JULIA HUA FANG, PhD
Research Assistant Professor, Department of Psychology
Project Director, Office of Research
102 501 Building
University of Nebraska-Lincoln
Lincoln, Nebraska 68588-0206
(402) 472-0062

Biography

Before coming to the U.S. for her graduate study, Dr. Fang was an information development engineer in Huawei Technology Co. Ltd., China. She received her Master in Financial Economics from the Department of Economics and a PhD in Statistics, Research and Evaluation program from the School of Education, Ohio University in 2006. Her doctoral minors are in statistical computing, measurement and research design, accounting and finance. She is currently a Research Assistant Professor in the Department of Psychology and Project Director in the Office of Research at UNL.  

Dr. Fang's educational training and statistical consulting experience have given her a comparative advantage in solving multidisciplinary research problems. She has served as a statistical consultant in education, psychology, finance, economics, health care and engineering areas and participated in large-scale and statewide projects. She won a paper award at the 2006 Joint Research Conference on Statistics in quality industry and technology. Dr. Fang has expertise in multilevel/SEM modeling in growth studies and has been working on the power analysis of multilevel models and working with other professors in genetic algorithms for nonlinear classification. Working with Dr. Kimberly Andrews Espy, she is using advanced statistical modeling and clustering methods for complex developmental designs. Her current interests include growth mixture modeling, missing data analysis, statistical genetics, propensity score modeling, nonlinear classification and survey analysis.

 

Recent Publications

Journal Articles & Proceedings

Fang, H., Brooks, G. P., Rizzo, M. L., Espy, K. A., & Barcikowski, R. S. (in press). A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models in longitudinal data analysis. Journal of Modern Applied Statistical Methods.

Fang, H., Brooks, G. P., Rizzo, M. L., Espy, K. A., & Barcikowski, R. S. (in press). Power of models in longitudinal study: Findings from a full-crossed simulation design. Journal of Experimental Education.

Fang, H., Espy, K. A., Rizzo, M. L., & Wang, H. (in press). Identifying patterns of longitudinal data set with meaningful inflated missing values: A case study combining data mining and statistical techniques. 2007 Proceedings of the Joint Statistical Meetings, American Statistical Association, Section on Statistics in Epidemiology [CD-ROM], Alexandria, VA: American Statistical Association.

Wang, H., Fang, H., Sharif, H., & Wang, Z. (in press). Nonlinear classification by genetic algorithm with signed fuzzy measure. Proc. IEEE-FUZZ 2007.

Wang, H., Fang, H., Espy, K. A., Peng, D., & Sharif, H. (2007). A Bayesian multilevel modeling approach for data query in wireless sensor networks. Springer Lecture Notes in Computer Science (LNCS),Y. Shi et al. (Eds.): Part III, LNCS 4489, pp. 859–866.

Fang, H. (2006).  %hlmdata and %hlmrmpower: Traditional repeated measures vs. HLM for multilevel longitudinal data analysis - power and type I error rate comparison. Proceedings of the Thirty-First Annual SAS Users Group Conference, SAS Institute Inc., Cary, NC, 138-31.

Fang, H., Brooks, G. P., Rizzo, M. L., & Barcikowski, R. S. (2006). An empirical power analysis of hierarchical multivariate linear model under three covariance structures in longitudinal data analysis. 2006 Proceedings of the American Statistical Association, Section on General Methodology [CD-ROM], Alexandria, VA: American Statistical Association: pp.1187 - 1189.

Fang, H., Brooks, G. P., Rizzo, M. L., & Barcikowski, R. S. (2006). An empirical power analysis of multilevel linear model under three covariance structures in longitudinal data analysis. 2006 Proceedings of the American Statitical Association,Section on Quality and Productivity [CD-ROM], Alexandria, VA: American Statistical Association: pp.4399 - 4406.

 

Research Projects

Prenatal Tobacco Exposure: Perinatal & Genetic Risks
NIH/NIDA
Role: Co-PI