Small Sample Statistics for Incomplete Nonnormal Data: Extensions of Complete Data Formulae and a Monte Carlo Comparison
By Victoria Savalei. 2010
77 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/13466664ebd1a3b6a29fee7676af5c4ebf7264ee
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- George A. Marcoulides
- Carl F. Falk
- Tenko Raykov
- Milica Miočević
- Victoria Savalei
- R. van de Schoot
- Kristopher J Preacher
- Mike W.-L. Cheung
Citations in the corpus
- Multiple-Group Analysis for Structural Equation Modeling With Dependent Samples (2015, unknown): “However, simulation results in Nevitt and Hancock (2004) and Savalei (2010) suggest that TAML tend to have smaller Type I errors than nominal levels.”
- The Relationship Between Root Mean Square Error of Approximation and Model Misspecification in Confirmatory Factor Analysis Models (2012, methodology): “Some simulation studies report the RMSEA for the created misspecifications to communicate their severity (Nevitt & Hancock, 2004; Savalei, 2010).”
- Small Sample Size Solutions (2020, methodology): “Variations of this correction exist, but the one most studied (Nevitt & Hancock, 2004; see also Fouladi, 2000; Savalei, 2010) is a simplified correction proposed by Bartlett (1950) in the context of exploratory factor analysis.”
- Robust Two-Stage Approach Outperforms Robust Full Information Maximum Likelihood With Incomplete Nonnormal Data (2014, methodology): “Most simulation studies do not summarize the results regarding error variance estimates, as these estimates are rarely used in practice; however, Savalei (2010a) did report on the performance of the error variances and found similarly poor performance.”
- Scale Reliability Evaluation Under Multiple Assumption Violations (2016, methodology): “When components are nonnormal and data are missing and NMAR, as discussed in the preceding section, use of MLR can be recommended (cf. Savalei, 2010; see also next section), after inclusion of auxiliary variables in the model fitting process (see last section on how one could go about finding such…”