Book contents
- Measuring Compliance
- Measuring Compliance
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- 1 Measuring Compliance: The Challenges in Assessing and Understanding the Interaction between Law and Organizational Misconduct
- Part 1 The Compliance Industry, the State, and Measurement Needs
- Part 2 Quantitative Approaches to Measuring Corporate Compliance
- Part 3 Qualitative Approaches to Measuring Corporate Compliance
- Part 4 Mixed Methods and Building on Existing Compliance Research
- 14 Mixing and Combining Research Strategies and Methods to Understand Compliance
- 15 Using Meta-Analysis/Systematic Review to Examine Corporate Compliance
- 16 Data Simulations as a Means of Improving Compliance Measurement
- Index
- References
16 - Data Simulations as a Means of Improving Compliance Measurement
from Part 4 - Mixed Methods and Building on Existing Compliance Research
Published online by Cambridge University Press: 17 February 2022
Book contents
- Measuring Compliance
- Measuring Compliance
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- 1 Measuring Compliance: The Challenges in Assessing and Understanding the Interaction between Law and Organizational Misconduct
- Part 1 The Compliance Industry, the State, and Measurement Needs
- Part 2 Quantitative Approaches to Measuring Corporate Compliance
- Part 3 Qualitative Approaches to Measuring Corporate Compliance
- Part 4 Mixed Methods and Building on Existing Compliance Research
- 14 Mixing and Combining Research Strategies and Methods to Understand Compliance
- 15 Using Meta-Analysis/Systematic Review to Examine Corporate Compliance
- 16 Data Simulations as a Means of Improving Compliance Measurement
- Index
- References
Summary
Abstract: This chapter discusses how Monte Carlo Simulations (MCS) can be used to improve empirical studies of compliance. They are a form of stochastic simulation, which aim to imitate and represent real-world processes with the use of random variables. This chapter describes three applications of MCS using compliance-related examples, including (a) estimating total costs of noncompliance, (b) identifying the optimal sample size for a planned study, and (c) demonstrating potential bias in model estimates. Ultimately, MCS can assist the field of compliance in navigating certain problems faced by many research domains, such as replication problems.
- Type
- Chapter
- Information
- Measuring ComplianceAssessing Corporate Crime and Misconduct Prevention, pp. 285 - 301Publisher: Cambridge University PressPrint publication year: 2022
References
American Psychological Association. 2019. Publication Manual of the American Psychological Association, 7th ed., Washington, DC: American Psychological Association.Google Scholar
Barrett, Steven R. H., Speth, Raymond L., Eastham, Sebastian D. et al. 2015. Impact of the Volkswagen emissions control defeat device on US public health. Environmental Research Letters, 10(11).Google Scholar
Benjamin, Daniel J., Berger, James O., Johannesson, Magnus et al. 2017. Redefine statistical significance. Nature Human Behaviour, 2, 6–10.CrossRefGoogle Scholar
Button, Katherine S., Ioannidis, John P. A., Mokrysz, Claire et al. 2013. Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–76.Google ScholarPubMed
Cohen, Jacob. 1988. Statistical Power Analysis for the Behavioral Sciences, 2nd ed., Hillsdale, NJ: Erlbaum.Google Scholar
Cohen, Mark A. 2016. The costs of white-collar crime. In van Slyke, Shanna R., Benson, Michael L., and Cullen, Francis T., eds., The Oxford Handbook of White-Collar Crime. New York: Oxford University Press, pp. 78–98.Google Scholar
Crutzen, Rik and Peters, Gjalt-Jorn Y.. 2017. Targeting next generations to change common practice of underpowered research. Frontiers in Psychology, 8.CrossRefGoogle ScholarPubMed
Cumming, Geoff. 2014. The new statistics: Why and how. Psychological Science, 25(1), 7–29.CrossRefGoogle ScholarPubMed
Ferrarini, Alessandro. 2011. A fitter use of Monte Carlo simulations in regression models. Computational Ecology and Software, 1(4), 240–3.Google Scholar
Gentle, James E. 2017. Matrix Algebra: Theory, Computations and Applications in Statistics, 2nd ed., New York: Springer.CrossRefGoogle Scholar
Grinstead, Charles M. and Snell, J. Laurie. 1997. Introduction to Probability. Providence, RI: American Mathematical Society.Google Scholar
Harrell, Erika. 2019. Victims of Identity Theft, 2016. Washington, DC: Bureau of Justice Statistics.Google Scholar
Hunt, Priscilla and Miles, Jeremy N. V.. 2015. An introduction to Monte Carlo simulations in criminal psychology: Applications in evaluating biased estimators for recidivism. Journal of Criminal Psychology, 5(2), 149–56.CrossRefGoogle Scholar
