ere inputted into SPSS and as before the negatively worded items were recoded so that they matched the positive items. A reliability analysis was performed on the data to identify any items that were performing poorly and they were subsequently removed. Firstly, each of the item means were analysed, any item which had a mean outside of the range 2.5 - 3.5 was removed as these items were not efficiently discriminating between the two criterion groups The item means should lie within this range as one group should be scoring highly on each item while the other should obtain low scores on each item. Secondly, the standard deviations of each item was analysed and any that appeared to be different from the majority were removed. Thirdly, the inter-item correlations (Pearsons correlation coefficients) were analysed, any items which had a relationship of less than 0.5 (Pearsons r) with the majority of the other items were removed as this implies that they are not measuring the same construct as the majority. Next, the effect that each of the items had on the scale variance was considered. If an item radically effects the variation in the scale then it was deleted. The corrected Item-Total Correlation was then considered; if any item correlated with the total score of the rest of the items by less than 0.5 it was deleted. Lastly, the effect that each item would have on the Cronbachs coefficient alpha if it was deleted was analysed, if the deletion of any item significantly increased the alpha then it was removed.A Principal Component Analysis was then performed on the data so that the scale could be analysed in terms of the factors underlying the scale items. A component was deemed to be an underlying factor if its Eigenvalue, the amount of variance accounted for by each component or factor, exceeded 1. The presence of more than one factor means that the data was rotated using varimax in order to maximise the relationship between the variables ...
