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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.

Factor loading is a measure of how much the variation of an observed variable is explained by a factor in factor analysis. It is analogous to a correlation coefficient for the factor and the variable.

Eigenvalues in factor analysis represent the total variance explained by each factor. A factor with a high eigenvalue explains a larger portion of the total variance.

Factor rotation is a technique used in factor analysis to find the most interpretable placement of factors by maximising the loading for some variables and minimising for others.

Orthogonal rotation is a type of factor rotation in factor analysis that assumes factors are uncorrelated, maintaining a 90-degree angle between factor axes.

Oblique rotation allows for correlation between factors in factor analysis, recognizing that factors may be related rather than strictly orthogonal.

Communality is the proportion of variance in an observed variable that is accounted for by the common factors in factor analysis.

Factor scores are the values for each individual on the factors that have been identified in a factor analysis, indicating where they fall on each latent dimension.

Uniqueness (also called specificity or error variance) in factor analysis is the variance of an observed variable that is unique to it and not explained by the common factors.

A Scree Plot is a graph that shows the eigenvalues of factors or principal components in descending order, used to determine the number of factors to retain.

The Eigenvalue-One Criterion, also known as the Kaiser Criterion, suggests that in factor analysis, only factors with eigenvalues greater than one should be retained.

Factor extraction is the process of determining the number of factors and the initial form of those factors in factor analysis.

Exploratory Factor Analysis is a factor analysis technique that assumes no prior theory regarding the structure or number of factors, and seeks to uncover the underlying structure of data.

Confirmatory Factor Analysis tests a hypothesized factor structure by assessing the fit of a measurement model specified by the researcher based on prior theory.

Factorial Invariance refers to the extent to which the same factor structures hold across different groups or conditions in factor analysis.

Maximum Likelihood Estimation in factor analysis is a method of estimation that finds the set of factor loadings and unique variances that make the sample data most probable.

Principal Components Analysis is often used as a technique to simplifying data before factor analysis by creating new uncorrelated variables that successively maximize variance.

Latent Variables in factor analysis are variables that are not directly observed but are inferred from the observed variables, often representing underlying constructs.

The Chi-Square Test of Goodness-of-Fit in factor analysis is used to determine if a hypothesized model fits the observed data well by comparing expected and observed frequencies.