Journal of Survey Statistics and Methodology最新文献_第4页

Discussion of the 2022 Hansen Lecture: “The Evolution of the Use of Models in Survey Sampling” 2022年Hansen讲座:“调查抽样中模型使用的演变”讨论

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-07-26 DOI: 10.1093/jssam/smad030

F. Breidt

引用次数: 0

Estimating the Size of Clustered Hidden Populations 估计聚类隐藏种群的大小

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-07-17 DOI: 10.1093/jssam/smad025

Laura J Gamble, L. Johnston, P. Pham, P. Vinck, Katherine R. McLaughlin

{"title":"Estimating the Size of Clustered Hidden Populations","authors":"Laura J Gamble, L. Johnston, P. Pham, P. Vinck, Katherine R. McLaughlin","doi":"10.1093/jssam/smad025","DOIUrl":"https://doi.org/10.1093/jssam/smad025","url":null,"abstract":"\u0000 Successive sampling population size estimation (SS-PSE) is a method used by government agencies, aid organizations, and researchers around the world to estimate the size of hidden populations using data from respondent-driven sampling surveys. SS-PSE addresses a specific need in estimation, since many countries rely on having accurate size estimates to plan and allocate finite resources to address the needs of hidden populations. However, SS-PSE relies on several assumptions, one of which requires the underlying social network of the hidden population to be fully connected. We propose two modifications to SS-PSE for estimating the size of hidden populations whose underlying social network is composed of disjoint clusters. The first method is a theoretically straightforward extension of SS-PSE, but it relies on prior information that may be difficult to obtain in practice. The second method extends the Bayesian SS-PSE model by introducing a new set of parameters that allow for clustered estimation without requiring the additional prior information. After providing theoretical justification for both novel methods, we then assess their performance using simulations and apply the Clustered SS-PSE method to a population of internally displaced persons in Bamako, Mali.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48480602","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}

引用次数: 0

Multivariate Small-area Estimation for Mixed-type Response Variables With Item Nonresponse 项目无响应的混合型响应变量的多变量小面积估计

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-28 DOI: 10.1093/jssam/smad018

Haoliang Sun, Emily J. Berg, Zhengyuan Zhu

引用次数: 0

Hansen Lecture 2022: The Evolution of the Use of Models in Survey Sampling Hansen讲座2022:调查抽样中模型使用的演变

4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-28 DOI: 10.1093/jssam/smad021

Richard Valliant

引用次数: 0

Survey Consent to Administrative Data Linkage: Five Experiments on Wording and Format 调查同意行政数据联动:五项措辞与格式实验

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-27 DOI: 10.1093/jssam/smad019

A. Jäckle, Jonathan Burton, M. Couper, Thomas F. Crossley, Sandra Walzenbach

{"title":"Survey Consent to Administrative Data Linkage: Five Experiments on Wording and Format","authors":"A. Jäckle, Jonathan Burton, M. Couper, Thomas F. Crossley, Sandra Walzenbach","doi":"10.1093/jssam/smad019","DOIUrl":"https://doi.org/10.1093/jssam/smad019","url":null,"abstract":"\u0000 To maximize the value of the data while minimizing respondent burden, survey data are increasingly linked to administrative records. Record linkage often requires the informed consent of survey respondents and failure to obtain consent reduces sample size and may lead to selection bias. Relatively little is known about how best to word and format consent requests in surveys. We conducted a series of experiments in a probability household panel and an online access panel to understand how various features of the design of the consent request can affect informed consent. We experimentally varied: (i) the readability of the consent request, (ii) placement of the consent request in the survey, (iii) consent as default versus the standard opt-in consent question, (iv) offering additional information, and (v) a priming treatment focusing on trust in the data holder. For each experiment, we examine the effects of the treatments on consent rates, objective understanding of the consent request (measured with knowledge test questions), subjective understanding (how well the respondent felt they understood the request), confidence in their decision, response times, and whether they read any of the additional information materials. We find that the default wording and offering additional information do not increase consent rates. Improving the readability of the consent question increases objective understanding but does not increase the consent rate. However, asking for consent early in the survey and priming respondents to consider their trust in the administrative data holder both increase consent rates without negatively affecting understanding of the request.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44943344","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}

引用次数: 0

Pseudo-Bayesian Small-Area Estimation 伪贝叶斯小面积估计

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-19 DOI: 10.1093/jssam/smad012

