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Everything about Parallel analysis

Statistical method

Parallel analysis, also known as Horn's parallel analysis, is a statistical method used to determine the number of components to keep in a principal component analysis or factors to keep in an exploratory factor analysis. It is named after psychologist John L. Horn, who created the method, publishing it in the journal Psychometrika in 1965. The method compares the eigenvalues generated from the data matrix to the eigenvalues generated from a Monte-Carlo simulated matrix created from random data of the same size. Horn developed the method as a correction to the Kaiser criterion, arguing that sampling error and least-squares capitalization inflate eigenvalues in sample data, causing the Kaiser rule to overestimate the number of factors to retain. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Parallel analysisA 5-minute orientation
  2. Skim a recent paper: Generalized Analysis of Load-Independent ZCS Parallel-Resonant InverterInfluential work since 2022
  3. Pick up a classic book: Job scheduling strategies for parallel processingThe most-reprinted book on the subject
  4. Read the landmark paper: PARAFAC: Parallel factor analysisCited 447 times
  5. Go sideways: Exploratory factor analysisA closely related topic
  6. Go sideways: Factor analysisA closely related topic

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🗂️ The subject at a glance

  1. Evaluation and comparison with alternatives
  2. Implementation

🏆 Landmark papers (most cited)

🆕 Influential since 2022

đź“š Books worth reading

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