Many self-report measures of attitudes, beliefs, personality, and pathology include items that can be easily manipulated by respondents. For example, an individual may deliberately attempt to manipulate or distort responses to simulate grossly exaggerated physical or psychological symptoms in order to reach specific goals such as, for example, obtaining financial compensation, avoiding being charged with a crime, avoiding military duty, or obtaining drugs. This article introduces the package sgr that can be used to perform fake data analysis according to the sample generation by replacement approach. The package includes functions for making simple inferences about discrete/ordinal fake data. The package allows to quantify uncertainty in inferences based on possible fake data as well as to study the implications of fake data for empirical results.
Multivariate, Psychometrics, Distributions, Econometrics, Environmetrics, NumericalMathematics, Pharmacokinetics, Robust, SocialSciences
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For attribution, please cite this work as
Lombardi & Pastore, "The R Journal: sgr: A Package for Simulating Conditional Fake Ordinal Data", The R Journal, 2014
BibTeX citation
@article{RJ-2014-019,
author = {Lombardi, Luigi and Pastore, Massimiliano},
title = {The R Journal: sgr: A Package for Simulating Conditional Fake Ordinal Data},
journal = {The R Journal},
year = {2014},
note = {https://doi.org/10.32614/RJ-2014-019},
doi = {10.32614/RJ-2014-019},
volume = {6},
issue = {1},
issn = {2073-4859},
pages = {164-177}
}