Online Short Courses

courses

 

Over the course of the year Stats Central teaches short courses aimed at researchers across all disciplines. Offerings in 2022 include:

  • Sample Size and Power Calculations: February 8 and 9 , 9.00am-1.00pm each day

  • Introduction to R: Offered twice per year

    • 1st Schedule: April and May - Dates: TBA

    • 2nd Schedule: August - Dates: TBA

  • Introductory Statistics for Researchers using R: Offered twice per year

    • 1st Schedule: May - Dates: TBA

    • 2nd Schedule: August - Dates: TBA

  • Introductory Statistics for Researchers using SPSS: May - Dates: TBA

  • Introduction to Regression Modelling in R: August/September - Dates: TBA

*We are offering online courses with 20% discounted rates.*

The courses will be delivered remotely using online teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the courses.

 

2022 - Sample Size and Power Calculations

8-9 February

Course Overview

Power calculations and sample size determination are essential parts of planning a scientific study. In this online course (run over two half-days) we will introduce the basic principles of precision-based and power-based sample size calculations. Using practical examples, we will demonstrate how to perform sample size calculations for common designs in single-sample studies and for comparing groups, as well as discussing practical issues such as multiple comparisons. No knowledge of statistical software will be required.

Course Outline

This course will cover topics including:

  • Introduction and motivation

  • Basic principles of power and sample size calculations

  • Precision-based sample size calculations

  • Power-based sample size calculations

  • Complex power calculations

  • Practical considerations & other issues

Presenter and Expertise: Mark Donoghoe, Statistical Consultant

Course Requirement: You will need to use a computer during the course.

Date: Tuesday 8 and Wednesday 9 February 2022

Duration: 9.00am to 1.00pm - Each day

Location: Online

You will receive a certificate of completion for the course.

2021 - Introduction to Regression Modelling in R

30 August - 3 September

Course Overview

The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models. Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables you to take a systematic approach to key steps in analysis (like assumption checking) and to extend your skills to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models).

This short course, taking place over five half-days, is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools - you should know about the t-test, linear regression, analysis of variance and know something about orthogonal and nested designs. If you have not used R before, we strongly recommend you learn basic features of R and how to use the RStudio interface to R before this course.

An outline of topics included in this regression course is below.

Course Outline

  • Linear regression — Simple linear regression, including assumptions, influential observations and inference. Equivalence of two-sample t-test and linear regression.
  • Linear models with multiple predictor variables — Multiple regression. Analysis of variance (ANOVA), including multiple comparisons
  • More linear models — Paired and blocked designs. ANCOVA. Factorial experiments. Interactions in regression.
  • Model selection — Cross-validation. Information criteria (AIC, BIC). Penalised estimation (LASSO).
  • Mixed effects models — Random effects. Linear mixed effects models. Inference for mixed models, including likelihood ratio tests, parametric bootstrap, hypothesis tests, confidence intervals. Correlated random effects.
  • Extensions — Spline smoothers, including diagnostics and interactions. Generalised linear models (GLMs).

Course Requirements: You will need to use a computer during the course. Some familiarity with introductory statistics and R will be assumed.

 

Date: Monday 30 August to Friday 3 September 2021 (five morning sessions)

Duration: 9.00am - 12.30pm each day

Location: Online

2021 - Introduction to R

August 10-11 (course 1) and 17-18 (course 2)

Course Overview

R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R. It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers.

This course will be held over two half-days and will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is self-paced and focused on developing practical skills.

Course Outline

This course will cover topics including:

  • Basics of interacting with R – calculations, saving variables so you can reuse them, data types and structures, organising R code in scripts

  • Tidyverse – a basic introduction to tidy R code

  • Data – reading in and organising data (from spreadsheets) with dplyr

  • Plotting – make beautiful figures with ggplot

Course Requirement: You will need a computer with administrator access (to install R and RStudio software before attending the course).

 

*This is a popular course so we are running the course twice. Tickets are limited.*

Please choose ONE course ONLY and attend the course that you are enrolled.

