The Statistical Consulting Center (SCC) is a service and research unit of the Nova Southeastern University’s Health Professions Division, administratively located in the Office of Assessment, Evaluation and Faculty Development. Its staff provides statistical services to faculty, primary researchers, graduate students, and staff of the University.The SCC emphasizes an integrated, comprehensive statistical consulting service, covering all aspects of a quantitative research project ranging from the initial study design through to the presentation of the final research conclusions. More specifically, the SCC offers the following services:
Proposal Presentation and Study Design
SCC provides free consulting services on the statistical aspects of research grants and/or contract proposals. These include design and sampling methods, power and sample size calculations, and choice of statistical methodology.
Researchers are encouraged to make contact with the SCC early in the planning process of a quantitative study. Consulting may involve power calculations and help in determining sample sizes needed to achieve various degrees of precision for sample estimators. Statistical advice on matters of measurement or instrument design is also provided.
The SCC provides advice on database design and management, as well as on transferring datasets across platforms (e.g., Mac to PC) and across software (e.g., JMP to SPSS).
Choice of Statistical Methods
The SCC provides consulting services regarding the choice of appropriate and contemporary statistical methodology.
Use of Statistical Software
The SCC maintains a staff of consultants to help researchers implement statistical methodology using one or more of the standard statistical computer programs. At present these programs include SAS, SPSS, JMP, LISREL, AMOS, and Winsteps. Support is also available on the platform Windows or Macintosh.
Interpretation of Results
The SCC staff assists researchers in interpreting the results of various statistical methods and in determining what conclusions are statistically justifiable.
Presentation of Results
The SCC staff offers advice on presenting the results of statistical analyses, including graphical presentation.
For projects that require more statistical services than SCC can offer without charge, SCC and its staff are available to submit joint, collaborative research grant/contract proposals or to enter into subcontract agreements. Such agreements might cover any or all of the various statistical/quantitative aspects of the research.
The SCC presents workshops on statistical methods and statistical software. SAS and SPSS workshops, as well as a statistics review workshop, are offered each fall and winter. Other recent workshops include: Calculating Sample Size, Structural Equation Modeling, Missing Data, and Survival Analysis.
The SCC is located in the room 1522b of the Terry Building, in the Health Professions Division of Nova Southeastern University. Statistical consulting is available by phone, drop-in visit*, or email. More in-depth statistical consulting is available by appointment. Collaborative/joint research should be arranged with the SCC director, Patrick C. Hardigan.
*It is recommended to call first so that we can serve you better.
Patrick C. Hardigan, Ph.D.
Psychometrics, Structural Equation Modeling
Manuel J. Carvajal, Ph.D.
Professor of Economics and The Honors College, Florida International University
Econometrics, Experimental Design
L. Leanne Lai, Ph.D.
Associate Professor of Pharmacy Administration
Generalized Linear Models, Sample Size Estimation
Raymond L. Ownby, MD, Ph.D.
Professor and Chair of the Department of Psychiatry and Behavioral Medicine. He is the recipient of multiple grants from the National Institutes of Mental Health. He has specific expertise in research methods related to behavior and psychopharmacology, including longitudinal studies and the measurement of intervention effects over time. Statistical methods that he has interests in include latent variables modeling such as structural equation models, confirmatory factor analysis as well as mixed effects regression models.
Sarah Ransdell, Ph.D.
Associate Professor, College of Health Care Sciences
Multiple Regression, Structural Equation Modeling
Gabriel Suciu, Ph.D.
Associate Professor of Public Health
Clinical Trials, Biostatistics