Journal of Official Statistics 

About: Journal of Official Statistics is an academic journal published by De Gruyter Open. The journal publishes majorly in the area(s): Population & Estimator. It has an ISSN identifier of 0282-423X. It is also open access. Over the lifetime, 801 publications have been published receiving 16016 citations. The journal is also known as: JOS & Journal of official statistics.

To mix or not to mix data collection modes in surveys.

Abstract: Traditionally in social surveys and official statistics data were collected either by an interviewer visiting a respondent or through a self-administered mail questionnaire. In the second half of the twentieth century this picture changed rapidly. Telephone surveys became increasingly popular, reaching their peak in the 1990s. Advances in computer technology in the last thirty years have made computer-assisted survey methods possible, including methods for Internet and web surveys. This variety of data collection methods led to methodological questions, such as, which method to choose? which is best? Recently in survey practice multiple modes of data collection or mixed-modes have become more and more popular. In this article I will outline the advantages and disadvantages of mixed-mode survey designs, starting with an overview of common forms of mixed-mode design and discussing reasons for using more than one mode in a survey. This overview will end with a discussion of practical issues and an agenda for future research.

Probability based estimation theory for respondent driven sampling

Abstract: Many populations of interest present special challenges for traditional survey methodology when it is difficult or impossible to obtain a traditional sampling frame. In the case of such “hidden” populations at risk of HIV/AIDS, many researchers have resorted to chain-referral sampling. Recent progress on the theory of chain-referral sampling has led to Respondent Driven Sampling (RDS), a rigorous chain-referral methodwhich allows unbiased estimation of the target population. In this article we present new probability-theoretic methods for making estimates fromRDS data. The new estimators offer improved simplicity, analytical tractability, and allow the estimation of continuous variables. An analytical variance estimator is proposed in the case of estimating categorical variables. The properties of the estimator and the associated variance estimator are explored in a simulation study, and compared to alternative RDS estimators using data from a study of New York City jazz musicians. The new estimator gives results consistent with alternative RDS estimators in the study of jazz musicians, and demonstrates greater precision than alternative estimators in the simulation study.

Income Measurement Error in Surveys: A Review

Abstract: 20233, U.S.A. Acknowledgments: We are grateful to Martin David, Kent Marquis, and Betsy Martin for their useful comments and suggestions. An early draft of this paper was made available as a background document to participants in the “Cognitive Aspects of Survey Methodology II” conference held in Charlottesville, VA, in June 1997. A highly condensed version of the paper will appear in the proceedings monograph from the conference — Sirken, M., D. Herrmann, S. Schechter, N. Schwarz, J. Tanur, and R. Tourangeau (eds.), Cognition and Survey Research, New York, John Wiley and Sons, (forthcoming). The opinions expressed herein are the authors’ and do not necessarily represent the official views or positions of either the Bureau of the Census or the Bureau of Labor Statistics. Income Measurement Error in Surveys: A Review

Nearest Neighbor Imputation for Survey Data

Abstract: Imputation is commonly applied to compensate for nonresponse in sample surveys (Kalton 1981; Sedransk 1985; Rubin 1987). The nearest neighbor imputation (NNI) method is used in many surveys conducted at Statistics Canada, the U.S. Bureau of Labor Statistics, and the U.S. Census Bureau, and this trend will continue because of the availability of a computer software, the Generalized Edit and Imputation System, which provides a simple way of performing NNI (Cotton 1991; Rancourt, SaÈrndal, and Lee 1994; Kovar, Whitridge, and MacMillan 1998). Let us begin with an introduction of the NNI method in the simplest case. Consider a bivariate sample (x1; y1),. . . ; …xn; yn) and suppose that r of the n y-values are observed (respondents), the rest of m ˆ n ÿ r y-values are missing (nonrespondents), and all x-values are observed. For simplicity we assume that yr‡1; . . . ; yn are missing. The NNI method imputes a missing yj, r ‡ 1 # j # n, by yi, where 1 # i # r and i is the nearest neighbor of j measured by the x-variable, i.e., i satis®es