ESRA 2013 Sessions

Weighting: approach and sources 1Mrs Kim De Cuyper
There are numerous reasons why a given sample may not be representative. The main reasons for this are uncontrollable deviations from randomness which may arise from numerous sources: systematic non response, deficient address material, interviewer bias, heterogeneous contact probabilities, etc. Moreover, it will rarely occur that the raw data 100% match the population, especially since data validation and data cleaning also impact this. To eliminate bias as much as possible and to correspond to the population, weighting is applied. Weighting is a complex process and goes through various phases in which the approach towards weighting as well as the sources are key and determine the validity of the weighting.
In terms of approach a distinction can be made between:
- types of weight: probability or design, post-stratification or non-response, national, population, etc.
- single stage or multi stage weighting
- variables to weight on
- method: iterative proportionate fitting (IPF, a.k.a. Rim Weighting or Raking), linear weighting, etc.
- software package: SPSS, Quantum, etc.
- trimming
- imputation of missing data
The sources for weighting are utmost important and can divided in two main groups
- public vs private data
- type of data
o individuals vs households in consumer surveys
o workers vs establishments (in -or excluding group structures) in business surveys
A well designed weighting procedure results in a high weighting efficiency and a high effective sample percentage.

Therefore this session invites papers looking into one or more of the different weighting angles as per above. Paper givers are invited to send in an abstract of no longer than 1000 words.

Weighting: approach and sources 2Mrs Kim De Cuyper
There are numerous reasons why a given sample may not be representative. The main reasons for this are uncontrollable deviations from randomness which may arise from numerous sources: systematic non response, deficient address material, interviewer bias, heterogeneous contact probabilities, etc. Moreover, it will rarely occur that the raw data 100% match the population, especially since data validation and data cleaning also impact this. To eliminate bias as much as possible and to correspond to the population, weighting is applied. Weighting is a complex process and goes through various phases in which the approach towards weighting as well as the sources are key and determine the validity of the weighting.
In terms of approach a distinction can be made between:
- types of weight: probability or design, post-stratification or non-response, national, population, etc.
- single stage or multi stage weighting
- variables to weight on
- method: iterative proportionate fitting (IPF, a.k.a. Rim Weighting or Raking), linear weighting, etc.
- software package: SPSS, Quantum, etc.
- trimming
- imputation of missing data
The sources for weighting are utmost important and can divided in two main groups
- public vs private data
- type of data
o individuals vs households in consumer surveys
o workers vs establishments (in -or excluding group structures) in business surveys
A well designed weighting procedure results in a high weighting efficiency and a high effective sample percentage.

Therefore this session invites papers looking into one or more of the different weighting angles as per above. Paper givers are invited to send in an abstract of no longer than 1000 words.