Course 1: Tuesday 10 and Wednesday 11 August, 9.30am -1.00pm each day

OR

Course 2: Tuesday 17 and Wednesday 18 August, 9.30am -1.00pm each day

Location: Online

You will receive a certificate of completion for the course.

2021 - Introductory Statistics for Researchers Using R

23-26 August

Important Notes - please read

1. Remote course

The course will be delivered remotely using online teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the practical periods.

2. Participants must have basic R skills prior to course

This is not an introductory course in using the statistical software package, R. Basic R coding will not be taught. To do this course successfully, you must have basic proficiency in using the R package. All examples and exercises used in this course are done using R. We want to ensure everyone is able to follow the course, and no participant is disappointed.

If you have basic R skills, that's excellent, please complete this quick task HERE. Once you have completed this and emailed your results you will be given a code to allow you to register for Introductory Statistics for Researchers.

If you do not have basic R skills, but want to do the Introductory Statistics for Researchers course, you can enrol in our Introduction to R course that runs twice ahead of this course, August 10-11 (course 1) and 17-8 (course 2), register HERE. Please email stats.central@unsw.edu.au once you have registered, and you will be given a code to allow you to register for Introductory Statistics for Researchers.

Course Overview

This course is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher. The theory behind the statistical procedures outlined in the course will, in general, not be discussed.

A range of statistical analyses will be discussed in the course, as described in the course outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in R. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work in the course.

Course Outline

Revision

  • Study design - experimental and observational studies, sample size
  • Descriptive statistics – mean, mode, standard deviation, inter-quartile range, correlation
  • Data visualisation - boxplot, histogram, scatterplot

Introduction to statistical inference

  • Uncertainty, p-values, significance/evidence
  • T-test (comparing two groups)
  • Checking model assumptions

Analysis of continuous responses with linear models

  • Linear regression
  • ANOVA / ANCOVA

Analysis of categorical responses

  • Chi-square tests
  • Logistic regression

Course Requirement: You will need to use a computer during the course. You will also need administrator rights to install software needed for the course onto your computer. Basic proficiency in using the R statistical package is essential.

 

Date: Monday 23 to Thursday 26 2021

Duration: 9.00am to 1.00pm each day

Location: Online

You will receive a certificate of completion for the course.

2021 - Introductory Statistics for Researchers Using SPSS

17 - 20 May

New Course - First Time Offer

Important Notes - please read

1. Remote course

The course will be delivered remotely using online teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the practical periods.

2. Access to SPSS software

You will need access to the SPSS software package to complete this course successfully. If you have a UNSW zID, you can access SPSS through myAccess. If you do not have a UNSW zID, we cannot provide access to SPSS.

3. Participants must have basic SPSS skills prior to course

This is not an introductory course in using the statistical software package, SPSS. Basic SPSS use (including syntax) will not be taught. To do this course successfully, you must have basic proficiency in using the SPSS package, such as importing and opening data, obtaining basic descriptive statistics, and what the syntax window is and how to use it. All examples and exercises used in this course are done using SPSS. We want to ensure everyone is able to follow the course and no participant is disappointed.

You should be able to complete this task HERE before attending the course. If you need guidance, here are a couple of websites that are great places to learn a bit about SPSS:

SPSS Tutorials

UCLA Institute for Digital Education and Research SPSS pages

Course Overview

In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"

How does one decide which statistical procedure is the most appropriate? What do all the sections of the output mean?

This course is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher. The theory behind the statistical procedures outlined in the course will, in general, not be discussed.

Statistical analyses require specialised software to perform calculations. In this course, we use the statistical program SPSS, although researchers may have another statistical package available to them.

A range of statistical analyses will be discussed in the course, as described in the course outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in SPSS. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work in the course. You will need basic SPSS proficiency to carry out the practical work; we will teach only the additional SPSS commands needed for these analyses.

Do you need to have previous experience using the program SPSS?

It is essential to have basic proficiency in using the SPSS statistical package. Please read "Important Notes" above.

In this course, the statistical software package used is SPSS exclusively. We do not use or demonstrate any other package.

