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Data Editing for Complex Surveys in Presence Of Administrative Data: An Application to Fss 2013 Livestock Survey Data Based on The Joint Sequential Use Of Different R Packages

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  • Elena Catanese

    (Italian National Institute of Statistics (Istat))

Abstract

Data editing and imputation (E&I) in complex sample business surveys is a task which is usually split into two steps to gain efficiency in terms of time and human resources: first selective editing techniques are applied to the primary target estimates variables in order to identify a potential set of influential errors that require usually manual editing and a second part of automatic identification and imputation of inconsistencies and missing values. Within this framework, the present paper reviews the Italian top-down data editing strategy adopted and automated imputation showing the experience applied to 2013 Farm Structure Survey livestock data.In this edition this process has been entirely carried out in the R environment by means of different R packages.

Suggested Citation

  • Elena Catanese, 2016. "Data Editing for Complex Surveys in Presence Of Administrative Data: An Application to Fss 2013 Livestock Survey Data Based on The Joint Sequential Use Of Different R Packages," Romanian Statistical Review, Romanian Statistical Review, vol. 64(2), pages 101-117, June.
    • Handle: RePEc:rsr:journl:v:64:y:2016:i:2:p:101-117
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    References listed on IDEAS

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    1. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.
    2. Hron, K. & Templ, M. & Filzmoser, P., 2010. "Imputation of missing values for compositional data using classical and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3095-3107, December.
    3. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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    More about this item

    Keywords

    Selective editing; data editing; Business Surveys; Automated Edit Rules; Imputation of Missing Values; Compositional Data; Random vs. Systematic Errors; Influential non Influential Errors; Statistical software R;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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