Assessing the Impact of Non-Random Measurement Error on Inference

A Sensitivity Analysis Approach

Dados Bibliográficos

ID6342201
AutoresMax Gallop (0000-0001-6352-4301, University of Strathclyde), Simon Weschle (0000-0002-5106-5301, Fundación Juan March)
Ano2019
Volume7
Fascículo2
Páginas367-384
Data de publicação2019-04-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPolitical Science Research and Methods (JOURNAL)
Identificadores do periódicoISSN: 2049-8470 • E-ISSN: 2049-8489
EditoraCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/psrm.2016.53
OpenAlexW2573644465
IdiomaEN
Citações recebidas10
Referências citadas49

Many commonly used data sources in the social sciences suffer from non-random measurement error, understood as mis-measurement of a variable that is systematically related to another variable. We argue that studies relying on potentially suspect data should take the threat this poses to inference seriously and address it routinely in a principled manner. In this article, we aid researchers in this task by introducing a sensitivity analysis approach to non-random measurement error. The method can be used for any type of data or statistical model, is simple to execute, and straightforward to communicate. This makes it possible for researchers to routinely report the robustness of their inference to the presence of non-random measurement error. We demonstrate the sensitivity analysis approach by applying it to two recent studies

Data mining · Data science · Econometrics · Inference · Machine learning · Observational error · Random variable · Robustness (evolution · Sensitivity (control systems · Statistical inference · Statistics · Suspect · Advanced Causal Inference Techniques · Computational and Text Analysis Methods · Computer Science · Engineering · Mathematics · Psychology · Qualitative Comparative Analysis Research · Artificial Intelligence