Auditory processing disorder (APD) is a label that describes a variable set of symptoms that share a common feature of difficulty listening to sounds in the absence of an actual audiological deficit (Moore, 2006). Clinical assessment of APD typically involves a test battery consisting of tests designed to examine the integrity of various auditory processes of the central auditory nervous system. Individuals with APD have difficulty recognising speech when parts of the signal are missing. One category of tests used to assess the extent to which this deficit is associated with reduced performance on the task is the low-pass filtered speech test. The University of Canterbury Adaptive Speech Test-Filtered Words (UCAST-FW) is a computer-based adaptive low-pass filtered speech test developed for the assessment of auditory processing skills in adults and children. Earlier studies with the UCAST-FW (McGaffin, 2007; Sincock, 2008; Heidtke, 2010; Abu-Hijleh, 2011) have produced encouraging results. However, there appear to be a number of confounding factors. The UCAST-FW is testing New Zealand listeners using an Australian recording of American test material. The purpose of the current study was to develop a new four-alternative forced choice test to replace the Northwestern University Children’s Perception of Speech (NU-CHIPS) stimuli the UCAST-FW currently utilises. The new test consists of 98 sets of four test items, (one target item and three foil alternatives) designed to be used in a four-alternative forced choice picture-pointing procedure. Phonemic analysis of the new word list and the NU-CHIPS word lists revealed a similar distribution of phonemes for target words of both word lists. The development of the new word list is described and the clinical applicability is explored.

To control various response biases and rater errors in noncognitive assessment, multidimensional forced choice (MFC) measures have been proposed as an alternative to single-statement Likert-type scales. Historically, MFC measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for inter-individual comparisons. However, with the recent advent of classical test theory and item response theory scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components of personnel and educational assessment systems. This dissertation presents developments concerning a GGUM-based MFC model henceforth referred to as the GGUM-RANK. Markov Chain Monte Carlo (MCMC) algorithms were developed to estimate GGUM-RANK statement and person parameters directly from MFC rank responses, and the efficacy of the new estimation algorithm was examined through computer simulations and an empirical construct validity investigation. Recently derived GGUM-RANK item information functions and information indices were also used to evaluate overall item and test quality for the empirical study and to give insights into differences in scoring accuracy between two-alternative (pairwise preference) and three-alternative (triplet) MFC measures for future work. This presentation concludes with a discussion of the research findings and potential applications in workforce and educational setting.

Multidimensional forced-choice questionnaires can reduce the impact of numerous response biases typically associated with Likert scales. However, if scored with traditional methodology these instruments produce ipsative data, which has psychometric problems, such as constrained total test score and negative average scale inter-correlation. Ipsative scores distort scale relationships and reliability estimates, and make interpretation of scores problematic. This research demonstrates how Item Response Theory (IRT) modeling may be applied to overcome these problems. A multidimensional IRT model for forced-choice questionnaires is introduced, which is suitable for use with any forced-choice instrument composed of items fitting the dominance response model, with any number of measured traits, and any block sizes (i.e. pairs, triplets, quads etc.). The proposed model is based on Thurstone's framework for comparative data. 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. Simulation studies show how the latent traits are recovered from the comparative binary data under different conditions. The Thurstonian IRT model is also tested with real participants in both research and occupational assessment settings. 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.