Statistical tools for research at Michigan State University: state of the art (2013)

1. Statistical tools for research at Michigan State University: state of the art (2013) Brian A. Maurer, Director Steven Pierce, Associate Director Center of Statistical Training and Consulting Michigan State University

2. Outline • Introduction to CSTAT • Brief description of the process of doing research • Identification of software tools at MSU that can be used at each stage in the research process

3. CSTAT is a professional service & research unit offering: • Training workshops • Statistical consulting services & research partnerships We aim to increase the quality of our clients’ research.

4. Consulting Team Brian A. Maurer, PhD, Director Steven J. Pierce, PhD, Assoc. Director Sarah Hession, PhD, Asst. Director/Sr. Statistician Frank Lawrence, PhD, Sr. Statistician Dhruv Sharma, PhD, Sr. Biostatistician Current search for Sr. Biostatistician (target date for hire is Jan 2014). 4-6 doctoral student consultants (MA/MS) We can also refer you to other MSU specialists.

5. Training Workshops • Half-day sessions on: • Statistical software • Specific statistical methods or topics • Day long in depth workshops (summer) • Hands-on practice in computer labs • Taught by MSU faculty experts

6. Consulting Services • Support research (not coursework) • Interactive, one-on-one approach • Customized to your project Usefulat any stage—from study planning all the way to responding to peer reviewers!

7. Levels of Consulting Service

8. Register for Consulting or Workshops at www.cstat.msu.edu

9. The research process • Study design • Data collection • Data processing • Data analysis and statistical modeling • Graphics and report writing

10. Study design • Importance of research objectives and aims • Detailed plan linking data to aims via appropriate statistical methods • Experimental and sampling designs • Randomization • Statistical models • Sample size calculations

11. Data collection • Determined largely by the specific field of inquiry • Automation and accumulation of errors • Accuracy and repeatability

12. Data processing • Limit the number of “human” steps • Data cleaning – ensuring accuracy • Data transformation and manipulation • Data storage and security • Documentation and metadata

13. Data analysis and statistical modeling

14. Data analysis and statistical modeling • A wide variety of techniques are available • Often data dictates modeling strategies that depart substantially from standard approaches

15. Data analysis and statistical modeling (cont.) • Specialized techniques often require collaboration and team work • Examples of such techniques include Bayesian analysis, structural equation modeling, etc.

16. Graphics and report writing • Need informative and accurate graphics • Publication standards vary widely among journals • Tailor graphics to audience and mode of presentation

17. Documenting the research process • Reproducible scripts versus point and click operations • REDCap (Research Electronic Data Capture)

18. Multipurpose software packages • Flexibility • Licensing options • Availability in computer labs • Overlap among packages

19. Multipurpose software packages available in MSU computer labs • SAS* • SPSS* • R (open source shareware)* • STATA* • Matlab* • SYSTAT • Mathematica • Statmost * CSTAT workshop offered

20. Study design software • Gpower – shareware that has an array of basic options for power analysis • PASS – has a wide variety of study designs for which power analyses are available • Optimal design plus – shareware specifically for hierarchical study designs

21. Specialized statistical software • MPLUS – structural equation models (SEM) • LISREL – another SEM package • HLM – hierarchical linear models • WinBUGS, OpenBUGS, JAGS – Bayesian analysis packages using Monte Carlo methods • ArcGIS – spatial data analysis

22. Questions?