Longitudinal surveys provide valuable data for tracking change in a cohort of individuals over time. Respondents are often asked to provide similar, if not the same, data at multiple time points. One could argue that this unnecessarily increases respondent burden, especially for information that does not change frequently. One way to reduce burden while still capturing up-to-date information may be to implement dependent interviewing (DI), where the respondent is provided information from the last data collection to aid in answering the current survey. If the information is still correct, then no change is needed, but if incorrect, the respondent has the option to change the response. To test this, we implemented two different versions of DI in a self-administered web survey and compared these against a traditional version of the web survey. We examined respondent burden by analyzing timing data and respondent enjoyment by analyzing debriefing questions. To assess the success of the implementation, we looked at timing data and undesirable behavior (missing data and backtracking). Finally, to evaluate measurement error, we looked at the number of meaningful changes. We found that DI is faster, more enjoyable, easily executed by the respondent (more so in one of our experimental formats), and significant measurement error was not introduced. In addition, DI provided consistency in the data, minimizing the noise introduced by nonmeaningful changes. The findings have significant implications for implementing DI in self-administered modes without an interviewer present.
{"title":"Reducing Burden in a Web Survey through Dependent Interviewing","authors":"Curtiss Engstrom, J. Sinibaldi","doi":"10.1093/jssam/smad006","DOIUrl":"https://doi.org/10.1093/jssam/smad006","url":null,"abstract":"\u0000 Longitudinal surveys provide valuable data for tracking change in a cohort of individuals over time. Respondents are often asked to provide similar, if not the same, data at multiple time points. One could argue that this unnecessarily increases respondent burden, especially for information that does not change frequently. One way to reduce burden while still capturing up-to-date information may be to implement dependent interviewing (DI), where the respondent is provided information from the last data collection to aid in answering the current survey. If the information is still correct, then no change is needed, but if incorrect, the respondent has the option to change the response. To test this, we implemented two different versions of DI in a self-administered web survey and compared these against a traditional version of the web survey. We examined respondent burden by analyzing timing data and respondent enjoyment by analyzing debriefing questions. To assess the success of the implementation, we looked at timing data and undesirable behavior (missing data and backtracking). Finally, to evaluate measurement error, we looked at the number of meaningful changes. We found that DI is faster, more enjoyable, easily executed by the respondent (more so in one of our experimental formats), and significant measurement error was not introduced. In addition, DI provided consistency in the data, minimizing the noise introduced by nonmeaningful changes. The findings have significant implications for implementing DI in self-administered modes without an interviewer present.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44304074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linkage errors in probabilistically matched data sets can cause biases in the estimation of regression coefficients. This article proposes an approach to obtain consistent estimates and valid inference that relies on instrumental variables. The novelty of the method is to show that instrumental variables arise naturally in the course of probabilistic record linkage thereby allowing for off-the-shelf implementation. Relative to existing approaches, the instrumental variable approach does not require integration of the record linkage and regression analysis steps, the estimation of complex models of linkage error, or computationally expensive methods to estimate standard errors. The instrumental variables approach performs well in Monte Carlo simulations of an environment highlighting a many-to-one linkage problem.
{"title":"Implicates as Instrumental Variables: An Approach for Estimation and Inference with Probabilistically Matched Data","authors":"Dhiren Patki, M. Shapiro","doi":"10.1093/jssam/smad005","DOIUrl":"https://doi.org/10.1093/jssam/smad005","url":null,"abstract":"\u0000 Linkage errors in probabilistically matched data sets can cause biases in the estimation of regression coefficients. This article proposes an approach to obtain consistent estimates and valid inference that relies on instrumental variables. The novelty of the method is to show that instrumental variables arise naturally in the course of probabilistic record linkage thereby allowing for off-the-shelf implementation. Relative to existing approaches, the instrumental variable approach does not require integration of the record linkage and regression analysis steps, the estimation of complex models of linkage error, or computationally expensive methods to estimate standard errors. The instrumental variables approach performs well in Monte Carlo simulations of an environment highlighting a many-to-one linkage problem.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46248838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The United Kingdom’s Living Costs and Food (LCF) Survey has a relatively small sample size but produces estimates which are widely used, notably as a key input to the calculation of weights for consumer price indices. There has been a recent call for the use of additional data sources to improve the estimates from the LCF. Since some LCF variables are shared with the much larger Labour Force Survey (LFS), we investigate combining data from these surveys using composite calibration to improve the precision of estimates from the LCF. We undertake model selection to choose a suitable set of common variables for the composite calibration using the effect on the estimated variances for national and regional totals of important LCF variables. The variances of estimates for common variables are reduced to around 5 percent of their original size. Variances of national estimates are reduced (across several quarters) by around 10 percent for expenditure and 25 percent for income; these are the variables of primary interest in the LCF. Reductions in the variances of regional estimates vary more but are mostly large when using common variables at the regional level in the composite calibration. The composite calibration also makes the LCF estimates for employment status almost consistent with the outputs of the LFS, which is an important property for users of the statistics. A novel alternative method for variance estimation, using stored information produced by the composite calibration, is also presented.
