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Research Articles

Expanding the methodological toolkit of criminology and criminal justice with the Total Error Framework

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Pages 112-129 | Received 01 Oct 2021, Accepted 27 Jul 2022, Published online: 23 Aug 2022

References

  • Amaya, A., P. P. Biemer, and D. Kinyon. 2020. “Total Error in a Big Data World: Adapting the TSE Framework to Big Data.” Journal of Survey Statistics and Methodology 8: 89–119. doi:10.1093/jssam/smz056.
  • Anderson, R., J. Kasper, and M. Frankel. 1979. Total Survey Error: Applications to Improve Health Surveys. San Francisco, CA: Jossey-Bass.
  • Andresen, M. A. 2006. “Crime Measures and the Spatial Analysis of Criminal Activity.” British Journal of Criminology 46: 258–285. doi:10.1093/bjc/azi054.
  • Andresen, M. A., and G. W. Jenion. 2010. “Ambient Populations and the Calculation of Crime Rates and Risk.” Security Journal 23 (2): 114–133. doi:10.1057/sj.2008.1.
  • Andresen, M. A., and S. J. Linning. 2012. “The (In)appropriateness of Aggregating across Crime Types.” Applied Geography 35 (1–2): 275–282. doi:10.1016/j.apgeog.2012.07.007.
  • Andresen, M. A., N. Malleson, W. Steenbeek, M. Townsley, and C. Vandeviver. 2020. “Minimum Geocoding Match Rates: An International Study of the Impact of Data and Areal Unit Sizes.” International Journal of Geographical Information Science 34 (7): 1306–1322. doi:10.1080/13658816.2020.1725015.
  • Aneesh, A. 2009. “Global Labor: Algocratic Modes of Organization.” Sociological Theory 27 (4): 347–370. doi:10.1111/j.1467-9558.2009.01352.x.
  • Baker, R. P. 2017. “Big Data: A Survey Research Perspective.” In Total Survey Error: Improving Quality in the Era of Big Data, edited by P. P. Biemer, E. D. de Leeuw, S. Eckman, B. Edwards, F. Kreuter, L. Lyberg, C. Tucker, and B. West, 47–70. Hoboken: Wiley.
  • Bakker, B. F. M. 2010. “Micro-integration: State of the Art.” In Report on WP1. State of the Art on Statistical Methodologies for Data Integration, edited by ESSnet on Data Integration, 77–107. Luxembourg: Eurostat.
  • Bakker, B. F. M. 2012. “Estimating the Validity of Administrative Variables.” Statistica Neerlandica 66 (1): 8–17. doi:10.1111/j.1467-9574.2011.00504.x.
  • Biderman, A. D., and J. P. Lynch. 1991. Understanding Crime Statistics: Why the UCR Diverges from the NCS. New York: Springer-Verlag.
  • Biemer, P. P. 2010. “Total Survey Error: Design, Implementation, and Evaluation.” The Public Opinion Quarterly 74 (5): 817–848. doi:10.1093/poq/nfq058.
  • Biemer, P. P. 2016. “Errors and Inference.” In Big Data and Social Science: A Practical Guide to Methods and Tools, edited by I. Foster, R. Ghani, R. S. Jarmin, F. Kreuter, and J. Lane, 265–297. Boca Raton, FL: CRC Press.
  • Biemer, P. P., and A. Amaya. 2021. Total Error Frameworks for Found Data. In Big Data Meets Survey Science. A Collection of Innovative Methods, edited by C. A. Hill, P. P. Biemer, T. D. Buskirk, L. Japec, A. Kirchner, S. Kolenikov, and L. E. Lyberg., 133–161. Hoboken: John Wiley & Sons.
  • Biemer, P. P., and L. E. Lyberg. 2003. Introduction to Survey Quality. Hoboken: Wiley.
  • Biemer, P. P., and L. E. Lyberg. 2020. “Total Survey Error.” SAGE Research Methods Foundations edited by P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, and R. A. Williams https://doi.org/10.4135/9781526421036888444
  • Boggs, S. L. 1965. “Urban Crime Patterns.” American Sociological Review 30 (6): 899–908. doi:10.2307/2090968.