G. Datta, Juhyung Lee, Jiacheng Li

{"title":"Pseudo-Bayesian Small-Area Estimation","authors":"G. Datta, Juhyung Lee, Jiacheng Li","doi":"10.1093/jssam/smad012","DOIUrl":"https://doi.org/10.1093/jssam/smad012","url":null,"abstract":"\u0000 In sample surveys, a subpopulation is referred to as a “small area” or “small domain” if it does not have a large enough sample that alone will yield an adequately accurate estimate of a characteristic. In small-area estimation, the sample size from various subpopulations is often too small to accurately estimate its mean, and so one borrows strength from similar subpopulations through an appropriate model based on relevant covariates. The empirical best linear unbiased prediction (EBLUP) method has been the dominant frequentist model-based approach in small-area estimation. This method relies on estimation of model parameters based on the marginal distribution of the data. As an alternative to this method, the observed best prediction (OBP) method estimates the parameters by minimizing an objective function that is implied by the total mean squared prediction error. We use this objective function in the Fay–Herriot model to construct a pseudo-posterior distribution for the model parameters under nearly noninformative priors for them. Data analysis and simulation show that the pseudo-Bayesian estimators (PBEs) compete favorably with the OBPs and EBLUPs. The PBE estimates are robust to mean misspecification and have good frequentist properties. Being Bayesian by construction, they automatically avoid negative estimates of standard errors, enjoy a dual justification, and provide an attractive alternative to practitioners.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48464247","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}

引用次数: 0

Maximum Entropy Design by a Markov Chain Process 马尔可夫链过程的最大熵设计

4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-14 DOI: 10.1093/jssam/smad010

Yves Tillé, Bardia Panahbehagh

引用次数: 0

A Comprehensive Overview of Unit-Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling 信息抽样下小面积估算调查数据的单位级建模综述

4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-14 DOI: 10.1093/jssam/smad020

Paul A Parker, Ryan Janicki, Scott H Holan

引用次数: 2

Comparison of Unit-Level Small Area Estimation Modeling Approaches for Survey Data Under Informative Sampling 信息抽样下调查数据单位级小面积估算建模方法比较

4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-14 DOI: 10.1093/jssam/smad022

Paul A Parker, Ryan Janicki, Scott H Holan

引用次数: 1

Leveraging Predictive Modelling from Multiple Sources of Big Data to Improve Sample Efficiency and Reduce Survey Nonresponse Error 利用多来源大数据的预测建模，提高样本效率，减少调查无响应误差

IF 2.1 4区数学

Journal of Survey Statistics and Methodology Pub Date : 2023-06-10 DOI: 10.1093/jssam/smad016

David Dutwin, Patrick Coyle, I. Bilgen, N. English

{"title":"Leveraging Predictive Modelling from Multiple Sources of Big Data to Improve Sample Efficiency and Reduce Survey Nonresponse Error","authors":"David Dutwin, Patrick Coyle, I. Bilgen, N. English","doi":"10.1093/jssam/smad016","DOIUrl":"https://doi.org/10.1093/jssam/smad016","url":null,"abstract":"\u0000 Big data has been fruitfully leveraged as a supplement for survey data—and sometimes as its replacement—and in the best of worlds, as a “force multiplier” to improve survey analytics and insight. We detail a use case, the big data classifier (BDC), as a replacement to the more traditional methods of targeting households in survey sampling for given specific household and personal attributes. Much like geographic targeting and the use of commercial vendor flags, we detail the ability of BDCs to predict the likelihood that any given household is, for example, one that contains a child or someone who is Hispanic. We specifically build 15 BDCs with the combined data from a large nationally representative probability-based panel and a range of big data from public and private sources, and then assess the effectiveness of these BDCs to successfully predict their range of predicted attributes across three large survey datasets. For each BDC and each data application, we compare the relative effectiveness of the BDCs against historical sample targeting techniques of geographic clustering and vendor flags. Overall, BDCs offer a modest improvement in their ability to target subpopulations. We find classes of predictions that are consistently more effective, and others where the BDCs are on par with vendor flagging, though always superior to geographic clustering. We present some of the relative strengths and weaknesses of BDCs as a new method to identify and subsequently sample low incidence and other populations.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45867293","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}

引用次数: 0