{"title":"OUP accepted manuscript","authors":"","doi":"10.1093/jssam/smac010","DOIUrl":"https://doi.org/10.1093/jssam/smac010","url":null,"abstract":"","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61006260","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}
{"title":"OUP accepted manuscript","authors":"","doi":"10.1093/jssam/smac017","DOIUrl":"https://doi.org/10.1093/jssam/smac017","url":null,"abstract":"","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61006852","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}
{"title":"OUP accepted manuscript","authors":"","doi":"10.1093/jssam/smac021","DOIUrl":"https://doi.org/10.1093/jssam/smac021","url":null,"abstract":"","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61006890","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}
{"title":"OUP accepted manuscript","authors":"","doi":"10.1093/jssam/smac013","DOIUrl":"https://doi.org/10.1093/jssam/smac013","url":null,"abstract":"","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61007181","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}
{"title":"OUP accepted manuscript","authors":"","doi":"10.1093/jssam/smac018","DOIUrl":"https://doi.org/10.1093/jssam/smac018","url":null,"abstract":"","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61006861","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}
{"title":"OUP accepted manuscript","authors":"","doi":"10.1093/jssam/smac007","DOIUrl":"https://doi.org/10.1093/jssam/smac007","url":null,"abstract":"","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61006193","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}
Graham Kalton, Ismael Flores Cervantes, Carlos Arieira, Mike Kwanisai, Elizabeth Radin, Suzue Saito, Anindya K DE, Stephen McCracken, Paul Stupp
The units at the early stages of multi-stage area samples are generally sampled with probabilities proportional to their estimated sizes (PPES). With such a design, an overall equal probability (EP) sample design would yield a constant number of final stage units from each final stage cluster if the measures of size used in the PPES selection at each sampling stage were directly proportional to the number of final stage units. However, there are often sizable relative differences between the measures of size used in the PPES selections and the number of final stage units. Two common approaches for dealing with these differences are: (1) to retain a self-weighting sample design, allowing the sample sizes to vary across the sampled primary sampling units (PSUs) and (2) to retain the fixed sample size in each PSU and to compensate for the unequal selection probabilities by weighting adjustments in the analyses. This article examines these alternative designs in the context of two-stage sampling in which PSUs are sampled with PPES at the first stage, and an equal probability sample of final stage units is selected from each sampled PSU at the second stage. Two-stage sample designs of this type are used for household surveys in many countries. The discussion is illustrated with data from the Population-based HIV Impact Assessment surveys that were conducted using this design in several African countries.
{"title":"DEALING WITH INACCURATE MEASURES OF SIZE IN TWO-STAGE PROBABILITY PROPORTIONAL TO SIZE SAMPLE DESIGNS: APPLICATIONS IN AFRICAN HOUSEHOLD SURVEYS.","authors":"Graham Kalton, Ismael Flores Cervantes, Carlos Arieira, Mike Kwanisai, Elizabeth Radin, Suzue Saito, Anindya K DE, Stephen McCracken, Paul Stupp","doi":"10.1093/jssam/smaa020","DOIUrl":"10.1093/jssam/smaa020","url":null,"abstract":"<p><p>The units at the early stages of multi-stage area samples are generally sampled with probabilities proportional to their estimated sizes (PPES). With such a design, an overall equal probability (EP) sample design would yield a constant number of final stage units from each final stage cluster if the measures of size used in the PPES selection at each sampling stage were directly proportional to the number of final stage units. However, there are often sizable relative differences between the measures of size used in the PPES selections and the number of final stage units. Two common approaches for dealing with these differences are: (1) to retain a self-weighting sample design, allowing the sample sizes to vary across the sampled primary sampling units (PSUs) and (2) to retain the fixed sample size in each PSU and to compensate for the unequal selection probabilities by weighting adjustments in the analyses. This article examines these alternative designs in the context of two-stage sampling in which PSUs are sampled with PPES at the first stage, and an equal probability sample of final stage units is selected from each sampled PSU at the second stage. Two-stage sample designs of this type are used for household surveys in many countries. The discussion is illustrated with data from the Population-based HIV Impact Assessment surveys that were conducted using this design in several African countries.</p>","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11288091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44810005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-19eCollection Date: 2023-02-01DOI: 10.1093/jssam/smab038
Yajuan Si, Steve Heeringa, David Johnson, Roderick J A Little, Wenshuo Liu, Fabian Pfeffer, Trivellore Raghunathan
Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the United States. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worth and sociodemographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.
{"title":"Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics.","authors":"Yajuan Si, Steve Heeringa, David Johnson, Roderick J A Little, Wenshuo Liu, Fabian Pfeffer, Trivellore Raghunathan","doi":"10.1093/jssam/smab038","DOIUrl":"10.1093/jssam/smab038","url":null,"abstract":"<p><p>Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the United States. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worth and sociodemographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.</p>","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9874997/pdf/smab038.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10584238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motivated misreporting occurs when respondents give incorrect responses to survey questions to shorten the interview; studies have detected this behavior across many modes, topics, and countries. This paper tests whether motivated misreporting affects responses in a large survey of household purchases, the US Consumer Expenditure Interview Survey. The data from this survey inform the calculation of the official measure of inflation, among other uses. Using a parallel web survey and multiple imputation, this article estimates the size of the misreporting effect without experimentally manipulating questions in the survey itself. Results suggest that household purchases are underreported by approximately five percentage points in three sections of the first wave of the survey. The approach used here, involving a web survey built to mimic the expenditure survey, could be applied in other large surveys where budget or logistical constraints prevent experimentation.