  • Bottomley, A. K., and K. Pease. 1986. Crime and Punishment: Interpreting the Data. Milton Keynes: Open University Press.
  • Braga, A. A., and R. V. Clarke. 2014. “Explaining high-risk Concentrations of Crime in the City: Social Disorganization, Crime Opportunities, and Important Next Steps.” Journal of Research in Crime and Delinquency 51: 480–498. doi:10.1177/0022427814521217.
  • Buil-Gil, D., A. Moretti, and S. H. Langton. 2021. “The Accuracy of Crime Statistics: Assessing the Impact of Police Data Bias on Geographic Crime Analysis.” Journal of Experimental Criminology. doi:10.1007/s11292-021-09457-y.
  • Bunge, M. 2006. Chasing Reality. Strife over Realism. Toronto: University of Toronto Press.
  • Chen, Y., Y. Li, and J. Li. 2016. “Investigating the Influence of Tree Coverage on Property Crime: A Case Study in the City of Vancouver, British Columbia, Canada.” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences B2: 695–702. doi:10.5194/isprs-archives-XLI-B2-695-2016.
  • Cochran, W. G. 1953. Sampling Techniques. New York: Wiley.
  • Connelly, R., C. J. Playford, V. Gayle, and C. Dibben. 2016. “The Role of Administrative Data in the Big Data Revolution in Social Science Research.” Social Science Research 59: 1–12. doi:10.1016/j.ssresearch.2016.04.015.
  • Curtis, R. 2010. “Getting Good Data from People that Do Bad Things: Effective Methods and Techniques for Conducting Research with hard-to-reach and Hidden Population.” In Offenders and Offending. Learning about Crime from Criminals, edited by W. Bernasco, 163–180. London: Willan.
  • De Kimpe, L., M. Walrave, T. Snaphaan, L. J. R. Pauwels, W. Hardyns, K. Ponnet 2019. “Research Note: An Investigation of Reporting Behavior among Cybercrime Victims.” European Journal of Crime, Criminal Law and Criminal Justice. 29(1): 66–78. doi:10.1163/15718174-bja10019
  • de Leeuw, Edith, and W. de Heer. 2002. “Trends in Household Survey Nonresponse: A Longitudinal and International Comparison.” In Survey Nonresponse, edited by R. M. Groves, D. A. Dillman, J. L. Eltinge, and R. J. A. Little, 41–54. New York: Wiley.
  • Deming, W. E. 1944. “On Errors in Surveys.” American Sociological Review 9 (4): 359–369. doi:10.2307/2085979.
  • Favaretto, M., E. De Clercq, and B. S. Elger. 2019. “Big Data and Discrimination: Perils, Promises and Solutions. A Systematic Review.” Journal of Big Data 6(12) . doi: 10.1186/s40537-019-0177-4.
  • Federal Police. 2021a. “Criminaliteitsstatistieken.” Available online: http://www.stat.policefederale.be/criminaliteitsstatistieken/interactief/ (Accessed 15 April 2022).
  • Federal Police. 2021b. “Veiligheidsmonitor 2018.” Grote tendensen. Available online: http://www.moniteurdesecurite.policefederale.be/assets/pdf/2018/reports/Grote_tendensen_Analyses_VMS2018.pdf (Accessed 15 April 2022).
  • Groves, R. M. 1989. Survey Errors and Survey Costs. New York: Wiley.
  • Groves, R. M., F. J. Fowlers Jr., M. P. Couper, J. M. Lepkowski, E. Singer, and R. Tourangeau. 2009. Survey Methodology. 2nd ed. Hoboken: Wiley.
  • Groves, R. M., F. J. Fowlers Jr., M. P. Couper, E. Singer, and R. Tourangeau. 2004. Survey Methodology. Hoboken: Wiley.
  • Groves, R. M., and L. Lyberg. 2010. “Total Survey Error: Past, Present, and Future.” Public Opinion Quarterly 74 (5): 849–879. doi:10.1093/poq/nfq065.