{"title":"Combining National Surveys with Composite Calibration to Improve the Precision of Estimates from the United Kingdom's Living Costs and Food Survey","authors":"T. Merkouris, Paul A. Smith, A. Fallows","doi":"10.1093/jssam/smad001","DOIUrl":"https://doi.org/10.1093/jssam/smad001","url":null,"abstract":"\u0000 The United Kingdom’s Living Costs and Food (LCF) Survey has a relatively small sample size but produces estimates which are widely used, notably as a key input to the calculation of weights for consumer price indices. There has been a recent call for the use of additional data sources to improve the estimates from the LCF. Since some LCF variables are shared with the much larger Labour Force Survey (LFS), we investigate combining data from these surveys using composite calibration to improve the precision of estimates from the LCF. We undertake model selection to choose a suitable set of common variables for the composite calibration using the effect on the estimated variances for national and regional totals of important LCF variables. The variances of estimates for common variables are reduced to around 5 percent of their original size. Variances of national estimates are reduced (across several quarters) by around 10 percent for expenditure and 25 percent for income; these are the variables of primary interest in the LCF. Reductions in the variances of regional estimates vary more but are mostly large when using common variables at the regional level in the composite calibration. The composite calibration also makes the LCF estimates for employment status almost consistent with the outputs of the LFS, which is an important property for users of the statistics. A novel alternative method for variance estimation, using stored information produced by the composite calibration, is also presented.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42475076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Income is an important economic indicator to measure living standards and individual well-being. In Germany, different data sources yield ambiguous evidence for analyzing the income distribution. The Tax Statistics (TS)—an income register recording the total population of more than 40 million taxpayers in Germany for the year 2014—contains the most reliable income information covering the full income distribution. However, it offers only a limited range of socio-demographic variables essential for income analysis. We tackle this challenge by enriching the tax data with information on education and working time from the Microcensus, a representative 1 percent sample of the German population. We examine two types of data fusion methods well suited to the specific data fusion scenario of the TS and the Microcensus: missing-data methods and performant prediction models. We conduct a simulation study and provide an empirical application comparing the proposed data fusion methods, and our results indicate that Multinomial Regression and Random Forest are the most suitable methods for our data fusion scenario.
