Both methods yield the same diagnosis on approximately 58.7% (obtained as 131/223) of the 223 patients. Summing all 4 diagonal numbers of Table 2.1 (i.e = 131) gives us an indication of the degree of consistency between the clinical and the research diagnoses. A high degree of consistency between different methods permits each method to validate the other, and eventually be used with confidence on a routine basis. Fenning, Craig, Tanenberg-Karant, and Bromet (1994) conducted this study to investigate the extent of agreement between clinical and Research Diagnoses, using the following 4 diagnostic categories : Schizophrenia Bipolar Disorder Depression Other Psychiatric diagnoses are difficult to make due to the fuzzy boundaries that define various psychiatric disorders. The second method known as Research Diagnosis is based on a strict application of research criteria. public hospital, or a community unit), and does not rely on a rigorous application of research criteria. The first method named Clinical Diagnosis (also known as Facility Diagnosis ) is used in a service facility (e.g. Lord Kelvin ( ) The Problem Table 2.1 shows the distribution of 223 psychiatric patients by diagnosis category and method used to make diagnosis. But when you cannot your knowledge is of meager and unsatisfactory kind. This chapter also treats the problem of missing ratings 2 CONTENTS 2.1 The Problem Kappa for 2 Raters on a 2-Level Measurement Scale.17 Chance-Agreement Correction Cohen s Kappa Definition What is Chance Agreement Dealing with Missing Data Kappa for 2 Raters on a Multiple-Level Measurement Scale Dealing with Missing Ratings Kappa for Multiple Raters on a Multiple-Level Measurement Scale Defining Agreement Among 3 raters or More Computing Inter-Rater Reliability.30 Fleiss Chance-Agreement Probability.32 Conger s Chance-Agreement Probability Kappa Coefficient and the Paradoxes Kappa s Dependency on Trait Prevalence Kappa s Dependency on Marginal Homogeneity Weighting the Kappa Coefficient Some Alternative Kappa-Like Coefficients.43 Alternative Generalization of Scott s π Coefficient.44 Light s Generalized Kappa Coefficient.44 BAK and PABAK Coefficients Concluding RemarksĢ -16- Chapter 2 : The Kappa Coefficient :AReview When you can measure what you are speaking about, and express it in numbers, you know something about it. We explore the case of 2 raters and 2 response categories before expanding to the more general situation of multiple raters and multiple-item response scales. The different components of Kappa are teased apart and their influence on the agreement coefficient discussed. Its simple user interfaceĪnd workflow allows for fast training and self-learning for occasional users.1 CHAPTER The Kappa Coefficient: AReview OBJECTIVE This chapter aims at presenting the Kappa coefficient of Cohen (1960), its meaning, and its limitations. Saphir is the industry standard PTA software, used by nearly all major IOC’s, NOC’s, Independents and Service Companies. The development of deconvolution allows us to combine several of these shut-ins in time to provide information much deeper into the reservoir than would normally be feasible by a shut-in alone. Specific operations require specific processing, such as multirate gas tests, interference tests, multilayer tests, etc. The diagnostic plot of choice is the loglog plot where the pressure and the Bourdet derivative allow the identification of reservoir geometry and properties, these are then matched with models. The pressure response from these shut-ins can be used to provide information about the reservoir within the radius of investigation for radial flow or the area of investigation for more complex geometries. The data are captured during dedicated well test operations such as DSTs or production tests or during routine operational shut-ins by Permanent Downhole Gauges (PDG). Pressure Transient Analysis, or PTA, is, in most cases, about analyzing high frequency, high resolution shut-in data.
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