Evaluating the quality of survey and administrative data with generalized multitrait-multimethod models
|Starts:||13:00 25 Apr 2016|
|Ends:||14:00 25 Apr 2016|
|What is it:||Seminar|
|Who is it for:||University staff, Alumni, Current University students|
|Speaker:||Dr Daniel Oberski|
Administrative register data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect.
We introduce the "generalized multitrait-multimethod" (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and register to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-register data from the German Federal Employment Agency on income from and duration of employment, and a simulation study evaluates the estimates obtained.
This seminar is presented by Dr Daniel Oberski, Assistant Professor in the Department of Methodology and Statistics at Tilburg University.
Dr Daniel Oberski
Role: Assistant Professor
Organisation: Tilburg University
Travel and Contact Information
Humanities Bridgeford Street