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Data mining based quality analysis on informants involved applied research

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Abstract

Attitudinal questions are widely applied in the statistical questionnaire surveys, but the reliability of answers, affected by the psychological tendency of informants, is in doubt for the existence of systematic psychological errors. In this case, control and experimental groups were built up in the work for the sake of investigation and analysis. As a result, it was found that the selection tendencies of systematic psychological errors were derived from the settings of questionnaire answers, and there were always some rules to be followed. On this account, the researchers of statistical surveys are required to abide by the psychological tendency laws of informants and set up statistical questionnaires scientifically and rationally. In this way, the overall quality of survey data can be enhanced.

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References

  1. Xu, X.: Some statistical problems related to GDP accounting. Financ. Trade. Econ. 4, 5–10 (2009)

  2. Pang, Z., Niu, C.: Construction of nonsampling error function—a discussion of non-response error based on incomplete sample frame. Stat. Inf. Forum. 12, 15–19 (2013)

    Google Scholar 

  3. Wang, H., Jin, Y.: Analytical methods for the error effect of precision evaluation of statistical data. Stat. Inf. Forum 9, 10–16 (2009)

    Google Scholar 

  4. Zipei, T.: Big data. Guangxi Normal University Press, Guangxi Province (2013)

    Google Scholar 

  5. Dang, W.: Research on the evaluation methods of macro statistical data quality based on SAM. Stat. Inf. Forum 8, 8–14 (2013)

    Google Scholar 

  6. Zhang, F., Zhu, S., Cong, R.: Investigation and analysis of statistical data accuracy and the influencing factors. World Survey. Res. 9, 47–49 (2013)

    Google Scholar 

  7. Feng, L., Zhou, J.: Assessment methods of government statistical data accuracy. Stat. Res. 6, 78–84 (2013)

    Google Scholar 

  8. Fan, H.: Total quality management of survey data. Stat. Res. 11, 53–56 (2010)

    Google Scholar 

  9. Wang, Y.: Quality control of questionnaires. J. Bus. Econ. 4, 25–27 (2003)

    Google Scholar 

  10. Cai, H.: Context and questionnaires. J.Sun Yatsen Univ. 3, 115–128 (2004)

    Google Scholar 

  11. Maddison, A.: The world economy—a millennial perspective. OECD Development Centre, Paris (2001)

    Google Scholar 

  12. Zhou, Q., Luo, J.: The risk management using limit theory of statistics on extremes on the big data era. J. Comput. Theor. Nanosci. 12, 6237–6243 (2015). doi:10.1166/jctn.2015.4661

    Article  Google Scholar 

  13. Zhou, Q., Luo, J.: Empirical test of efficiency comparison between PPS estimation and simple random sampling. Adv. Sci. Lett. 5(1), 437–440 (2012). doi:10.1166/asl.2012.3166

    Article  Google Scholar 

  14. Zhou, Q., Luo, J.: Artificial neural network based grid computing of E-government scheduling for emergency management. Comput. Syst. Sci. Eng. 30(5), 327–335 (2015)

    MathSciNet  Google Scholar 

  15. Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014). doi:10.1109/TKDE.2013.109

    Article  Google Scholar 

  16. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014). doi:10.1109/TKDE.2013.34

    Article  MATH  Google Scholar 

  17. Han, L., Ong, H.Y.: Parallel data intensive applications using MapReduce: a data mining case study in biomedical sciences. Clust. Comput. 18(1), 403–418 (2015). doi:10.1007/s10586-014-0405-9

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the Sciences Foundation of Jiangsu Province (No. 14ZWD001).

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Correspondence to Qingyuan Zhou.

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Xie, J., Luo, J. & Zhou, Q. Data mining based quality analysis on informants involved applied research. Cluster Comput 19, 1885–1893 (2016). https://doi.org/10.1007/s10586-016-0657-7

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  • DOI: https://doi.org/10.1007/s10586-016-0657-7

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