Sensory neurons assemble to form networks that process inputs coming from the senses. Through synaptic connections neurons interact and create complex dynamical states in response to these inputs. Networks with different connectivity patterns are thought to display different states and therefore subserve different computational goals. In this thesis, we mainly study brain rhythms, a dynamical state that occurs in various neural structures. Rhythms are emergent oscillations that typically occur in homogeneous recurrent networks, whose neurons have identical properties and are densely interconnected. Many sensory systems comprise neurons with opposite ON and OFF responses to inputs. We show that homogenous recurrent networks fail to sustain rhythms when ON and OFF neurons are present in equal proportions. This happens even when the network is subjected to spatially correlated inputs, which are known to promote synchronized oscillations. In this context, we adapted the so-called linear response theory to include networks containing ON and OFF neurons with different intrinsic properties. In this asymmetric case, oscillations can be recovered. A simpler approach is to segregate the ON and OFF populations, thus producing two oscillating subnetworks. The dynamics of purely feedforward networks are studied next. These networks are composed of two or more populations. The populations are connected in a serial fashion, but neurons are unconnected within the populations. This connectivity scheme is drastically different from the fully recurrent network. Yet, this network is shown to display oscillatorylike properties when subjected to spatially correlated stimulation under certain conditions. We also find that this network can implement various types of gain control, depending on the noise in the system and the strength of synaptic interactions. These results establish some unexpected links between feedforward and recurrent networks. Along the way, we apply our results and conclusions to a well-characterized sensory network, the electrosensory system of weakly electric fish.

This dissertation is composed of three studies that examined illness-related stigma, depressive symptoms and attachment style among patients living with HIV and Hepatitis C (HCV). The first study examined the psychometric properties of a brief HIV Stigma Scale (B-HSS) in a sample of adult patients living with HIV (PHA) (n=94). The second study developed and explored the psychometric properties of the HCV Stigma Scale in a sample of adult patients living with HCV (PHC) (n =92). Psychometric properties were evaluated with classical test theory and item response theory methodology. The third study explored whether illness-related stigma mediated the relationship between insecure attachment styles (anxious attachment or avoidant attachment) and depressive symptoms among PHA (n =72) and PHC (n=83). From June to December 2008, patients were recruited to participate in a questionnaire study at the outpatient clinics in The Ottawa Hospital. Findings indicated that the 9-item B-HSS is a reliable and valid measure of HIV stigma with items that are highly discriminatory, which indicates that items are highly effective at discriminating patients with different levels of stigma. The 9-item HCV Stigma Scale was also found to be reliable and valid with highly discriminatory items that effectively differentiate PHC. Construct validity for both scales was supported by relationships with theoretically related constructs: depression and quality of life. Among PHA, when HIV stigma was controlled the relationship between anxious attachment style and depression was not significant. However, the relationship between avoidant attachment style and depressive symptoms decreased but remained significant. Among PHC when HCV stigma was controlled the relationship between insecure attachment styles and depressive symptoms was not significant. Dissertation results emphasize the importance of identifying patients experiencing illness-related stigma and the relevance of addressing stigma and attachment style when treating depressive symptoms among PHA and PHC.

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. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate

Light-matter interactions lead to a variety of interesting phenomena, for example photosynthesis which is a process fundamental to life on earth. There exists many different spectroscopic methods to measure light-matter interactions, for example UV/Vis spectroscopy, that can provide information about electronically excited states. However, numerical methods and theory are important to model and gain understanding of these experiments. Quantum chemistry provides that understanding, giving the possibility to numerically calculate molecular properties like excitation energies. The aim of this thesis was to implement a reduced-space algorithm in Dalton, to solve an eigenvalue equation obtained by response theory, for the calculation of excitation energies of molecular systems. There already was a similar algorithm in Dalton, that was able to perform these calculations. However, in a different module of Dalton used mainly for complex response theory, an algorithm to obtain eigenvalues was missing. The new implementation was similar to the existing one, except for the division of the reduced space into even and odd parts used in the complex response module. The thesis starts with a quick introduction of light-matter interactions and proceeds with a description of many-body theory, including numerical methods used in that field. In the end of the theoretical part, the eigenvalue equation, used to calculate excitation energies, is derived. In the following section, the reduced-space algorithm is described. In the end of the thesis, numerical results obtained with the algorithm are presented, including a small basis set and method study. The comparison with the existing implementation of the similar algorithm verified the successful implementation of the algorithm presented in this thesis.

