IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v183y2020i1p37-59.html
   My bibliography  Save this article

Improved secondary analysis of linked data: a framework and an illustration

Author

Listed:
  • Ray Chambers
  • Andrea Diniz da Silva

Abstract

Applications that use linked data are now part of mainstream social science research, though they generally do not take linkage error into consideration. Solutions that correct for the bias caused by these errors have been proposed but are not yet embedded in the various analysis procedures in common use. Secondary analyses based on linked data can therefore be potentially misleading. We review some recent approaches to non‐deterministic data linkage together with a framework for secondary analysis of the linked data which makes use of paradata produced by the linkage process to correct this bias. We also describe a new method for secondary analysis of linked data that builds on this framework and show how it can be used for estimation of a set of domain means based on linked data. We then illustrate this approach via an empirical study based on record linkage of agricultural producers in four states of Brazil aimed at producing estimates of agricultural output by industry. Our study considers register‐to‐register linkage as well as sample‐to‐register linkage, and we show results for the traditional Fellegi–Sunter approach to record linkage as well as for a newer linkage procedure based on the use of classification trees and bagging.

Suggested Citation

  • Ray Chambers & Andrea Diniz da Silva, 2020. "Improved secondary analysis of linked data: a framework and an illustration," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 37-59, January.
    • Handle: RePEc:bla:jorssa:v:183:y:2020:i:1:p:37-59
    DOI: 10.1111/rssa.12477
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12477
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12477?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. P. Lahiri & Michael D. Larsen, 2005. "Regression Analysis With Linked Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 222-230, March.
    2. Rachel S. Franklin, 2013. "The Roles of Population, Place, and Institution in Student Diversity in A merican Higher Education," Growth and Change, Wiley Blackwell, vol. 44(1), pages 30-53, March.
    3. Kim, Gunky & Chambers, Raymond, 2012. "Regression analysis under incomplete linkage," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2756-2770.
    4. Gunky Kim & Raymond Chambers, 2012. "Regression Analysis under Probabilistic Multi‐Linkage," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(1), pages 64-79, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Szymkowiak Marcin & Wilak Kamil, 2021. "Repeated weighting in mixed-mode censuses," Economics and Business Review, Sciendo, vol. 7(1), pages 26-46, March.
    2. Li‐Chun Zhang & Tiziana Tuoto, 2021. "Linkage‐data linear regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 522-547, April.
    3. N. Salvati & E. Fabrizi & M. G. Ranalli & R. L. Chambers, 2021. "Small area estimation with linked data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 78-107, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li‐Chun Zhang & Tiziana Tuoto, 2021. "Linkage‐data linear regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 522-547, April.
    2. Ben Powell & Paul A. Smith, 2020. "Computing expectations and marginal likelihoods for permutations," Computational Statistics, Springer, vol. 35(2), pages 871-891, June.
    3. Han Ying, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 30-34, August.
    4. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2018. "Identification, data combination, and the risk of disclosure," Quantitative Economics, Econometric Society, vol. 9(1), pages 395-440, March.
    5. Vo, Thanh Huan & Chauvet, Guillaume & Happe, André & Oger, Emmanuel & Paquelet, Stéphane & Garès, Valérie, 2023. "Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    6. Ying Han, 2020. "Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 30-34, August.
    7. Angelo Moretti & Natalie Shlomo, 2023. "Improving Probabilistic Record Linkage Using Statistical Prediction Models," International Statistical Review, International Statistical Institute, vol. 91(3), pages 368-394, December.
    8. N. Salvati & E. Fabrizi & M. G. Ranalli & R. L. Chambers, 2021. "Small area estimation with linked data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 78-107, February.
    9. Martha J. Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2020. "How Well Do Automated Linking Methods Perform? Lessons from US Historical Data," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 997-1044, December.
    10. Al-Kandari Noriah M. & Lahiri Partha, 2016. "Prediction of a Function of Misclassified Binary Data," Statistics in Transition New Series, Polish Statistical Association, vol. 17(3), pages 429-447, September.
    11. Dasylva Abel, 2018. "Design-Based Estimation with Record-Linked Administrative Files and a Clerical Review Sample," Journal of Official Statistics, Sciendo, vol. 34(1), pages 41-54, March.
    12. Afshin Fallah & Mohsen Mohammadzadeh, 2010. "Bayesian regression analysis with linked data using mixture normal distributions," Statistical Papers, Springer, vol. 51(2), pages 421-430, June.
    13. Durrant, Gabriele B. & D'Arrigo, Julia & Steele, Fiona, 2011. "Using field process data to predict best times of contact conditioning on household and interviewer influences," LSE Research Online Documents on Economics 52201, London School of Economics and Political Science, LSE Library.
    14. Kim, Gunky & Chambers, Raymond, 2012. "Regression analysis under incomplete linkage," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2756-2770.
    15. Deborah Wagner & Mary Lane, 2014. "The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software," CARRA Working Papers 2014-01, Center for Economic Studies, U.S. Census Bureau.
    16. Noriah M. Al-Kandari & Partha Lahiri, 2016. "Prediction Of A Function Of Misclassified Binary Data," Statistics in Transition New Series, Polish Statistical Association, vol. 17(3), pages 429-447, September.
    17. Bera Sabyasachi & Chatterjee Snigdhansu, 2020. "High dimensional, robust, unsupervised record linkage," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 123-143, August.
    18. John M. Abowd & Joelle Abramowitz & Margaret C. Levenstein & Kristin McCue & Dhiren Patki & Trivellore Raghunathan & Ann M. Rodgers & Matthew D. Shapiro & Nada Wasi & Dawn Zinsser, 2021. "Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning," Working Papers 21-35, Center for Economic Studies, U.S. Census Bureau.
    19. Catherine G. Massey, 2016. "Playing with Matches: An Assessment of Accuracy in Linked Historical Data," CARRA Working Papers 2016-05, Center for Economic Studies, U.S. Census Bureau.
    20. Sarah Tahamont & Zubin Jelveh & Aaron Chalfin & Shi Yan & Benjamin Hansen, 2019. "Administrative Data Linking and Statistical Power Problems in Randomized Experiments," NBER Working Papers 25657, National Bureau of Economic Research, Inc.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:183:y:2020:i:1:p:37-59. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.