Cognitive Behavior Therapy for Obsessive-Compulsive Behavior in Children with Autism Spectrum Disorder: A Comparison of Hierarchical Linear Modelling and Visual Analysis

Behavioral researchers commonly use single subject designs to evaluate the effects of a given treatment. Several different methods of data analysis are used, each with their own set of methodological strengths and limitations. Visual inspection is commonly used as a method of analyzing data which assesses the variability, level, and trend both within and between conditions (Cooper, Heron, & Heward, 2007). In an attempt to quantify treatment outcomes, researchers developed two methods for analysing data called Percentage of Non-overlapping Data Points (PND) and Percentage of Data Points Exceeding the Median (PEM). The purpose of the present study is to compare and contrast the use of Hierarchical Linear Modelling (HLM), PND and PEM in single subject research. The present study used 39 behaviours, across 17 participants to compare treatment outcomes of a group cognitive behavioural therapy program, using PND, PEM, and HLM on three response classes of Obsessive Compulsive Behaviour in children with Autism Spectrum Disorder. Findings suggest that PEM and HLM complement each other and both add invaluable information to the overall treatment results. Future research should consider using both PEM and HLM when analysing single subject designs, specifically grouped data with variability.

Exploring the use of prediction and summarization to increase students' listening and reading comprehension /

This study examined the effect of expHcitly instructing students to use a repertoire of reading comprehension strategies. Specifically, this study examined whether providing students with a "predictive story-frame" which combined the use of prediction and summarization strategies improved their reading comprehension relative to providing students with generic instruction on prediction and summarization. Results were examined in terms of instructional condition and reading ability. Students from 2 grade 4 classes participated in this study. The reading component of the Canadian Achievement Tests, Second Edition (CAT/2) was used to identify students as either "average or above average" or "below average" readers. Students received either strategic predication and summarization instruction (story-frame) or generic prediction and summarization instruction (notepad). Students were provided with new but comparable stories for each session. For both groups, the researcher modelled the strategic tools and provided guided practice, independent practice, and independent reading sessions. Comprehension was measured with an immediate and 1-week delayed comprehension test for each of the 4 stories, hi addition, students participated in a 1- week delayed interview, where they were asked to retell the story and to answer questions about the central elements (character, setting, problem, solution, beginning, middle, and ending events) of each story. There were significant differences, with medium to large effect sizes, in comprehension and recall scores as a fimction of both instructional condition and reading ability. Students in the story-frame condition outperformed students in the notepad condition, and average to above average readers performed better than below average readers. Students in the story-frame condition outperformed students in the notepad condition on the comprehension tests and on the oral retellings when teacher modelling and guidance were present. In the cued recall sessions, students in the story-frame instructional condition recalled more correct information and generated fewer errors than students in the notepad condition. Average to above average readers performed better than below average readers across comprehension and retelling measures. The majority of students in both instructional conditions reported that they would use their strategic tool again.

Forecasting the Yield Curve of Government Bonds: A Comparative Study

For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields