Everything about Factor analysis
Statistical method
Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors plus "error" terms, hence factor analysis can be thought of as a special case of errors-in-variables models. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Factor analysisA 5-minute orientation
- Skim a recent paper: Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021Influential work since 2022
- Pick up a classic book: Structural Steel DesignThe most-reprinted book on the subject
- Read the landmark paper: THE THEORETICAL FOUNDATIONS OF PRINCIPAL FACTOR ANALYSIS, CANONICAL FACTOR ANALYSIS, AND ALPHA FACTOR ANALYSISCited 675 times
- Go sideways: Principal component analysisA closely related topic
- Go sideways: Exploratory factor analysisA closely related topic
📖 Definitions
Noun A statistical method used to describe variability among observed correlated variables in terms of one or more unobserved variables (called factors).
Noun A statistical study performed using the method.
🏆 Landmark papers (most cited)
THE THEORETICAL FOUNDATIONS OF PRINCIPAL FACTOR ANALYSIS, CANONICAL FACTOR ANALYSIS, AND ALPHA FACTOR ANALYSIS
Multiple factor analysis.
Alpha Factor Analysis
Independent Factor Analysis
Factor Analysis
Evolving factor analysis
Exploratory Factor Analysis
Understanding factor analysis
🆕 Influential since 2022
Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
Global, regional, and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
Global, regional, and national burden of low back pain, 1990–2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021
Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis
📚 Books worth reading
Structural Steel DesignJack C. McCormac · 1965
Latent variable modelsJohn C. Loehlin · 1987
Factor analysisRichard L. Gorsuch · 1974
Modern factor analysisHarry Horace Harman · 1960
A Concise Guide to Market ResearchErik Mooi · 2011
Contextual designHugh Beyer, Karen Holtzblatt · 1997
Active alphaAlan H. Dorsey · 2007
Factor analysisEdward Eugene Cureton, Edward E. Cureton · 1983
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Principal component analysis Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data…
- Exploratory factor analysis In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set…
- The Vectors of Mind The Vectors of Mind is a book published by American psychologist Louis Leon Thurstone in 1935 that summarized Thurstone's methodology for multiple factor…
- Confirmatory factor analysis In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. It is used to test…
- Linear discriminant analysis Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization…
- Whitening transformation A whitening transformation or sphering transformation is a linear transformation that transforms a vector of random variables with a known covariance matrix…
- Covariance matrix In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance…
- Sparse PCA Sparse principal component analysis (SPCA or sparse PCA) is a technique used in statistical analysis and, in particular, in the analysis of multivariate…