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A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys

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  • Reza C. Daniels

    () (SALDRU, School of Economics, University of Cape Town)

Abstract

In this paper the Total Survey Error (TSE) paradigm is combined with detailed data quality indicators to develop a framework for investigating micro data quality. The TSE framework is widely used in the survey methodology literature to identify different components of error that arise in the survey process. Consequently, it provides a very useful typology for researchers to understand which data quality issues are relevant in applied work based on these surveys. In order to demonstrate how the framework sheds light on micro data quality, two labour market household surveys conducted by Statistics South Africa are reviewed, spanning a time-frame from 1995-2007. It is argued that efforts to improve data quality should involve a virtuous interaction between producers and consumers of micro data and should be considered an evolving process. For producers of data, the preparation and publication of detailed data quality frameworks is recommended, and two examples of these frameworks are reviewed. For consumers of data, judicious analyses of the univariate, bivariate and multivariate relationships in public-use versions of the datasets can help shed light on different components of survey error, and should be communicated back to survey organisations. Ultimately, improving data quality is about being more explicit about the limitations of data production at each stage of the process, which does not stop at initial public release.

Suggested Citation

  • Reza C. Daniels, 2012. "A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys," SALDRU Working Papers 90, Southern Africa Labour and Development Research Unit, University of Cape Town.
  • Handle: RePEc:ldr:wpaper:90
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    File URL: http://opensaldru.uct.ac.za/bitstream/handle/11090/606/2012_90.pdf?sequence=1
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    References listed on IDEAS

    as
    1. Nicola Branson & Martin Wittenberg, 2014. "Reweighting South African National Household Survey Data to Create a Consistent Series Over Time: A Cross-Entropy Estimation Approach," South African Journal of Economics, Economic Society of South Africa, vol. 82(1), pages 19-38, March.
    2. Nicola Branson, 2009. "Re-weighting the OHS and LFS National household Survey Data to create a consistent series over time: A Cross Entropy Estimation Approach," SALDRU Working Papers 38, Southern Africa Labour and Development Research Unit, University of Cape Town.
    3. Servaas Van Der Berg & Megan Louw, 2004. "Changing Patterns Of South African Income Distribution: Towards Time Series Estimates Of Distribution And Poverty," South African Journal of Economics, Economic Society of South Africa, vol. 72(3), pages 546-572, September.
    4. Martin Wittenberg, 2006. "Research Note: Errors In The October Household Survey 1994 Available From The South African Data Archive," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 766-768, December.
    5. Rosalia Vazquez-Alvarez, 2003. "Anchoring Bias and Covariate Nonresponse," University of St. Gallen Department of Economics working paper series 2003 2003-19, Department of Economics, University of St. Gallen.
    6. Nicola Branson & Martin Wittenberg, 2007. "The Measurement Of Employment Status In South Africa Using Cohort Analysis, 1994-2004," South African Journal of Economics, Economic Society of South Africa, vol. 75(2), pages 313-326, June.
    7. Reza Daniels & Sandrine Rospabé, 2005. "Estimating an Earnings Function from Coarsened Data by an Interval Censored Regression Procedure," Working Papers 05091, University of Cape Town, Development Policy Research Unit.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Data Quality Evaluation and Assessment; Total Survey Error;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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