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Statistical matching of sample survey data: application to integrate Iranian time use and labour force surveys

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  • Zahra Rezaei Ghahroodi

    (University of Tehran)

Abstract

Survey data are still contemplated as one of the main sources in official statistics. However, due to the high cost of conducting a survey, as well as the respondent burden, it may not be possible to collect all variables of interest in a data set. To obtain a more comprehensive source of data, one possible way is to integrate available data from different data sets such as already existing data, administrative registers, and official surveys. This helps to minimize the shortcomings of each survey and to maximize their advantages. In this paper, a mixed method at the micro-level has been applied to integrate data sourced from two surveys, involving the ‘Iranian Labour Force Survey’ and the ‘Iranian Time Use Survey’ which have been performed in the Fall of 2015. Thereby, besides increasing the coverage of the variables from two sources, we could also study the peculiarities of work and life qualities. For this objective, we develop a statistical matching micro approach by proposing the conditional predictive Dirichlet distribution and conditional predictive multinomial distribution in the regression step of mixed methods. In the end, the quality of matching along with the similarity of marginal distributions of specific variables (variables of interest) pre-and-post the integration are assessed by some similarity measures and the Kolmogorov–Smirnov test.

Suggested Citation

  • Zahra Rezaei Ghahroodi, 2023. "Statistical matching of sample survey data: application to integrate Iranian time use and labour force surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 1023-1051, September.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:3:d:10.1007_s10260-023-00693-2
    DOI: 10.1007/s10260-023-00693-2
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    References listed on IDEAS

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    1. Antonio D’Ambrosio & Massimo Aria & Roberta Siciliano, 2012. "Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 227-258, July.
    2. Pierre Walthery & Jonathan Gershuny, 2019. "Improving Stylised Working Time Estimates with Time Diary Data: A Multi Study Assessment for the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1303-1321, August.
    3. Nancy Ruggles & Richard Ruggles, 1974. "A Strategy for Merging and Matching Microdata Sets," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 2, pages 353-371, National Bureau of Economic Research, Inc.
    4. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    5. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    6. Z. Rezaei Ghahroodi & M. Ganjali, 2013. "A Bayesian approach for analysing longitudinal nominal outcomes using random coefficients transitional generalized logit model: an application to the labour force survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1425-1445, July.
    7. Fernando Rios-Avila, 2018. "Quality of Match for Statistical Matches Using the American Time Use Survey 2013, the Survey of Consumer Finances 2013, and the Annual Social and Economic Supplement 2014," Economics Working Paper Archive wp_914, Levy Economics Institute.
    8. Z. Rezaei Ghahroodi & M. Ganjali & F. Harandi & D. Berridge, 2011. "Bivariate transition model for analysing ordinal and nominal categorical responses: an application to the Labour Force Survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(4), pages 817-832, February.
    9. Pier Luigi Conti & Daniela Marella & Mauro Scanu, 2016. "Statistical Matching Analysis for Complex Survey Data With Applications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1715-1725, October.
    10. Tao Wang & Hongyu Zhao, 2017. "A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms," Biometrics, The International Biometric Society, vol. 73(3), pages 792-801, September.
    11. Marella, Daniela & Scanu, Mauro & Luigi Conti, Pier, 2008. "On the matching noise of some nonparametric imputation procedures," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1593-1600, September.
    12. Conti, Pier Luigi & Marella, Daniela & Scanu, Mauro, 2008. "Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 354-365, December.
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