Narrative data enable evaluators to understand other people's viewpoints without predetermining those viewpoints by using preselected questionnaire categories. Narrative data yield rich detail, insight, and information. However, reducing narrative data into meaningful conclusions is difficult and time consuming, and requires attention, commitment, and skill on the part of trained coders. The personal and situational characteristics of the coders (called value inertia and cognitive limitation biases) affect data reduction. The effects of coder exposure to expected project outcomes and the level of coder research methodology sophistication were investigated. Coders considered either sophisticated or naive in research methodology were exposed to positive, ambiguous, or negative project outcome expectations. The coders reduced, or categorized, 25 open-ended interview response sets into previously established positive, negative, and ambiguous statement-type, content-code categories. The effectiveness of coder training was also explored by computing generalizability (reliability) coefficients. High generalizability coefficients were found, regardless of level of exposure to project outcome expectations. This indicates that coders were able to code the same statements the same way and is an indication of the coders' ability to reproduce the results. Results of this study also indicate that evaluators should use sophisticated coders for the reduction of narrative data, given that option. Sophisticated coders appear more resistant to the effect of exposure to project outcome expectations, coding narrative data more positively with less variability than naive coders when exposed to positive outcome expectations.

The subject of this dissertation is an electro-optical processing (EOP) concept which, in its basic configuration, computes a discrete linear transform such as Fourier, Laplace, Hilbert, etc., as well as convolutions and correlations. It accepts input signals through an incoherent light source, performs high speed analog multiplications via a two-dimensional array of apertures in a chrome mask on the surface of a charge-coupled device (CCD), shifts and integrates intermediate results within the CCD, and presents the transformed signal as a data stream from the output shift register of the CCD. The EOP concept is described in detail where both serial and parallel configurations are developed. It is seen to be an efficient computer of matrix-vector products, matrix-matrix products, and multichannel correlations. The inclusion of feedback and a changeable CCD mask yields an architecture for higher order matrix operations such as matrix inversion, solution of simultaneous equations, etc. A functional model of an EOP matrix-vector multiplier is presented which describes the accumulated effect of errors in system elements from the LED through the CCD. Also described is removal of error introduced by biasing required of input and mask modulation functions in order that they represent bipolar quantities. An EOP spectrum analyzer based upon direct implementation of the discrete Fourier transform (DFT) is described and use of a Kaiser-Bessel window function applied to the CCD mask is described as a solution to the "spectral leakage" problem caused by sharp discontinuities at each end of a normal window of sampled data. Finally, application of a parallel EOP configuration to the synthetic aperture radar (SAR) problem is offered. An architecture utilizing separate in-phase and quadrature EOP channels is described. The system shows potential for providing at least modest resolution SAR imagery with an economy of size, weight, and power consumption.

The growing popularity of various Web 2.0 media has created massive amounts of user-generated content such as online reviews, blog articles, shared videos, forums threads, and wiki pages. Such content provides insights into web users' preferences and opinions, online communities, knowledge generation, etc., and presents opportunities for many knowledge discovery problems. However, several challenges need to be addressed: data collection procedure has to deal with unique characteristics and structures of various Web 2.0 media; advanced data selection methods are required to identify data relevant to specific knowledge discovery problems; interactions between Web 2.0 users which are often embedded in user-generated content also need effective methods to identify, model, and analyze. In this dissertation, I intend to address the above challenges and aim at three types of knowledge discovery tasks: (data) collection, selection, and investigation. Organized in this "CSI" framework, five studies which explore and propose solutions to these tasks for particular Web 2.0 media are presented. In Chapter 2, I study focused and hidden Web crawlers and propose a novel crawling system for Dark Web forums by addressing several unique issues to hidden web data collection. In Chapter 3 I explore the usage of both topical and sentiment information in web crawling. This information is also used to label nodes in web graphs that are employed by a graph-based tunneling mechanism to improve collection recall. Chapter 4 further extends the work in Chapter 3 by exploring the possibilities for other graph comparison techniques to be used in tunneling for focused crawlers. A subtree-based tunneling method which can scale up to large graphs is proposed and evaluated. Chapter 5 examines the usefulness of user-generated content in online video classification. Three types of text features are extracted from the collected user-generated content and utilized by several feature-based classification techniques to demonstrate the effectiveness of the proposed text-based video classification framework. Chapter 6 presents an algorithm to identify forum user interactions and shows how they can be used for knowledge discovery. The algorithm utilizes a bevy of system and linguistic features and adopts several similarity-based methods to account for interactional idiosyncrasies.

As libraries collect more and more data, it is worth taking some time to analyze the data we collect and effectively present it. This article details how to use visualization to investigate trends and make compelling arguments with data.

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