  • Hansen, M. H., W. N. Hurwitz, and W. G. Madow. 1953. Sample Survey Methods and Theory. Vol. 2. New York: Wiley.
  • Hardyns, W., T. Snaphaan, L. J. R. Pauwels. 2019. “Crime Concentrations and Micro Places: An Empirical Test of the ‘Law of Crime Concentration at Places‘ in Belgium.” Australian & New Zealand Journal of Criminology. 52(3): 390–410. doi:10.1177/0004865818807243
  • Harries, K. D. 1981. “Alternative Denominators in Conventional Crime Rates.” In Environmental Criminology, edited by P. J. Brantingham and P. L. Brantingham, 147–165. Prospect Heights: Waveland Press.
  • Hilbert, M. 2016. “Big Data for Development: A Review of Promises and Challenges.” Development Policy Review 34 (1): 135–174. doi:10.1111/dpr.12142.
  • Hox, J. J., E. D. de Leeuw, and D. A. Dillman. 2008. “The Cornerstones of Survey Research.” In International Handbook of Survey Research, edited by E. D. de Leeuw, J. J. Hox, and D. A. Dillman, 1–17. New York: Taylor & Francis Group.
  • Hsieh, Y. P., and J. Murphy. 2017. “Total Twitter Error: Decomposing Public Opinion Measurement on Twitter from a Total Survey Error Perspective.” In Total Survey Error in Practice, edited by P. P. Biemer, E. D. de Leeuw, S. Eckman, B. Edwards, F. Kreuter, L. E. Lyberg, N. C. Tucker, and B. T. West, 23–46. Hoboken: Wiley.
  • Japec, L., F. Kreuter, M. Berg, P. Biemer, P. Decker, C. Lampe, J. Lane, C. O’Neil, and A. Usher. 2015. “Big Data in Survey Research: AAPOR Task Force Report.” Public Opinion Quarterly 79 (4): 839–880. doi:10.1093/poq/nfv039.
  • Japec, L., and L. Lyberg. 2021. Big Data Initiatives in Official Statistics. In Big Data Meets Survey Science. A Collection of Innovative Methods, edited by C. A. Hill, P. P. Biemer, T. D. Buskirk, L. Japec, A. Kirchner, S. Kolenikov, and L. E. Lyberg., 275–302. Hoboken: John Wiley & Sons.
  • Johnson, T. P., and T. W. Smith. 2017. “Big Data and Survey Research: Supplement or Substitute?” In Seeing Cities through Big Data: Research, Methods and Applications in Urban Informatics, edited by P. V. Thakuriah, N. Tilahun, and M. Zellner, 113–126. Cham: Springer.
  • Kahneman, D., O. Sibony, and C. R. Sunstein. 2021. Noise: A Flaw in Human Judgment. New York: Little, Brown Spark.
  • Karr, A. F., A. P. Sanil, and D. L. Banks. 2006. “Data Quality: A Statistical Perspective.” Statistical Methodology 3 (2): 137–173. doi:10.1016/j.stamet.2005.08.005.
  • Keusch, F., S. Bähr, G.-C. Haas, F. Kreuter, and M. Trappmann. 2020. “Coverage Error in Data Collection Combining Surveys with Passive Measurement Using Apps: Data from a German National Survey.” Sociological Methods & Research 1–38. doi:10.1177/0049124120914924.
  • Kirkpatrick, K. 2017. “It’s Not the Algorithms, It’s the Data.” Communications of the ACM 60 (2): 21–23. doi:10.1145/3022181.
  • Kish, L. 1965. Survey Sampling. New York: Wiley.
  • Kitchin, R. 2014. “Big Data, New Epistemologies and Paradigm Shifts.” Big Data & Society 1 (1): 1–12. doi:10.1177/2053951714528481.
  • Kitchin, R. 2015. “The Opportunities, Challenges and Risks of Big Data for Official Statistics.” Statistical Journal of the IAOS 31 (3): 471–481. doi:10.3233/SJI-150906.
