Statistical Consulting

Details

Research Methods & Analysis:

This is an introduction to statistical inference for all branches of academic research. The course covers the following things.

  1. The foundations of statistics.
  2. Useful hypothesis tests.
  3. Hypothesis test p-values, size, power and confidence intervals.
  4. Statistical reasoning in modern research papers.
This course is delivered as a single 3 hour session. The course is lecture-style, during which student comments and questions are strongly encouraged. A full set of notes and several accessible journal articles are provided. Real data sets and analysis from the Monash Statistical Consulting Service will be provided as case studies.

The course ends with a guidance about learning more about statistics - for example, online courses, books, computer software and Monash courses.

Recommended or required prerequisites
No prior experience in statistics is needed.

Learning Outcomes

At the end of this course, you should:

  1. Understand sampling variance;
  2. Be able to articulate your empirical research in the language of formal hypotheses and statistical inferences;
  3. Appreciate the value of coherent data summaries, charts and tables;
  4. Know what further resources are available to you in order to augment your knowledge about statistics.

Details

Research Methods & Analysis: This is an introduction to the best computer software that you can use to manage and analyse data.

We will cover the advantages and the disadvantages of different interfaces, data visualization options and programming languages. Specifically, we will discuss Excel, R, SPSS, STATA and Graphpad Prism.

Tips on accessing and installing these programs will be provided (for example via open-source downloads, Monash’s MoVE system and free “cloud” settings).

A list of relevant training courses available to Monash students will also be provided.  Real data sets from the Monash Statistical Consulting Service will be used as case studies.

Using R and SPSS as examples, we will explore essential elements of working with data, including data set-up, analysis functions and basic coding. In addition, we will discuss general topics about data entry, data organization, and transferring data between software environments.

There are no requirements or prerequisites.

Learning Outcomes

At the end of this course, you will be able to:

  1. Select the right software for your research and data;
  2. Save time in your research by avoiding unnecessarily complex options, practices and habits;
  3. Know how to access further help and training on statistical software.

Details

Excellence in Research and Teaching - Research Methods & Analysis

This is an introduction to the basic statistical concepts and computer skills that are necessary for analysing data.  The focus is on how to make your empirical research useful and credible.  Real data sets from the Monash Statistical Consulting Service will be used as case studies.

There are 3 computer-lab style sessions, each of 3 hours duration.  During each session, everyone will be required to analyse data on their own computer.

Recommended or required prerequisites

There are no prerequisites, but taking Monash’s shorter “Statistics 101: Statistical Inference for Research” course would give you a head-start.

Please have a computer with R and RStudio installed before the first session starts.