Análisis comparativo de tres enfoques para evaluar el acuerdo entre observadores

Bibliographische Referenzen

• Agresti, A. (1989). An agreement model with kappa as parameter. Statistics and Probability Letters, 7, 271-273.
• Agresti, A. (1992). Modelling patterns of agreement and disagreement. Statistical Methods in Medical Research, 1, 201-218.
• Agresti, A. (2002). Categorical data analysis. 2nd edition. Hoboken, NJ: Wiley.
• Agresti, A., Ghosh, A. y Bini, M. (1995). Raking kappa: describing potential impact of marginal distributions on measure of agreement. Biometrical Journal, 37, 811-820.
• Aickin, M. (1990). Maximum likelihood estimation of agreement in the constant predictive probability model, and its relation to Cohen’s kappa. Biometrics, 46, 293-302.
• Ato, M., Benavente, A., Rabadán, R. y López, J.J. (2004). Modelos con mezcla de distribuciones para evaluar el acuerdo entre observadores. Metodología de las Ciencias del Comportamiento, V. Especial 2004, 47-54.
• Bennet, E.M., Alpert, R. y Goldstein, A.C. (1954). Communications through limited response questioning. Public Opinion Quarterly, 18, 303-308.
• Bloch, D.A. y Kraemer, H.C. (1989). 2 x 2 kappa coefficients: measures of agreement or association. Biometrics, 45, 269-287.
• Brennan, R.L. y Prediger, D. (1981). Coefficient kappa: somes uses, misuses and alternatives. Educational and Psychological Measurement, 41, 687-699.
• Byrt, T., Bishop, J. y Carlin, J.B. (1993). Bias, prevalence and kappa. Journal of Clinical Epidemiology, 46, 423-429.
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46.
• Cronbach, L.J., Gleser, G.C. y Rajaratnam, J. (1972). The dependability of behavioral measurements. New York, NY: Wiley.
• Darroch, J.M. y McCloud, P.I. (1986). Category distinguishability and observer agreement. Australian Journal of Statistics, 28, 371-388.
• Dillon, W.R. y Mullani, N. (1984). A probabilistic latent class model for assessing inter-judge reliability. Multivariate Behavioral Research, 19, 438-458.
• Dunn, C. (1989). Design and analysis of reliability studies: the statistical evaluation of measurement errors. Cambridge, UK: Cambridge University Press.
• Feinstein, A. y Cichetti, D. (1990). High agreement but low kappa: I. The problem of two paradoxes. Journal of Clinical Epidemiology, 43, 543-549.
• Fleiss, J.L., Cohen, J. y Everitt, B.S. (1969). Large sample standard errors of kappa and weighted kappa. Psychological Bulletin, 72, 323-327.
• Graham, P. (1995). Modelling covariante effects in observer agreement studies: the case of nominal agreement. Statistics in Medicine, 14, 299-310.
• Guggenmoos-Holtzmann, I. (1993). How reliable are change-corrected measures of agreement. Statistics in Medicine, 12, 2.191-2.205.
• Guggenmoos-Holtzmann, I. (1996). The meaning of kappa: probabilistic concepts of reliability and validity revisited. Journal of Clinical Epidemiology, 49, 775-782.
• Guggenmoos-Holtzmann, I. y Vonk, R. (1998). Kappa-like indices of observer agreement viewed from a latent class perspective. Statistics in Medicine, 17, 797-812.
• Hoehler, F.K. (2000). Bias and prevalence effects on kappa viewed in terms of sensitivity and specificity. Journal of Clinical Epidemiology, 53, 499-503.
• Holley, W. y Guilford, J.P. (1964). A note on the G-index of agreement. Educational and Psychological Measuerement, 24, 749-753.
• Hsu, L.M. y Field, R. (2003). Interrater agreement measures: comments on kappan, Cohen’s kappa, Scott’s p and Aickin’s a. Understanding Statistics, 2, 205-219.
• Janson, S. y Vegelius, J. (1979). On generalizations of the G-index and the phi coefficient to nominal scales. Multivariate Behavioral Research, 14, 255-269.
• Lantz, C.A. y Nebenzahl, E. (1996). Behavior and interpretation of the k statistics: resolution of the two paradoxes. Journal of Clinical Epidemiology, 49, 431-434.
• Lin, L., Hedayat, A.S., Sinha, B. y Yang, M. (2002). Statistical methods in assessing agreement: models, issues and tools. Journal of the American Statistical Association, 97, 257-270.
• Martín, A. y Femia, P. (2004). Delta: a new measure of agreement between two raters. British Journal of Mathematical and Statistical Psychology, 57, 1-19.
• Maxwell, A.E. (1977). Coefficients of agreement between observers and their interpretation. British Journal of Psychiatry, 116, 651-655.
• Schuster, C. (2002). A mixture model approach to indexing rater agreement. British Journal of Mathematical and Statistical Psychology, 55, 289-303.
• Schuster, C. y von Eye, A. (2001). Models for ordinal agreement data. Biometrical Journal, 43, 795-808.
• Schuster, C. y Smith, D.A. (2002). Indexing systematic rater agreement with a latent-class model. Psychological Methods, 7, 384-395.
• Scott, W.A. (1955). Reliability of content analysis: the case of nominal scale coding. Public Opinion Quarterly, 19, 321-325.
• Shoukri, M.M. (2004). Measures of Interobserver Agreement. Boca Raton, Fl. CRC Press.
• Shrout, P.E, y Fleiss, J.L. (1973). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 2, 420-428.
• Spitznagel, E.I. y Helzer, J.E. (1985). A proposed solution to the base rate problem in the kappa statistics. Archives of General Psychiatry, 42, 725-728.
• Tanner, M.A. y Young, M.A. (1985a). Modeling agreement among raters. Journal of the American Psychological Association, 80, 175-180.
• Tanner, M.A. y Young, M.A. (1985b). Modeling ordinal scale disagreement. Psychological Bulletin, 98, 408-415.
• Vermunt, J.K. (1997). LEM: a general program for the analysis of categorical data. Tilburg: University of Tilburg.
• Von Eye, A. y Mun, E.Y. (2005). Analyzing Rater Agreement. Mahwah, NJ: Lawrence Erlbaum Associates.
• Zwick, R. (1988). Another look at interrater agreement. Psychological Bulletin, 103, 374-378.