  • Kitchin, R., and G. McArdle. 2016. “What Makes Big Data, Big Data? Exploring the Ontological Characteristics of 26 Datasets.” Big Data & Society 3 (1): 1–10. doi:10.1177/2053951716631130.
  • Klinger, D. A., and G. S. Bridges. 1997. “Measurement Error in calls-for-service as an Indicator of Crime.” Criminology 35 (4): 705–726. doi:10.1111/j.1745-9125.1997.tb01236.x.
  • Kreuter, F. 2021. “What Surveys Really Say.” Nature 600: 614–615. doi:10.1038/d41586-021-03604-1.
  • Lavrakas, P. J. 2013. “Applying a Total Error Perspective for Improving Research Quality in the Social, Behavioral, and Marketing Sciences.” Public Opinion Quarterly 77 (3): 831–850. doi:10.1093/poq/nft033.
  • Liao, D., M.E. Berzofsky, G.L. Couzens, I. Thomas, and A. Cooper. 2021. “Improving Quality of Administrative Data: A Case Study with FBI’s National incident-based Reporting System Data.” In Big Data Meets Survey Science. A Collection of Innovative Methods, edited by C. A. Hill, P. P. Biemer, T. D. Buskirk, L. Japec, A. Kirchner, S. Kolenikov, and L. E. Lyberg, 217–244. Hoboken: John Wiley & Sons.
  • Little, R. J. A., and D. B. Rubin. 2002. Statistical Analysis with Missing Data. 2nd ed. Hoboken: Wiley.
  • Lynch, J. P., and L. A. Addington, eds. 2007. Understanding Crime Statistics: Revisiting the Divergence of the NCVS and UCS. Cambridge: Cambridge University Press.
  • Lynch, J. P., and J. P. Jarvis. 2008. “Missing Data and Imputation in the Uniform Crime Reports and the Effects on National Estimates.” Journal of Contemporary Criminal Justice 24 (1): 69–85. doi:10.1177/1043986207313028.
  • Malleson, N., and M. A. Andresen. 2015. “The Impact of Using Social Media Data in Crime Rate Calculations: Shifting Hot Spots and Changing Spatial Patterns.” Cartography and Geographic Information Science 42 (2): 112–121. doi:10.1080/15230406.2014.905756.
  • Malleson, N., and M. A. Andresen. 2016. “Exploring the Impact of Ambient Population Measures on London Crime Hotspots.” Journal of Criminal Justice 46: 42–63. doi:10.1016/j.jcrimjus.2016.03.002.
  • Mosher, C. J., T. D. Miethe, and D. M. Phillips. 2002. The Mismeasure of Crime. Thousand Oaks: Sage.
  • Neyman, J. 1934. “On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection.” Journal of the Royal Statistical Society 97 (4): 558–625. doi:10.2307/2342192.
  • Peeters, R., and M. Schuilenburg. 2020. “The Algorithmic Society: An Introduction.” In The Algorithmic Society, edited by M. Schuilenburg and R. Peeters, 1–15. New York: Routledge.
  • Proximus, 2020. “Financiële Resultaten van de Proximus Groep Volledig Jaar 2019.” Available online: https://www.proximus.com/nl/news/2020/financial-results-q4-2019# (accessed on 15 April 2022).
  • Ratcliffe, J. H. 2021. “Policing and Public Health Calls for Service in Philadelphia.” Crime Science 10 Article 5: 10.1186/s40163-021-00141-0.
  • Reid, G., F. Zabala, and A. Holmberg. 2017. “Extending TSE to Administrative Data: A Quality Framework and Case Studies from Stats NZ.” Journal of Official Statistics 33 (2): 477–511. doi:10.1515/jos-2017-0023.
  • Renzenbrink, T. 2019. “Algoritmen voor een Eerlijkere Stad.” Tada, June 3. Accessed 12 March 2021. https://tada.city/nieuws/algoritmen-voor-een-eerlijkere-stad/
  • Richardson, R., J. Schultz, and K. Crawford. 2019. “Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice.” New York University Law Review 94: 192–233.
