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标定数据的AGREEment是一个计算机程序，为调查一个或者多个评判对象分类为名目尺度类别时的情况。它允许名目尺度判断之间协议的计算方法（科恩kappa或D2方法）并且提供几种选项。AGREE是一个程序，是格罗宁根大学的Roel Popping开发的。
在很多科学领域和实际调查中，目标被分为几个级别，根据他们一个或多个判定的性能。比如，一个职位的申请是否合适，面试者ㄨ要被分为健康或者不健康的，建议可能被分为可行和不可行的，等等。
Agreement 通常被认为是一个特殊姓的联合。但是这是不同的。它对确定通常编码者之间相似内容的行为（从广义上讲）和这些行为的认同度是很重要的。一个编码者的这些行 为不需要从其它的预测。关于agreement, 最重要的是行为内容的相似和比率，和决定这些行为的认知度的目标。基本的一个协议指数的概念是在利率同意考虑obser-vations部分。
AGREEment on nominal data is a computer program developed for research situations where one or more judges classify objects into nominal scale categories. It allows the calculation of measures of agreement (Cohen’s kappa or the D2 measure) between nominal scale judgements and provides several options. AGREE is a program that has been developed by Roel Popping from the University of Groningen.
In many fields of scientific and practical research, objects are being divided into classes according to their properties by one or more judges. For example, applicants for a position may be qualified or not, patients may be divided into healthy and unhealthy, proposals may be divided into workable and unworkable, etc.
Agreement is very often considered as a special kind of association. There are differences however. It is important to determine the similarity of the content of behavior (in a broad sense) between coders in general with the degree of identity of this behavior. The behavior of one coder does not have to be predicted from that of the other. In the case of association one investigates the strength of the linear relationship between variables. Here the goal is to predict the values of one variable from those of the other. With regard to agree-ment, most important is the similarity of the content of behavior between rates, with the goal of determining the degree of identity of this behavior. The basic idea of an agreement index is looking at the fraction of obser-vations on which rates agree.
Very often the coders have to assign the objects to a variable having a nominal scale. In the situation where a measure of nominal scale agreement is to be computed, the choice of the agreement index is determined by whether or not the coders knew the response categories a priori. When the set of categories is known, this set is identical for all coders, and all objects would be assigned to one of the categories. Another situation is when the response categories have to be developed by the coders during the assigning process. In this situation each coder may finish with a different set and number of categories. This situation arises most in pilot studies, where the investigator wishes to find a set of response categories that will be used in the main investigation. In the first situation the agreement index kappa performs best, while in the second situation the index D2 is to be preferred. Extensions of these indices are available for nearly all research situations that are realistic.
The computer program AGREE 7 can be used in computing the two indices kappa and D2 in many research situations.
