<p>This tutorial presents a multimethod framework for assessing the latent structure of psychometric measures, integrating exploratory, confirmatory, and resampling-based techniques. The proposed approach guides researchers through a step-by-step process that includes Exploratory Graph Analysis (EGA), Parallel Analysis (PA), Bootstrap Exploratory Graph Analysis (bootEGA), Bootstrap Exploratory Factor Analysis (bootEFA), and Confirmatory Factor Analysis (CFA) with model fit evaluation. Each method contributes complementary evidence to support structural decisions and improve replicability, reducing the risks of overfitting and model misspecification. The tutorial is illustrated with four examples: three based on simulated data and one using real-world empirical data. The simulated examples represent different structural scenarios commonly encountered in psychometric research: Example 1—A relatively simple structure: three factors, two of which are highly correlated. Example 2 – A challenging scenario due to high dimensional complexity: four factors, strongly correlated in pairs. Example 3 – A scenario involving cross-loadings: two moderately correlated factors, with one item loading substantially on both. Example 4 – An empirical application using real-world data, introducing recent developments in bifactor modeling and the Generalized Total Entropy Index (GenTEFI), to address complex latent structures in psychometric networks. A final discussion highlights the strengths and limitations of this multimethod strategy and its implications for making more valid, replicable inferences about the latent structure of psychological measures. The framework ultimately aims to strengthen the methodological rigor of psychometric evaluation.</p>