Adults with language impairment have poor language skills, but may or may not have reading deficits. For those identified with literacy difficulties as children, deficits in the skills related to sublexical level decoding skills can persist into adulthood. Decoding deficits, current or remediated, would implicate the sublexical interaction of orthography and phonology. In addition, the heterogeneous reading profiles of adults with language impairment provide an opportunity to examine whether the deficits in this bidirectional link between orthography and phonology are related to reading deficit specifically or language impairment globally. Sixty adults, 30 with language impairment and 30 with typical language, participated in this study. Nonword stimuli, orthogonally varied based on orthographic and phonological neighborhood density, were presented in two sublexical tasks, nonword repetition and nonword spelling. The experiment revealed a pattern of similar responses for adults with and without language impairment. However, adults with impaired language had significantly poorer performance on both the nonword repetition task and the nonword spelling task. Additional analyses indicated that both language and literacy measures predicted the overall performance on the orthogonally varied nonword repetition and nonword spelling tasks. This study highlights the existence of a bidirectional influence between phonological and orthographic processing. For adults with language impairment it appears that this bidirectional link is intact at the sublexical level.

Extending prior research related to attitude development and intra-attitudinal consistency, the current study aims to evaluate various factors that predict attitudes toward public education and understand how they impact the direction and consistency of participants’ attitudes. In an effort to develop our knowledge of the factors that predict these attitudes among members of differing populations, the researcher will administer surveys that assess the three components of participants’ attitudes as well as the factors that predict them to public school teachers and parents of students in the United States (US) and in France. It is predicted that considerations like social identity and the structure of the two education systems will affect participants’ attitudes differently depending on their role (teacher or student) and location (France or US), while other aspects, such as the status of the profession of the teacher and the public perceptions of public education, will impact all participants’ attitudes similarly. This research will allow for an understanding of both the direction and consistency of attitudes toward public education, hypothesizing consistent attitudes among parents and inconsistent attitudes among teachers, based on the number and types of factors each set of the population considers when formulating their attitudes.

Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.

Inductive logic programming is a discipline investigating invention of clausal theories from observed examples such that for given evidence and background knowledge we are finding a hypothesis covering all positive examples and excluding all negative ones. In this thesis we extend an existing work on template consistency to general consistency. We present a three-phase algorithm DeMeR decomposing the original problem into smaller subtasks, merging all subsolutions together yielding a complete solution and finally refining the result in order to get a compact final hypothesis. Furthermore, we focus on a method how each individual subtask is solved and we introduce a generate-and-test method based on the probabilistic history-driven approach for this purpose. We analyze each stage of the proposed algorithms and demonstrate its impact on a runtime and a hypothesis structure. In particular, we show that the first phase of the algorithm concentrates on solving the problem quickly at the cost of longer solutions whereas the other phases refine these solutions into an admissible form. Finally, we prove that our technique outperforms other algorithms by comparing its results for identifying common structures in random graphs to existing systems.

Doctor of Philosophy / Department of Statistics / Weixing Song / The regression model has been given a considerable amount of attention and played a significant role in data analysis. The usual assumption in regression analysis is that the variances of the error terms are constant across the data. Occasionally, this assumption of homoscedasticity on the variance is violated; and the data generated from real world applications exhibit heteroscedasticity. The practical importance of detecting heteroscedasticity in regression analysis is widely recognized in many applications because efficient inference for the regression function requires unequal variance to be taken into account. The goal of this thesis is to propose new testing procedures to assess the adequacy of fitting parametric variance function in heteroscedastic regression models. The proposed tests are established in Chapter 2 using certain minimized L[subscript]2 distance between a nonparametric and a parametric variance function estimators. The asymptotic distribution of the test statistics corresponding to the minimum distance estimator under the fixed model and that of the corresponding minimum distance estimators are shown to be normal. These estimators turn out to be [sqrt]n consistent. The asymptotic power of the proposed test against some local nonparametric alternatives is also investigated. Numerical simulation studies are employed to evaluate the nite sample performance of the test in one dimensional and two dimensional cases. The minimum distance method in Chapter 2 requires the calculation of the integrals in the test statistics. These integrals usually do not have a tractable form. Therefore, some numerical integration methods are needed to approximate the integrations. Chapter 3 discusses a nonparametric empirical smoothing lack-of-fit test for the functional form of the variance in regression models that do not involve evaluation of integrals. empirical smoothing lack-of-fit test can be treated as a nontrivial modification of Zheng (1996)'s nonparametric smoothing test and Koul and Ni (2004)'s minimum distance test for the mean function in the classic regression models. The asymptotic normality of the proposed test under the null hypothesis is established. Consistency at some fixed alternatives and asymptotic power under some local alternatives are also discussed. Simulation studies are conducted to assess the nite sample performance of the test. The simulation studies show that the proposed empirical smoothing test is more powerful and computationally more efficient than the minimum distance test and Wang and Zhou (2006)'s test.

