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IRT modeling of forced choice   2 Abstract Multidimensional forced-choice formats can significantly reduce the impact of numerous response biases typically associated with rating scales. However, if scored with classical methodology these questionnaires produce ipsative data, which leads to distorted scale relationships and makes comparisons between individuals problematic. This research demonstrates how Item Response Theory (IRT) modeling may be applied to overcome these problems. A multidimensional IRT model based on Thurstone's framework for comparative data is introduced, which is suitable for use with any forced-choice questionnaire composed of items fitting the dominance response model, with any number of measured traits, and any block sizes (i.e. pairs, triplets, quads etc.). Thurstonian IRT models are normal ogive models with structured factor loadings, structured uniquenesses, and structured local dependencies.These models can be straightforwardly estimated using structural equation modeling (SEM) software Mplus. A number of simulation studies are performed to investigate how latent traits are recovered under various forced-choice designs, and to provide guidelines for optimal questionnaire design. An empirical application is given to illustrate how the model may be applied in practice. It is concluded that when the recommended design guidelines are met, scores estimated from forced-choice questionnaires with the proposed methodology reproduce the latent traits well.Keywords: forced-choice format, forced-choice questionnaires, ipsative data, comparative judgment, multidimensional IRT IRT modeling of forced choiceItem response modeling of forced-choice questionnairesThe most popular way of presenting questionnaire items is through rating scales (Likert scales), where participants are asked to rate a statement using some given categories (for example, ranging from "strongly disagree" to "strongly agree", or from "never" to "always", etc.). It is well-known that such format (single-stimulus format) can lead to various response biases, for instance because participants do not interpret the rating categories in the same way (Friedman & Amoo, 1999) between statements according to the extent these statements describe their preferences or behavior. When there are 2 statements in a block, respondents are simply asked to select the statement that better describes them. For blocks of 3, 4 or more statements, respondents may be asked to rank-order the statements, or to select one statement which is "most like me" and one which is "least like me" (i.e., to provide a partial ranking).Because it is impossible to endorse every item, the forced-choice format eliminates uniform biases such as acquiescence responding (Cheung & Chan, 2002), and can increase operational validity by reducing "halo" effects (Bartram, 2007). However, there are serious problems with the way the forced-choice questionnaires have been scored traditionally.IRT modeling of forced choiceTypically, rank orders of items in a block are reversed ...

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In this chapter we describe the social problem-solving model that has generated most of the research and training programs presented in the remaining chapters of this volume. We also describe the major assessment methods and instruments that have been used to measure social problemsolving ability and performance in research as well as clinical practice.The term social problem solving refers to the process of problem solving as it occurs in the natural environment or "real world" (D'Zurilla & Nezu, 1982). The adjective social is not meant to limit the study of problem solving to any particular type of problem. It is used in this context only to highlight the fact that we are interested in problem solving that influences one's adaptive functioning in the real-life social environment. Hence, the study of social problem solving deals with all types of problems that might affect a person's functioning, including impersonal problems (e.g., insufficient finances, stolen property), personal or intrapersonal problems (emotional, behavioral, cognitive, or health problems), interpersonal problems (e.g., marital conflicts, family disputes), as well as broader community and societal problems (e.g., crime, racial discrimination). The model of social problem solving presented in this chapter was originally introduced by D' Zurilla and Goldfried (1971) II

This study investigated the effect the number of observed variables (p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, the sample size, and the magnitude of factor loadings. The results showed that the effect of p on the population CFI and TLI depended on the type of specification error, whereas a higher p was associated with lower values of the population RMSEA regardless of the type of model misspecification. In finite samples, all three fit indices tended to yield estimates that suggested a worse fit than their population counterparts, which was more pronounced with a smaller sample size, higher p, and lower factor loading.