ACSPRI Course Resources

This is an introductory course designed for the individual with limited or no previous experience with qualitative techniques of data collection and analysis.

This course is designed for participants who have undertaken a qualitative study in their work or study and would like to build their theoretical knowledge and applied skill base in qualitative research.

This is an intermediate, applied course covering a range of the most commonly used statistical procedures. It aims to provide participants with an ability to understand, run and interpret these procedures.This course will further enhance your ability to understand research based literature where these procedures were employed.

This course is intended for applied data analysts, including academics and postgraduate students, policy specialists and others. It will examine questions dealt with in public policy, the social sciences and industry, using real data. This includes surveys, and economics and public health data. The unit will help build participants’ ability to undertake rigorous statistical analysis, including means, confidence intervals and linear regression in R, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research. It provides the foundational skills needed for the ACSPRI course Advanced Statistical Analysis using R.

This course is a step by step interactive introduction for participants with no experience with R and RStudio. Notes will be provided and a set of exercises, covering a variety of data sets, will be run together during the lecture and also individually at the end of the day. The course content is particularly suited for those involved in research in the business, education, social and health sciences.

Participants will first be introduced to the foundations of R such as libraries, matrix manipulation, objects, and data frames. Participants will use R Markdown in RStudio to generate reports, tables and graphics to Word, PDF and HTML files. Once the foundations have been covered a variety of data management skills will introduced (e.g. filtering data, merging data). The production of descriptive statistics tables and graphs will be covered so that participants can easily use RStudio to explore data and produce reports.

The last session on each day will finish with a set of exercises for participants to run on their own and practice what they have learned that day. Please note that due to the short 2-day structure, there will not be any time set aside for analysing participant’s own data.

Designed for the applied users of R, this master-class will show you how to access spatial data from a number of sources, match this with geographic shape files, analyse spatial patterns, link these data to information from surveys, and create interactive maps to highlight important findings.

This course will be run over 2 days in three sessions per day:

• 9.30 am - 11.30 am - Session 1
• 12.00 pm - 1.30 pm - Session 2
• 2.30 pm - 4.30 pm - Session 3

This course is being held online via Zoom and run on Australian Eastern Standard Time (GMT +10)

This course is a step by step interactive introduction for participants with no experience with R and RStudio. Notes will be provided and a set of exercises, covering a variety of data sets, will be run together during the lecture and also individually at the end of the day. The course content is particularly suited for those involved in research in the business, education, social and health sciences.

Participants will first be introduced to the foundations of R such as libraries, matrix manipulation, objects, and data frames. Participants will use R Markdown in RStudio to generate reports, tables and graphics to Word, PDF and HTML files. Once the foundations have been covered a variety of data management skills will introduced (e.g. filtering data, merging data). The production of descriptive statistics tables and graphs will be covered so that participants can easily use RStudio to explore data and produce reports.

The last session on each day will finish with a set of exercises for participants to run on their own and practice what they have learned that day. Please note that due to the short 2-day structure, there will not be any time set aside for analysing participant’s own data.

Machine Learning techniques are becoming increasingly popular across areas of research from computer science to various disciplines of medicine. This branch of artificial intelligence relates to algorithms that learn from data based on specific tasks and performance measures. This course is an introductory applied course, with exercises in R and Python to run various ML algorithms.

Classification, prediction and model selection issues will be discussed. Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.

This course primarily focuses on the application of specific ML techniques rather than the complex mathematics behind the ML algorithms and discussion of some of the uses in ML techniques in publications will be discussed at the end of the course.

This course is designed as an introduction to mixed effects modelling. These models involve data arising from longitudinal studies or studies where the data exhibits some form of hierarchy, and sometimes referred to as multilevel modelling.

Mixed effects modelling is used when observations are not independent of each other (e.g., clustered data, repeated measures). This type of analysis is regularly used in such areas as educational research when studying the performance of students within schools and in medical research when investigating the outcomes over time following major trauma.

Mixed effects refer to the inclusion of both fixed effects (i.e., the variables that are constant across individuals) and random effects (i.e., account for variability among subjects around the relationships captured by the fixed effects).

This course will be discussing the linear mixed effects models in which the outcome of interest is continuous. Discussion of some of the uses of mixed effects models in publications will be discussed at the end of the course.

