The following book is excellent in answering your question. if you dont have access, I may find the exact page with the answer and send to you. good luck
Waltz C., Strickland O., Lenz E. (2010; 4th ed.). Measurement in Nursing and Health Research Fourth Edition. Springer; New York.
The following book is excellent in answering your question. if you dont have access, I may find the exact page with the answer and send to you. good luck
Waltz C., Strickland O., Lenz E. (2010; 4th ed.). Measurement in Nursing and Health Research Fourth Edition. Springer; New York.
The methodology described in the following free, brief articles remedies the shortcomings of all alternative methods.
No alternative methodology provides this set of features...
Can be applied to ratings that are categorical or ordered.
Specifically identifies points of convergence as well as of divergence between raters or methods (i.e., systems or devices).
Explicitly identifies the most accurate model for relating ratings made by raters or methods.
Models are expressed in the original measurement metric, and they are transparent and intuitive.
Provides a chance-corrected and maximum-corrected index of strength of effect: this facilitates direct assessment (comparison) of reliability between (different) pairs of raters.
Provides exact p-values.
Supports multiple methods of assessing potential cross-generalizability of the models if the raters or methods were used to assess an independent random sample of observations.
Prevents paradoxical confounding attributable to combining data from raters (methods) that provide disparate responses (e.g., see http://optimalprediction.com/files/pdf/V4A6.pdf)
Pin-points the areas of reliable agreement and disagreement between pairs of rates, facilitating efficient calibration and potentially leading to refinement of training methodology.
Which of these features is not important in research?
Well, perhaps these references could be of assistance to you:
Bennett, W. L., Foot, K., & Xenos, M. (April 01, 2011). Narratives and Network Organization: A Comparison of Fair Trade Systems in Two Nations. Journal of Communication, 61, 2.)
Hayes, A. F., & Krippendorff, K. (2007). “Answering the call for a standard reliability measure for coding data.” Communication Methods and Measures, Vol. 1, No. 1, 77-89.
Krippendorff, K. (2011). “Computing Krippendorff
’s alpha-reliability.” Philadelphia: Annenberg School for Communication Departmental Papers.
Krippendorff, K. (2004). Content analysis: An introduction to its methodology. Thousand Oaks, California: Sage.
Krippendorff, K. (2004) “Reliability in content analysis: Some common misconceptions and recommendations.” in Human Communication Research. Vol. 30, pp. 411-433.
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.
Van Selm, M., & Jankowski, N. (2004). “Content analysis of internet-based documents.” In M. van Selm & N. Jankowski (Eds.), Researching new media: An advanced-level textbook. Thousand Oaks, CA: Sage Publications.
Depending on the scales of measure and the number of attributes and the variance of attributes you might want to use more than one reliability measure. Krippendorff's Alpha is great but it is also very sensitive when it comes to variables with low variance. So you might want to add percent agreement and Cohen's Kappa.