A Signal Detection Theory Approach for Predicting Immunity to Pandemic Vaccine Fake News

2023 | HICSS | Citations: 0

Authors: Barajas, J.; John, R.

Abstract: Managing a contagious disease pandemic, such as , requires that the public unde ... Expand

Abstract: Managing a contagious disease pandemic, such as , requires that the public understand and cooperate with behavioral guidelines to reduce viral transmission. This research uses Signal Detection Theory (SDT) to explore how U.S. adults distinguish between true and false information related to COVID-19 vaccines. A total of 372 U.S. adults categorized 17 true and 17 false COVID-19 vaccine headlines. Item Response Theory analyses suggest that the ability to identify true information about the pandemic is a construct that can be reliably measured with our novel methodology. Signal Detection Theory analyses indicate high accuracy (AUC = 0.861), with no bias favoring either true or false responses. Overall, U.S. adults correctly classified 7 of 10 true and 8 of 10 false headlines. Multiple regression analyses on individual performance metrics reveal substantially lower accuracy among conservatives and those with lower scores on a measure of Actively Open-minded Thinking. Implications and limitations of these findings within the pandemic news context are discussed. Collapse

Topics: misinformation fake news pandemic social media covid 19

Methods: machine learning survey multiple linear regression survey design regression analysis method

Theories: signal detection theory

Tell me the Truth: Separating Fact from Fiction in Social Media Following Extreme Events

2021 | HICSS | Citations: 1

Authors: Byrd, Katie; John, Richard

Abstract: With increased reliance on social media to spread important information during e ... Expand

Abstract: With increased reliance on social media to spread important information during extreme events, users’ reported inability to distinguish fact from fiction is a growing concern. This experiment (N=398) tests whether feedback training improves performance in identifying true and false social media content during extreme events. Respondents completed two sets of 16 binary classification judgments (true or false) of actual social media posts following either natural disasters or soft-target terror attacks. Respondents randomly assigned to the feedback training condition received feedback after each of the 16 training judgments, while those assigned to the control condition did not receive any feedback following the training judgments. Feedback training did not increase social media content classification performance for either natural disasters or soft-target terror events. Individuals’ performance for correctly identifying false content was negatively related to selfidentified political conservatism and was positively related to a measure of cognitive reflection. Collapse

Topics: social media natural hazard misinformation fake news deception detection

Methods: machine learning regression analysis method experimental group psychometrics self reported survey

Theories: signal detection theory big five model

Breaking free from the limitations of classical test theory: Developing and measuring information systems scales using item response theory

2017 | Information & Management | Citations: 7

Authors: Rusch, Thomas; Lowry, Paul Benjamin; Mair, Patrick; Treiblmaier, Horst

Abstract: Information systems (IS) research frequently uses survey data to measure the int ... Expand

Abstract: Information systems (IS) research frequently uses survey data to measure the interplay between technological systems and human beings. Researchers have developed sophisticated procedures to build and validate multi-item scales that measure latent constructs. The vast majority of IS studies uses classical test theory (CTT), but this approach suffers from three major theoretical shortcomings: (1) it assumes a linear relationship between the latent variable and observed scores, which rarely represents the empirical reality of behavioral constructs; (2) the true score can either not be estimated directly or only by making assumptions that are difficult to be met; and (3) parameters such as reliability, discrimination, location, or factor loadings depend on the sample being used. To address these issues, we present item response theory (IRT) as a collection of viable alternatives for measuring continuous latent variables by means of categorical indicators (i.e., measurement variables). IRT offers several advantages: (1) it assumes nonlinear relationships; (2) it allows more appropriate estimation of the true score; (3) it can estimate item parameters independently of the sample being used; (4) it allows the researcher to select items that are in accordance with a desired model; and (5) it applies and generalizes concepts such as reliability and internal consistency, and thus allows researchers to derive more information about the measurement process. We use a CTT approach as well as Rasch models (a special class of IRT models) to demonstrate how a scale for measuring hedonic aspects of websites is developed under both approaches. The results illustrate how IRT can be successfully applied in IS research and provide better scale results than CTT. We conclude by explaining the most appropriate circumstances for applying IRT, as well as the limitations of IRT. Collapse

Topics: website hedonistic information system open source source code brainstorming

Methods: confirmatory factor analysis data transformation survey survey design descriptive statistic

Developing and Measuring IS Scales Using Item Response Theory

2013 | International Conference on Information Systems | Citations: 0

Authors: Rusch, Thomas; Mair, Patrick; Lowry, Paul; Treiblmaier, Horst

Abstract: Information Systems (IS) research frequently uses survey data to measure the int ... Expand

Abstract: Information Systems (IS) research frequently uses survey data to measure the interplay between technological systems and human beings. Researchers have developed sophisticated procedures to build and validate multi-item scales that measure latent constructs. Most studies use classical test theory (CTT), which suffers from several theoretical shortcomings. We discuss these problems and present item response theory (IRT) as a viable alternative. Subsequently, we use the CTT approach as well as Rasch models (a class of restrictive IRT models) to develop a scale for measuring the hedonic aspects of websites. The results illustrate how IRT can not only be successfully applied in IS research but also provide improved results over CTT approaches. Collapse

Topics: website hedonistic information system logistics management brainstorming marketing management

Methods: survey simulation survey design data transformation confirmatory factor analysis

Key Considerations for Designing Electronic Medical Records Systems

2007 | Americas Conference on Information Systems | Citations: 0

Authors: Wright, Edward; Avital, Michel

Abstract: Subsequent to earlier work investigating the slow adoption rate of Electronic Me ... Expand

Abstract: Subsequent to earlier work investigating the slow adoption rate of Electronic Medical Record (EMR) technology, we investigated the influence of known antecedents of EMR adoption on post adoption success. Data collected from 388 physicians users suggests that ease of use of an EMR system is by far the most significant and substantive factor of enduring success regardless of physician age, gender, or practice size. Implications for the design of EMR systems are discussed. Collapse

Topics: productivity ease of use system support technology adoption electronic health record

Methods: survey SEM tool data transformation survey design structural equation modeling

Model management system for IRT-based test construction decision support system

2000 | Decision Support Systems | Citations: 0

Authors: Wu, Ing-Long

Abstract: Decision support system (DSS) has become widespread for some specific domains in ... Expand

Abstract: Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time. Collapse

Topics: anonymity Pascal logistics management decision support system

Methods: design artifact computational algorithm experiment mathematical model knapsack problem