This master-class provides a foundation for those wishing to utilise structural equation modelling (SEM) to explore and test complex relationships.

The course is designed as an applied introduction to SEM using Stata, aimed at providing participants with a sound understanding of when to use SEM and how to assess and report their models.

This course is intended for applied data analysts, including academics and postgraduate students, policy specialists and others. It will examine questions dealt with in public policy, the social sciences and industry, using real data. This includes surveys, and economics and public health data. The unit will help build participants’ ability to undertake rigorous statistical analysis, including means, confidence intervals and linear regression in R, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research. It provides the foundational skills needed for the ACSPRI two-day masterclass Spatial Analysis in R: Online, and the week-long course Advanced Statistical Analysis using R.

This is an introductory unit in statistical methods with the emphasis on statistical techniques applicable to the social sciences, although these introductory techniques are also relevant to the health sciences.

This course is intended for applied data analysts, including academics and postgraduate students, policy specialists and others. It will examine questions dealt with in public policy, the social sciences and industry, using real data. This includes surveys, and economics and public health data. The unit will help build participants’ ability to undertake rigorous statistical analysis, including means, confidence intervals and linear regression in R, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research. It provides the foundational skills needed for the ACSPRI two-day masterclass Spatial Analysis in R: Online, and the week-long course Advanced Statistical Analysis using R.

This course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool.

This is an introductory unit in statistical methods with the emphasis on statistical techniques applicable to the social sciences, although these introductory techniques are also relevant to the health sciences.

This course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool.

This is an introductory unit in statistical methods with the emphasis on statistical techniques applicable to the social sciences, although these introductory techniques are also relevant to the health sciences.

This course is intended for applied data analysts, including academics and postgraduate students, policy specialists and others. It will examine questions dealt with in public policy, the social sciences and industry, using real data. This includes surveys, and economics and public health data. The unit will help build participants’ ability to undertake rigorous statistical analysis, including means, confidence intervals and linear regression in R, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research. It provides the foundational skills needed for the ACSPRI two-day masterclass Spatial Analysis in R: Online, and the week-long course Advanced Statistical Analysis using R.

This course is intended for applied data analysts, including academics and postgraduate students, policy specialists and others. It will examine questions dealt with in public policy, the social sciences and industry, using real data. This includes surveys, and economics and public health data. The unit will help build participants’ ability to undertake rigorous statistical analysis, including means, confidence intervals and linear regression in R, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research. It provides the foundational skills needed for the ACSPRI two-day masterclass Spatial Analysis in R: Online, and the week-long course Advanced Statistical Analysis using R.

This course is a step by step interactive introduction for participants with no experience with R and RStudio. Notes will be provided and a set of exercises, covering a variety of data sets, will be run together during the lecture and also individually at the end of the day. The course content is particularly suited for those involved in research in the business, education, social and health sciences.

Participants will first be introduced to the foundations of R such as libraries, matrix manipulation, objects, and data frames. Participants will use R Markdown in RStudio to generate reports, tables and graphics to Word, PDF and HTML files. Once the foundations have been covered a variety of data management skills will introduced (e.g. filtering data, merging data). The production of descriptive statistics tables and graphs will be covered so that participants can easily use RStudio to explore data and produce reports.

The last session on each day will finish with a set of exercises for participants to run on their own and practice what they have learned that day. Please note that due to the short 2-day structure, there will not be any time set aside for analysing participant’s own data.

This is an introductory unit in statistical methods with the emphasis on statistical techniques applicable to the social sciences, although these introductory techniques are also relevant to the health sciences.

This course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool.

This is an introductory unit in statistical methods with the emphasis on statistical techniques applicable to the social sciences, although these introductory techniques are also relevant to the health sciences.

This course is intended for applied data analysts, including academics and postgraduate students, policy specialists and others. It will examine questions dealt with in public policy, the social sciences and industry, using real data. This includes surveys, and economics and public health data. The unit will help build participants’ ability to undertake rigorous statistical analysis, including means, confidence intervals and linear regression in R, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research. It provides the foundational skills needed for the ACSPRI two-day masterclass Spatial Analysis in R: Online, and the week-long course Advanced Statistical Analysis using R.

This course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool.

This is an introductory unit in statistical methods with the emphasis on statistical techniques applicable to the social sciences, although these introductory techniques are also relevant to the health sciences.