Suchergebnisse

Mattia, A. de, et al. ; We analyse the large-scale clustering in Fourier space of emission line galaxies (ELG) from the Data Release 16 of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey. The ELG sample contains 173 736 galaxies covering 1170 deg2 in the redshift range 0.6 < z < 1.1. We perform a BAO measurement from the post-reconstruction power spectrum monopole, and study redshift space distortions (RSD) in the first three even multipoles. Photometric variations yield fluctuations of both the angular and radial survey selection functions. Those are directly inferred from data, imposing integral constraints which we model consistently. The full data set has only a weak preference for a BAO feature (1.4σ). At the effective redshift zeff = 0.845 we measure D_{\rm V}(z_{\rm eff})/r_{\rm drag} = 18.33_{-0.62}^{+0.57}, with DV the volume-averaged distance and rdrag the comoving sound horizon at the drag epoch. In combination with the RSD measurement, at zeff = 0.85 we find f\sigma _8(z_{\rm eff}) = 0.289_{-0.096}^{+0.085}, with f the growth rate of structure and σ8 the normalization of the linear power spectrum, D_{\rm H}(z_{\rm eff})/r_{\rm drag} = 20.0_{-2.2}^{+2.4} and DM(zeff)/rdrag = 19.17 ± 0.99 with DH and DM the Hubble and comoving angular distances, respectively. These results are in agreement with those obtained in configuration space, thus allowing a consensus measurement of fσ8(zeff) = 0.315 ± 0.095, D_{\rm H}(z_{\rm eff})/r_{\rm drag} = 19.6_{-2.1}^{+2.2} and DM(zeff)/rdrag = 19.5 ± 1.0. This measurement is consistent with a flat ΛCDM model with Planck parameters. ; AdM acknowledges support from the P2IO LabEx (ANR-10-LABX-0038) in the framework 'Investissements d'Avenir' (ANR-11-IDEX-0003-01) managed by the Agence Nationale de la Recherche (ANR, France). This work was supported by the ANR eBOSS project (ANR-16-CE31-0021) of the French National Research Agency. AR acknowledges support from the ERC advanced grant LIDA. AR, CZ, and AT acknowledge support from the SNF grant 200020_175751. AJR is grateful for support from the Ohio State University Center for Cosmology and Particle Physics. SA is supported by the European Research Council through the COSFORM Research Grant (#670193). SA was supported by the MICUES project, funded by the European Union's Horizon 2020 research programme under the Marie Sklodowska-Curie Grant Agreement No. 713366 (InterTalentum UAM). VGP acknowledges support from the European Union's Horizon 2020 research and innovation programme (ERC grant #769130). YW and GBZ are supported by NSFC Grants 11925303, 11720101004, 11673025 and 11890691. G-BZ is also supported by the National Key Basic Research and Development Program of China (No. 2018YFA0404503), and a grant of CAS Interdisciplinary Innovation Team. YW is also supported by the Nebula Talents Program of NAOC. GR acknowledges support from the National Research Foundation of Korea (NRF) through Grant Nos. 2017R1E1A1A01077508 and 2020R1A2C1005655 funded by the Korean Ministry of Education, Science and Technology (MoEST), and from the faculty research fund of Sejong University.

"Recently, survey methodology literature has put forward responsive and adaptive survey designs as means to make efficient tradeoffs between survey quality and survey costs. The designs, however, restrict quality-cost assessments to nonresponse error, while there are various design features that impact also measurement error, e.g. the survey mode, the type of questionnaire (long or condensed) and the type of reporting (self or proxy). Extension of adaptive survey design to measurement error is, however, not straightforward when a survey has many and diverse survey items. An adaptive survey design needs to make an overall choice of design features that applies to all survey items simultaneously. In this paper, we investigate adaptive survey designs that account for both nonresponse and measurement error. In order to do so, we model the underlying causes for differences in measurement error between design features. This leads to response styles or response latencies. We tailor efforts so that either response style propensities are minimized or constrained. We illustrate the ideas with a case study on the 2008 Dutch Labor Force Survey. The design features in this study are the type of reporting (self-reporting only versus proxy-reporting allowed), and the number of face-to-face calls." (author's abstract)

