Correcting Measurement Error in Content Analysis

References

• Bauman, K. E., & Koch, G. G. (1983). Validity of self-reports and descriptive and analytical conclusions: The case of cigarette smoking by adolescents and their mothers. American Journal of Epidemiology, 118(1), 90–98. doi:10.1093/oxfordjournals.aje.a113620
   PubMed Web of Science ®Google Scholar
• Benoit, K. R., Conway, D., Lauderdale, B. E., Laver, M., & Mikhaylov, S. (2016). Crowd-sourced text analysis: Reproducible and agile production of political data. American Political Science Review, 110(2), 278–295. doi:10.1017/S0003055416000058
   Web of Science ®Google Scholar
• Bross, I. (1954). Misclassification in 2 X 2 tables. Biometrics, 10(4), 478–486. doi:10.2307/3001619
   Web of Science ®Google Scholar
• Buonaccorsi, J. P. (2010). Measurement error: Models, methods, and applications. Boca Raton, FL: CRC Press.
   Google Scholar
• Grimmer, J., King, G., & Superti, C. (2015). The unreliability of measures of intercoder reliability, and what to do about it. Retrieved from http://stanford.edu/~jgrimmer/Handbib.pdf
   Google Scholar
• Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1(1), 77–89. doi:10.1080/19312450709336664
   Google Scholar
• Krippendorff, K. (2004a). Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks, CA: Sage.
   Google Scholar
• Krippendorff, K. (2004b). Reliability in content analysis. Some common misconceptions and recommendations. Human Communication Research, 30(3), 411–433. doi:10.1111/j.1468-2958.2004.tb00738.x
   Web of Science ®Google Scholar
• Krippendorff, K. (2008). Systematic and random disagreement and the reliability of nominal data. Communication Methods and Measures, 2(4), 323–338. doi:10.1080/19312450802467134
   Google Scholar
• Krippendorff, K. (2011). Agreement and information in the reliability of coding. Communication Methods and Measures, 5(2), 93–112. doi:10.1080/19312458.2011.568376
   Google Scholar
• Krippendorff, K., & Craggs, R. (2016). The reliability of multi-valued coding of data. Communication Methods and Measures, 10(4), 181–198. doi:10.1080/19312458.2016.1228863
   Web of Science ®Google Scholar
• Küchenhoff, H., Lederer, W., & Lesaffre, E. (2007). Asymptotic variance estimation for the misclassification SIMEX. Computational Statistics & Data Analysis, 51(12), 6197–6211. doi:10.1016/j.csda.2006.12.045
   Web of Science ®Google Scholar
• Küchenhoff, H., Mwalili, S. M., & Lesaffre, E. (2006). A general method for dealing with misclassification in regression: The misclassification SIMEX. Biometrics, 62(1), 85–96. doi:10.1111/j.1541-0420.2005.00396.x
   PubMed Web of Science ®Google Scholar
• Kuha, J., & Skinner, C. (1997). Categorical data analysis and misclassification. In L. E. Lyberg, P. Biemer, M. Collins, E. D. De Leeuw, C. Dippo, N. Schwarz, & D. Trewin (Eds.), Survey measurement and process quality (pp. 633–670). New York, NY: Wiley.
   Google Scholar
• Lederer, W., & Küchenhoff, H. (2006). A short introduction to the SIMEX and MCSIMEX. R News, 6(4), 26–31.
   Google Scholar
• Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content analysis in mass communication: Assessment and reporting of intercoder reliability. Human Communication Research, 28(4), 587–604. doi:10.1111/j.1468-2958.2002.tb00826.x
   Web of Science ®Google Scholar
• Lovejoy, J., Watson, B. R., Lacy, S., & Riffe, D. (2014). Assessing the reporting of reliability in published content analyses: 1985–2010. Communication Methods and Measures, 8(3), 207–221. doi:10.1080/19312458.2014.937528
   Google Scholar
• Marshall, R. J. (1990). Validation study methods for estimating exposure proportions and odds ratios with misclassified data. Journal of Clinical Epidemiology, 43(9), 941–947. doi:10.1016/0895-4356(90)90077-3
   PubMed Web of Science ®Google Scholar
• Mikhaylov, S., Laver, M., & Benoit, K. R. (2012). Coder reliability and misclassification in the human coding of party manifestos. Political Analysis, 20(1), 78–91. doi:10.1093/pan/mpr047
   Web of Science ®Google Scholar
• Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, CA: Sage.
   Google Scholar
• R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
   Google Scholar
• Riffe, D., & Freitag, A. (1997). A content analysis of content analyses: Twenty-five years of Journalism Quarterly. Journalism & Mass Communication Quarterly, 74(3), 515–524. doi:10.1177/107769909707400306
   Web of Science ®Google Scholar
• Scharkow, M., & Bachl, M. (2016). How measurement error in content analysis and self-reported media use leads to minimal media effect findings in linkage analyses: A simulation study. Political Communication, 1–21. doi:10.1080/10584609.2016.1235640
   Google Scholar
• Schmid-Petri, H., Adam, S., Schmucki, I., & Häussler, T. (2015). A changing climate of skepticism: The factors shaping climate change coverage in the US press. Public Understanding of Science. doi:10.1177/0963662515612276
   PubMed Web of Science ®Google Scholar
• Schuck, A. R. T., Boomgaarden, H. G., & De Vreese, C. H. (2013). Cynics all around? The impact of election news on political cynicism in comparative perspective. Journal of Communication, 63(2), 287–311. doi:10.1111/jcom.12023
   Web of Science ®Google Scholar
• Schwartz, J. E. (1985). The neglected problem of measurement error in categorical data. Sociological Methods & Research, 13(4), 435–466. doi:10.1177/0049124185013004001
   Web of Science ®Google Scholar
• Volkens, A. (2007). Strengths and weaknesses of approaches to measuring policy positions of parties. Electoral Studies, 26(1), 108–120. doi:10.1016/j.electstud.2006.04.003
   Web of Science ®Google Scholar