Examining conservation compliance with randomized response technique analyses.

Abstract

Understanding violations of laws or social norms designed to protect natural resources from overexploitation is a priority for conservation research and management. Because direct questioning about stigmatized behaviors can produce biased responses, researchers have adopted more complex, indirect questioning techniques. The randomized response technique (RRT) is one of the most powerful indirect survey methods, yet analyses of these data require sophisticated statistical models. To date, there has been limited user-friendly software to analyze RRT data, particularly for models that combine information from multiple RRT questions. We developed an R package, zapstRR (ZoologicAl Package for RRT) that provides functions for 3 RRT models that can be applied to single or multiple RRT questions. With these functions, researchers can estimate the prevalence of conservation noncompliance, determine the number of violations by individuals, perform regressions for univariate and multivariate RRT data, and correct prevalence estimates for evasive-response bias. We illustrate the use of these estimators for RRT data through an examination of 2 case studies: illegal bird hunting where the interview consisted of a standard RRT question design and a novel implementation designed to offer further anonymity to respondents and reveal the impact of educational interventions on illegal bushmeat consumption. The case studies demonstrate how the models can work in tandem to uncover distinct patterns within RRT data sets. The case studies also show how several assumptions are central to the application of the multivariate models.