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- Articles and reports: 11-522-X202100100021Description: Istat has started a new project for the Short Term statistical processes, to satisfy the coming new EU Regulation to release estimates in a shorter time. The assessment and analysis of the current Short Term Survey on Turnover in Services (FAS) survey process, aims at identifying how the best features of the current methods and practices can be exploited to design a more “efficient” process. In particular, the project is expected to release methods that would allow important economies of scale, scope and knowledge to be applied in general to the STS productive context, usually working with a limited number of resources. The analysis of the AS-IS process revealed that the FAS survey incurs substantial E&I costs, especially due to intensive follow-up and interactive editing that is used for every type of detected errors. In this view, we tried to exploit the lessons learned by participating to the High-Level Group for the Modernisation of Official Statistics (HLG-MOS, UNECE) about the Use of Machine Learning in Official Statistics. In this work, we present a first experiment using Random Forest models to: (i) predict which units represent “suspicious” data, (ii) to assess the prediction potential use over new data and (iii) to explore data to identify hidden rules and patterns. In particular, we focus on the use of Random Forest modelling to compare some alternative methods in terms of error prediction efficiency and to address the major aspects for the new design of the E&I scheme.Release date: 2021-10-15
- Articles and reports: 12-001-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 12-001-X201500214236Description:
We propose a model-assisted extension of weighting design-effect measures. We develop a summary-level statistic for different variables of interest, in single-stage sampling and under calibration weight adjustments. Our proposed design effect measure captures the joint effects of a non-epsem sampling design, unequal weights produced using calibration adjustments, and the strength of the association between an analysis variable and the auxiliaries used in calibration. We compare our proposed measure to existing design effect measures in simulations using variables like those collected in establishment surveys and telephone surveys of households.
Release date: 2015-12-17 - Articles and reports: 82-003-X201500614196Description:
This study investigates the feasibility and validity of using personal health insurance numbers to deterministically link the CCR and the Discharge Abstract Database to obtain hospitalization information about people with primary cancers.
Release date: 2015-06-17 - Articles and reports: 12-001-X201300211870Description:
At national statistical institutes experiments embedded in ongoing sample surveys are frequently conducted, for example to test the effect of modifications in the survey process on the main parameter estimates of the survey, to quantify the effect of alternative survey implementations on these estimates, or to obtain insight into the various sources of non-sampling errors. A design-based analysis procedure for factorial completely randomized designs and factorial randomized block designs embedded in probability samples is proposed in this paper. Design-based Wald statistics are developed to test whether estimated population parameters, like means, totals and ratios of two population totals, that are observed under the different treatment combinations of the experiment are significantly different. The methods are illustrated with a real life application of an experiment embedded in the Dutch Labor Force Survey.
Release date: 2014-01-15 - 6. Methodological challenges in analyzing nutrition data from the Canadian Community Health Survey - Nutrition ArchivedArticles and reports: 11-522-X200600110394Description:
Statistics Canada conducted the Canadian Community Health Survey - Nutrition in 2004. The survey's main objective was to estimate the distributions of Canadians' usual dietary intake at the provincial level for 15 age-sex groups. Such distributions are generally estimated with the SIDE application, but with the choices that were made concerning sample design and method of estimating sampling variability, obtaining those estimates is not a simple matter. This article describes the methodological challenges in estimating usual intake distributions from the survey data using SIDE.
Release date: 2008-03-17 - 7. A design-based procedure for the analysis of experiments embedded in complex sample surveys ArchivedArticles and reports: 11-522-X20030017702Description:
This paper proposes a procedure to test hypotheses about differences between sample estimates observed under alternative survey methodologies.
Release date: 2005-01-26 - Articles and reports: 11-522-X20020016728Description:
Nearly all surveys use complex sampling designs to collect data and these data are frequently used for statistical analyses beyond the estimation of simple descriptive parameters of the target population. Many procedures available in popular statistical software packages are not appropriate for this purpose because the analyses are based on the assumption that the sample has been drawn with simple random sampling. Therefore, the results of the analyses conducted using these software packages would not be valid when the sample design incorporates multistage sampling, stratification, or clustering. Two commonly used methods for analysing data from complex surveys are replication and Taylor linearization techniques. We discuss the use of WESVAR software to compute estimates and replicate variance estimates by properly reflecting complex sampling and estimation procedures. We also illustrate the WESVAR features by using data from two Westat surveys that employ complex survey designs: the Third International Mathematics and Science Study (TIMSS) and the National Health and Nutrition Examination Survey (NHANES).