{"title":"Evaluating Data Fusion Methods to Improve Income Modeling","authors":"Jana Emmenegger, R. Münnich, Jannik Schaller","doi":"10.1093/jssam/smac033","DOIUrl":"https://doi.org/10.1093/jssam/smac033","url":null,"abstract":"\u0000 Income is an important economic indicator to measure living standards and individual well-being. In Germany, different data sources yield ambiguous evidence for analyzing the income distribution. The Tax Statistics (TS)—an income register recording the total population of more than 40 million taxpayers in Germany for the year 2014—contains the most reliable income information covering the full income distribution. However, it offers only a limited range of socio-demographic variables essential for income analysis. We tackle this challenge by enriching the tax data with information on education and working time from the Microcensus, a representative 1 percent sample of the German population. We examine two types of data fusion methods well suited to the specific data fusion scenario of the TS and the Microcensus: missing-data methods and performant prediction models. We conduct a simulation study and provide an empirical application comparing the proposed data fusion methods, and our results indicate that Multinomial Regression and Random Forest are the most suitable methods for our data fusion scenario.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44968251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Small area estimation (SAE) has become an important tool in official statistics, used to construct estimates of population quantities for domains with small sample sizes. Typical area-level models function as a type of heteroscedastic regression, where the variance for each domain is assumed to be known and plugged in following a design-based estimate. Recent work has considered hierarchical models for the variance, where the design-based estimates are used as an additional data point to model the latent true variance in each domain. These hierarchical models may incorporate covariate information but can be difficult to sample from in high-dimensional settings. Utilizing recent distribution theory, we explore a class of Bayesian hierarchical models for SAE that smooth both the design-based estimate of the mean and the variance. In addition, we develop a class of unit-level models for heteroscedastic Gaussian response data. Importantly, we incorporate both covariate information as well as spatial dependence, while retaining a conjugate model structure that allows for efficient sampling. We illustrate our methodology through an empirical simulation study as well as an application using data from the American Community Survey.
{"title":"Conjugate Modeling Approaches for Small Area Estimation with Heteroscedastic Structure","authors":"Paul A Parker, Scott H Holan, Ryan Janicki","doi":"10.1093/jssam/smad002","DOIUrl":"https://doi.org/10.1093/jssam/smad002","url":null,"abstract":"Abstract Small area estimation (SAE) has become an important tool in official statistics, used to construct estimates of population quantities for domains with small sample sizes. Typical area-level models function as a type of heteroscedastic regression, where the variance for each domain is assumed to be known and plugged in following a design-based estimate. Recent work has considered hierarchical models for the variance, where the design-based estimates are used as an additional data point to model the latent true variance in each domain. These hierarchical models may incorporate covariate information but can be difficult to sample from in high-dimensional settings. Utilizing recent distribution theory, we explore a class of Bayesian hierarchical models for SAE that smooth both the design-based estimate of the mean and the variance. In addition, we develop a class of unit-level models for heteroscedastic Gaussian response data. Importantly, we incorporate both covariate information as well as spatial dependence, while retaining a conjugate model structure that allows for efficient sampling. We illustrate our methodology through an empirical simulation study as well as an application using data from the American Community Survey.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136081685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online panel surveys are often criticized for their inability to cover the offline population, potentially resulting in coverage error. Previous research has demonstrated that non-internet users in fact differ from online individuals on several sociodemographic characteristics. In attempts to reduce coverage error due to missing the offline population, several probability-based online panels equip offline households with an internet connection and a simple computer or tablet. However, the question remains whether the recruitment of offline individuals for an online panel leads to substantial changes in survey estimates. That is, it is unclear whether estimates derived from the survey data are affected by the differences between the groups of online and offline individuals. Against this background, we investigate how the inclusion of the previously offline population into the German Internet Panel affects various survey estimates such as voting behavior and social engagement. Overall, we find little evidence for the claim that equipping otherwise offline individuals with online access affects the estimates derived from previously online individuals only.
{"title":"Equipping the Offline Population with Internet Access in an Online Panel: Does It Make a Difference?","authors":"Ruben L. Bach, Carina Cornesse, Jessica Daikeler","doi":"10.1093/jssam/smad003","DOIUrl":"https://doi.org/10.1093/jssam/smad003","url":null,"abstract":"\u0000 Online panel surveys are often criticized for their inability to cover the offline population, potentially resulting in coverage error. Previous research has demonstrated that non-internet users in fact differ from online individuals on several sociodemographic characteristics. In attempts to reduce coverage error due to missing the offline population, several probability-based online panels equip offline households with an internet connection and a simple computer or tablet. However, the question remains whether the recruitment of offline individuals for an online panel leads to substantial changes in survey estimates. That is, it is unclear whether estimates derived from the survey data are affected by the differences between the groups of online and offline individuals. Against this background, we investigate how the inclusion of the previously offline population into the German Internet Panel affects various survey estimates such as voting behavior and social engagement. Overall, we find little evidence for the claim that equipping otherwise offline individuals with online access affects the estimates derived from previously online individuals only.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45916954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The inverse probability weighting (IPW) method is commonly used to deal with missing-at-random outcome (response) data collected by surveys with complex sampling designs. However, IPW methods generally assume that fully observed predictor variables are available for all sampled units, and it is unclear how to appropriately implement these methods when one or more independent variables are subject to missing values. Multiple imputation (MI) methods are well suited for a variety of missingness patterns but are not as easily adapted to complex sampling designs. In this case study, we consider the National Survey of Morbidity and Risk Factors (EMENO), a multistage probability sample survey. To understand the strengths and limitations of using either missing data treatment method for the EMENO, we present an extensive simulation study modeled on the EMENO health survey, with the target analysis being the estimation of population prevalence of hypertension as well as the association between hypertension and income. Both variables are subject to missingness. We test a variety of IPW and MI methods in simulation and on empirical data from the survey, assessing robustness by varying missingness mechanisms, proportions of missingness, and strengths of fitted response propensity models.