Course Outline

Summarising and Graphing Data

Ways of presenting data (histograms, boxplots), measures of centre and spread, analysing tables, correlation, and confidence intervals.

Comparing Groups

Hypothesis testing concepts - power, significance, P-value. Comparing two groups (t and Wilcoxon tests).

Comparing many groups - with one treatment factor (one-way ANOVA or Kruskal-Wallis test; multiple comparison tests).

Finding Relationships

Correlation, predicting relationships (regression - simple).

Other topics covered

Non-normal data - transformations and non-parametric tests. If time is available, we will briefly discuss sample size and power.

Course Requirement: You will need to use a computer during the course. You will also need access to the SPSS software package (see note above on access to SPSS). Basic proficiency in using the SPSS statistical package is essential.

 

Date: Monday 17 to Thursday 20 May, 2021

Duration: 9.00 am to 1.00 pm each day

Location: Online

 

2020 - Introduction to Python for Data Science

1 and 2 September

Course Overview

Python is a widely used programming language to manipulate, analyze, and visualize data. It is one of the most popular languages for Data Science, especially when dealing with complex, uncurated or text datasets.

This course assumes that you have never used Python before, but you have some basic programming knowledge. You will learn how to obtain and install Python, which is open-source software, and Jupyter Notebook, which is an interactive computational environment, in which you can combine code execution, rich text, mathematics, plots and rich media.

This two half-days introduction to Python will cover some useful features of Python for data science. It will discuss various online resources available to further develop your data science skills using Python.

Course Outline

This course will cover topics including:

• Python overview

• Jupyter Notebook

• Basic Python programming

• Typical process of data science

• Techniques to manipulate and analyze datasets

• Result visualization

• Selected statistical analysis and/or machine learning examples

Presenter and Expertise: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)

Course Requirements: You will need to use a computer during the course.

 

Date: Tuesday 1st to Wednesday 2nd September 2020 (two morning sessions)

Duration: 10.00am - 1.00pm each day

Location: Online

 

2020 - Text Analytics in Python (Advanced)

8 and 9 September

Course Overview

More than 70% of the data on the internet is unstructured. Among them, text is the most common form that appears in almost all data sources. For example, text data such as emails, online reviews, tweets, news and reports hold valuable information and insight for most research and applications. Text analytics, usually involving techniques from text mining or natural language processing (NLP), can automatically uncover patterns and extract meaning/context from these unstructured texts.

This course assumes that you have basic Python programming knowledge, or have previously attended "Introduction to Python for Data Science" from Stats Central. This course will provide you the foundation to process and analyze text.

In this course, we will cover some useful Python features and libraries for text processing and analysis. We will touch on some advanced topics such as sentiment analysis, text classification, and/or topic extraction.

 

Course Outline

This course will cover topics including:

• Jupyter Notebook

• Basic text operations in Python

• Text analytics and NLP

• Tokenization, stopwords, lexicon normalization, POS tagging

• Sentiment analysis and text classification

Presenter and Expertise: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)

Course Requirements: You will need to use a computer during the course.

 

Date: Tuesday 8 to Wednesday 9 September 2020 (two morning sessions)

Duration: 10.00am - 1.00pm each day

Location: Online

 

2020 - Study Design

19 May

Course Overview

Good study design is crucial for answering your research questions. No amount of post processing or statistical expertise can compensate for poor or inadequate study design. In this course you will lean the basic concepts of study design including how to;

  • Randomize, so that your sample can be used make inferences about the real world (the population).

  • Implement appropriate controls and manipulation to infer causation.

  • Determine adequate sample size.

We will then move on to advanced concepts, which will focus on how blocking and stratification can reduce variability and improve power (the ability to answer your research question) using a smaller sample size (hence less resources).

Practical Component

  • How to randomise – Random allocation of subjects to treatments and random sampling

  • Simple power analysis – How much data do we need to answer the question of interest?

  • We will use free online tools and the software packages, either Excel and G*Power; or R (optional).

 

Course Requirement: You will need a computer with Excel (or equivalent) and G*Power installed and access to the course.

Duration: 9.00am to 1.00pm

Location: Online