Test selection in psychological assessment is guided, both explicitly and implicitly, by how informative tests are with regard to a trait of interest. Most existing formulations of test information are sensitive to subpopulation variation, with the result that test information will vary from sample to sample. Recently, measures of test information have been developed that quantify the potential informativeness of the test. These indices are defined by the properties of the test, as distinct from the properties of the sample or examinee. As of yet, however, measures of potential information have been developed only for unidimensional tests. In practice, psychological tests are often multidimensional. Furthermore, multidimensional tests are often used to estimate one specific trait among many. This study develops measures of potential test information for multidimensional tests, as well as measures of marginal potential test information---test information with regard to one trait within a multidimensional test. In Study 1, the performance of the metrics was tested in data simulated from unidimensional, first-order multidimensional, second-order, and bifactor models. In Study 2, measures of marginal and multidimensional potential test information are applied to a set of neuropsychological data collected as part of Rush University's Memory and Aging Project. In simulated data, marginal and multidimensional potential test information were sensitive to the changing dimensionality of the test. In observed neuropsychological data, five traits were identified. Verbal abilities were most closely correlated with probable dementia. Both indices of marginal potential test information identify the Mini Mental Status Exam as the best measure of that trait. More broadly, greater marginal potential test information calculated with regard to verbal abilities was associated with greater criterion validity. These measures allow for the direct comparison of two multidimensional tests that assess the same trait, facilitating test selection and improving the precision and validity of psychological assessment.

Dimensional models of schizotypy and associated traits have taken on current relevance in the DSM-5 (http://www.dsm5.org) proposal for personality disorder (PD), which includes a personality trait initially conceptualized as a five-facet schizotypy domain and then simplified into a three-facet psychoticism domain that has yet to be evaluated extensively. In this study, I (1) reviewed the literature to develop hypotheses about the content and boundaries of the schizotypy domain, and (2) measured this content in a mixed sample of students and patents with 657 usable protocols at Time 1 (193 Notre Dame students, 301 University of Iowa students; 163 outpatients) and 263 usable protocols (74 Notre Dame students, 76 University of Iowa students, 113 outpatients) at Time 2, at least 1.5 weeks later. I then (3) evaluated confirmatory models including DSM-5 schizotypy and psychoticism facet models and other empirically grounded models and (4) used the best confirmatory model to provide item pools for classically constructing scales of schizotypy facets. This four-factor structure provided content pools used to create four corresponding scales: Unusual Perceptions, Unusual Beliefs, Dissociation Proneness, and Cognitive and Communicative Peculiarity. Additionally, (5) I used item response theory (IRT)-based analyses to evaluate items in these facet scales, both in terms of the level of schizotypy they best measure and the strength of their relations to the schizotypy construct. I also (6) examined the short-term test-retest reliability of the schizotypy scales, as well as that of the established measures used in this study; new and existing measures were comparably stable. Finally, (7) I evaluated schizotypy's convergent and discriminant validity in relation to three other types of traits: (a) those correlated with the domain (e.g., Obsessive Compulsive Disorder [OCD] and non-delusional mistrust), (b) other higher level traits (i.e., measures of the 3 factor and 5-factor models of higher order personality/temperament), and (c) familially related traits (e.g., social anxiety). Overall, the schizotypy facet measures appeared to assess moderate amounts of variance that were unexplained by the established measures of personality, temperament, and psychopathology that were included in this study. The implications of adding a schizotypy trait to the overall personality trait taxonomy are discussed.

Magnetic nanoparticles (MNPs) have found use in a wide variety of biomedical applications including hyperthermia, imaging and drug delivery. Certain physical properties, such as the ability to generate heat in response to an alternating magnetic field, make these structures ideal for such purposes. This study's objective was to elucidate the mechanisms primarily responsible for RF MNP heating and determine how such processes affect polymer solutions that might be useful in drug delivery. 15-20 nm magnetite (Fe3O4) nanoparticles at 0.2% and 0.5% concentrations were heated with RF fields of different strengths (200 Oe, 400 Oe and 600 Oe) in water and in 0.5% gellan gum solution. Mixing and fan cooling were used in an attempt to improve accuracy of data collection. Specific absorption rate (SAR) values were determined experimentally for each combination of solvent, concentration and field strength. Theoretical calculation of SAR was performed using a model based on linear response theory. Mixing yielded greater precision in experimental determination of SAR while the effects of cooling on this parameter were negligible. Solutions with gellan gum displayed smoother heating over time but no significant changes in SAR values. This was attributed to low polymer concentration and lack of structural phase transition. The LRT model was found to be adequate for calculating SAR at low polymer concentration and was useful in identifying Neel relaxation as the dominant heating process. Heating trials with MNPs in 2% agar confirmed Neel relaxation to be primarily responsible for heat generation in the particles studied.

