A distance education case study: A comparison of instructional delivery methods practiced by higher education faculty and emerging standards

The purpose of this research was to compare the delivery methods as practiced by higher education faculty teaching distance courses with recommended or emerging standard instructional delivery methods for distance education. Previous research shows that traditional-type instructional strategies have been used in distance education and that there has been no training to distance teach. Secondary data, however, appear to suggest emerging practices which could be pooled toward the development of standards. This is a qualitative study based on the constant comparative analysis approach of grounded theory.^ Participants (N = 5) of this study were full-time faculty teaching distance education courses. The observation method used was unobtrusive content analysis of videotaped instruction. Triangulation of data was accomplished through one-on-one in-depth interviews and from literature review. Due to the addition of non-media content being analyzed, a special time-sampling technique was designed by the researcher--influenced by content analyst theories of media-related data--to sample portions of the videotape instruction that were observed and counted. A standardized interview guide was used to collect data from in-depth interviews. Coding was done based on categories drawn from review of literature, and from Cranton and Weston's (1989) typology of instructional strategies. The data were observed, counted, tabulated, analyzed, and interpreted solely by the researcher. It should be noted however, that systematic and rigorous data collection and analysis led to credible data.^ The findings of this study supported the proposition that there are no standard instructional practices for distance teaching. Further, the findings revealed that of the emerging practices suggested by proponents and by faculty who teach distance education courses, few were practiced even minimally. A noted example was the use of lecture and questioning. Questioning, as a teaching tool was used a great deal, with students at the originating site but not with distance students. Lectures were given, but were mostly conducted in traditional fashion--long in duration and with no interactive component.^ It can be concluded from the findings that while there are no standard practices for instructional delivery for distance education, there appears to be sufficient information from secondary and empirical data to initiate some standard instructional practices. Therefore, grounded in this research data is the theory that the way to arrive at some instructional delivery standards for televised distance education is a pooling of the tacitly agreed-upon emerging practices by proponents and practicing instructors. Implicit in this theory is a need for experimental research so that these emerging practices can be tested, tried, and proven, ultimately resulting in formal standards for instructional delivery in television education. ^

Monitoring the Accuracy of Multiple Occupancy Forecasts

Corporate executives closely monitor the accuracy of their hotels' occupancy fore- casts since important decisions are based upon these predictions. This study lists the criteria for selecting an appropriate error measure. It discusses several evaluation methods focusing on statistical significance tests and demonstrates the use of two adequate evaluation methods: Mincer- Zamowitz's efficiency test and Wilcoxon's Non-Parametric Matched-Pairs Signed- Ranks test.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Essays on the impact of stakeholders' sentiment on the financial decision making process

The most important factor that affects the decision making process in finance is the risk which is usually measured by variance (total risk) or systematic risk (beta). Since investors’ sentiment (whether she is an optimist or pessimist) plays a very important role in the choice of beta measure, any decision made for the same asset within the same time horizon will be different for different individuals. In other words, there will neither be homogeneity of beliefs nor the rational expectation prevalent in the market due to behavioral traits. This dissertation consists of three essays. In the first essay, “ Investor Sentiment and Intrinsic Stock Prices”, a new technical trading strategy was developed using a firm specific individual sentiment measure. This behavioral based trading strategy forecasts a range within which a stock price moves in a particular period and can be used for stock trading. Results indicate that sample firms trade within a range and give signals as to when to buy or sell. In the second essay, “Managerial Sentiment and the Value of the Firm”, examined the effect of managerial sentiment on the project selection process using net present value criterion and also effect of managerial sentiment on the value of firm. Final analysis reported that high sentiment and low sentiment managers obtain different values for the same firm before and after the acceptance of a project. Changes in the cost of capital, weighted cost of average capital were found due to managerial sentiment. In the last essay, “Investor Sentiment and Optimal Portfolio Selection”, analyzed how the investor sentiment affects the nature and composition of the optimal portfolio as well as the portfolio performance. Results suggested that the choice of the investor sentiment completely changes the portfolio composition, i.e., the high sentiment investor will have a completely different choice of assets in the portfolio in comparison with the low sentiment investor. The results indicated the practical application of behavioral model based technical indicator for stock trading. Additional insights developed include the valuation of firms with a behavioral component and the importance of distinguishing portfolio performance based on sentiment factors.

Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Determinants and effects of electoral party coalitions: The case of Brazil

Since the 1985 return to democracy, Brazilian politicians have resorted to vote-pooling arrangements to elect representatives. A puzzle thus presents itself: What drives parties to join these electoral cartels? The dissertation unraveled the incentives party elites have to participate in coalitions under a presidencialist system of government. I also investigated the effect of electoral coalitions on congressional representation. I applied a model of binary outcomes and relied on standard deviations to assess the ideological homogeneity/heterogeneity of electoral coalitions. I also calculated the Index of Disproportionality to measure the gaps between the proportion of votes and seats received by all parties in Brazil with and without electoral coalitions. Finally, I assessed the effects of the electoral formula on proportionality. An unexpected exogenous factor resulted crucial in explaining proportional electoral coalition building: The district's majoritarian election for governor. In each district, political actors often synchronize coalition partners to maximize winning results while minimizing electoral efforts.

Three essays on corporate governance and meeting-beating or missing analyst forecasts

The beginning of the 21st century was plagued with unprecedented instances of corporate fraud. In an attempt to address apparent non-existent or “broken” corporate governance policies, sweeping measures of financial reporting reform ensued, having specific requirements relating to the composition of audit committees, the interaction between audit committees and external auditors, and procedures concerning auditors’ assessment of client risk. The purpose of my dissertation is to advance knowledge about “good” corporate governance by examining the association between meeting-or-beating analyst forecasts and audit fees, audit committee compensation, and audit committee tenure and “busyness”. Using regression analysis, I found the following: (1) the frequency of meeting-or-just beating (just missing) analyst forecasts is negatively (positively) associated with audit fees, (2) the extent by which a firm exceeds analysts’ forecasts is positively (negatively) associated with audit committee compensation that is predominately equity-based (cash-based), and (3) the likelihood of repeatedly meeting-or-just beating analyst forecasts is positively associated with audit committee tenure and “busyness”. These results suggest that auditors consider clients who frequently meet-or-just beat forecasts as being less “risky”, and clients that frequently just miss as being more “risky”. The results also imply that cash-based director compensation is more successful in preserving the effectiveness of the audit committee’s financial reporting oversight role, that equity-based compensation motivates independent audit committee directors to focus on short-term performance thereby aligning their interests with management, and that audit committee director tenure and the degree of director “busyness” can affect an audit committee member’s effectiveness in providing financial reporting oversight. Collectively, my dissertation provides additional insights regarding corporate governance practices and informs policy-makers for future relevant decisions.^

Essays on the Impact of Stakeholders' Sentiment on the Financial Decision Making Process

The most important factor that affects the decision making process in finance is the risk which is usually measured by variance (total risk) or systematic risk (beta). Since investors' sentiment (whether she is an optimist or pessimist) plays a very important role in the choice of beta measure, any decision made for the same asset within the same time horizon will be different for different individuals. In other words, there will neither be homogeneity of beliefs nor the rational expectation prevalent in the market due to behavioral traits. This dissertation consists of three essays. In the first essay, Investor Sentiment and Intrinsic Stock Prices, a new technical trading strategy is developed using a firm specific individual sentiment measure. This behavioral based trading strategy forecasts a range within which a stock price moves in a particular period and can be used for stock trading. Results show that sample firms trade within a range and show signals as to when to buy or sell. The second essay, Managerial Sentiment and the Value of the Firm, examines the effect of managerial sentiment on the project selection process using net present value criterion and also effect of managerial sentiment on the value of firm. Findings show that high sentiment and low sentiment managers obtain different values for the same firm before and after the acceptance of a project. The last essay, Investor Sentiment and Optimal Portfolio Selection, analyzes how the investor sentiment affects the nature and composition of the optimal portfolio as well as the performance measures. Results suggest that the choice of the investor sentiment completely changes the portfolio composition, i.e., the high sentiment investor will have a completely different choice of assets in the portfolio in comparison with the low sentiment investor. The results indicate the practical application of behavioral model based technical indicators for stock trading. Additional insights developed include the valuation of firms with a behavioral component and the importance of distinguishing portfolio performance based on sentiment factors.

