The QRCC Statistical Consultant team is passionate about statistics and research design and excitement about the idea of answering a broad range of advanced statistical questions from across disciplines.
Deepali Advani specializes in econometrics, spatial analysis, ArcGIS, and fundamental statistical tests using Excel, SPSS, and Stata.
She is a fourth-year doctoral student in the Economics Department at the Graduate Center. She has taught several undergraduate statistics courses. She has worked in both in academic research and industry, using both qualitative and quantitative data and designing surveys. She has experience working with data on consumer expenditure, geospatial data, transportation, and SQL datasets.
Safa Shehab specializes in linear models, mixed-effects models and clinical research designs using R and SPSS.
She is a fifth-year doctoral student in the Clinical Psychology program at Queens College. She is interested in Clinical Neuropsychology. Safa has been involved in research in academic and medical settings where she worked on studies including clinical trials, registries, and epidemiological surveys. Her doctoral dissertation investigates the effects of a smartphone-based breathing intervention on stress, mood, and cognition in young adults.
Christen N Madsen II, Ph.D., specializes in mixed-effects modeling, experimental design, and R programming.
He has worked in both academic research labs and industry using behavioral, sociological, corpus, survey, neuroelectrical, and ocular data. He has worked as a statistical consultant for over 4 years.
Kalina Gjicali, Ph.D., specializes in general(ized) linear models (GLM) and structural equation modeling (SEM) using SPSS, Mplus, and HLM software.
Kalina is an alumna from Educational Psychology at The Graduate Center. She has applied research experience in longitudinal data analyses, program evaluation utilizing randomized control trial (RCT) methods, psychometrics, and large-scale assessments. Her multi-study dissertation, titled Mathematics Attitudes and Mathematics Performance: Novel Approaches towards Noncognitive Educational Measurement, Applications to Large-scale Assessment Data, and Examinations of Multigroup Invariance, focused on measuring and examining the attitude-achievement relation of high school students in the U.S.