This study investigated the item parameter recovery of two methods of factor analysis. The methods researched were a traditional factor analysis of tetrachoric correlation coefficients and an IRT approach to factor analysis which utilizes marginal maximum likelihood estimation using an EM algorithm (MMLE-EM). Dichotomous item response data was generated under the 2-parameter normal ogive model (2PNOM) using PARDSIM software. Examinee abilities were sampled from both the standard normal and uniform distributions. True item discrimination, a, was normal with a mean of .75 and a standard deviation of .10. True b, item difficulty, was specified as uniform [-2, 2]. The two distributions of abilities were completely crossed with three test lengths (n= 30, 60, and 100) and three sample sizes (N = 50, 500, and 1000). Each of the 18 conditions was replicated 5 times, resulting in 90 datasets. PRELIS software was used to conduct a traditional factor analysis on the tetrachoric correlations. The IRT approach to factor analysis was conducted using BILOG 3 software. Parameter recovery was evaluated in terms of root mean square error, average signed bias, and Pearson correlations between estimated and true item parameters. ANOVAs were conducted to identify systematic differences in error indices. Based on many of the indices, it appears the IRT approach to factor analysis recovers item parameters better than the traditional approach studied. Future research should compare other methods of factor analysis to MMLE-EM under various non-normal distributions of abilities.

Many social, biological and technological systems can be viewed as complex networks with a large number of interacting components. However despite recent advancements in network theory, a satisfactory description of dynamic processes arising in such cooperative systems is a subject of ongoing research. In this dissertation the emergence of dynamical complexity in networks of interacting stochastic oscillators is investigated. In particular I demonstrate that networks of two and three state stochastic oscillators present a second-order phase transition with respect to the strength of coupling between individual units. I show that at the critical point fluctuations of the global order parameter are characterized by an inverse-power law distribution and I assess their renewal properties. Additionally, I study the effect that different types of perturbation have on dynamical properties of the model. I discuss the relevance of those observations for the transmission of information between complex systems.

Item-response theory (IRT)assumes that the model fits the data. One commonly overlooked aspect of model-fit assessment is an examination of personfit, or person-fit assessment (PFA). One reason that PFA lacks popularity among psychometricians is that comprehensive software is notpresent.This dissertation outlines the development and testing ofa new software package, called wizirt, that will begin to meet this need. This software package provides a wide gamut of tools to the user but is currently limited to unidimensional, dichotomous, and parametricmodels. The wizirt package is built in the open source language R, where it combines the capabilities of a number of other R packages under a single syntax.In addition to the wizirt package, I have created a number of resources to help users learn to use the package. This includes support for individuals who have never used R before, as well as more experienced R users.

Item-response theory (IRT)assumes that the model fits the data. One commonly overlooked aspect of model-fit assessment is an examination of personfit, or person-fit assessment (PFA). One reason that PFA lacks popularity among psychometricians is that comprehensive software is notpresent.This dissertation outlines the development and testing ofa new software package, called wizirt, that will begin to meet this need. This software package provides a wide gamut of tools to the user but is currently limited to unidimensional, dichotomous, and parametricmodels. The wizirt package is built in the open source language R, where it combines the capabilities of a number of other R packages under a single syntax.In addition to the wizirt package, I have created a number of resources to help users learn to use the package. This includes support for individuals who have never used R before, as well as more experienced R users.

Thesis advisor: Zhushan Mandy Li / Differential item functioning (DIF) occurs when examinees of equal ability from different groups have different probabilities of correctly responding to certain items. DIF analysis aims to identify potentially biased items to ensure the fairness and equity of instruments, and has become a routine procedure in developing and improving assessments. This study proposed a DIF detection method using regularization techniques, which allows for simultaneous investigation of all items on a test for both uniform and nonuniform DIF. In order to evaluate the performance of the proposed DIF detection models and understand the factors that influence the performance, comprehensive simulation studies and empirical data analyses were conducted. Under various conditions including test length, sample size, sample size ratio, percentage of DIF items, DIF type, and DIF magnitude, the operating characteristics of three kinds of regularized logistic regression models: lasso, elastic net, and adaptive lasso, each characterized by their penalty functions, were examined and compared. Selection of optimal tuning parameter was investigated using two well-known information criteria AIC and BIC, and cross-validation. The results revealed that BIC outperformed other model selection criteria, which not only flagged high-impact DIF items precisely, but also prevented over-identification of DIF items with few false alarms. Among the regularization models, the adaptive lasso model achieved superior performance than the other two models in most conditions. The performance of the regularized DIF detection model using adaptive lasso was then compared to two commonly used DIF detection approaches including the logistic regression method and the likelihood ratio test. The proposed model was applied to analyzing empirical datasets to demonstrate the applicability of the method in real settings. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

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