{"title":"Preferred Reporting Items for Complex Sample Survey Analysis (PRICSSA)","authors":"A. Seidenberg, R. Moser, B. West","doi":"10.1093/jssam/smac040","DOIUrl":"https://doi.org/10.1093/jssam/smac040","url":null,"abstract":"\u0000 Methodological issues pertaining to transparency and analytic error have been widely documented for publications featuring analysis of complex sample survey data. The availability of numerous public use datasets to researchers without adequate training in using these data likely contributes to these problems. In an effort to introduce standards for reporting analyses of survey data and promote replication, we propose the Preferred Reporting Items for Complex Sample Survey Analysis (PRICSSA), an itemized checklist to guide researchers publishing analyses using complex sample survey data. PRICSSA is modeled after other checklists (e.g., PRISMA, CONSORT) that have been widely adopted for other research designs. The PRICSSA items include a variety of survey characteristics, such as data collection dates, mode(s), response rate, and sample selection process. In addition, essential analytic information—such as sample sizes for all estimates, missing data rates and imputation methods (if applicable), disclosing if any data were deleted, specifying what survey weight and sample design variables were used along with method of variance estimation, and reporting design-adjusted standard errors/confidence intervals for all estimates—are also included. PRICSSA also recommends that authors make all corresponding software code available. Widespread adoption of PRICSSA will help improve the quality of secondary analyses of complex sample survey data through transparency and promote scientific rigor and reproducibility.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42455728","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}
Kyle Endres, E. Heiden, Ki H. Park, M. Losch, K. Harland, Anne L Abbott
{"title":"Experimenting with QR Codes and Envelope Size in Push-to-Web Surveys","authors":"Kyle Endres, E. Heiden, Ki H. Park, M. Losch, K. Harland, Anne L Abbott","doi":"10.1093/jssam/smad008","DOIUrl":"https://doi.org/10.1093/jssam/smad008","url":null,"abstract":"\u0000 Survey researchers are continually evaluating approaches to increase response rates, especially those that can be implemented with little or no costs. In this study, we experimentally evaluated whether or not including Quick Response (QR) codes in mailed recruitment materials for self-administered web surveys increased web survey participation. We also assessed whether mailing these materials in a non-standard envelope size (6 × 9 inch) yielded a higher response rate than invitations mailed in a standard, #10 envelope (4.125 × 9.5 inch). These experiments were embedded in a sequential mixed-mode (dual-frame phone and web) statewide survey. Including a QR code (in addition to a URL) significantly increased the response rate compared to invitations that only included a URL in our study. As expected, a consequence of including the QR code was an elevated number of completions on smartphones or tablets among households randomly assigned to the QR code condition. The use of a larger (6 × 9 inch) envelope did not affect the overall response rate but did significantly boost the response rate for the landline sample (envelopes addressed to “STATE resident”) while having little effect for the wireless sample (envelopes addressed by name). This study suggests that incorporating both QR codes and larger (6 × 9 inch) envelopes in mail recruitment materials for web surveys is a cost-effective approach to increase web participation.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44973615","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":"Recent Advances in Data Integration","authors":"J. Sakshaug, R. Steorts","doi":"10.1093/jssam/smad009","DOIUrl":"https://doi.org/10.1093/jssam/smad009","url":null,"abstract":"\u0000 The availability of both survey and non-survey data sources, such as administrative data, social media data, and digital trace data, has grown rapidly over the past decade. With this expansion in data, the statistical, methodological, computational, and ethical challenges around integrating multiple data sources have also grown. This special issue addresses these challenges by highlighting recent innovations and applications in data integration and related topics.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48178429","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":"Panel Conditioning in A Probability-based Longitudinal study: A Comparison of Respondents with Different Levels of Survey Experience","authors":"Fabienne Kraemer, Henning Silber, Bella Struminskaya, Matthias Sand, Michael Bosnjak, Joanna Koßmann, Bernd Weiß","doi":"10.1093/jssam/smad004","DOIUrl":"https://doi.org/10.1093/jssam/smad004","url":null,"abstract":"Abstract Learning effects due to repeated interviewing, also known as panel conditioning, are a major threat to response quality in later waves of a panel study. To date, research has not provided a clear picture regarding the circumstances, mechanisms, and dimensions of potential panel conditioning effects. In particular, the effects of conditioning frequency, that is, different levels of experience within a panel, on response quality are underexplored. Against this background, we investigated the effects of panel conditioning by using data from the GESIS Panel, a German mixed-mode probability-based panel study. Using two refreshment samples, we compared three panel cohorts with differing levels of