Novel methodology for in situ carbon dioxide enrichment of benthic ecosystems

Future climate change will likely represent a major stress to shallow aquatic and coastal marine communities around the world. Most climate change research, particularly in regards to increased pCO2 and ocean acidification, relies on ex situ mesocosm experimentation, isolating target organisms from their environment. Such mesocosms allow for greater experimental control of some variables, but can often cause unrealistic changes in a variety of environmental factors, leading to “bottle effects.” Here we present an in situ technique of altering dissolved pCO2within nearshore benthic communities (e.g., macrophytes, algae, and/or corals) using submerged clear, open-top chambers. Our technique utilizes a flow-through design that replicates natural water flow conditions and minimizes caging effects. The clear, open-top design additionally ensures that adequate light reaches the benthic community. Our results show that CO2 concentrations and pH can be successfully manipulated for long durations within the open-top chambers, continuously replicating forecasts for the year 2100. Enriched chambers displayed an average 0.46 unit reduction in pH as compared with ambient chambers over a 6-month period. Additionally, CO2 and HCO3 – concentrations were all significantly higher within the enriched chambers. We discuss the advantages and disadvantages of this technique in comparison to other ex situ mesocosm designs used for climate change research.

Accuracy of Hotel Feasibility Study Projections

Hotel feasibility studies are critical in the determination of hotel construction, sales and refinancing. Discrepancies have been reported between forecasted results and actual operating results. The author, with funding provided by the Hilton corporation, examines whether such studies under- state or overstate occupancy, average rate, and net income.

Essays in book -tax income differences and analyst forecasts /recommendations

Prior research suggests that book-tax income differences (BTD) relate to both firms' earnings quality and operating performance. In this dissertation, I explore whether and how financial analysts signal the implications of BTD efficiently. This dissertation is comprised of three essays on BTD. The three essays seek to develop a better understanding of how financial analysts utilize information reflected in BTD (derived from the ratio of taxable income to book income). The first essay is a review and discussion of prior research regarding BTD. The second essay of this dissertation investigates the role of BTD in indicating the consensus and dispersion of analyst recommendations. I find that sell recommendations are positively related to BTD. I also document that analyst coverage has a positive effect on the standard deviation of consensus recommendations with respect to BTD. The third essay is an empirical analysis of analysts' forecast optimism, analyst coverage, and BTD. I find a negative association between forecast optimism and BTD. My results are consistent with a larger BTD being associated with less forecast bias. Overall, I interpret the sum of the evidence as being consistent with BTD reflecting information about earnings quality, and consistent with analysts examining and using this information in making decisions regarding both forecasts and recommendations.

Effects of In Situ CO2 Enrichment on the Structural and Chemical Characteristics of the Seagrass Thalassia Testudinum

Seagrasses commonly display carbon-limited photosynthetic rates. Thus, increases in atmospheric pCO2, and consequentially oceanic CO2(aq) concentrations, may prove beneficial. While addressed in mesocosms, these hypotheses have not been tested in the field with manipulative experimentation. This study examines the effects of in situ CO2(aq) enrichment on the structural and chemical characteristics of the tropical seagrass, Thalassia testudinum. CO2(aq) availability was manipulated for 6 months in clear, open-top chambers within a shallow seagrass meadow in the Florida Keys (USA), reproducing forecasts for the year 2100. Structural characteristics (leaf area, leaf growth, shoot mass, and shoot density) were unresponsive to CO2(aq) enrichment. However, leaf nitrogen and phosphorus content declined on average by 11 and 21 %, respectively. Belowground, non-structural carbohydrates increased by 29 %. These results indicate that increased CO2(aq) availability may primarily alter the chemical composition of seagrasses, influencing both the nutrient status and resilience of these systems.

Flood risk analysis of the White Oak Creek watershed in Oak Ridge, Tennessee using MIKE SHE and MIKE 11 software

The purpose of this study was to determine the flooding potential of contaminated areas within the White Oak Creek watershed in the Oak Ridge Reservation in Tennessee. The watershed was analyzed with an integrated surface and subsurface numerical model based on MIKE SHE/MIKE 11 software. The model was calibrated and validated using five decades of historical data. A series of simulations were conducted to determine the watershed response to 25 year, 100 year and 500 year precipitation forecasts; flooding maps were generated for those events. Predicted flood events were compared to Log Pearson III flood flow frequency values for validation. This investigation also provides an improved understanding of the water fluxes between the surface and subsurface subdomains as they affect flood frequencies. In sum, this study presents crucial information to further assess the environmental risks of potential mobilization of contaminants of concern during extreme precipitation events.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.