« Data and Analysis


No dates are currently scheduled.

Description 

The aim of this micro-credential is to equip enrollees with the skills and knowledge to engage with biosocial research either directly as a researcher, or as a policy maker critically engaging with the most recent research.

Topics 

  1. Biosocial research frameworks
  2. Designing biosocial research
  3. Data quality considerations
  4. Missing data in biosocial research
  5. Interpreting biosocial research findings

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Specify a biosocial research question and conceptualise it using biosocial frameworks
  2. Communicate and critique existing biosocial research in a rigorous manner
  3. Understand the assumptions, strengths and limitations of biosocial research data
  4. Understand approaches to missing data in biosocial research
  5. Design or critique a biosocial data research study

Indicative assessment 

Assignment 1 - Introductions and identification of research question (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Biosocial research design (1,500 words, 80% of final mark) LO: 3, 4, 5

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential is undertaken as a stand-alone course.

Details 

Course Code: DATA03

Workload: 22 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 15 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Associate Professor Naomi Priest


This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

The aim of this micro-credential is to equip students with the skills and knowledge to analyse existing data to create new social science and policy insights.

Topics 

  1. The concept and practice of statistical hypothesis tests
  2. Descriptive statistics and distributional analysis
  3. Introducing multivariate analysis – linear regression
  4. Extending multivariate analysis – non-linear regression
  5. The power and practice of longitudinal data analysis

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Explain the key concepts of data analysis
  2. Outline the strengths and weaknesses of existing datasets from an analysis perspective
  3. Outline a hypothesis test and explain the use of null and alternative hypotheses, as well as one and two-sided tests
  4. Identify the appropriate analytical technique for different types of variables
  5. Discuss some of the main assumptions underlying different techniques
  6. Design or critique an analysis plan

Indicative assessment 

Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential is undertaken as a stand-alone course.

Details 

Course Code: DATA05

Workload: 21 hours 

  • Contact hours: 8 hours
  • Individual study and assessment: 13 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Professor Nicholas Biddle

 

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

This micro-credential builds upon data manipulation and visualisation skills to cover statistical principles of good study design and data analysis using statistical modelling in the context of clinical research. Enrollees will gain experience exploring patterns in data and inferring relationships between variables. Common misuses of data analysis, including “p-hacking” and overfitting, will be discussed in depth. The course will emphasise the importance of reproducible analyses, and enrolees will learn good practice through the creation of a reproducible analysis workflow using Rmarkdown.

Learning outcomes 

Upon successful completion, enrolees will have the knowledge and skills to:

  1. Explore datasets within the R environment
  2. Apply statistical models to infer treatment effects in a randomised controlled clinical trial
  3. Interpret and presents the results of data analyses
  4. Apply principles of good study design in clinical research
  5. Build a data analysis workflow

Indicative assessment 

Enrolees will critically assess a published work for which the data has been made available. They will import the accompanying data into R, and demonstrate they can interpret the main findings, and reproduce aspects of the analysis (approximately 1000 words total).

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as a stand-alone course or as part of a stack including:

Escape from Excel: Data Wrangling and Visualisation in the Health and Environmental Sciences using R

Details 

Course Code: DATA08

Workload: 21 hours 

  • Contact hours: 7 hours: In person sessions: 9, 16, 23 September 2 - 4.30pm
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 9

Contact: Dr Terry Neeman and Professor Eric Stone


This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

The aims of this micro-credential is to equip enrollees with the skills and knowledge to extend their survey data analysis experience beyond that which is taught in Data analysis and interpretation, with a focus on longitudinal data analysis.

Topics 

  1. Differencing
  2. Fixed and random effects data analysis
  3. Controlling for sample attrition in longitudinal data analysis

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Explain the key concepts of longitudinal data analysis
  2. Outline the strengths and weaknesses of existing longitudinal datasets from an analysis perspective
  3. Identify the appropriate analytical technique for longitudinal data analysis
  4. Discuss some of the main assumptions underlying different techniques
  5. Design or critique an analysis plan

Indicative assessment 

Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6

Assumed knowledge 

Completion of ANU Micro-credential Using Longitudinal Studies to Inform Public Policy and Data Analysis and Interpretation (or equivalent).

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as part of a stack by completing Using Longitudinal Studies to Inform Public Policy and Data Analysis and Interpretation.

Details 

Course Code: DATA25

Workload: 21 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Professor Nicholas Biddle

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

This micro-credential provides a broad overview of the theory of qualitative research, and examines the basic skills involved in the application of these methods in social research, demography and population studies. Qualitative methods are defined, and their uses and limitations explored. Qualitative methods are compared with quantitative methods, and approaches to the integration of qualitative data are reviewed.

