Figure 2 displays the IRFs for two different items (Item 1 and Item 2) having different values of bi; the value of bi for Item 1 is -1 (i.e., b1 = -1) and the value of bi for Item 2 is 1 (i.e., b2 = 1). Notice how the value of the bi specifies the horizontal location of the item’s IRF; as bi increases, the IRF shifts to the right and the item becomes more difficult. In this manner, Item 2 is more difficult than Item 1, such that for any given level of ability there is a higher probability of correctly responding to Item 1 than to Item 2. Notice that the probability of correct response to Item 1 equals .5 at an ability value of -1, as would be expected given that b1 = -1. Similarly, the probability of correct response to Item 2 equals .5 at an ability value of 1, as would be expected given that b2 = -1.
The 1PL model shown above is equivalent to the Rasch model, proposed by the Danish mathematician George Rasch. It is relevant to note, however, that other forms of the 1PL model exist in which a scaling factor that is constant across all items is included in the model. In some instances, the scaling factor D = 1.7 is included in the exponent, and in other instances the scaling factor is described by an alternative constant value (symbolized by “a”) that leads to the best fit to the data.