Analysis of sociodemographic factors influencing students' data visualization literacy

The rapid pace at which data is created has required the creation of new tools to extract information from large amounts of data. Data visualization has proven effective in facilitating access to essential information from a dataset. For this reason, it is critical to examine Data Visualization Literacy (DVL), particularly in the context in which learning occurs, in schools. Studies related to this topic have been consulted, however, the research done so far to understand the influence of sociodemographic factors on the ability to read, interpret, and draw conclusions from data visualizations has not reached a consensus. Therefore, this study aims to bridge the controversy surrounding the topic by examining whether Age, Sex, Field of studies in High School (FSHS), Current level of education (CLE), and Current field of studies (CFS) predict students’ responses to data visualization questions. In this study, data collection was done through an online survey, which not only con- tained questions about the sociodemographic characteristics of the students, but also a section intended for data visualization questions. The non-probability convenience sampling technique was used and after processing the collected data, a total of 153 responses were obtained. To analyze the data, 6 binary logistic regressions were developed, each referring to one of the 6 data visualization questions contained in the survey, in order to compare the findings of this study with those previously supported by other authors. The results suggest that all variables except CLE were important factors in predicting students’ ability to answer the data visualization questions correctly.