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See Also

  • The R Project for Statistical Computing
    http://www.ecu.edu.au/faculties/computing-health-and-science/research-activity/research-support/research-consultant-statistician/related-content/see-also/the-r-project-for-statistical-computing
  • The R Project for Statistical Computing

Research Consultant (Statistician)

The Research Consultant (Statistician) for the Faculty of Computing, Health and Science provides advice on experimental design and data analysis for postgraduate research students (Masters, Ph.D. by research), postdoctoral researchers and staff. The research consultant also delivers workshops on experimental design and analysis planning, and using the R language for data analysis and graphics.

Consultations

If you require advice then please contact the research consultant either by phone or by email. Some questions are better addressed by phone, some by email and/or a meeting depending on the circumstance. If you communicate by email then please include the following information:

  • Your name;
  • School or research centre;
  • Degree of study;
  • Supervisor’s name; and
  • Specific items that require advice.

If you are unclear about experimental design or statistical approaches then be sure that you first have a clear understanding of your research question and appropriate hypotheses. If you have data that you wish to analyse then this must first be explored by you before seeking statistical advice. If you have completed some analyses and would like feedback on the approach or method then please include a detailed written explanation of the analysis in question.

Workshops

New Masters or PhD candidate are encouraged to enrol in Workshop I and II to help with experimental design and statistical planning, and to learn the R statistical computing language so that you can analyse data without relying on propriety software (R is free).

These workshops will begin November 2012:

  1. Experimental Design & Analysis Planning (One day): this workshop is intended for commencing postgraduate research students but is open to more advanced students, postdoctoral researchers and staff. The workshop covers approaches to experimental design, hypothesis formulation, statistical power, philosophical foundations of statistical modelling, critiques of frequentist statistics and best practice approaches to modelling techniques.
  2. Introduction to Data and Applied Modelling in R (Two days): This workshop provides an introduction to the R language, data manipulation, graphics, exploratory analysis techniques and statistical modelling (e.g. classical tests, analysis of variance). The material is relevant to all disciplines within the Faculty. The workshop is designed for people new to R. Techniques will be taught using real datasets and a session is focussed on ‘your data’. This workshop assumes that participants have solid understanding of statistics. Eligibility: Research Masters and Ph.D. students, Postdoctoral Fellows and Staff.
  3. **Applied modelling in R for Health and Medical Sciences (One day): Intended for researchers in fields such as sports, health and medical sciences this workshop will focus on more advanced statistical modelling techniques not covered in ‘Introduction to Data and Applied Modelling in R’: mixed effects models, longitudinal analysis, Cox proportional hazard modelling. Techniques will be taught using real datasets and a session is focussed on ‘your data’. Eligibility: Research Masters and Ph.D. students, Postdoctoral Fellows and Staff; Completion of ‘Introduction to Data and Applied Modelling in R’.
  4. **Applied modelling in R for Natural Sciences (One day): Intended for researchers in fields such as ecology, conservation biology and environmental sciences this workshop will focus on more advanced statistical modelling techniques not covered in ‘Introduction to Data and Applied Modelling in R’: multivariate analysis (clustering, multidimensional scaling, PERMANOVA), generalized mixed modelling and additive modelling. Techniques will be taught using real datasets and a session is focussed on ‘your data’. Eligibility: Research Masters and Ph.D. students, Postdoctoral Fellows and Staff; Completion of ‘Introduction to Data and Applied Modelling in R’.

**These workshops will be offered in 2013 and are open to those who have completed Workshop II, or have demonstrated experience using R at an intermediate level.

Workshop structures

Workshop I: Experimental design and analysis planning

  • Study design
  • Statistical planning
  • ‘Your study’

Workshop II: Introduction to Data and Applied Modelling in R

  • Data
  • Graphics
  • Descriptive statistics
  • Classical tests
  • General linear modelling
  • Generalized linear modelling
  • Generalized linear modelling
  • Factor analysis
  • Structural equation modelling

Workshop III: Applied modelling in R for Health and Medical Sciences

  • Generalized Estimating Equations
  • Mixed effects modelling
  • Proportional hazard models

Workshop IV: Applied modelling in R for Natural Sciences

  • Multivariate modelling
  • Mixed effects modelling
  • Generalized Additive Modelling

Addition information

Workshop II – Two Days

Workshops III and IV – One Day

  • Focus on problem solving in specific cases
  • Must have completed Workshop II

Contact

Research Consultant (Statistician)

To arrange an appointment, contact Dr Neil Collier.

Availability

Monday – Friday, Joondalup Campus

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