Estimation in Nonprobability Sampling // Different inference
procedures are proposed in the literature to correct for
selection bias that might be introduced with non-random
selection mechanisms. A class of methods to correct for
selection bias is to apply a statistical model to predict the
units not in the sample (super-population modeling). Other
studies use calibration or Statistical Matching (statistically
match nonprobability and probability samples). To date, the
more relevant methods are weighting by Propensity Score
Adjustment (PSA). The Propensity Score Adjustment method was
originally developed to construct weights by estimating
response probabilities and using them in HorvitzThompson type
estimators. This method is usually used by combining a non-
probability sample with a reference sample to construct
propensity models for the non-probability sample. Calibration
can be used in a posterior way to adding information of
auxiliary variables. Propensity scores in PSA are usually
estimated using logistic regression models. Machine learning
classification algorithms can be used as alternatives for
logistic regression as a technique to estimate propensities.
The package 'NonProbEst' implements some of these methods and
thus provides a wide options to work with data coming from a
non-probabilistic sample.