Common methodological challenges encountered with multiple systems estimation studies
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
Multiple systems estimation refers to a class of inference procedures that are commonly used to estimate the size of hidden populations based on administrative lists. In this paper we discuss some of the common challenges encountered in such studies. In particular, we summarize theoretical issues relating to the existence of maximum likelihood estimators, model identifiability, and parameter redundancy when there is sparse overlap among the lists. We also discuss techniques for matching records when there are no unique identifiers, exploiting covariate information to improve estimation, and addressing missing data. We offer suggestions for remedial actions when these issues/challenges manifest. The corresponding R coding packages that can assist with the analyses of multiple systems estimation data sets are also discussed.
Vincent , K , Sharifi Far , S & Papathomas , M 2020 , ' Common methodological challenges encountered with multiple systems estimation studies ' , Crime and Delinquency , vol. OnlineFirst . https://doi.org/10.1177/0011128720981900
Crime and Delinquency
Copyright © 2020 the Author(s). This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1177/0011128720981900.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.