population

A dataset of a simulated fictitious population of 50,000 individuals. Further details on the generation of the dataset can be found in Ferri-Garc&lt;U+00ED&gt;a and Rueda (2018). The variables present in the dataset are the following:<ul>
<li>education_primaria. A binary variable indicating if the highest academic level achieved by the individual is Primary Education.</li>
<li>education_secundaria. A binary variable indicating if the highest academic level achieved by the individual is Secondary Education.</li>
<li>education_terciaria. A binary variable indicating if the highest academic level achieved by the individual is Tertiary Education.</li>
<li>age. A numeric variable, with values ranging from 18 to 100, indicating the age of the individual.</li>
<li>sex. A binary variable indicating if the individual is a man.</li>
<li>language. A binary variable indicating if the individual is a native.</li>
</ul>

datasets

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 Horvitz<e2><80><93>Thompson 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.

Luis Martc3<ad>n

NonProbEst

Estimation in Nonprobability Sampling

population function

An object of class <code>data.frame</code> with 50000 rows and 6 columns.

population: A full population

Description

Usage

Arguments

Format

References