8. Outliers, Leverage Observations, and Influential Cases in Factor Analysis: Using Robust Procedures to Minimize Their Effect

79 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Best-Practice Recommendations for Defining, Identifying, and Handling Outliers (2013, methodology): “Regarding robust approaches, we recommend a two-stage robust procedure (Yuan & Bentler, 1998) or a direct robust method using iteratively reweighted least squares (Yuan & Zhong, 2008; Zhong & Yuan, 2011).”
Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, background): “Conversely, good leverage points – outliers with very small residuals from the model line despite lying far from the center of the data cloud – can actually lower standard errors on estimates of regression coefficients (Yuan & Zhong, 2008).”