For example, measurement error may be introduced by imprecision, inaccuracy, or poor scaling of the items within an instrument (i.e., issues of internal consistency) instability of the measuring instrument in measuring the same subject over time (i.e., issues of test-retest reliability) and instability of the measuring instrument when measurements are made between coders (i.e., issues of IRR). Measurement error ( E) prevents one from being able to observe a subject’s true score directly, and may be introduced by several factors.
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Where the variance of the observed scores is equal to the variance of the true scores plus the variance of the measurement error, if the assumption that the true scores and errors are uncorrelated is met. Although it is beyond the scope of the current paper to provide a comprehensive review of the many IRR statistics that are available, references will be provided to other IRR statistics suitable for designs not covered in this tutorial. Computational examples include SPSS and R syntax for computing Cohen’s kappa for nominal variables and intra-class correlations (ICCs) for ordinal, interval, and ratio variables. This paper will provide an overview of methodological issues related to the assessment of IRR, including aspects of study design, selection and computation of appropriate IRR statistics, and interpreting and reporting results. However, many studies use incorrect statistical analyses to compute IRR, misinterpret the results from IRR analyses, or fail to consider the implications that IRR estimates have on statistical power for subsequent analyses.
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The assessment of inter-rater reliability (IRR, also called inter-rater agreement) is often necessary for research designs where data are collected through ratings provided by trained or untrained coders.