Hunt, Priscilla, Anderson, James, and Saunders, Jessica. 2017. The price of justice: New national and state-level estimates of the judicial and legal costs of crime to taxpayers. American Journal of Criminal Justice, 42(2), 231–54.CrossRefGoogle Scholar
Johnson, Matthew S. 2020. Regulation by shaming: Deterrence effects of publicizing violations of workplace safety and health laws. American Economic Review, 110(6), 1866–904.CrossRefGoogle Scholar
Kano, Yutaka and Takai, Keiji. 2011. Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variant model. Journal of Multivariate Statistics, 102(9), 1241–55.Google Scholar
Kenny, David A., Kaniskan, Burcu, and McCoach, D. Betsy. 2015. The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507.CrossRefGoogle Scholar
Kvarven, Amanda, Strømland, Eirik, and Johannesson, Magnus. 2020. Comparing meta-analyses and preregistered multiple-laboratory replication projects. Nature Human Behaviour, 4(4), 423–34.CrossRefGoogle ScholarPubMed
Lakens, Daniel, Adolfi, Federico G., Albers, Casper J. et al. 2018. Justify your alpha. Nature Human Behaviour, 2, 168–71.CrossRefGoogle Scholar
Little, Roderick J. A. and Rubin, Donald B.. 2002. Statistical Analysis with Missing Data. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Metropolis, Nicholas and Ulam, Stan. 1949. The Monte Carlo method. Journal of the American Statistical Association, 44(247), 335–41.CrossRefGoogle ScholarPubMed
Muthén, Linda K. and Muthén, Bengt O.. 1998–2017. Mplus User’s Guide, 8th ed., Los Angeles, CA: Muthén & Muthén.Google Scholar
Muthén, Linda K. and Muthén, Bengt O.. 2002. How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620.CrossRefGoogle Scholar
Nicholson, Jody S., Deboeck, Pascal R., and Howard, Wayland. 2017. Attrition in developmental psychology: A review of modern missing data reporting and practices. International Journal of Behavioral Development, 41(1), 143–53.CrossRefGoogle Scholar
Nuijten, Michèle B., van Assen, Marcel A. L. M., Coosje, L. Veldkamp, S., and Wicherts, Jelte M.. 2015. The replication paradox: Combining studies can decrease accuracy of effect size estimates. Review of General Psychology, 19(2), 172–82.CrossRefGoogle Scholar
Open Science Collaboration. 2015. Estimating the reproducibility of psychological science. Science, 349(6251), 943–44.Google Scholar
Peugh, James L. and Enders, Craig K.. 2004. Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74(4), 525–56.Google Scholar
Rice, Marnie E. and Harris, Grant T.. 2005. Comparing effect sizes in follow-up studies: ROC Area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615–20.CrossRefGoogle Scholar
Rorie, Melissa, Alper, Mariel, Schell-Busey, Natalie, and Simpson, Sally S.. 2018. Using meta-analysis under conditions of definitional ambiguity: The case of corporate crime. Criminal Justice Studies: A Critical Journal of Crime, Law and Society, 31(1), 38–61.CrossRefGoogle Scholar
Schafer, Joseph L. and Graham, John W.. 2002. Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–77.CrossRefGoogle ScholarPubMed
Schell-Busey, Natalie M. 2009. The deterrent effects of ethics codes for corporate crime: A meta-analysis. PhD dissertation. ProQuest.Google Scholar
Shimshack, Jay P. and Ward, Michael B.. 2005. Regulator reputation, enforcement, and environmental compliance. Journal of Environmental Economics and Management, 50, 519–40.CrossRefGoogle Scholar
Sigal, Matthew J. and Chalmers, R. Philip. 2016. Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136–56.Google Scholar
Simmons, Joseph P., Nelson, Leif D., and Simonsohn, Uri. 2011. False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–66.CrossRefGoogle ScholarPubMed
Stigler, Stephen M. 1991. Stochastic simulation in the nineteenth century. Statistical Science, 6(1), 89–97.CrossRefGoogle Scholar
Strømland, Eirik. 2019. Preregistration and reproducibility. Journal of Economic Psychology, 75. doi: 10.1016/j.joep.2019.01.006.Google Scholar
Sweeten, Gary. 2020. Standard errors in quantitative criminology: Taking stock and looking forward. Journal of Quantitative Criminology, 36, 263–72.Google Scholar
Szucs, Denes and Ioannidis, John P. A.. 2017. Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLoS Biology, 15(3), 1–18.Google Scholar
Thomopoulos, Nicholas T. 2013. Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models. New York: Springer.CrossRefGoogle Scholar
Wasserstein, Ronald L., Schirm, Allen L., and Lazar, Nicole A.. 2019. Moving to a world beyond p < .05. The American Statistician, 73, 1–19.CrossRefGoogle Scholar
West, Matthew P., Rorie, Melissa, and Cohen, Mark A.. 2020. The “pliability” of criminological analyses: Assessing bias in regression estimates using Monte Carlo simulations. Journal of Quantitative Criminology, 36, 371–94.Google Scholar
Zhang, Zhiyong. 2014. Monte Carlo based statistical power analysis for mediation models: Methods and software. Behavior Research Methods, 46(4), 1184–98.Google Scholar