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Everything about Structural equation modeling

Form of causal modeling that fit networks of constructs to data

Structural equation modeling (SEM) is a diverse set of methods used by scientists for both observational and experimental research. SEM is used mostly in the social and behavioral science fields, but it is also used in econometrics, epidemiology, business, and other fields. By a standard definition, SEM is "a class of methodologies that seeks to represent hypotheses about the means, variances, and covariances of observed data in terms of a smaller number of 'structural' parameters defined by a hypothesized underlying conceptual or theoretical model". Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Structural equation modelingA 5-minute orientation
  2. Skim a recent paper: Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied exampleInfluential work since 2022
  3. Pick up a classic book: A beginner's guide to structural equation modelingThe most-reprinted book on the subject
  4. Read the landmark paper: Structural equation modeling in practice: A review and recommended two-step approach.Cited 35,358 times
  5. Go sideways: Confirmatory factor analysisA closely related topic
  6. Go sideways: Confirmatory composite analysisA closely related topic

📖 Definitions

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🗂️ The subject at a glance

  1. History
  2. General steps and considerations
  3. Extensions, modeling alternatives, and statistical kin

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

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