Release date: 2004-09-13
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- Articles and reports: 11-522-X202100100021Description: Istat has started a new project for the Short Term statistical processes, to satisfy the coming new EU Regulation to release estimates in a shorter time. The assessment and analysis of the current Short Term Survey on Turnover in Services (FAS) survey process, aims at identifying how the best features of the current methods and practices can be exploited to design a more “efficient” process. In particular, the project is expected to release methods that would allow important economies of scale, scope and knowledge to be applied in general to the STS productive context, usually working with a limited number of resources. The analysis of the AS-IS process revealed that the FAS survey incurs substantial E&I costs, especially due to intensive follow-up and interactive editing that is used for every type of detected errors. In this view, we tried to exploit the lessons learned by participating to the High-Level Group for the Modernisation of Official Statistics (HLG-MOS, UNECE) about the Use of Machine Learning in Official Statistics. In this work, we present a first experiment using Random Forest models to: (i) predict which units represent “suspicious” data, (ii) to assess the prediction potential use over new data and (iii) to explore data to identify hidden rules and patterns. In particular, we focus on the use of Random Forest modelling to compare some alternative methods in terms of error prediction efficiency and to address the major aspects for the new design of the E&I scheme.Release date: 2021-10-15
- Articles and reports: 12-001-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 12-001-X201500214236Description:
We propose a model-assisted extension of weighting design-effect measures. We develop a summary-level statistic for different variables of interest, in single-stage sampling and under calibration weight adjustments. Our proposed design effect measure captures the joint effects of a non-epsem sampling design, unequal weights produced using calibration adjustments, and the strength of the association between an analysis variable and the auxiliaries used in calibration. We compare our proposed measure to existing design effect measures in simulations using variables like those collected in establishment surveys and telephone surveys of households.
Release date: 2015-12-17 - Articles and reports: 82-003-X201500614196Description:
This study investigates the feasibility and validity of using personal health insurance numbers to deterministically link the CCR and the Discharge Abstract Database to obtain hospitalization information about people with primary cancers.
Release date: 2015-06-17 - Articles and reports: 12-001-X201300211870Description:
At national statistical institutes experiments embedded in ongoing sample surveys are frequently conducted, for example to test the effect of modifications in the survey process on the main parameter estimates of the survey, to quantify the effect of alternative survey implementations on these estimates, or to obtain insight into the various sources of non-sampling errors. A design-based analysis procedure for factorial completely randomized designs and factorial randomized block designs embedded in probability samples is proposed in this paper. Design-based Wald statistics are developed to test whether estimated population parameters, like means, totals and ratios of two population totals, that are observed under the different treatment combinations of the experiment are significantly different. The methods are illustrated with a real life application of an experiment embedded in the Dutch Labor Force Survey.
Release date: 2014-01-15 - 6. Methodological challenges in analyzing nutrition data from the Canadian Community Health Survey - Nutrition ArchivedArticles and reports: 11-522-X200600110394Description:
Statistics Canada conducted the Canadian Community Health Survey - Nutrition in 2004. The survey's main objective was to estimate the distributions of Canadians' usual dietary intake at the provincial level for 15 age-sex groups. Such distributions are generally estimated with the SIDE application, but with the choices that were made concerning sample design and method of estimating sampling variability, obtaining those estimates is not a simple matter. This article describes the methodological challenges in estimating usual intake distributions from the survey data using SIDE.
Release date: 2008-03-17 - 7. A design-based procedure for the analysis of experiments embedded in complex sample surveys ArchivedArticles and reports: 11-522-X20030017702Description:
This paper proposes a procedure to test hypotheses about differences between sample estimates observed under alternative survey methodologies.
Release date: 2005-01-26 - Articles and reports: 11-522-X20020016728Description:
Nearly all surveys use complex sampling designs to collect data and these data are frequently used for statistical analyses beyond the estimation of simple descriptive parameters of the target population. Many procedures available in popular statistical software packages are not appropriate for this purpose because the analyses are based on the assumption that the sample has been drawn with simple random sampling. Therefore, the results of the analyses conducted using these software packages would not be valid when the sample design incorporates multistage sampling, stratification, or clustering. Two commonly used methods for analysing data from complex surveys are replication and Taylor linearization techniques. We discuss the use of WESVAR software to compute estimates and replicate variance estimates by properly reflecting complex sampling and estimation procedures. We also illustrate the WESVAR features by using data from two Westat surveys that employ complex survey designs: the Third International Mathematics and Science Study (TIMSS) and the National Health and Nutrition Examination Survey (NHANES).
Release date: 2004-09-13
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