In graded paired comparisons (GPCs), two items are compared using a multipoint rating scale. GPCs are expected to reduce faking compared with Likert-type scales and to produce more reliable, less ipsative trait scores than traditional binary forced-choice formats. To investigate the statistical properties of GPCs, we simulated 960 conditions in which we varied six independent factors and additionally implemented conditions with algorithmically optimized item combinations. Using Thurstonian IRT models, good reliabilities and low ipsativity of trait score estimates were achieved for questionnaires with 50% unequally keyed item pairs or equally keyed item pairs with an optimized combination of loadings. However, in conditions with 20% unequally keyed item pairs and equally keyed conditions without optimization, reliabilities were lower with evidence of ipsativity. Overall, more response categories led to higher reliabilities and nearly fully normative trait scores. In an empirical example, we demonstrate the identified mechanisms under both honest and faking conditions and study the effects of social desirability matching on reliability. In sum, our studies inform about the psychometric properties of GPCs under different conditions and make specific recommendations for improving these properties.
