Estimating classification error under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)

Both registers and surveys can contain classication errors. These errors can be estimated by making use of information that is obtained when making use of a combined dataset. We propose a new method based on latent class modelling that estimates the number of classification errors in the multiple sources, and simultaneously takes impossible combinations with other variables into account. Furthermore, we use the latent class model to multiply impute a new variable, which enhances the quality of statistics based on the combined dataset. The performance of this method is investigated by a simulation study, which shows that whether the method can be applied depends on the entropy of the LC model and the type of analysis a researcher is planning to do. Furthermore, the method is applied to a combined dataset from Statistics Netherlands

A framework for privacy preserving digital trace data collection through data donation

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

lavaan.survey:An R Package for complex survey analysis of structural equation models

This paper introduces the R package lavaan.survey, a user-friendly interface to design-based complex survey analysis of structural equation models (SEMs). By leveraging existing code in the lavaan and survey packages, the lavaan.survey package allows for SEM analyses of stratified, clustered, and weighted data, as well as multiply imputed complex survey data. lavaan.survey provides several features such as SEMs with replicate weights, a variety of resampling techniques for complex samples, and finite population corrections, features that should prove useful for SEM practitioners faced with the common situation of a sample that is not iid