ESS round 10 mode experiments in Great Britain and Finland. Findings on mode effects

Abstract

One of the major problems in estimating the mode measurement effect in (mixed-mode) surveys is that isolation of the causal effect of mode on measurement is difficult due to the fact that selection and measurement effects are (potentially) correlated. In this report I use experimental data collected in round 11 of the European Social Survey (ESS) to study the size of mode effects in Great Britain and Finland. Respondents were randomized into a condition in which recruitment and interviewing was done using face-to-face interviewing, or a condition in which a push-to-web design was used. Here respondents were invited by postal mail, and aksed to do the survey online. Paper was used to convert nonrespondents or for respondents requesting to do the survey on paper. The report analyses mode effects, and aims to control for selection effects to assess the size of mode-effects across 111 numeric variables in the European Social Survey.

Publication
ESS methodological reports 2024

Documentation: https://sourcethemes.com/academic/docs/managing-content/

title: “Dynamic Time Warping-based imputation of long gaps in human mobility trajectories” authors: [McCool,D.M., Lugtig, P. & Schouten, J.G.] date: 2024-11-21T00:00:00+01:00 doi: “doi: 10.48550/arXiv.2410.16096”

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abstract: “Individual mobility trajectories are difficult to measure and often incur long periods of missingness. Aggregation of this mobility data without accounting for the missingness leads to erroneous results, underestimating travel behavior. This paper proposes Dynamic Time Warping-Based Multiple Imputation (DTWBMI) as a method of filling long gaps in human mobility trajectories in order to use the available data to the fullest extent. This method reduces spatiotemporal trajectories to time series of particular travel behavior, then selects candidates for multiple imputation on the basis of the dynamic time warping distance between the potential donor series and the series preceding and following the gap in the recipient series and finally imputes values multiple times. A simulation study designed to establish optimal parameters for DTWBMI provides two versions of the method. These two methods are applied to a real-world dataset of individual mobility trajectories with simulated missingness and compared against other methods of handling missingness. Linear interpolation outperforms DTWBMI and other methods when gaps are short and data are limited. DTWBMI outperforms other methods when gaps become longer and when more data are available.”

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tags: [sensors, official statistics, app, missing data, statistics netherlands,location data, imputation] categories: [] featured: false

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