Conference Programme 2015

Conference floor plans and map
Tuesday 14th July      Wednesday 15th July      Thursday 16th July      Friday 17th July     


Wednesday 15th July, 09:00 - 10:30 Room: HT-101

Advanced survey estimation methods for treatment of non-sampling errors 1

Convenor Dr Alina Matei (University of Neuchatel, Switzerland )
Coordinator 1Professor Giovanna Ranalli (University of Perugia, Italy)

Session Details

Non-sampling errors can be generated by nonresponse, frame imperfections, measurement and data processing errors. We focus here on nonresponse and coverage errors as a possible source of non-negligible bias. Nonresponse is determined by the failure to obtain fully or partially information from the sampled units. Coverage or frame errors are caused by the gap between the target population and the sampling frame.

The proposed session will bring together presentations proposing advanced estimation methods for treatment of these types of non-sampling errors. The presentations will focus on methodologies to deal with nonresponse, frame imperfections or both. Methods using propensity score methods or (generalized) calibration to handle nonresponse, estimation methods for multiple-frame sampling, joint calibration for nonresponse and frame imperfections, calibration in the presence of domain misclassification, etc. are invited to be submitted in this session.

Paper Details

1. Estimation of proportions in dual frames using ordinal logistic regression.
Dr Arcos Antonio (University of Granada)
Dr Maria Del Mar Rueda (University of Granada)
Mr David Molina (University of Granada)

In this work we consider estimation techniques from dual frame surveys in the case of estimation of proportions when the variable of interest has ordinal outcomes. We propose to describe the joint distribution of the class indicators by an ordinal model. Ordinal model assisted estimators and ordinal model calibration estimators are introduced for class frequencies in a population by using the two different approaches to estimation. Theoretical properties are investigated for these estimators. An application in a complex survey is also included.


2. Indirect Sampling and the Multiple Frame Surveys
Professor Manuela Maia (Universidade Católica Portuguesa- Faculdade de Economia e Gestão)

Indirect sampling is an alternative approach to classical sampling theory in dealing with the problem of overlapping sampling frames on survey estimates. In this paper, a comparison is made between the optimal estimator of Deville and Lavallée and two classes of estimators - Domain Membership estimator and Unit Multiplicity estimator - translated into the context of Indirect Sampling. Next, a comparison is made of these estimators, taking in consideration the different kinds of cases that occur in a dual frame context, and the variance of the optimal estimator Deville and Lavallée is presented. Finally, the results of a simulation study


3. The Blocked Imputation Approach as a Method to incorporate Information on why Data are Missing
Dr Peter Lugtig (Utrecht University)
Miss Roline Kamphuis (Utrecht University)
Dr Shahab Jolani (Utrecht University)

A new blocked-imputation method will be presented to deal with missing data in longitudinal surveys. Usually, information on why a unit is missing is recorded (e.g. refusal, noncontact, out-of scope), is recorded, but this information is then never used in the imputation process. The new approach used this information to improve the quality of the imputation process. The approach is illustrated with a simulation study and data from the British Household Panel Survey.


4. Weighting adjustment for nonignorable nonresponse with a heterogeneous structure of the variable of interest
Dr Alina Matei (Universityof Neuchatel, Switzerland)
Ms Caren Hasler (Universityof Neuchatel, Switzerland)

In the case of nonignorable nonresponse, the unit response probabilities
depend on the variable of interest. It is assumed that the variable of interest is sampled from a population which can be described as a mixture of some hidden subpopulations; a typical example of such a variable is income. Given the relationship between the variable of interest and the response probabilities, the latter can also be described by a such mixture. We consider that auxiliary information is available for all the sampled units. Two approaches that underline the mixture structure are considered to estimate the response probabilities through logistic regression.