Leverhulme Lecture: Item Non-response in Surveys - Make it up or throw it out?

Date:

07/03/2019

Organised by:

University of Essex

Presenter:

Christopher R. Bollinger is the Leverhulme Visiting Professor at ISER during 2018-2019. He is a Sturgill Professor in the Department of Economics at the University of Kentucky. He received his B.A. in Economics at Michigan State University and earned both an M.S. and Ph.D. in economics at the University of Wisconsin, Madison.

Professor Bollinger’s research has focused on data quality issues and the impact of survey data quality on estimation of micro economic models. His work has focused on both measurement error and item non-response. His work on measurement error has been published in journals such as Journal of Applied Econometrics, the Journal of the American Statistical Association, and the Journal of Econometrics. His work on non-response has been published in journals such as the Review of Economics and Statistics, Journal of Labor Economics and the Journal of Political Economy. Professor Bollinger also has interests in Urban Economics and Labor Economics. His work in these areas has been published in journals such as Journal of Labor Economics, Labour Economics and the Journal of Urban Economics. Professor Bollinger has served as both an associate editor and co-editor for the Southern Economic Journal, and an associate editor for the Journal of Econometric Methods. He has also served as the Associate Director of the U.K. Center for Poverty Research, Director of Graduate Studies for the Economics Department, and the Director of the Center for Business and Economic Research. He currently serves on the Consensus Forecast Group for the State of Kentucky.

Level:

Intermediate (some prior knowledge)

Contact:

proficio@essex.ac.uk
01206 873077

Map:

View in Google Maps  (CO4 3SQ)

Venue:

2N2.4.16
University of Essex
Wivenhoe Park
Colchester
Essex

Description:

Students will learn how missing data – item non-response in surveys – can impact estimation.  The course will focus upon missing data in a linear regression setting.   We will being by considering basic and advanced imputation methods and when then can be safely used.  The course will focus upon the assumption of missing at random and when it can apply and when it may not.  The course will examine approaches using auxiliary data – particularly matches to administrative and other non-survey data – both to examine the validity of the missing at random assumption and to address missing data.

Cost:

£40 per participant

Website and registration:

Region:

East of England

Keywords:

Data Quality and Data Management , Quantitative Data Handling and Data Analysis, Students will learn how missing data – item non-response in surveys – can impact estimation. The course will focus upon missing data in a linear regression setting. We will being by considering basic and advanced imputation methods and when then can be

Related publications and presentations:

Data Quality and Data Management
Quantitative Data Handling and Data Analysis

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