This chapter explores factor analysis, a statistical method used to reduce data in empirical research. It begins with an introduction to the foundational concepts of factor analysis, detailing its relevance and application across various fields. Using the example of attributes that consumers value in toothpaste, particular emphasis is placed on the use of factor analysis to reduce large sets of variables or item batteries in surveys. The nuances between principal component analysis and principal factor analysis are discussed, highlighting how they address different analytical needs by making different underlying theoretical assumptions. The chapter also covers advanced topics such as the Kaiser–Meyer–Olkin measure, Bartlett's sphericity test, the correlation matrix to assess suitability for factor analysis, factor extraction techniques, the interpretation of eigenvalues, varimax rotation, factor loadings, communalities, factor scores. Furthermore, the use of factor analysis with software such as R, SPSS and Stata is demonstrated to enhance the reader’s understanding of how to perform such analyses can be carried out in applied scenarios.