Quartimax

Quartimax rotation is an orthogonal rotation criterion used in exploratory factor analysis to simplify the interpretation of factor loadings. It aims to simplify the rows of the loading matrix by maximizing the sum of squared loadings across factors for each variable, which tends to make each variable load highly on as few factors as possible. Equivalently, quartimax can be viewed as applying varimax rotation to the transposed loading matrix.

In practice, quartimax produces a simple structure in the sense that variables tend to cluster around a single factor, whereas each factor may load on many variables. This can aid interpretation when the researcher is interested in identifying dominant associations between variables and factors.

Quartimax is one of several orthogonal rotation options, along with varimax and equamax. It is less commonly used than varimax but can be informative in certain datasets or theoretical contexts. As with all rotations, quartimax preserves the orthogonality of factors and does not change the communalities or the overall amount of explained variance; it only redistributes variance among loadings.

Limitations include potential instability with small samples and sensitivity to measurement scales; rotation choice can influence downstream conclusions about factor structure.

In software, quartimax is available in major statistical packages under rotation options in factor analysis procedures (for example, in R, SPSS, and SAS).