ODISSEI Lunch Lecture: Reconciliation of inconsistent data sources using hidden Markov models
oktober 26 @ 12:00 - 13:00
On Tuesday 26 October from 12:00-13:00 hours, dr. Paulina Pankowska (VU Amsterdam) presents her research on the use of hidden Markov models to produce consistent statistics with inconsistent sources.
The use of hidden Markov Models can be a complicated and expensive procedure. Therefore, it is preferable to use the error parameter estimates as a correction factor for a number of years. However, this might lead to biased structural estimates if measurement error changes over time or if the data collection process changes. Pankowska’s results on these issues are highly encouraging and imply that the suggested method is appropriate for NSI’s. Specifically, linkage error only leads to substantial bias in very extreme scenarios. Moreover, measurement error parameters are largely stable over time if no major changes in the data collection process occur. However, when a substantial change in the data collection process occurs, such as a switch from dependent (DI) to independent (INDI) interviewing, re-using measurement error estimates is not advisable. These results are more informative for those who are looking to improve the quality of their data and reduce measurement error by using multiple sources on the same variable for the same sample.
We warmly invite you to attend this lecture, learn more about Pankowska’s research, and participate in the Q&A and discussion after the lecture.
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