HSPM 8233: Quantitative Research and Evaluation Methods

This course introduces students to the fundamentals and practical application of quantitative methodologies for answering policy and policy-relevant health services research questions. It discusses foundational concepts related to study conceptualization, study design, data analysis and interpretation, and reporting. Topics covered in this course include foundations of health services and policy research, research design, sampling, measurement, univariate and bivariate analysis, regression, multilevel analysis, panel data analysis, exploratory factor analysis, confirmatory factor analysis and structural equation modeling. Hands-on application of research methods is integrated throughout the course through the use of a statistical software application and real-world data

Semi-Automated Analysis of Large Privacy Policy Corpora

Regulators, policy makers, and consumers are interested in proactively identifying services with acceptable or compliant data use policies, privacy policies, and terms of service. Academic requirements engineering researchers and legal scholars have developed qualitative, manual approaches to conducting requirements analysis of policy documents to identify concerns and compare services against preferences or standards. In this research, we develop and present an approach to conducting large-scale, qualitative, prospective analyses of policy documents with respect to the wide-variety of normative concerns found in policy documents. Our approach uses techniques from natural language processing, including topic modeling and summarization. We evaluate our approach in an exploratory case study that attempts to replicate a manual legal analysis of roughly 200 privacy policies from seven domains in a semi-automated fashion at a larger scale. Our findings suggest that this approach is promising for some concerns

Community Policing and Intelligence-Led Policing: An Examination of Convergent or Discriminant Validity

Purpose Despite increased scholarly inquiry regarding intelligence-led policing (ILP) and popularity among law enforcement agencies around the globe, ambiguity remains regarding the conceptual foundation and appropriate measurement of ILP. Although most scholars agree that ILP is indeed a unique policing philosophy, there is less consensus regarding the relationship between ILP and the ever-present model of community-oriented policing (COP). Consequently, there is a clear need to study the empirical distinctions and overlaps in these policing philosophies as implemented by US law enforcement agencies. The paper aims to discuss these issues. Design/methodology/approach Data were gleaned from the 2007 LEMAS and 2009 NIJ Intelligence surveys. A total of 227 unique police agencies in the USA are included. A series of bivariate, exploratory factor analyses and structural models are used to determine discriminatory or convergent validity across COP and ILP constructs. Findings The goal was to answer the question: are these two policing philosophies are being implemented as separate and distinct strategies? Results of our exploratory and structural models indicate that COP and ILP loaded on unique latent constructs. This affirms the results of the bivariate correlations, and indicates that COP and ILP have discriminant measurement validity. In other words, COP and ILP are conceptually distinct, even when implemented in police departments across the USA. Implications of these findings and suggestions for future research are discussed. Originality/value This is the first study to empirically test the discriminant or convergent validity of COP and ILP

Simulation of the Long-Term Effects of Decentralized and Adaptive Investments in Cross-Agency Interoperable and Standard IT Systems

Governments have come under increasing pressure to promote horizontal flows of information across agencies, but investment in cross-agency interoperable and standard systems have been minimally made since it seems to require government agencies to give up the autonomies in managing own systems and its outcomes may be subject to many external and interaction risks. By producing an agent-based model using 'Blanche' software, this study provides policy-makers with a simulation-based demonstration illustrating how government agencies can autonomously and interactively build, standardize, and operate interoperable IT systems in a decentralized environment. This simulation designs an illustrative body of 20 federal agencies and their missions. A multiplicative production function is adopted to model the interdependent effects of heterogeneous systems on joint mission capabilities, and six social network drivers (similarity, reciprocity, centrality, mission priority, interdependencies, and transitivity) are assumed to jointly determine inter-agency system utilization. This exercise simulates five policy alternatives derived from joint implementation of three policy levers (IT investment portfolio, standardization, and inter-agency operation). The simulation results show that modest investments in standard systems improve interoperability remarkably, but that a wide range of untargeted interoperability with lagging operational capabilities improves mission capability less remarkably. Nonetheless, exploratory modeling against the varying parameters for technology, interdependency, and social capital demonstrates that the wide range of untargeted interoperability responds better to uncertain future states and hence reduces the variances of joint mission capabilities. In sum, decentralized and adaptive investments in interoperable and standard systems can enhance joint mission capabilities substantially and robustly without requiring radical changes toward centralized IT management.Public IT Investment, Interoperability, Standardization, Social Network, Agent-Based Modeling, Exploratory Modeling

Purchasing behavior for pirated products: a structural equation modeling approach on Bangladeshi consumers

The purpose of this study is to explore the factors influencing the Bangladeshi consumers’ perception toward purchasing pirated products. To explore these factors, this study had conducted a survey among the Bangladeshi consumers’. The data analysis was conducted through exploratory factor analysis, confirmatory factor analysis and structural equation modeling to test the hypotheses. Among the four variables, individual personalities and economic conditions are the most significant followed by social influences and pricing of pirated products. The outcome of this research showed a comprehensively integrated framework for policy maker and business enterprise to understand the dynamic relationships among dimensions of social influence, pricing, economy, and personality to understand the consumers’ perception toward purchasing pirated products. Further research is needed to examine these factors with additional samples before generalization can be made

Auto Insurance Tenure Prediction and Analysis

The purpose of this project is to understand the main factors that drive customer tenure within auto insurance industry for six or more years. The analysis is based on three years of the J.D. Power Auto Insurance survey data. For the analysis, multiple binary machine learning algorithms were implemented and measured to classify whether customers would stay with the same insurer for more than six years. Random forest was found to be the most robust model as compared to logistic regression, decision trees, and xgboost

Electronic government and civic engagement: Citizen interaction with government via Web portals

This exploratory study examines civic engagement with e-government via Web sites. It provides an analytical framework that integrates both the supply and the demand sides of citizen interaction with e-government. In modeling three dimensions of online civic engagement (government information access, service transactions, and contributing to government policy-making processes), the study framework incorporates a number of variables, including political activism, civic involvement, perceived benefits and difficulties, information channels, and demographic characteristics. Based on a national sample of Internet users, the study highlights the importance of the supply side (availability of e-government) for promoting civic engagement. Furthermore, political activism is found to be related positively to accessing government policy information and contributing to policy-making processes. The study results also confirm the significant impact of perceived benefits in fostering online civic engagement. Future research can benefit from this study by utilizing a more comprehensive model, treating various dimensions of online engagement separately, and conducting an in-depth analysis of the elements of perceived benefits

The Impacts of the COVID-19 Pandemic on Mental Health across Different Genders and Sexualities

Current studies report an increase in psychological distress as a result of the COVID-19 pandemic. This study is interested in examining mental health disparities and how the COVID-19 pandemic has disproportionately impacted marginalized groups—and more specifically, those identified by sex, gender, and sexuality—compared with the general population. This study also considers the effects and ramifications of different policy measures taken during the course of the pandemic. We perform exploratory data modeling and analysis on several important and publicly available datasets taken during the pandemic on mental health and COVID-19 infection data across various identity groups to look for significant disparities, correlations, and causations across different times and identities. This paper uses these analyses to suggest policy measures that could improve public wellness during future public health crises, and in particular across different identities