**Victoria Savalei,
Ph.D.**

Assistant
Professor

Department
of Psychology

University
of British Columbia

**Class Websites:**

Psyc 546Y Structural
Equation Modeling

Psyc 546H Psychological
Measurement

Psyc 546X Applied
Multivariate Analysis

**Computer Code and
Supplementary Materials for Research Papers: **

Savalei, V., & Yuan,
K.-H. (2009). On the
model-based bootstrap with missing data: obtaining a p-value for a test of
exact fit. *Multivariate Behavioral
Research, 44,* 741-763.

**Sample dataset:** holz-misMAR.dat

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.

**Supporting EQS code:** EQS code for
obtaining the .ets file, EQS code for
analyzing bootstrapped data

**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: **

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.

**Selected Papers:**

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). The relationship between RMSEA
and model misspecification in CFA models. *Educational
and Psychological Measurement*.

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*.

Rhemtulla, M., Brosseau-Liard, P., & Savalei, V. (2012).
How many categories is enough to treat data as continuous? A comparison of
robust continuous and categorical SEM estimation methods under a range of
non-ideal situations. *Psychological
Methods.* Advance online publication. doi: 10.1037/a0029315

Savalei,
V., & Rhemtulla, M. (2012). The performance of
robust test statistics with categorical data. *British Journal of Mathematical and Statistical Psychology.* Advance
online publication. doi: 10.1111/j.2044-8317.2012.02049.x

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.

Biesanz, J. C., Falk, C., & Savalei, V. (2010). Inferences
and estimation for indirect effects: Missing data, non-normality, and
sample size. *Multivariate Behavioral Research, 45*, 661-701.

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., & Yuan, K.-H. (2009). On the model-based bootstrap with missing data: obtaining a p-value for
a test of exact fit. *Multivariate
Behavioral Research, 44,* 741-763.

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. (2008). Is the ML chi-square ever robust to nonnormality? A
cautionary note with missing data. *Structural
Equation Modeling, 15*, 1-22*. *

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 * Political Psychology, 27*, 895-924*. *

**Contact:**

Victoria
Savalei

University
of British Columbia

Department
of Psychology

2136 West
Mall

Vancouver,
BC V6T 1Z4

Canada

Phone:
(604) 822-2296

Email: vsavalei at ubc dot ca