{"title":"Handling Missing Values in Surveys With Complex Study Design: A Simulation Study","authors":"N. Kalpourtzi, James R. Carpenter, G. Touloumi","doi":"10.1093/jssam/smac039","DOIUrl":"https://doi.org/10.1093/jssam/smac039","url":null,"abstract":"\u0000 The inverse probability weighting (IPW) method is commonly used to deal with missing-at-random outcome (response) data collected by surveys with complex sampling designs. However, IPW methods generally assume that fully observed predictor variables are available for all sampled units, and it is unclear how to appropriately implement these methods when one or more independent variables are subject to missing values. Multiple imputation (MI) methods are well suited for a variety of missingness patterns but are not as easily adapted to complex sampling designs. In this case study, we consider the National Survey of Morbidity and Risk Factors (EMENO), a multistage probability sample survey. To understand the strengths and limitations of using either missing data treatment method for the EMENO, we present an extensive simulation study modeled on the EMENO health survey, with the target analysis being the estimation of population prevalence of hypertension as well as the association between hypertension and income. Both variables are subject to missingness. We test a variety of IPW and MI methods in simulation and on empirical data from the survey, assessing robustness by varying missingness mechanisms, proportions of missingness, and strengths of fitted response propensity models.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48401788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is growing interest within National Statistical Institutes in combining available datasets containing information on a large variety of social domains. Statistical matching approaches can be used to integrate data sources through a common set of variables where each dataset contains different units that belong to the same target population. However, a common problem is related to the assumption of conditional independence among variables observed in different data sources. In this context, an auxiliary dataset containing all the variables jointly can be used to improve the statistical matching by providing information on the correlation structure of variables observed across different datasets. We propose modifying the prediction models from the auxiliary dataset through a calibration step and show that we can improve the outcome of statistical matching in a variety of settings. We evaluate the proposed approach via simulation and an application based on the European Union Statistics for Income and Living Conditions and Living Costs and Food Survey for the United Kingdom.
{"title":"Improving Statistical Matching when Auxiliary Information is Available","authors":"Angelo Moretti, N. Shlomo","doi":"10.1093/jssam/smac038","DOIUrl":"https://doi.org/10.1093/jssam/smac038","url":null,"abstract":"\u0000 There is growing interest within National Statistical Institutes in combining available datasets containing information on a large variety of social domains. Statistical matching approaches can be used to integrate data sources through a common set of variables where each dataset contains different units that belong to the same target population. However, a common problem is related to the assumption of conditional independence among variables observed in different data sources. In this context, an auxiliary dataset containing all the variables jointly can be used to improve the statistical matching by providing information on the correlation structure of variables observed across different datasets. We propose modifying the prediction models from the auxiliary dataset through a calibration step and show that we can improve the outcome of statistical matching in a variety of settings. We evaluate the proposed approach via simulation and an application based on the European Union Statistics for Income and Living Conditions and Living Costs and Food Survey for the United Kingdom.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48388115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Raghunathan, K. Kirtland, Ji Li, K. White, B. Murthy, Xia Lin, Latreace Harris, L. Gibbs-Scharf, E. Zell
Immunization Information Systems are confidential computerized population-based systems that collect data from vaccination providers on individual vaccinations administered along with limited patient-level characteristics. Through a data use agreement, Centers for Disease Control and Prevention obtains the individual-level data and aggregates the number of vaccinations for geographical statistical areas defined by the US Census Bureau (counties or equivalent statistical entities) for each vaccine included in system. Currently, 599 counties, covering 11 states, collect and report data using a uniform protocol. We combine these data with inter-decennial population counts from the Population Estimates Program in the US Census Bureau and several covariates from a variety of sources to develop model-based estimates for each of the 3,142 counties in 50 states and the District of Columbia and then aggregate to the state and national levels. We use a hierarchical Bayesian model and Markov Chain Monte Carlo methods to obtain draws from the posterior predictive distribution of the vaccination rates. We use posterior predictive checks and cross-validation to assess the goodness of fit and to validate the models. We also compare the model-based estimates to direct estimates from the National Immunization Surveys.