  • Robinson, W. S. 1950. “Ecological Correlations and the Behavior of Individuals.” American Sociological Review 15 (3): 351–357. doi:10.2307/2087176.
  • Rummens, A., T. Snaphaan, N. Van de Weghe, D. Van den Poel, L. J. R. Pauwels, W. Hardyns. 2021. “Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime? ISPRS International Journal of Geo-Information 10(6): Article 369. doi:10.3390/ijgi10060369
  • Saris, W. E. 2014. “Total Survey Error.” In Encyclopedia of Quality of Life and well-being Research, edited by A. C. Michalos. Dordrecht: Springer 6703–6704 . doi:10.1007/978-94-007-0753-5_3034.
  • Saris, W. E., and F. Andrews. 1991. “Evaluation of Measurement Instruments Using a Structural Modeling Approach.” In Measurement Errors in Surveys, edited by P. P. Biemer, R. M. Groves, L. E. Lyberg, N. A. Mathiowetz, and S. Sudman, 575–597. New York: Wiley.
  • Schober, M. F., J. Pasek, L. Guggenheim, C. Lampe, and F. G. Conrad. 2016. “Social Media Analyses for Social Measurement.” Public Opinion Quarterly 80 (1): 180–211. doi:10.1093/poq/nfv048.
  • Skogan, W. G. 1977. “Dimensions of the Dark Figure of Unreported Crime.” Crime and Delinquency 23 (1): 41–50. doi:10.1177/001112877702300104.
  • Snaphaan, T. and W. Hardyns. 2021. “Environmental Criminology in the Big Data Era” European Journal of Criminology. 18(5): 713–734. doi:10.1177/1477370819877753
  • Statistics New Zealand. 2016. “Guide on Reporting on Administrative Data Quality.” Accessed 26 March 2021. https://www.stats.govt.nz/methods/guide-to-reporting-on-administrative-data-quality
  • Thakuriah, P. V., N. Tilahun, and M. Zellner. 2017. “Big Data and Urban Informatics: Innovations and Challenges to Urban Planning and Knowledge Discovery.” In Seeing Cities through Big Data: Research, Methods and Applications in Urban Informatics, edited by P. V. Thakuriah, N. Tilahun, and M. Zellner, 11–45. Cham: Springer.
  • United Nations. 1969. Methodology and Evaluation of Population Registers and Similar Systems. New York: Statistical Office of the United Nations. 15.
  • United Nations Economic Commission for Europe. 2014. “A Suggested Framework for the Quality of Big Data.” Accessed 26 March 2021. https://statswiki.unece.org/download/attachments/108102944/Big%20Data%20Quality%20Framework%20-%20final-%20Jan08-2015.pdf
  • van de Weijer, S. G. A., R. Leukfeldt, and W. Bernasco. 2019. “Determinants of Reporting Cybercrime: A Comparison between Identity Theft, Consumer Fraud, and Hacking.” European Journal of Criminology 16 (4): 486–508. doi:10.1177/1477370818773610.
  • Wallgren, A., and B. Wallgren. 2007. Register-based Statistics: Administrative Data for Statistical Purposes. Chichester: Wiley.
  • Warner, B. D., and G. L. Pierce. 1993. “Reexamining Social Disorganization Theory Using Calls to the Police as a Measure of Crime.” Criminology 31 (4): 493–517. doi:10.1111/j.1745-9125.1993.tb01139.x.
  • Wikström, P.-O. H. 1991. Urban Crime, Criminals, and Victims. The Swedish Experience in an Anglo-American Comparative Perspective. New York: Springer.
  • Wikström, P.-O. H., and C. Kroneberg. 2022. “Analytic Criminology: Mechanisms and Methods in the Explanation of Crime and Its Causes.” Annual Review of Criminology 5: 179–203. doi:10.1146/annurev-criminol-030920-091320.
  • Zhang, L.-C. 2012. “Topics of Statistical Theory for register-based Statistics and Data Integration.” Statistica Neerlandica 66 (1): 41–63. doi:10.1111/j.1467-9574.2011.00508.x.

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