Factor analysis - Simple English Wikipedia, the free encyclopedia

Factor analysis is a way to look at many pieces of information and find groups among them. These groups are called "factors".^[1]

Factor analysis is used to see if many things are related or similar. For example, if a person is asked many questions, factor analysis could be used to see if some of the answers are similar.^[2]

This method is used in many fields. For example, in psychology, it is used to find out if certain behaviors or feelings are related. In business, it can be used to see if different products are liked by the same group of people.

Factor analysis makes it easier to understand large amounts of data. It can help to show important patterns and relationships in the data. It can also help to simplify complex information.^[3]

References

[change | change source]

↑ Gorsuch, Richard L. (2013-05-13). Factor Analysis. Psychology Press. ISBN 978-1-134-92078-5.
↑ Kline, Paul (2014-02-25). An Easy Guide to Factor Analysis. Routledge. ISBN 978-1-317-72560-2.
↑ Fabrigar, Leandre R.; Wegener, Duane T. (2011-12-22). Exploratory Factor Analysis. Oxford University Press. ISBN 978-0-19-981351-3.

Statistics

Descriptive statistics

Continuous data

Center	Mean arithmetic geometric harmonic cubic generalized/power Median Mode
Dispersion	Variance Standard deviation Average absolute deviation Coefficient of variation Percentile Range Interquartile range
Shape	Central limit theorem Moments Skewness Kurtosis L-moments

Count data

Index of dispersion

Summary tables

Dependence

Graphics

Data collection

Study design	Population Statistic Effect size Statistical power Optimal design Sample size determination Replication Missing data
Survey methodology	Sampling stratified cluster Standard error Opinion poll Questionnaire
Controlled experiments	Scientific control Randomized experiment Randomized controlled trial Random assignment Blocking Interaction Factorial experiment
Adaptive Designs	Adaptive clinical trial Up-and-Down Designs Stochastic approximation
Observational Studies	Cross-sectional study Cohort study Natural experiment Quasi-experiment

Statistical inference

Statistical theory

Frequentist inference

Point estimation	Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization Lehmann–Scheffé theorem Median unbiased Plug-in
Interval estimation	Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife
Testing hypotheses	1- & 2-tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons
Parametric tests	Likelihood-ratio Score/Lagrange multiplier Wald

Specific tests

Z-test (normal) Student's t-test F-test
Goodness of fit	Chi-squared G-test Kolmogorov–Smirnov Anderson–Darling Lilliefors Jarque–Bera Normality (Shapiro–Wilk) Likelihood-ratio test Model selection Cross validation AIC BIC
Rank statistics	Sign Sample median Signed rank (Wilcoxon) Hodges–Lehmann estimator Rank sum (Mann–Whitney) Nonparametric anova 1-way (Kruskal–Wallis) 2-way (Friedman) Ordered alternative (Jonckheere–Terpstra)

Bayesian inference

Correlation	Pearson product-moment Partial correlation Confounding variable Coefficient of determination
Regression analysis	Errors and residuals Regression validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines (MARS)
Linear regression	Simple linear regression Ordinary least squares General linear model Bayesian regression
Non-standard predictors	Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity
Generalized linear model	Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions
Partition of variance	Analysis of variance (ANOVA, anova) Analysis of covariance Multivariate ANOVA Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

Categorical

Multivariate

Time-series

General	Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality
Specific tests	Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey
Time domain	Autocorrelation (ACF) partial (PACF) Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR)
Frequency domain	Spectral density estimation Fourier analysis Wavelet Whittle likelihood

Survival

Survival function	Kaplan–Meier estimator (product limit) Proportional hazards models Accelerated failure time (AFT) model First hitting time
Hazard function	Nelson–Aalen estimator
Test	Log-rank test

Applications

Biostatistics	Bioinformatics Clinical trials / studies Epidemiology Medical statistics
Engineering statistics	Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification
Social statistics	Actuarial science Census Crime statistics Demography Econometrics Jurimetrics National accounts Official statistics Population statistics Psychometrics
Spatial statistics	Cartography Environmental statistics Geographic information system Geostatistics Kriging