{"title":"Constructing State and National Estimates of Vaccination Rates from Immunization Information Systems","authors":"T. Raghunathan, K. Kirtland, Ji Li, K. White, B. Murthy, Xia Lin, Latreace Harris, L. Gibbs-Scharf, E. Zell","doi":"10.1093/jssam/smac042","DOIUrl":"https://doi.org/10.1093/jssam/smac042","url":null,"abstract":"\u0000 Immunization Information Systems are confidential computerized population-based systems that collect data from vaccination providers on individual vaccinations administered along with limited patient-level characteristics. Through a data use agreement, Centers for Disease Control and Prevention obtains the individual-level data and aggregates the number of vaccinations for geographical statistical areas defined by the US Census Bureau (counties or equivalent statistical entities) for each vaccine included in system. Currently, 599 counties, covering 11 states, collect and report data using a uniform protocol. We combine these data with inter-decennial population counts from the Population Estimates Program in the US Census Bureau and several covariates from a variety of sources to develop model-based estimates for each of the 3,142 counties in 50 states and the District of Columbia and then aggregate to the state and national levels. We use a hierarchical Bayesian model and Markov Chain Monte Carlo methods to obtain draws from the posterior predictive distribution of the vaccination rates. We use posterior predictive checks and cross-validation to assess the goodness of fit and to validate the models. We also compare the model-based estimates to direct estimates from the National Immunization Surveys.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41952610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gemechu Aga, David C Francis, Filip Jolevski, Jorge Rodriguez Meza, Joshua Seth Wimpey
Abstract Informal business activity is ubiquitous around the world, but it is nearly always uncaptured by administrative data, registries, or commercial sources. For this reason, there are rarely adequate sampling frames available for survey implementers wishing to measure the activity and characteristics of the sector. This article applies a well-established sampling method for rare and/or clustered populations, Adaptive Cluster Sampling (ACS), to a novel population of informal businesses. Generally, it shows that efficiency gains through the application of ACS, when compared to Simple Random Sampling (SRS), are large, particularly at higher levels of fieldwork effort. In particular, ACS efficiency gains over SRS remain sizable at higher values of initial starting samples, but with comparatively high expansion thresholds, which can reduce the fieldwork effort.
{"title":"An Application of Adaptive Cluster Sampling to Surveying Informal Businesses","authors":"Gemechu Aga, David C Francis, Filip Jolevski, Jorge Rodriguez Meza, Joshua Seth Wimpey","doi":"10.1093/jssam/smac037","DOIUrl":"https://doi.org/10.1093/jssam/smac037","url":null,"abstract":"Abstract Informal business activity is ubiquitous around the world, but it is nearly always uncaptured by administrative data, registries, or commercial sources. For this reason, there are rarely adequate sampling frames available for survey implementers wishing to measure the activity and characteristics of the sector. This article applies a well-established sampling method for rare and/or clustered populations, Adaptive Cluster Sampling (ACS), to a novel population of informal businesses. Generally, it shows that efficiency gains through the application of ACS, when compared to Simple Random Sampling (SRS), are large, particularly at higher levels of fieldwork effort. In particular, ACS efficiency gains over SRS remain sizable at higher values of initial starting samples, but with comparatively high expansion thresholds, which can reduce the fieldwork effort.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135794712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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