probability of endorsing that item (). The exact shape and location of the S-shaped curve for each item depends on the estimated difficulty and discrimination parameters; a reverse S-shaped curve indicates negative discrimination, which suggests a poorly performing item that is best excluded. Curves that form a straight horizontal line indicate that the item provides no discrimination or is undefined, while a straight vertical line indicates perfect discrimination just above and below that point, but no ability to distinguish between those with levels of the latent trait further above or below it. See Ainsworth (2011) for a detailed discussion. Items were evaluated at the level of the subscale with the intent of maintaining the broad framework of the SIP. Inadequate items were removed individually based on manual inspection of ICCs. Because ICCs on a subscale are a function of how those items work together (and not just the sum of individual ICCs), subscale ICCs were re-evaluated after each step in the reduction process. Fit was initially evaluated using chi-square analyses, with non-significant values indicating appropriate model fit. However, since the significance of model fit tends to increase with larger χ2 sample sizes, the priority was to achieve consensus among root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI) fit indices. For RMSEA, good fit was defined as a test statistic .05, with values .08 considered adequate fit (). ≤ ≤ Good fit for CFI and TLI was defined by a test statistic .95, with adequate fit attained by values ≥ of .90 or larger (). Following the removal of all poorly functioning items as demonstrated by ≥ model fit indices and visual inspection of ICCs, chi-square difference tests were conducted to determine whether the removal of additional items significantly improved model fit across further iterations of the subscale. For instances in which two items shared the same ICC characteristics (suggesting potential redundancy), items were evaluated for uniqueness relative to other items and clinical relevance for chronic pain and retained or removed accordingly. Since this process was exploratory in nature, removal of items occasionally produced changes in ICCs which suggested that inclusion of that item may yield better model fit. In such cases, effects of re-adding
probability of endorsing that item (). The exact shape and location of the S-shaped curve for each item depends on the estimated difficulty and discrimination parameters; a reverse S-shaped curve indicates negative discrimination, which suggests a poorly performing item that is best excluded. Curves that form a straight horizontal line indicate that the item provides no discrimination or is undefined, while a straight vertical line indicates perfect discrimination just above and below that point, but no ability to distinguish between those with levels of the latent trait further above or below it. See Ainsworth (2011) for a detailed discussion. Items were evaluated at the level of the subscale with the intent of maintaining the broad framework of the SIP. Inadequate items were removed individually based on manual inspection of ICCs. Because ICCs on a subscale are a function of how those items work together (and not just the sum of individual ICCs), subscale ICCs were re-evaluated after each step in the reduction process. Fit was initially evaluated using chi-square analyses, with non-significant values indicating appropriate model fit. However, since the significance of χ2 model fit tends to increase with larger sample sizes, the priority was to achieve consensus among root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI) fit indices. For RMSEA, good fit was defined as a test statistic ≤ .05, with values ≤ .08 considered adequate fit (). Good fit for CFI and TLI was defined by a test statistic ≥ .95, with adequate fit attained by values of ≥ .90 or larger (). Following the removal of all poorly functioning items as demonstrated by model fit indices and visual inspection of ICCs, chi-square difference tests were conducted to determine whether the removal of additional items significantly improved model fit across further iterations of the subscale. For instances in which two items shared the same ICC characteristics (suggesting potential redundancy), items were evaluated for uniqueness relative to other items and clinical relevance for chronic pain and retained or removed accordingly. Since this process was exploratory in nature, removal of items occasionally produced changes in ICCs which suggested that inclusion of that item may yield better model fit. In such cases, effects of re-adding