Source: http://www2.psych.ubc.ca/~vsavalei/
Timestamp: 2019-04-25 00:04:19+00:00

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This is a subset of the Holzinger & Swineford (1939) dataset. The original dataset contained 26 cognitive ability measures in junior high students. The subset used here contains 9 cognitive ability measures, hypothesized to load on three different factors: Spatial, Verbal, and Memory (3 indicators per factor). Missing data have artificially been created by deleting observations on the last two variables for 30 of the 145 cases. The missing values have been deleted conditioning on values of other variables, creating MAR data. This dataset is analyzed in the sample R code below.
R code:� Transformation #1, Transformation #2, Transformation #3.
Direct EQS code (does not require any R code to be run first): Transformation #1, Transformation #2; Transformation #3� is not yet available in EQS.
Savalei, V. (in press). The relationship between RMSEA and model misspecification in CFA models. Educational and Psychological Measurement.
R code:� Will be available shortly.
My research interests lie primarily in the field of structural equation modeling (SEM), and include model evaluation and testing and approaches to dealing with difficult kinds of data such as incomplete, nonnormal, and categorical data.
Brosseau-Liard, P., Savalei, V., and Li, L. (in press). An investigation of the sample performance of two non-normality corrections for RMSEA. Multivariate Behavioral Research.
Savalei, V. (in press). Understanding Robust Corrections in Structural Equation Modeling. Structural Equation Modeling.
Savalei, V., & Falk, C. (in press). Robust two-stage approach outperforms robust FIML with incomplete nonnormal data. Structural Equation Modeling.
Savalei, V., & Rhemtulla, M. (2012). On obtaining estimates of the fraction of missing information from full information maximum likelihood. Structural Equation Modeling, 19, 477-494.
Falk, C., & Savalei, V. (2011). The relationship between unstandardized and standardized alpha, true reliability, and the underlying measurement model. Journal of Personality Assessment, 93, 445-453.
Savalei, V. (2011). What to do about zero frequency cells when estimating polychoric correlations? Structural Equation Modeling, 18, 253-273.
Savalei, V. (2010). Expected vs. observed information in SEM with incomplete normal and nonnormal data. Psychological Methods, 15, 352-367.
Savalei, V. (2010). Small sample statistics for incomplete nonnormal data: extensions of complete data formulae and a Monte Carlo comparison. Structural Equation Modeling, 17, 245�268.
Savalei, V., & Bentler, P. M. (2009). A two-stage ML approach to missing data: theory and application to auxiliary variables. Structural Equation Modeling, 16, 477-497.
Savalei, V., & Kolenikov, S. (2008). Constrained vs. unconstrained estimation in structural equation modeling. Psychological Methods, 13, 150-170.
Savalei, V. (2006). Logistic approximation to the normal: The KL rationale. Psychometrika, 71, 763-767.
Savalei, V., & Bentler, P.M. (2005). A statistically justified pairwise ML method for incomplete nonnormal data: A comparison with direct ML and pairwise ADF. Structural Equation Modeling, 12, 183-214.
Sears, D.O., & Savalei, V. (2006). The political color line in America: many �peoples of color� or black exceptionalism? Political Psychology, 27, 895-924.

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