In press ; Population studies are often characterized by a plethora of data that includes quantitative to qualitative variables. The main focus of this study was to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing underlying structures in large datasets used to investigate cognitive ageing. Principal component analysis (PCA) was used to obtain main cognitive dimensions (based on the continuous neurocognitive test variables) and MCA to detect and explore relationships of cognitive, clinical, physical and lifestyle categorical variables across the low-dimensional space. Altogether the technique allows to not only simplify complex data, providing a detailed description of the data and yielding a simple and exhaustive analysis, but also to handle a large and diverse dataset comprised of quantitative, qualitative, objective and subjective data. Two PCA dimensions were identified (general cognition/executive function and memory) and two main MCA dimensions were retained. As expected, poorer cognitive performance was associated with older age, less school years, unhealthier lifestyle indicators and presence of pathology. Interestingly, the first MCA dimension indicated the clustering of general/executive function and lifestyle indicators and education, while the second association between memory and clinical parameters and age. The clustering analysis with object scores method was used to identify groups sharing similar characteristics within each of the identified dimensions. Following MCA findings, the weaker cognitive clusters in terms of memory and executive function comprised individuals with characteristics contributing to a higher MCA dimensional mean score (age, less education and presence of indicators of unhealthier lifestyle habits and/or clinical pathologies). MCA provided a powerful tool to explore complex ageing data, covering multiple and diverse variables, showing not only if a relationship exists between variables but also how they are related, offering at the same time statistical results can be seen both analytically and visually.