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Hamidreza Navvabpour, Mohadese Safakish,
Volume 4, Issue 1 (9-2010)
Abstract
Parallel to social developments and increasing use of statistics in planning, demand for producing and disseminating social and economic information have been risen dramatically. Survey sampling is one of the methods of gathering such information. The quality of survey data is important for planners and researches. In recent years, the concept of response burden has been expressed as one of the new components of the data quality. Unfortunately, In National Statistical System of Iran nothing has been done to evaluate response burden in order to improve survey data quality. In this paper, we introduce existing methods of measuring response burden and express other countries experiences to assess perceived response burden in surveys. A common approach to assess response burden is designing a Perceived Response Burden survey and carry it out along with the host survey. In order to illustrate how to design, conduct and analyze results of this survey, a Perceived Response Burden survey which has been conducted in Faculty of Economic, Allameh Tabatabaei University, to measure response burden impose on respondents in Health Survey is presented.
Roshanak Aliakbari Saba, Alireza Zahedian, Marzieh Arbabi,
Volume 9, Issue 1 (9-2015)
Abstract
Annual estimation of average household incomes is one of the main goals of the household income and expenditure survey in Iran. So, regarding importance of accuracy of gathered data and reasons that lead to error in measuring household income, in this paper, model-based methods are used for estimating income measurement error and adjusting sample households declared income for 2011 household income and expenditure survey.
Zahra Rezaei Ghahroodi, Hasan Ranji, Alireza Rezaei,
Volume 15, Issue 1 (9-2021)
Abstract
In most surveys, the occupation and job-industry related questions are asked through open-ended questions, and the coding of this information into thousands of categories is done manually. This is very time consuming and costly. Given the requirement of modernizing the statistical system of countries, it is necessary to use statistical learning methods in official statistics for primary and secondary data analysis. Statistical learning classification methods are also useful in the process of producing official statistics. The purpose of this article is to code some statistical processes using statistical learning methods and familiarize executive managers about the possibility of using statistical learning methods in the production of official statistics. Two applications of classification statistical learning methods, including automatic coding of economic activities and open-ended coding of statistical centers questionnaires using four iterative methods, are investigated. The studied methods include duplication, support vector machine (SVM) with multi-level aggregation methods, a combination of the duplication method and SVM, and the nearest neighbor method.
Shaho Zarei,
Volume 15, Issue 2 (3-2022)
Abstract
The most widely used model in small area estimation is the area level or the Fay-Herriot model. In this model, it is typically assumed that both the area level random effects (model errors) and the sampling errors have a Gaussian distribution. However, considerable variations in error components (model errors and sampling errors) can cause poor performance in small area estimation. In this paper, to overcome this problem, the symmetric α-stable distribution is used to deal with outliers in the error components. The model parameters are estimated with the empirical Bayes method. The performance of the proposed model is investigated in different simulation scenarios and compared with the existing classic and robust empirical Bayes methods. The proposed model can improve estimation results, in particular when both error components are normal or have heavy-tailed distribution.
Roshanak Aliakbari Saba, Nasrin Ebrahimi, Lida Kalhori Nadrabadi, Asieh Abbasi,
Volume 15, Issue 2 (3-2022)
Abstract
The ranked set sampling method uses the ranking information of the units to provide a more representative sample of the population to the survey designers. The sampling distribution is closer to the actual distribution of the population. In this article, to ensure the effectiveness of ranked set sampling in extensive surveys conducted to prepare official statistics, we intend to use this sampling method to improve the efficiency of key estimates of household expenditure and income survey of the Statistics Center of Iran. The results show that using ranked set sampling to design household expenditure and income surveys can improve the efficiency of key estimates of the study, provided that the ranking variable used has a high correlation with the main variables of the study. Obviously, in the absence of a suitable and available variable for ranking the units, the information of the sampling frame can be used to construct a ranking variable correlated with the key variables of the survey.
Dr Zahra Rezaei Ghahroodi, Zhina Aghamohamadi,
Volume 16, Issue 1 (9-2022)
Abstract
With the advent of big data in the last two decades, in order to exploit and use this type of data, the need to integrate databases for building a stronger evidence base for policy and service development is felt more than ever. Therefore, familiarity with the methodology of data linkage as one of the methods of data integration and the use of machine learning methods to facilitate the process of recording records is essential. In this paper, in addition to introducing the record linkage process and some related methods, machine learning algorithms are required to increase the speed of database integration, reduce costs and improve record linkage performance. In this paper, two databases of the Statistical Center of Iran and Social Security Organization are linked.
Lida Kalhori Nadrabadi, Zohreh Fallah Mohsekhani,
Volume 16, Issue 1 (9-2022)
Abstract
In countries where labor force surveys are based on rotation samples and partially standard sample units at different periods, the number of changing statuses can be estimated and presented as flow statistics. The response error is one of the essential non-sampling errors in labor force statistics. This error is doubled in flow statistics. Usually, the error of classifying flow statistics is estimated using the interview method, which is costly and complex. This paper presents the process of estimating flow statistics and appropriate models for calculating the classification error for it. Also, according to Iran's sample rotation pattern, each model's feasibility is examined. Finally, the Markov latent class model, assuming inequality of transition probabilities based on the rotation pattern of Iran for labor force samples, is introduced as a fit model for estimating classification error for flow statistics in Iran using the labour force survey data of 2019 and 2020.
Alireza Movaffaghi Ardestani, Dr. Zahra Rezaei Ghahroodi,
Volume 17, Issue 1 (9-2023)
Abstract
Today, with the increasing access to administrative databases and the high volume of data registered in organizations, the traditional methods of data collection and analysis are not effective due to the response burden. Accordingly, the transition from traditional survey methods to modern methods of data collection and analysis with the register-based statistics approach has received more and more attention from statistical data analysts. In register-based methods, it is especially important to create an integrated database by linking database records of different organizations. Many record linkage algorithms have been developed using the Fellegi and Sunter model. The Fellegi-Sunter model does not leverage information contained in field values and does not care about specific possible values of a string variable (more common and less common values). In this article, a method that can be able to infuse these differences in specific possible values of a string variable in the Fellegi-Sunter model is presented. On the other, the model proposed by Fellegi-Sunter, as well as the method for adjusting the matching weights in the frequency-based record linkage, binding in this paper, are based on the assumption of conditional independence. In some applications of record linkage, this assumption is not met in agreement or disagreement of common variables which are used for matching. One solution used in such a case is to use log-linear model which allows interactions between matching variables in the model.
In this article, we deal with two generalizations of Fellegi-Sunter model, one with the correction of the matching weights and the other with using a log-linear model with interactions in absence of conditional independence. The proposed methods are implemented on labour force data set of Statistical Centre of Iran using R.
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