This function gives a coupled student test, confidence intervals for the difference between a pair of means and optional, compliance limits for a pair of samples (Armitage and Berry, 1994; Altman, 1991). Readers are referred to the following documents, which contain measures of the agreement: Chen CC, Barnhart HX. Evaluation of compliance with intraclassade correlation coefficient and correlation coefficient for data with repeated measurements. Comput Stat Data Anal 2013;60:132-45. In the dataset example, each student`s answers are recorded in a line. Your English and math scores are presented in the English and Math variables. This format is already suitable for the t-coupled sample test, so no further restructuring is required. Bland JM, DG Altman. Statistical methods to assess the agreement between two methods of clinical measurement.
Lancet 1986; 1:307-10. Although we cannot “prove” zero without a difference between test results, we can use equivalency tests to determine whether the average difference between test results is small enough to be considered (clinically) insignificant. Bland and Altmans Accord Limitations (LOA) approach the problem in this way, providing an estimate of an area in which 95% of the differences between test results are expected to decrease (provided these differences are distributed normally).2,3 LOA are calculated as “s_` if the scope of the LOA includes differences considered clinically significant. , this result would indicate that the match between the tests is not satisfactory. THE LOA are also often represented graphically, presenting the average result for each subject with the difference between these results. Figure 1 shows this tool using a hypothetical example. Bland and Altman caution that LOA is only a significant result if the average and variance of test results are consistent in the range of test results.3 In other words, LOA should not be used if the agreement between the tests varies with the measured amount. This could occur if the tests yielded similar results to subjects with results within a normal range, but which show poor agreement for subjects outside that area. It is often asked whether the measurements of two different observers (sometimes more than two) or two different techniques yield similar results. This is called concordance or condore or reproducibility between measurements. Such an analysis examines the pairs of measurements, either categorically or numerically both, with each pair being performed on a person (or a pathology slide or an X-ray).
The correspondence between the measurements refers to the degree of correspondence between two (or more) measures.