{'fraction_non_alphanumeric': 0.026392961876832845, 'fraction_numerical': 0.0019550342130987292, 'mean_word_length': 4.720670391061453, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Agreement II: Categorical measures

Following on a previous article, we will start to look at agreement for categorical data, that is, the extent to which two raters provide the same ratings, where the ratings use a scale, or set of options, with a small number of categories (say, 2–5). Depending on the kind of raters involved, the agreement can be viewed as telling us about reliability or validity : thus if the two raters are clinicians, then we are concerned with reliability; whereas if one is a gold standard diagnostic test and the other is a screening test, then we are examining validity. While noted in the statistical literature, this is not a distinction that is always made in research practice. Our focus here will be on measuring agreement, that is, calculating a single number that expresses the degree of agreement. It is also possible to model agreement, that is, to provide a description that goes beyond a single number, but this is more complex and rarely used in the psychiatric research literature.