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• W3166770166 abstract "We tested a novel method for studying human experience (thoughts and affect). We utilized Cognitive-Affective Maps (CAMs)–an approach to visually represent thoughts and their affective connotations as networks of concepts that individuals associate with a given event. Using an innovative software tool, we recruited a comparative sample of ( n = 93) Canadians and ( n = 100) Germans to draw a CAM of their experience (events, thoughts, feelings) with the Covid-19 pandemic. We treated these CAM networks as a series of directed graphs and examined the extent to which their structural properties (latent and emotional) are predictive for the perceived coronavirus threat (PCT). Across multiple models, we found consistent and significant relationships between these network variables and the PCT in both the Canadian and German sample. Our results provide unique insights into individuals' thinking and perceptions of the viral outbreak. Our results also demonstrate that a network analysis of CAMs' properties is a promising method to study the relationship between human thought and affective connotation. We suggest that CAMs can bridge several gaps between qualitative and quantitative methods. Unlike when using quantitative tools (e.g., questionnaires), participants' answers are not restricted by response items as participants are free to incorporate any thoughts and feelings on the given topic. Furthermore, as compared to traditional qualitative measures, such as structured interviews, the CAM technique may better enable researchers to objectively assess and integrate the substance of a shared experience for large samples of participants." @default.
• W3166770166 created "2021-06-22" @default.
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• W3166770166 date "2021-06-11" @default.
• W3166770166 modified "2023-10-01" @default.
• W3166770166 title "A Novel Network Approach to Capture Cognition and Affect: COVID-19 Experiences in Canada and Germany" @default.
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• W3166770166 doi "https://doi.org/10.3389/fpsyg.2021.663627" @default.
• W3166770166 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8226173" @default.
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