Description: Quantitative analysis of multimodal data refers to the use of statistical and mathematical techniques to interpret and extract information from datasets that come from multiple sources or modalities. These modalities can include text, images, audio, and numerical data, among others. This approach allows researchers and analysts to combine different types of data to gain a more comprehensive and rich understanding of a specific phenomenon or problem. The main characteristics of this analysis include the integration of heterogeneous data, the application of advanced statistical models, and the ability to make meaningful comparisons across different modalities. The relevance of quantitative analysis of multimodal data lies in its ability to address complex questions that cannot be adequately answered using a single type of data. By combining information from various sources, patterns, trends, and relationships can be identified that might otherwise go unnoticed. This approach is particularly valuable in diverse fields such as psychology, sociology, medicine, and artificial intelligence, where understanding the interaction between different types of data is crucial for theory development and informed decision-making.
