deletions | additions       diff --git a/Abbreviating_ICAR.tex b/Abbreviating_ICAR.tex  index 6bf8c2f..a223d6e 100644  --- a/Abbreviating_ICAR.tex  +++ b/Abbreviating_ICAR.tex 
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\label{sec:abbreviation}  Several methods can be used to pick out and determine which items should be included in an abbreviated ICAR test. One way would be, as done in a recent study (\textbf{cite}), to use an evolutionary algorithm to search the composition space of item combinations. An evolutionary algorithm will not try all possible combinations, but will instead try to explore the space iteratively to find the best combination. Unfortunately, it is possible for evolutionary algorithms to get space in local maximums that are substantially worse than the global maximum. By contrast, exhaustive search tries all the possibilities and is guaranteed to find the global maximum. The downside to this is that it is very computationally expensive. For instance, if one desires to make a 10-item abbreviation of a 200-item test, there are 2.2451e+16 possible combinations. In our case, we decided that we wanted a 5-item version made from items of the existing 16-item sample test. Exhaustive search is possible due to the relatively few possibilities (4368), so evolutionary algorithms was not used.  Our criteria validity was the correlation between the abbreviated scale and the full 16-item scale. Thus, we calculated the validity correlation for every possible combination. Initially, we used the two datasets obtained from prior studies using the Danish translation of the 16-item test. However, the correlation between the validity correlations across datasets was only .20. Re We  reasoned that this was likely due to the fairly small sample sizes (N=72 and N=54). Thus, we sought a larger dataset. We found that the psych package [ref] has a built in dataset with the 16-item test (N=1449). This dataset however had some missing values. We created two parallel versions of this dataset: 1) one with missing data imputed, and 2) one with complete cases only (N=1248). The data were imputed with the VIM package without noise [single imputation; ref]. The correlations between the criteria validities for the four datasets are shown in Table .  As expected, all correlations were possible showing that we have at least some signal. The correlation between the two parallel versions using the psych dataset were .99, indicating the lack of problems with the imputation. For this reason, we used the imputed version for further analysis.  Next the different combinations were sorted by their results correlated with the results of ICAR16. A specific item type (3D rotation) occurred more than once in most The mean validity  of the combinations. This item type is unfortunately abbreviated versions were  very time consuming, which is why it was assessed similar across datasets (range .84  to only include one of this item type in the chosen combination. With this rule, option \textbf{#XXX} was chosen whose results has .86). Figure shows  a correlation of \textbf{0.WW} with combined density and histogram for  the results distribution  of ICAR16. criteria validities for the imputed dataset.  The best possible combination has a correlation of \textbf{0.VV} with ICAR16, which is a modest difference with our chosen combination. It was asserted that it abbreviation  wasworth the trade-off to decrease the modest difference in their correlations and heavily decrease the time taken to finish the ICAR5.