sgr: A Package for Simulating Conditional Fake Ordinal Data

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Authors Luigi Lombardi, Massimiliano Pastore
Journal/Conference Name The R Journal
Paper Category
Paper Abstract 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. In this article, we discuss the sgr package that we have developed for running fake data analysis according to the sample generation by replacement (SGR) approach (Lombardi and Pastore, 2012). SGR is a data simulation procedure to generate artificial samples of fake discrete/ordinal data. The main characteristic of the SGR approach is that it allows detailed explorations of what outcomes are produced by particular sets of faking assumptions. By changing the input in the faking model parameters and showing the effect on the outcome of a model, SGR provides a what-if-analysis of the faking scenarios. Therefore, SGR can be used to quantify uncertainty in inferences based on possible fake data as well as to evaluate the implications of fake data for statistical results. To illustrate, let us consider the following example where a researcher is interested in studying the relationship between therapy-uncompliance indicators (e.g., forgetting the treatment) and unsafe behaviors indicators (e.g., drinking alcohol) in a group of liver transplant patients. Generally, patients diagnosed with alcohol dependence who follow a pharmaceutical regimen after the liver transplant would deliberately answer fraudulently a question about drinking alcohol due to abstinence from ethanol and social desirability factors (e.g. Foster et al., 1997). In this context, an SGR analysis can help in testing for potential influence of faking the drinking alcohol self-report measure on the strength of the relationship between therapy-uncompliance and unsafe behaviors indicators. More specifically, how sensitive are the empirical associations to possible fake observations in the drinking alcohol self-report measure? Are the conclusions still valid under one or more scenarios of faking (e.g., slight, moderate, and extreme faking) for the drinking alcohol variable? In general, SGR takes an interpretation perspective by incorporating in a global model all the available information about the process of faking and the underlying true model representation. This makes SGR related in spirit to other statistical approaches such as, for example, uncertainty and sensitivity analysis (Helton et al., 2006) and prospective power analysis (Cohen, 1988) which are all characterized by an attempt to directly quantify uncertainty of general statistics computed on the data by means of specific hypotheses.
Date of publication 2014
Code Programming Language R

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