Master of Science / Department of Statistics / Weixing Song / The purpose of this report is to propose and evaluate two lack-of-fit test procedures to check the adequacy of the regression functional forms in the standard Tobit regression models. It is shown that testing the null hypothesis for the standard Tobit regression models amounts testing a new equivalent null hypothesis of the classic regression models. Both procedures are constructed based on the empirical variants of a minimum distance, which measures the squared difference between a nonparametric estimator and a parametric estimator of the regression functions fitted under the null hypothesis for the new regression models. The asymptotic null distributions of the test statistics are investigated, as well as the power for some fixed alternatives and some local hypotheses. Simulation studies are conducted to assess the finite sample power performance and the robustness of the tests. Comparisons between these two test procedures are also made.

This thesis deals with the differences in time preferences, individual discount rates, among non-Romany (Czechs) and Romany ethnic group in the Czech Republic. Subjects of almost homogenous ethnic groups -- low income, low education, currently unemployed -- were subjected to experiments based on an in-depth questionnaire (Romany N = 35, N = 25 non-Romany). In addition to time preferences, their relationship with the socio-economic and demographic characteristics was inspected. The sub-tests showed a difference across ethnic groups in regard to the discounting models. Romany ethnic group is better characterized by an inconsistent model compared to the non-Romany using an more consistent model. Participants from both groups, however, appear to be very impatient, with discount rates closer to the values of individuals dependent on addictive substances. Probably because of insufficient sample size, no statistically significant correlation was found between the discount rates and socio-economic and demographic factors. Financial (il)literacy among Romany proved to be an important factor affecting the formation of savings. Finally, the recorded signal effect of expenditures on signal goods among the Romany ethnic group is briefly analyzed.

A case study was used to survey 308 teacher trainees in western Kenya to investigate the extent to which pre-service teachers in two Kenyan teacher training colleges are prepared to integrate technology in teaching. . The study uses the technological pedagogical and content knowledge (TPACK) framework to understand the knowledge needed by the pre-service teachers to integrate technology effectively. Data was gathered using the Survey of Pre-Service Teachers' Knowledge of Teaching and Technology and three open-ended questions. Data from the survey does not distinguish the TPACK variable among the respondents. The data suggests that the pre-service teachers rate themselves highly on the other six TPACK subscales of technological knowledge, content knowledge, pedagogical knowledge, pedagogical content knowledge, technological pedagogical knowledge, and technological content knowledge. Further, the data suggests that the respondents' personal use of technology, to a large extent, influences how they use technology in classrooms. Lastly, the data indicates that the survey instrument is inadequate in capturing all the TPACK subscales in this population as it shows weak internal consistency. These findings imply that faculty in these colleges need to be more intentional and deliberate in teaching the trainees how to integrate technology in lessons. Policymakers and college administrators may also influence the teachers' personal use of technology to inculcate into the trainees tested methods of technology integration. Another implication is that future research could employ other supplementary methods, in addition to surveys, to find out the levels of technology integration in the teacher trainees.

One of the biggest challenges for the modern society is that of climate change. Despite the growing accumulation of scientific evidence that points towards a strong need for action to be made regarding anthropogenic (human made) climate change, there is a lack of unity in what actions are needed and an outspread passivity amongst both establishments and the public. The reason behind this is attributed to lack of belief in anthropogenic climate change, and lack of pro-environmental attitudes amongst the public. Furthermore, these issues have been found to relate to identity related beliefs and attitudes that conflict with pro-environmental beliefs and attitudes, where political orientation has proven to be a strong factor. One way of dealing with these issues could be through informational influences. By presenting people to information shaped in different ways, one could increase the belief in anthropogenic climate change and pro-environmental attitudes. This study examined the relationship between three different informational influences, and its potential effect on climate change beliefs and attitudes. Furthermore, this study examined the relationship between demographic factors such as age, gender and political orientation with regards to their potential effect on climate change beliefs and attitudes. 449 participants completed a survey with intent to measure the potential effects informational influences and demographic factors had on climate change beliefs and attitudes. Despite that indications where found, no significant results could be identified for the informational influences. All demographic factors had some significant effect on climate change beliefs or attitudes, where political orientation was the strongest influencing factor. This relates to earlier research and further implications were discussed for future studies.

What is the relationship between judging something as good and being motivated to acton this judgment? Motivational internalism is the thesis that there is a necessaryconnection between moral judgments and motivation. Externalists typically believe that ajudgment-independent desire is needed for the moral judgment to be motivating. Tobridge the gap between internalism and externalism, a few philosophers have appealed totheories of identification-based moral judgments. This implies that although moraljudgments may not be necessarily motivating in general, it could be possible to define acertain kind of identification-based moral judgment that is necessarily motivating. HereinI will examine the role identification plays for moral motivation. I will first analyse anargument that uses an identification-incompatible moral judgment to show thatinternalism is false. I will argue that this argument is unconvincing and that identificationdoes not preclude identification-incompatible moral judgments from being motivating.The identification-based argument hence does not support that internalism is false.Second, I will argue that identification can provide the motivational force needed to makecertain identity-based moral judgments necessarily motivating. This identification-basedaccount does however, I will argue, not support internalism. Despite presenting a kind ofmoral judgment that is necessarily motivating, it is an externalist account of theconnection between moral judgments and motivation since a judgment-independentdesire is a necessary source of its motivational force.