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This article explores how traditional scores obtained from different forced-choice (FC) formats relate to their true scores and item response theory (IRT) estimates. Three FC formats are considered from a block of items, and respondents are asked to (a) pick the item that describes them most (PICK), (b) choose the two items that describe them the most and the least (MOLE), or (c) rank all the items in the order of their descriptiveness of the respondents (RANK). The multi-unidimensional pairwise-preference (MUPP) model, which is extended to more than two items per block and different FC formats, is applied to obtain the responses to each item block. Traditional and IRT (i.e., expected a posteriori) scores are computed from each data set and compared. The aim is to clarify the conditions under which simpler traditional scoring procedures for FC formats may be used in place of the more appropriate IRT estimates for the purpose of inter-individual comparisons. Six independent variables are considered: response format, number of items per block, correlation between the dimensions, item discrimination level, and sign-heterogeneity and variability of item difficulty parameters. Results show that the RANK response format outperforms the other formats for both the IRT estimates and traditional scores, although it is only slightly better than the MOLE format. The highest correlations between true and traditional scores are found when the test has a large number of blocks, dimensions assessed are independent, items have high discrimination and highly dispersed location parameters, and the test contains blocks formed by positive and negative items.

Forced-choice questionnaires have been proposed as a way to control some response biases associated with traditional questionnaire formats (e.g., Likert-type scales). Whereas classical scoring methods have issues of ipsativity, item response theory (IRT) methods have been claimed to accurately account for the latent trait structure of these instruments. In this article, the authors propose the multi-unidimensional pairwise preference two-parameter logistic (MUPP-2PL) model, a variant within Stark, Chernyshenko, and Drasgow's MUPP framework for items that are assumed to fit a dominance model. They also introduce a Markov Chain Monte Carlo (MCMC) procedure for estimating the model's parameters. The authors present the results of a simulation study, which shows appropriate goodness of recovery in all studied conditions. A comparison of the newly proposed model with a Brown and Maydeu's Thurstonian IRT model led us to the conclusion that both models are theoretically very similar and that the Bayesian estimation procedure of the MUPP-2PL may provide a slightly better recovery of the latent space correlations and a more reliable assessment of the latent trait estimation errors. An application of the model to a real data set shows convergence between the two estimation procedures. However, there is also evidence that the MCMC may be advantageous regarding the item parameters and the latent trait correlations.

Two models for confirmatory factor analysis of multitrait-multimethod data (MTMM) were assessed, the correlated traits-correlated methods (CTCM), and the correlated traits-correlated uniqueness models (CTCU). Two Monte Carlo experiments (100 replications per cell) were performed to study the behavior of these models in terms of magnitude and direction of bias, and accuracy of estimates. Study one included a single indicator per trait-method combination, and it manipulated three independent variables: matrix type, from three traits-three methods to six traits-six methods; correlation among method factors, from zero to .6; and model type (CTCM and CTCU). Study two included simulated MTMM matrices with two or more indicators per trait-method combination. Again, three independent variables were manipulated: number of indicators per trait-method combination, from 2 to 5; correlation among methods; and model type, CTCM and CTCU. The results from study one showed that the CTCU model performed very well for MTMM designs with a single indicator per trait-method combination, and consistently better than the CTCM model. However, the results from study two showed that the CTCM model worked reasonably well and better than the CTCU model when more than two indicators per trait-method combination were available. Despite the CTCM model's allowance for correlation between methods, results pointed to better estimates when methods were orthogonal. The main conclusion of the present article is that the use of CTCU models in the situations described in study one and the use of CTCM models in those represented in study two could be recommended.

Item response theory (IRT) provides valuable methods for the analysis of the psychometric properties of a psychological measure. However, IRT has been mainly used for assessing achievements and ability rather than personality factors. This paper presents an application of the IRT to a personality measure. Thus, the psychometric properties of a new emotional adjustment measure that consists of a 28-six graded response items is shown. Classical test theory (CTT) analyses as well as IRT analyses are carried out. Samejima's (1969) graded-response model has been used for estimating item parameters. Results show that the bank of items fulfills model assumptions and fits the data reasonably well, demonstrating the suitability of the IRT models for the description and use of data originating from personality measures. In this sense, the model fulfills the expectations that IRT has undoubted advantages: (1) The invariance of the estimated parameters, (2) the treatment given to the standard error of measurement, and (3) the possibilities offered for the construction of computerized adaptive tests (CAT). The bank of items shows good reliability. It also shows convergent validity compared to the Eysenck Personality Inventory (EPQ-A; Eysenck & Eysenck, 1975 ) and the Big Five Questionnaire (BFQ; Caprara, Barbaranelli, & Borgogni, 1993 ).