How to Obtain Valid Inference under Unit Nonresponse?

Missing data form a ubiquitous problem in scientific research, especially since most statistical analyses require complete data. To evaluate the performance of methods dealing with missing data, researchers perform simulation studies. An important aspect of these studies is the generation of missing values in a simulated, complete data set: the amputation procedure. We investigated the methodological validity and statistical nature of both the current amputation practice and a newly developed and implemented multivariate amputation procedure. We found that the current way of practice may not be appropriate for the generation of intuitive and reliable missing data problems. The multivariate amputation procedure, on the other hand, generates reliable amputations and allows for a proper regulation of missing data problems. The procedure has additional features to generate any missing data scenario precisely as intended. Hence, the multivariate amputation procedure is an efficient method to accurately evaluate missing data methodology.

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“…Evaluations of weighting procedures (e.g. [31]) and likelihood-based methods (e.g. [32,33]) can be done as well.…”

Section: Discussionmentioning

confidence: 99%

Generating missing values for simulation purposes: a multivariate amputation procedure

Schouten

Lugtig

Vink

2018

Journal of Statistical Computation and Simulation

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“…This is in contrast to a situation when nothing is known about the nonrespondents, so that the missingness cannot be explained (Missing Not At Random, MNAR) (Schafer & Graham, 2002). When the missingness can be explained, it can be accounted for by method such as multiple imputation or weighting (Boeschoten et al, 2017). To minimize the number of respondents that are willing to participate but to not use the platform under investigation, the researcher should also focus on how often the target population makes use of the platform under investigation when determining which platform to use for research.…”

Section: Samplementioning

confidence: 98%

Digital trace data collection through data donation

Boeschoten,

Ausloos,

Moeller

et al. 2020

Preprint

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A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU's 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with "data download packages" (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens' digital life can be obtained and analyzed to answer social-scientific questions -with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a "total error framework" for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.

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Finding a Flexible Hot-Deck Imputation Method for Multinomial Data

Andridge

Bechtel

Thompson

2020

Journal of Survey Statistics and Methodology

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Detailed breakdowns on totals are often collected in surveys, such as a breakdown of total product sales by product type. These multinomial data are often sparsely reported with wide variability in proportions across units. In addition, there are often true zeros that differ across units even within industry; for example, one establishment sells jeans but not shoes, and another sells shoes but not socks. It is quite common to have large fractions of missing data for these detailed items, even when totals are relatively completely observed. Hot-deck imputation, which fills in missing data with observed data values, is an attractive approach. The entire set of proportions can be simultaneously imputed to preserve multinomial distributions, and zero values can be imputed. However, it is not clear what variant of the hot deck is best. We describe a large set of “flavors” of the hot deck and compare them through simulation and by application to data from the 2012 Economic Census. We consider different ways to create the donor pool: choosing one nearest neighbor (NN), choosing from five NNs, or using all units as the donor pool. We also consider different ways to impute from the donor: directly impute the donor’s vector of proportions or randomly draw from a multinomial distribution using this vector of proportions. We consider scenarios where a strong predictor of these multinomial distributions exists as well as when covariate information is weak.

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How to Obtain Valid Inference under Unit Nonresponse?

Cited by 4 publications

References 19 publications

Generating missing values for simulation purposes: a multivariate amputation procedure

Generating missing values for simulation purposes: a multivariate amputation procedure

Digital trace data collection through data donation

Finding a Flexible Hot-Deck Imputation Method for Multinomial Data

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