Psychometric Network Analysis is emerging as a complement to latent variable models for modeling the complexity of psychological processes. Network models allow for representing processes as systems of interacting elements and investigating the dynamics of stability and change over time. Network Psychometrics has become central to several subfields of Psychology (as well as to other social sciences), offering a novel theoretical perspective on phenomena such as personality traits, psychopathological dynamics, and attitudes. Conceptualizing such phenomena as networks allowed researchers to go beyond the assumption that psychological phenomena reflect the effects of unobservable latent variables, to focus instead on the possibility that they can emerge from the interactions among their basic constituents. I will introduce this approach starting from the theoretical foundations, discussing its main methodological declinations with a focus on longitudinal data, and showcasing applications in psychology.
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