Weighting and missing values
Our consulting services for weighting and missing values
Weighting
A "weight" is a multiplicative factor that can be used for any analysis of a survey data set with the aim of obtaining a less biased estimator for the population. A basic distinction is made between design and calibration weights. While design weights are indispensable for surveys with different selection probabilities, calibration can reduce potential nonresponse bias and the variance of the estimator.
Imputation of missing values
Imputation describes the process of replacing missing values with existing or estimated values using auxiliary variables to obtain a complete data set. Imputation is usually done at the item level and allows working with a complete data set. GESIS has many years of experience in the use of imputation procedures and advises on the appropriate imputation strategy for the respective survey data set.
Nonresponse bias analysis
Nonresponse bias can arise when participants in a survey differ systematically from non-participants. If the reasons for non-participation are associated with target variables, it is not possible to reliably infer the population parameter from the responses. Nonresponse bias analysis allows us to assess these biases. We give recommendations for dealing with nonresponse bias and point out possible limitations of surveys, when these suffer from nonresponse bias.
Cognitive pretesting
Empirically assess the comprehensibility and validity of questions in-depth
Sampling
We draw telephone samples and complex samples for you
Weighting and analysis of complex data
We perform weighting, imputation, or nonresponse bias analyses for you
GESIS survey guides
Practical guidelines on survey methodology topics
SQP - Survey quality predictor
Estimate the measurement quality of questions using meta-analysis
Meet the Experts - Season 1: Survey Methodology
Recorded lectures from our "Meet the Experts" series
QuestionLink
Harmonization of questions for selected constructs with recoding scripts and an R package for your own projects
