Journal of Survey Statistics and Methodology最新文献_第9页

OUP accepted manuscript OUP接受稿件

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2022-01-01 DOI: 10.1093/jssam/smac020

引用次数: 0

OUP accepted manuscript OUP接受稿件

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2022-01-01 DOI: 10.1093/jssam/smac010

引用次数: 1

OUP accepted manuscript OUP接受稿件

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2022-01-01 DOI: 10.1093/jssam/smac013

引用次数: 1

OUP accepted manuscript OUP接受稿件

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2022-01-01 DOI: 10.1093/jssam/smac017

引用次数: 1

OUP accepted manuscript OUP接受稿件

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2022-01-01 DOI: 10.1093/jssam/smac021

引用次数: 0

OUP accepted manuscript OUP接受稿件

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2022-01-01 DOI: 10.1093/jssam/smac018

引用次数: 4

OUP accepted manuscript OUP接受稿件

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2022-01-01 DOI: 10.1093/jssam/smac007

引用次数: 1

Deriving Priors for Bayesian Prediction of Daily Response Propensity in Responsive Survey Design: Historical Data Analysis Versus Literature Review. 在响应性调查设计中推导贝叶斯预测每日反应倾向的先验：历史数据分析与文献综述。

IF 1.6 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2021-10-26 eCollection Date: 2023-04-01 DOI: 10.1093/jssam/smab036

Brady T West, James Wagner, Stephanie Coffey, Michael R Elliott

{"title":"Deriving Priors for Bayesian Prediction of Daily Response Propensity in Responsive Survey Design: Historical Data Analysis Versus Literature Review.","authors":"Brady T West, James Wagner, Stephanie Coffey, Michael R Elliott","doi":"10.1093/jssam/smab036","DOIUrl":"10.1093/jssam/smab036","url":null,"abstract":"Responsive survey design (RSD) aims to increase the efficiency of survey data collection via live monitoring of paradata and the introduction of protocol changes when survey errors and increased costs seem imminent. Daily predictions of response propensity for all active sampled cases are among the most important quantities for live monitoring of data collection outcomes, making sound predictions of these propensities essential for the success of RSD. Because it relies on real-time updates of prior beliefs about key design quantities, such as predicted response propensities, RSD stands to benefit from Bayesian approaches. However, empirical evidence of the merits of these approaches is lacking in the literature, and the derivation of informative prior distributions is required for these approaches to be effective. In this paper, we evaluate the ability of two approaches to deriving prior distributions for the coefficients defining daily response propensity models to improve predictions of daily response propensity in a real data collection employing RSD. The first approach involves analyses of historical data from the same survey, and the second approach involves literature review. We find that Bayesian methods based on these two approaches result in higher-quality predictions of response propensity than more standard approaches ignoring prior information. This is especially true during the early-to-middle periods of data collection, when survey managers using RSD often consider interventions.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"11 2","pages":"367-392"},"PeriodicalIF":1.6,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080219/pdf/smab036.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9652642","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}

引用次数: 0

Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics. 海量数据的多重输入:在收入动态面板研究中的应用。

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2021-10-19 eCollection Date: 2023-02-01 DOI: 10.1093/jssam/smab038

Yajuan Si, Steve Heeringa, David Johnson, Roderick J A Little, Wenshuo Liu, Fabian Pfeffer, Trivellore Raghunathan

{"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":"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.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"11 1","pages":"260-283"},"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}

引用次数: 5

Underreporting of Purchases in the US Consumer Expenditure Survey 在美国消费者支出调查中少报购买

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2021-08-30 DOI: 10.1093/jssam/smab024

S. Eckman

引用次数: 3