Mary Malliaris

End-of-term course evaluations often use multiple-choice questions asking students to evaluate their experience with the class. Students are also asked to give an overall score to the instructor and to the course. To help instructors interpret the scores and work to improve their evaluations, we looked at 16 descriptive statements in 2,048 evaluations of course sections over a span of 4 years. Cluster analyses revealed that the course score related the most to Cluster 2 statements (feedback and challenge) and the instructor score to Cluster 4 statements (clear and stimulating presentations). We then analysed these specific clusters in more detail to provide specific recommendations to faculty on how to improve their student evaluation of teaching scores using this output. Overall, our study revealed several critical benefits of student evaluations of teaching effectiveness. Key findings include: (a) clear and informative teaching helps decrease student stress; (b) encouraging students to set and achieve challenging goals promotes personal and academic growth, and the fact that (c) students want to feel challenged, so they can grow. We discuss the implications of our findings and offer suggestions for future research.

To prepare for the next generation of digital health policies, we must understand how diverse populations build trust and find meaning in engaging with digitally mediated public health systems. Clinical trials (CTs) remain essential to health innovation, yet participation is persistently low. While digital platforms are increasingly used to expand outreach, disparities in institutional trust and digital access threaten equitable inclusion.To examine how institutional trust, digital engagement, and sociodemographic factors shape willingness to participate in clinical trials, as a case of digitally mediated health system engagement.This study used nationally representative survey data from the United States (N = 3,437). Exploratory and confirmatory factor analyses identified latent motivational pathways. Multivariate regression models examined associations between financial- and medical-based willingness to participate.Two distinct motivational profiles emerged: medical-based willingness (MBW) and financial-based willingness (FBW). MBW was positively associated with digital health-seeking behavior, clinical trial knowledge, and perceived care quality, which are the indicators of institutional trust and system engagement. FBW was associated with younger age, lower education, and economic vulnerability. Racial disparities in MBW were observed, with non-White respondents significantly less likely to report medically motivated participation.Equitable clinical trial participation depends on addressing both digital inclusion and institutional trust for the next generation. Policymakers should develop outreach strategies that tailor messaging by motivational profile and embed culturally responsive trust signals into digital platforms. These strategies can help foster a sense of meaning, enhance connection, and promote well-being among underrepresented populations which are key goals for next-generation health policy.

We propose two narratives to analyze monthly returns for the S&P 500 Index. The first narrative emphasizes variables that represent the macroeconomy: Fed Funds Effective Rate, Real M2, 10-Year T-Note minus 2-Year T-Note, Shiller Housing Index, industrial production, and 1-Year Expected Inflation. The second narrative focuses on microeconomic fundamentals that include earnings, CBOE Volatility, consumer sentiment, interest rates, global price of copper, and the Dollar Index. We perform a methodology of 348 rolling regressions for each narrative, each with a sample of 60 monthly observations, and estimate the significance of the independent variables considered. We conclude that the microeconomic narrative with its indicators tied to stock market activities correlates with monthly returns more closely than macro fundamentals do. The new insight from this paper is that it is beneficial to employ both narratives as complementary rather than as competitive.

Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a neural network model as an atheoretical non-linear approach to capture the relative importance of alternative macro variables, we show that these hypotheses generate different macro relevance. As an alternative to testing housing time series, we focus on bubble identification being hypothesis dependent. Model forecast errors (residuals) identify the potential presence of bubbles through standardized residual CUSUM tests for structural breaks. By testing for housing bubbles from these unstructured models, we generate conclusions on the presence of bubbles prior to the Great Financial Crisis and the post-pandemic periods. Competing macro hypotheses or narratives will generate different conclusions on the presence of bubbles and create bubble identification issues.

Purpose: The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles. Design/methodology/approach: The framework uses a training set of manually labeled examples to identify word features surrounding data set usage references. Using the word features and general entity identifiers, candidate data sets are extracted and scored separately at the sentence and document levels. Finally, the extracted data set references can be verified by the authors using a web-based verification module. Findings: This paper successfully addresses a significant gap in entity extraction literature by focusing on data set extraction. In the process, this paper: identified an entity-extraction scenario with specific characteristics that enable a multiphase approach, including a feasible author-verification step; defined the search space for word feature identification; defined scoring functions for sentences and documents; and designed a simple web-based author verification step. The framework is successfully tested on 178 articles authored by researchers from a large research organization. Originality/value: Whereas previous approaches focused on completely automated large-scale entity recognition from text snippets, the proposed framework is designed for a longer, high-quality text, such as a research publication. The framework includes a verification module that enables the request validation of the discovered entities by the authors of the research publications. This module shares some similarities with general crowdsourcing approaches, but the target scenario increases the likelihood of meaningful author participation.

The global financial crisis of 2007–2009 caused major economic disturbances in the oil market. In this paper, we consider five variables that describe the microeconomics of the supply of and demand for oil, and evaluate their importance before, during and after the global financial crisis. We consider five dissimilar regimes during the period of January 1986 to the end of 2020: two regimes prior to the global financial crisis, the regime during the crisis, and two regimes after the crisis. The main hypothesis tested is that oil fundamentals of supply and demand remained important, even though the five regimes were dissimilar. We built five boosted and over-fitted neural networks to capture the exact relationships between spot oil prices and oil data related to these prices. This analysis shows that, while the inputs into an accurate neural network can remain the same, the impact of each variable can change considerably during different regimes.

From the early 1970s to the Global Financial Crisis of 2007–09, U.S. crude oil production followed a declining trend. After the Global Financial Crisis, U.S. crude oil production increased rapidly. This paper addresses the important question “what economic factors have driven U.S. crude oil production since the Global Financial Crisis?”. We propose that factors such as: the price of oil, the one period lagged price of oil, the price of copper, the crude oil price volatility, the Trade Weighted U.S. Dollar Index, and the high yield index spread, are important explanatory variables. Using two modeling approaches, namely, multiple regression, and the random tree methodology, we conclude that the one month lagged price of oil is the most significant explanatory variable, among all considered, for the upward trend of U.S. oil production from 2009 to early 2020.

During the Global Financial Crisis of 2007-2009, the price of crude oil dropped markedly from about $140 per barrel in June 2008 to about $40 in early 2009. As Quantitative Easing allowed the U.S. economy to stabilize and return to slow growth, oil prices increased and averaged about $100 during 2010-13. Then in June 2014, they plunged dramatically to about $40. This paper makes an original contribution that explains the volatile behavior of global crude prices during the 2003-2018 period that includes the Global Financial Crisis. We propose a financial model that focuses on U.S. Quantitative Easing as the signature monetary policy tool to provide liquidity and restore economic growth. This model describes the price of oil as depending on six inputs: Federal Assets accumulated during the period of Quantitative Easing, the 10 Year Treasury Note, the price of copper, the Trade Weighted dollar, the S&P 500 Index and the U.S. High Yield Rate for Bonds rated CCC or below. Making use of 771 overlapping 52-week regressions to capture short run dynamics we find that Quantitative Easing was statistically significant only during 2009-10, while the U.S. High Yield Rate played a significant role explaining oil price changes, both during the crisis and also during the 2014-2017 period. The significance of the U.S. High Yield Rate is expected to continue in the future.