experience on several response quality indicators related to the mechanisms of reflection, satisficing, and social desirability. Overall, we find evidence for both negative (i.e., disadvantageous for response quality) and positive (i.e., advantageous for response quality) panel conditioning. Highly experienced respondents were more likely to satisfice by speeding through the questionnaire. They also had a higher probability of refusing to answer sensitive questions than less experienced panel members. However, more experienced respondents were also more likely to optimize the response process by needing less time compared to panelists with lower experience levels (when controlling for speeding). In contrast, we did not find significant differences with respect to the number of “don’t know” responses, nondifferentiation, the selection of first response categories and mid-responses, and the number of nontriggered filter questions. Of the observed differences, speeding showed the highest magnitude with an average increase of 6.0 percentage points for highly experienced panel members compared to low experienced panelists.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135189112","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":"Optimizing Data Collection Interventions to Balance Cost and Quality in a Sequential Multimode Survey","authors":"Stephanie M Coffey, Michael R Elliott","doi":"10.1093/jssam/smad007","DOIUrl":"https://doi.org/10.1093/jssam/smad007","url":null,"abstract":"Abstract High-quality survey data collection is getting more expensive to conduct because of decreasing response rates and rising data collection costs. Responsive and adaptive designs have emerged as a framework for targeting and reallocating resources during the data collection period to improve survey data collection efficiency. Here, we report on the implementation and evaluation of a responsive design experiment in the National Survey of College Graduates that optimizes the cost-quality tradeoff by minimizing a function of data collection costs and the root mean squared error of a key survey measure, self-reported salary. We used a Bayesian framework to incorporate prior information and generate predictions of estimated response propensity, self-reported salary, and data collection costs for use in our optimization rule. At three points during the data collection process, we implement the optimization rule and identify cases for which reduced effort would have minimal effect on the mean squared error (RMSE) of mean self-reported salary while allowing us to reduce data collection costs. We find that this optimization process allowed us to reduce data collection costs by nearly 10 percent, without a statistically or practically significant increase in the RMSE of mean salary or a decrease in the unweighted response rate. This experiment demonstrates the potential for these types of designs to more effectively target data collection resources to reach survey quality goals.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135648136","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}
Michael Jones, J Michael Brick, Wendy Van De Kerckhove
{"title":"Effects of Address Coverage Enhancement on Estimates from Address-Based Sampling Studies.","authors":"Michael Jones, J Michael Brick, Wendy Van De Kerckhove","doi":"10.1093/jssam/smab032","DOIUrl":"https://doi.org/10.1093/jssam/smab032","url":null,"abstract":"<p><p>For over a decade, address-based sampling (ABS) frames have often been used to draw samples for multistage area sample surveys in lieu of traditionally listed (or enumerated) address frames. However, it is well known that the use of ABS frames for face-to-face surveys suffer from undercoverage due to, for example, households that receive mail via a PO Box rather than being delivered to the household's street address. Undercoverage of ABS frames has typically been more prominent in rural areas but can also occur in urban areas where recent construction of households has taken place. Procedures have been developed to supplement ABS frames to address this undercoverage. In this article, we investigate a procedure called Address Coverage Enhancement (ACE) that supplements the ABS frame with addresses not found on the frame, and the resulting effects the addresses added to the sample through ACE have on estimates. Weighted estimates from two studies, the Population Assessment of Tobacco and Health Study and the 2017 US Program for the International Assessment of Adult Competencies, are calculated with and without supplemental addresses. Estimates are then calculated to assess if poststratifying analysis weights to control for urbanicity at the person level brings estimates closer to estimates from the supplemented frame. Our findings show that the noncoverage bias was likely minimal across both studies for a range of estimates. The main reason is because the Computerized Delivery Sequence file coverage rate is high, and when the coverage rate is high, only very large differences between the covered and not covered will result in meaningful bias.</p>","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":"11 2","pages":"340-366"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080217/pdf/smab032.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9274583","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}
{"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}
{"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}
{"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}
{"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}