iii Table 6. Results summary for tracked item pairs in the CONV condition with no LID .. 91 1 For simplicity, the term "individual" will be used throughout the document to denote the person who provides the responses to a data-collection instrument; the term is assumed to be synonymous with alternative terms such as "examinee" and "respondent," though it is acknowledged that each has domainspecific connotations. Along these lines, the data-collection instrument will be referred to as an "instrument," (representing alternative terms such as "test" and "questionnaire") and the term "trait" is used to denote the construct measured by the items (representing alternative terms such as "ability" and "proficiency"). Exceptions are made in rare instances when necessary to remain consistent with the literature. 2This initiative focuses on more accurate and efficient measurement of patient-reported symptoms and aspects of health-related quality of life. A primary goal of PROMIS is to develop instruments based on IRT methodologies in these domains that are publically available for the clinical research community.In IRT, estimates of respondents' traits (θ) are based not only on the responses they provide, but also the characteristics (i.e., parameters) of the items they are administered such as their difficulty -reflected by category boundary parameters (b) -their ability to differentiate among respondents -reflected by slope parameters (a) -and their susceptibility to guessing -reflected by lower asymptote parameters (c).One unidimensional IRT model frequently applied in health-outcomes settings is the graded response model (GRM;Samejima, 1969). The GRM is appropriate for item responses that fall in multiple ordered categories. It predicts the conditional probability of an individual responding in a particular category as a function of an individual's latent trait value and several item parameters.The GRM is considered a "difference model" (Thissen & Steinberg, 1986) or "indirect IRT model" (Embretson & Reise, 2000) because the probabilities are computed in two stages. Following Dodd, De Ayala, and Koch (1995), the probability that individual i will produce a response in category k or higher for item j is first computed as:where θ i is the individual's trait level, a j is the discrimination parameter for item j, and b jk is the category boundary for category k for item j. In the second step, the actual category 3 response probabilities for each response are computed by subtracting the cumulative probabilities from adjacent response categories conditional on θ using:There are a total of K -1 boundary parameters that need to be estimated for an item with K score categories. Figure 1 illustrates the probability of choosing each of the response options offered with an item from PROMIS designed to measure fatigue impact and experience according to the GRM. It shows that a person with a low level of fatigue would have a high probability of indicating that his fatigue made it "not at all hard to carry on a conversation." In ...

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