{"title":"Underreporting of Purchases in the US Consumer Expenditure Survey","authors":"S. Eckman","doi":"10.1093/jssam/smab024","DOIUrl":"https://doi.org/10.1093/jssam/smab024","url":null,"abstract":"\u0000 Motivated misreporting occurs when respondents give incorrect responses to survey questions to shorten the interview; studies have detected this behavior across many modes, topics, and countries. This paper tests whether motivated misreporting affects responses in a large survey of household purchases, the US Consumer Expenditure Interview Survey. The data from this survey inform the calculation of the official measure of inflation, among other uses. Using a parallel web survey and multiple imputation, this article estimates the size of the misreporting effect without experimentally manipulating questions in the survey itself. Results suggest that household purchases are underreported by approximately five percentage points in three sections of the first wave of the survey. The approach used here, involving a web survey built to mimic the expenditure survey, could be applied in other large surveys where budget or logistical constraints prevent experimentation.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46501698","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 2013 FECOND (Fertility, Contraception, and Sexual Dysfunction) probability telephone survey aims to monitor sexual health behaviors among fifteen to forty-nine year olds in France. We conducted a random experiment to compare a classic telephone survey (group T, n = 3,846 respondents) with two Internet-telephone mixed-mode protocols: a sequential Internet-telephone protocol (group S, n = 762, among which there were 462 Internet questionnaires), and a concurrent protocol (group C, n = 1,165, among which there were 208 Internet questionnaires). We compare telephone (T), sequential (S), and concurrent (C) samples on cooperation rates, break-off, and item nonresponse rates, sociodemographic characteristics, health behaviors, and seven sexual health behaviors and personal opinions questions. Reports on the most sensitive behaviors were expected to be more truthful and more prevalent on the Internet—and thus in the mixed-mode samples—than in the telephone sample. The cooperation rate (i.e., the response rate among the possible respondents selected during the initial telephone call) was higher in the classic telephone survey than in the sequential and concurrent mixed-mode protocols (88 percent for T versus 77 percent for S and 55 percent for C), where break-off and item nonresponse rates were also higher. Despite these lower response rates, mixed-mode samples showed better representativeness: their marginal distribution of sociodemographic characteristics was closer to that of the 2013 census, and they had higher R-indicators. A causal estimation of the measurement effect resulting from Internet administration found higher prevalence of three out of the seven sexual health behaviors and personal opinions in the sequential protocol compared to the classic telephone group; a similar pattern was found in the concurrent protocol. In addition, the variance of the weights of the mixed-mode protocols is lower, especially for the sequential design. Sequential telephone-Internet mixed-mode protocols nested in a probability telephone survey may be a good way to improve survey research on sensitive behaviors.
{"title":"Sequential and Concurrent Internet-Telephone Mixed-Mode Designs in Sexual Health Behavior Research","authors":"S. Legleye, Géraldine Charrance","doi":"10.1093/jssam/smab026","DOIUrl":"https://doi.org/10.1093/jssam/smab026","url":null,"abstract":"\u0000 The 2013 FECOND (Fertility, Contraception, and Sexual Dysfunction) probability telephone survey aims to monitor sexual health behaviors among fifteen to forty-nine year olds in France. We conducted a random experiment to compare a classic telephone survey (group T, n = 3,846 respondents) with two Internet-telephone mixed-mode protocols: a sequential Internet-telephone protocol (group S, n = 762, among which there were 462 Internet questionnaires), and a concurrent protocol (group C, n = 1,165, among which there were 208 Internet questionnaires). We compare telephone (T), sequential (S), and concurrent (C) samples on cooperation rates, break-off, and item nonresponse rates, sociodemographic characteristics, health behaviors, and seven sexual health behaviors and personal opinions questions. Reports on the most sensitive behaviors were expected to be more truthful and more prevalent on the Internet—and thus in the mixed-mode samples—than in the telephone sample. The cooperation rate (i.e., the response rate among the possible respondents selected during the initial telephone call) was higher in the classic telephone survey than in the sequential and concurrent mixed-mode protocols (88 percent for T versus 77 percent for S and 55 percent for C), where break-off and item nonresponse rates were also higher. Despite these lower response rates, mixed-mode samples showed better representativeness: their marginal distribution of sociodemographic characteristics was closer to that of the 2013 census, and they had higher R-indicators. A causal estimation of the measurement effect resulting from Internet administration found higher prevalence of three out of the seven sexual health behaviors and personal opinions in the sequential protocol compared to the classic telephone group; a similar pattern was found in the concurrent protocol. In addition, the variance of the weights of the mixed-mode protocols is lower, especially for the sequential design. Sequential telephone-Internet mixed-mode protocols nested in a probability telephone survey may be a good way to improve survey research on sensitive behaviors.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47716223","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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