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Research problem: During humanitarian crises, communities of people face various types of dangers. To counter the dangers, they need information in a short period. Such need creates the opportunity for misinformation. Such misinformation can result in information harms that can generate short- or long-term consequences. Literature review: Prior researchers have tackled the situation by using technical or behavioral approaches. Research question: What are the harms from misinformation? We propose a taxonomy of 15 information harms grouped in 8 categories and assess the perception of risk regarding the harms through a survey of respondents who have experienced crisis response situations. Methodology: This paper examines two scenarios, the 2013 Boston Marathon bombing and the 2017 Oroville Dam evacuation order crises, through two dimensions: Likelihood of occurrence and Level of impacts of the harms. Results and conclusions: Findings are presented through visualization and test results for significant differences of harms between scenarios. Similar groups of harms are identified with different severity levels based on post hoc analyses: those with 1. high likelihood and low impact (psychological and confusion harms), 2. low likelihood and low impact (reputation and privacy harms), and 3. low likelihood and high impact (physical, financial, safety, and social harms). In addition to establishing the taxonomy of misinformation harms, findings will have practical value in emergency response and recovery activities to effectively prioritize resources to minimize specific harms from misinformation in crises. Further research directions are also discussed. [ABSTRACT FROM AUTHOR]

Correspondence analysis is a statistical method that allows researchers to explore relationships among complex categorical variables. This paper will provide researchers with the theoretical and practical foundations for understanding and applying correspondence analysis to their own research agendas. Problem: Technical communicators use a variety of research methods and collect a variety of types of data. Of particular interest to technical communicators is categorical data, or data that are not traditionally quantitative. For instance, technical communicators often collect and analyze language data from a variety of texts. Analyzing this type of data can be difficult using traditional statistical methods. Key concepts: Variable types, a priori versus exploratory research designs, contingency tables, and data visualization are central to understanding the foundations of correspondence analysis. Key lessons: To conduct correspondence analysis, a researcher must walk through a series of steps including: (1) determining whether correspondence analysis is appropriate, (2) choosing a statistical software package, (3) running the correspondence analysis, and (4) interpreting and applying the results. Implications for practice: While correspondence analysis provides many useful insights into categorical data, a researcher must consider several things when deciding to use correspondence analysis. These include the potential to misinterpret and misapply the results of a correspondence analysis. [ABSTRACT FROM AUTHOR]