The purpose of the present study was to examine the invariance of the factor structure and item response model parameter estimates obtained from a set of 27 items selected from the 2002 and 2003 forms of Your First College Year (YFCY). The first major research question of the present study was: How similar/invariant are the factor structures obtained from two datasets (i.e., identical items, different people)? The first research question was addressed in two parts: (1) Exploring factor structures using the YFCY02 dataset; and (2) Assessing factorial invariance using the YFCY02 and YFCY03 datasets. After using exploratory and confirmatory and factor analysis for ordered data, a four-factor model using 20 items was selected based on acceptable model fit for the YFCY02 and YFCY03 datasets. The four factors (constructs) obtained from the final model were: Overall Satisfaction, Social Agency, Social Self Concept, and Academic Skills. To assess factorial invariance, partial and full factorial invariance were examined. The four-factor model fit both datasets equally well, meeting the criteria for partial and full measurement invariance. The second major research question of the present study was: How similar/invariant are person and item parameter estimates obtained from two different datasets (i.e., identical items, different people) for the homogenous graded response model (Samejima, 1969) and the partial credit model (Masters, 1982)? To evaluate measurement invariance using IRT methods, the item discrimination and item difficulty parameters obtained from the GRM need to be equivalent across datasets. The YFCY02 and YFCY03 GRM item discrimination parameters (slope) correlation was 0.828. The YFCY02 and YFCY03 GRM item difficulty parameters (location) correlation was 0.716. The correlations and scatter plots indicated that the item discrimination parameter estimates were more invariant than the item difficulty parameter estimates across the YFCY02 and YFCY03 datasets.

<p>The presented thesis is devoted to the field of organic light emitting</p><p>diodes (OLEDs). Time-dependent Kohn-Sham density functional theory</p><p>(TDDFT) is applied</p><p>in order to eludicate optical properties such as fluorescence and</p><p>phosphorescence for some of the most important materials. The</p><p>accuracy of TDDFT is evaluated with respect to the calculated absorption</p><p>and emission spectra for commonly used light emitting polymers.</p><p>A continuation of this work is devoted to Polyfluorene as this polymer</p><p>has proven to be very promising. In this study the chain</p><p>length dependence of its singlet and triplet excited states is</p><p>analyzed as well as the excited state structures.</p><p>Understanding the phosphorescence mechanism of tris(2-phenylpyridine)Iridium is</p><p>of importance in order to interpret the high efficiency of OLEDs</p><p>containing these specimens. The mechanism is analyzed by calculating</p><p>the electric transition dipole moments by means of TDDFT using</p><p>quadratic response functions.</p><p>As not only the optical properties are essential for effective</p><p>devices, electron transfer properties are addressed. The electron</p><p>transfer capability of the sulfur and nitrogen analogues of Oxadiazole</p><p>is evaluated through their internal reorganization energy.</p>

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

The assessment of noncognitive constructs poses a number of challenges that set it apart from traditional cognitive ability measurement. Of particular concern is the influence of response biases and response styles that can influence the accuracy of scale scores. One strategy to address these concerns is to use alternative item presentation formats (such as multidimensional forced choice (MFC) pairs, triads, and tetrads) that may provide resistance to such biases. A variety of strategies for constructing and scoring these forced choice measured have been proposed, though they often require large sample sizes, are limited in the way that statements can vary in location, and (in some cases) require a separate precalibration phase prior to the scoring of forced-choice responses. This dissertation introduces new item response theory models for estimating item and person parameters from rank-order responses indicating preferences among two or more alternatives representing, for example, different personality dimensions. Parameters for this new model, called the Hyperbolic Cosine Model for Rank order responses (HCM-RANK), can be estimated using Markov chain Monte Carlo (MCMC) methods that allow for the simultaneous evaluation of item properties and person scores. The efficacy of the MCMC parameter estimation procedures for these new models was examined via three studies. Study 1 was a Monte Carlo simulation examining the efficacy of parameter recovery across levels of sample size, dimensionality, and approaches to item calibration and scoring. It was found that estimation accuracy improves with sample size, and trait scores and location parameters can be estimated reasonably well in small samples. Study 2 was a simulation examining the robustness of trait estimation to error introduced by substituting subject matter expert (SME) estimates of statement location for MCMC item parameter estimates and true item parameters. Only small decreases in accuracy relative to the true parameters were observed, suggesting that using SME ratings of statement location for scoring might be a viable short-term way of expediting MFC test deployment in field settings. Study 3 was included primarily to illustrate the use of the newly developed IRT models and estimation methods with real data. An empirical investigation comparing validities of personality measures using different item formats yielded mixed results and raised questions about multidimensional test construction practices that will be explored in future research. The presentation concludes with a discussion of MFC methods and potential applications in educational and workforce contexts.

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Differential item functioning (DIF) can occur across age, gender, ethnic, and/or linguistic groups of examinee populations. Therefore, whenever there is more than one group of examinees involved in a test, a possibility of DIF exists. It is important to detect items with DIF with accurate and powerful statistical methods. While finding a proper DIP method is essential, until now most of the available methods have been dominated by applications to large scale testing contexts. Since the early 1990s, Ramsay has developed a nonparametric item response methodology and computer software, TestGraf (Ramsay, 2000). The nonparametric item response theory (IRT) method requires fewer examinees and items than other item response theory methods and was also designed to detect DIF. However, nonparametric IRT's Type I error rate for DIF detection had not been investigated. The present study investigated the Type I error rate of the nonparametric IRT DIF detection method, when applied to moderate-to-small-scale testing context wherein there were 500 or fewer examinees in a group. In addition, the Mantel-Haenszel (MH) DIF detection method was included. A three-parameter logistic item response model was used to generate data for the two population groups. Each population corresponded to a test of 40 items. Item statistics for the first 34 non-DIF items were randomly chosen from the mathematics test of the 1999 TEVISS (Third International Mathematics and Science Study) for grade eight, whereas item statistics for the last six studied items were adopted from the DIF items used in the study of Muniz, Hambleton, and Xing (2001). These six items were the focus of this study.

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