Bautista, Julian E., et al. ; We present the cosmological analysis of the configuration-space anisotropic clustering in the completed Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 galaxy sample. This sample consists of luminous red galaxies (LRGs) spanning the redshift range 0.6 < $z$ < 1, at an effective redshift of $z$eff = 0.698. It combines 174 816 eBOSS and 202 642 BOSS LRGs. We extract and model the baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) features from the galaxy two-point correlation function to infer geometrical and dynamical cosmological constraints. The adopted methodology is extensively tested on a set of realistic simulations. The correlations between the inferred parameters from the BAO and full-shape correlation function analyses are estimated. This allows us to derive joint constraints on the three cosmological parameter combinations: DM($z$)/rd, DH($z$)/rd, and fσ8($z$), where DM is the comoving angular diameter distance, DH is the Hubble distance, rd is the comoving BAO scale, f is the linear growth rate of structure, and σ8 is the amplitude of linear matter perturbations. After combining the results with those from the parallel power spectrum analysis of Gil-Marin et al., we obtain the constraints: DM/rd = 17.65 ± 0.30, DH/rd = 19.77 ± 0.47, and fσ8 = 0.473 ± 0.044. These measurements are consistent with a flat Lambda cold dark matter model with standard gravity. ; RP, SdlT, and SE acknowledge the support from the French National Research Agency (ANR) under contract ANR-16-CE31-0021, eBOSS. SdlT and SE acknowledge the support of the OCEVU Labex (ANR-11-LABX-0060) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the 'Investissements d'Avenir' French government programme managed by the ANR. MVM and SF are partially supported by Programa de Apoyo a Proyectos de Investigación e Inovación ca Teconológica (PAPITT) no. IA101518, no. IA101619, Proyecto LANCAD-UNAM-DGTIC-319, and LANCAD-UNAM-DGTIC-136. HGM acknowledges the support from la Caixa Foundation (ID 100010434) code LCF/BQ/PI18/11630024. SA is supported by the European Research Council through the COSFORM Research Grant (#670193). GR, PDC, and JM acknowledge support from the National Research Foundation of Korea (NRF) through Grant Nos. 2017R1E1A1A01077508 and 2020R1A2C1005655 funded by the Korean Ministry of Education, Science and Technology (MoEST), and from the faculty research fund of Sejong University. Numerical computations were done on the Sciama High Performance Compute (HPC) cluster, which is supported by the ICG, SEPNet, and the University of Portsmouth. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This research also uses resources of the HPC cluster ATOCATL-IA-UNAM México. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 693024).

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record. ; Empirical social science relies heavily on self-reported data, but subjects may misreport behaviors, especially sensitive ones such as crime or drug abuse. If a treatment influences survey misreporting, it biases causal estimates. We develop a validation technique that uses intensive qualitative work to assess survey misreporting and pilot it in a field experiment where subjects were assigned to receive cash, therapy, both, or neither. According to survey responses, both treatments reduced crime and other sensitive behaviors. Local researchers spent several days with a random subsample of subjects after surveys, building trust and obtaining verbal confirmation of four sensitive behaviors and two expenditures. In this instance, validation showed survey underreporting of most sensitive behaviors was low and uncorrelated with treatment, while expenditures were under reported in the survey across all arms, but especially in the control group. We use these data to develop measurement error bounds on treatment effects estimated from surveys. ; This study was funded by the National Science Foundation (SES-1317506), the World Bank's Learning on Gender and Conflict in Africa (LOGiCA) trust fund, the World Bank's Italian Children and Youth (CHYAO) trust fund, the Department of International Development, UK (DFID, GA-C1-RA2-114) via the Institute for the Study of Labor (IZA), a Vanguard Charitable Trust, the American People through the United States Agency for International Development (USAID, AID-OAA-A-12-00066) DCHA/CMM office, and the Robert Wood Johnson Health and Society Scholars Program at Harvard University (Cohort 5). The contents of this study are the sole responsibility of authors and do not necessarily reflect the views of their employers or any of these funding agencies or governments.

Declining response rates worldwide have stimulated interest in understanding what may be influencing this decline and how it varies across countries and survey populations. In this paper, we describe the development and validation of a short 9-item survey attitude scale that measures three important constructs, thought by many scholars to be related to decisions to participate in surveys, that is, survey enjoyment, survey value, and survey burden. The survey attitude scale is based on a literature review of earlier work by multiple authors. Our overarching goal with this study is to develop and validate a concise and effective measure of how individuals feel about responding to surveys that can be implemented in surveys and panels to understand the willingness to participate in surveys and improve survey effectiveness. The research questions relate to factor structure, measurement equivalence, reliability, and predictive validity of the survey attitude scale. The data came from three probability-based panels: the German GESIS and PPSM panels and the Dutch LISS panel. The survey attitude scale proved to have a replicable three-dimensional factor structure (survey enjoyment, survey value, and survey burden). Partial scalar measurement equivalence was established across three panels that employed two languages (German and Dutch) and three measurement modes (web, telephone, and paper mail). For all three dimensions of the survey attitude scale, the reliability of the corresponding subscales (enjoyment, value, and burden) was satisfactory. Furthermore, the scales correlated with survey response in the expected directions, indicating predictive validity.

In public opinion research, response latency is a measure of attitude accessibility, which is the ease or swiftness with which an attitude comes to mind when a respondent is presented with a survey question. Attitude accessibility represents the strength of the association in memory between an attitude object and an evaluation of the object. Recent research shows that attitude accessibility, as measured by response latency, casts light on a wide range of phenomena of public opinion and political behavior. We discuss response latency methodology for survey research and advocate the use of latent response latency timers (which are invisible both to respondents and interviewers) as a low cost, low-maintenance alternative to traditional methods of measuring response latency in public opinion surveys. We show that with appropriate model specification latent response latency timers may provide a suitable alternative to the more complicated and expensive interviewer-activated timers.

ObjectiveThe objective is to estimate the relative contributions of nonresponse, coverage, and measurement biases in survey estimates of voting.MethodsWe survey 3,000 Boston‐area households sampled from an address‐based frame matched, when possible, to telephone numbers. A two‐phase sampling design was used to follow up nonrespondents from phone interviews with personal interviews. All cases were then linked to voting records.ResultsNonresponse, coverage, and measurement‐biased survey estimates at varying stages of the study design. Coverage error linked to missing telephone numbers biased estimates that excluded nonphone households. Overall estimates including nonphone households and nonrespondent interviews include 25 percent relative bias equally attributable to measurement and nonresponse.ConclusionBias in voting measures is not limited to measurement bias. Researchers should also assess the potential for nonresponse and coverage biases.