Impact and developments on social science research of discrete latent variable models

Latent variable models have become increasingly important due to the growing complexity and volume of data in various disciplines. These models were first proposed in the social science context (Wiggins, 1955; Lazarsfeld and Henry, 1968; Goodman, 1970), and they are designed to handle data that often have hierarchical structures, such as those found in longitudinal studies or multilevel datasets. The core idea behind latent variable models is the assumption that certain variables that are not directly observable exist, termed latent variables. They enable researchers to capture and model the underlying structures and associations within the data that would otherwise be difficult to discern. The practical values of latent variable models extend beyond statistics, finding applications in diverse fields such as economics, psychology, and sociology. In these disciplines, the inclusion of latent variables in statistical models provides several advantages. First and foremost, these models offer a high degree of flexibility, allowing for the accommodation of complex dependency structures within the data. This capability is particularly useful when dealing with categorical and continuous variables. Furthermore, latent variable models facilitate straightforward interpretations, especially when the latent variables correspond to explanatory factors that are inherently unobservable. For instance, in psychological research, latent variables represent underlying traits or constructs that are not directly measurable but are inferred from observed behaviors or responses. This possibility of inferring latent factors provides a deeper understanding of the phenomena under study (Pennoni & Nakai, 2020). One important aspect is the potential for latent variable models to enable causal interpretations under specific conditions. By accounting for latent variables, researchers can control for unobserved confounding factors, thus allowing for more robust causal inferences. This capability is particularly valuable in fields where establishing causal associations is essential but challenging due to the presence of unmeasured confounders (Bartolucci, Pennoni, and Vittadini, 2016). The presentation delves into the recent advancements in discrete latent variable models (Bartolucci, Pandolfi, and Pennoni, 2022), highlighting how these models have been refined to better handle the complexities of modern datasets. The importance of these advancements in enhancing the interpretability and applicability of the models across the social science domain is emphasized (Bartolucci, Farcomeni, and Pennoni, 2013; Pennoni, 2014). Future developments are also considered to enhance the flexibility of latent variable models making them capable of dealing with increasingly complex and varied data structures such as social network data (Matias and Miele, 2017). Another critical area of focus is improving the accessibility and interpretability of latent variable models. While these models offer powerful tools for data analysis, their complexity can sometimes pose a barrier to practitioners. In conclusion, a comprehensive overview of the recent advances and applications of discrete latent variable models is presented, highlighting the importance of these models in addressing the challenges posed by complex and large datasets. The discussion underscores the pivotal role of latent variable models in advancing the analysis and understanding of phenomena across various fields, especially in social sciences, thus paving the way for new discoveries and applications.