to respondents hard to reach • The matter of data quality remains i.g) The duplication of responses among household members from census

  13. Research projects in Statistics Korea • Our current researches for MM surveys - MM design for 2015 census including IS questionnaire design to increase data quality - Estimation methods considering response differences by survey mode

  14. Future works • Research on questionnaire design in MM design • Question may differ on paper vs. internet questionnaire • Need to explore method to handle these differences • Research on statistical methodologies for differentmode effects • Selection bias, nonresponse bias, measurement error etc. • Understanding the respondent propensities by modes • Research for extended adoption of IS to establishment surveys

  15. Thank you and Good bye

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