Topics in this course include an introduction to the basic concepts of linear structure, explanation of model fit indicators, path analysis, confirmatory factor analysis, shoe-pull procedure and model validity verification. Additionally, there is a discussion of various intermediary and interference models and their mixed models, group comparison of group models, and a discussion of Bayesian analysis, all utilized as practical exercises through the application of SPSS, AMOS, and Smart PLS. Cross-level research topics are derived from nested data structures and theoretical models to the setting of hierarchical linear models (HLM) and context models, and the application of longitudinal research development and related research topics are discussed to achieve the teaching objectives of this course, helping students understand the concepts and practical applications of advanced quantitative research methods in recent years.