We applied IRT to the MS and MTS to

We applied IRT to the MS and MTS to examine the amount of information that is contained in each item and then use this information along with the item discrimination (a parameter) to suggest removing certain items if they contain little information and have low item discrimination. Item discrimination is an inverse function of item information, which in IRT models is a measure of reliability (Embretson & Reise, 2000). Importantly, total information is an incremental additive function in which each item contributes to the overall reliability of the scale. Currently there is no set standard for what level of item discrimination is good enough to be considered a good item, nor is there a standard for what is an adequate level of information that an item must contain. We believe that both the item information and item discrimination need to be taken into account when determining which items should be removed from a scale. For the current study, the criterion we used to remove an item was that the item information curve for an item had to be relatively flat, be below 0.50 on item information and have an a parameter below 1.50. Because there is no standard in the literature when using IRT to remove items, researchers should clearly state the criteria they used to determine which items could be removed from a scale. Zickar et al. (2002) recommend that items with a parameters above 1.0 should be retained and Hafsteins- son, Donovan, and Breland (2007) recommend that for shorter scales the threshold should be increased to 2.0.
Using our IRT sample (N= 948) we fit each factor of the MS and then fit the MTS as a unidimensional construct using the GRM. Table 5 contains the jtem parameters for the MS and MTS. Figure 1 contains the item information curves for each item in the MTS. Examination of the item parameters and information curves reveals that a number of item can be removed from both of these scales according to the criteria we set.
Specifically, items 1, 7, and 8 can be removed from the MTS and items 3, 4, 5, 6, 10, 12 can be removed from the MS. As can be clearly shown from Figure 1 and 2 these items contain little information and are flat across all possible levels of the maximizing construct. Plateaushaped information curves are not necessarily bad as this would indicate that the item is discriminating across a wide range of the latent trait. However, in the case of the items we removed these are not plateaued but rather completely flat lines relative to the other items indicating that no incremental information is being provided by these items.
Nenkov et al. (2008) revised the original 13-item Schwartz et al. (2002) scale and reduced the scale down to 6-items. There are some differences between the items they kept and the items that we kept from our IRT anal- ysis. Their 6-item scale contains three factors and con- sists of Alternative Search (items 2, 4) Decision Diffi- culty (items 7, 9) and High Standards (items 11, 12). We are confident in the items we recommended for removal because IRT provides the researcher with item level analysis about the information that a certain item contains in regards to an underlying construct. Now that we have suggested removing items from these scales, we reexamine the factor structure of these scales and then we will examine the correlations between the original scales and our revised scales with a number of other constructs that have been shown to be related to maximizing in the past.
3.4 Factor analysis of the revised MS
Given the results of our exploratory factor analysis and the IRT analysis, we revised the MS to a three-factor, eight-item structure by removing items that loaded in sufficiently on their respective factors and demonstrated low information functions (see Table 3). We conducted a CFA (using our CFA data set), on this revised structure and found that it met all of our criteria for model fit (see Table 1). Further, the reduction in the overall model chi- square statistic from the previous models suggests that our revised scale (Revised MS Short) is a more parsimonious version of the MS. We then tested a three-factor model based off Nenkov et al. factor structure with factor 1 (Alternative Search) containing items 1 and 2; Factor 2 (Decision Difficulty) containing items 7, 8, 9; and Factor 3 (High Standards) containing item 11 and 13. The new revised scale is presented in Table 6.