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Discuss the basic concepts of qualitative data collection;
  2. Critique existing qualitative research;
  3. Design a simple qualitative research project;
  4. Know the basic concepts for more advanced qualitative research and where to obtain further information

Indicative assessment 

Assignment 1 – Introductions and identification of research questions (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Research design (1,500 words, 80% of final mark) LO: 3, 4

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as a stand-alone course.

Details 

Course Code: DATA29

Workload: 21 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Professor Nicholas Biddle

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

Inference from social and population surveys rely on a sufficiently large sample of respondents that are representative of the population of interest. Some biases in sample selection can be adjusted for after survey completion (using weights), but biases based on unobserved characteristics cannot be. Regardless, the more representative the sample, the more likely it is that researchers will be able to accurately make inference for the population of interest. Larger sample sizes have less uncertainty around their estimates, but this comes at the costs of interviewer/recruitment time and respondent burden. There are a number of ways to select samples including simple random samples, stratified samples, clustered samples, or non-probability methods.

Topics 

  1. Sampling methodology with a focus on practical ways to design a sample recruitment strategy
  2. Critique a sampling method that has been used on a pre-existing survey

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Discuss the basic concepts of sampling for social and population surveys
  2. Critique sampling methodologies for existing surveys
  3. Design a simple sampling strategy that balances costs and error
  4. Know the basic concepts for more advanced sampling strategies and where to obtain further information

Indicative assessment 

Assignment 1 – Introductions and identification of research questions (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Research design (1,500 words, 80% of final mark) LO: 3, 4

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as a stand-alone course.

Details 

Course Code: DATA28

Workload: 21 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Professor Nicholas Biddle

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

The aims of this micro-credential is to equip enrollees with the skills and knowledge to extend their survey data analysis experience beyond that which is taught in Data Analysis and Interpretation.

Topics 

  1. Interaction terms and non-linear models for continuous variables
  2. Analysing non-linear dependent variables
  3. Analysing multi-level datasets
  4. Cluster and factor analysis

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Explain the key concepts of survey data analysis
  2. Outline the strengths and weaknesses of existing datasets from an analysis perspective
  3. Identify the appropriate analytical technique for complex data analysis
  4. Discuss some of the main assumptions underlying different techniques
  5. Design or critique an analysis plan

Indicative assessment 

Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6

Assumed knowledge 

Completion of ANU Micro-credential Data Analysis and Interpretation (or equivalent).

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as part of a stack by completing Data Analysis and Interpretation.

Details 

Course Code: DATA24

Workload: 21 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Professor Nicholas Biddle

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

The total survey error (TSE) paradigm is a useful rubric for both designing surveys and understanding strengths and weaknesses of existing surveys. The aim of this micro-credential is to provide a thorough grounding in survey error for the evaluation of existing surveys and planning of survey data collection. Enrollees will develop their skills by reviewing survey errors for an existing survey.

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Understand various sources of survey error
  2. Review and critique the methodology of existing surveys, grounded on the TSE paradigm
  3. Take survey error into consideration when undertaking or contracting for data collection

Indicative assessment 

Assignment 1 – Identification of existing survey for review (500 words, 20% of final mark)

Assignment 2 – Review and critique of existing survey (1,500 words, 80% of final mark)

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as a stand-alone course.

Details 

Course Code: DATA27

Workload: 21 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Associate Professor Benjamin Phillips

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

The aim of this course is to equip students with the skills and knowledge to extend their data analysis experience beyond that which is taught in Data Analysis and Interpretation.

Topics 

  1. Constructing time series datasets
  2. Analysing trends and seasonality
  3. Forecasting and predicting future outcomes

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Explain the key concepts of time series data analysis
  2. Outline the strengths and weaknesses of existing time series datasets from an analysis perspective
  3. Identify the appropriate analytical technique for time series data analysis
  4. Discuss some of the main assumptions underlying different techniques
  5. Design or critique an analysis plan

Indicative assessment 

Assignment 1 – Introductions and identification of data analysis questions (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Analysis plan (1,500 words, 80% of final mark) LO: 3, 4, 5, 6

Assumed knowledge 

Completion of ANU Micro-credential Data Analysis and Interpretation (or equivalent).

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential may be undertaken as part of a stack by completing Data Analysis and Interpretation.

Details 

Course Code: DATA22

Workload: 21 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 14 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Professor Nicholas Biddle

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

No dates are currently scheduled.

Description 

Longitudinal studies are powerful tools to establish causality, track and predict outcomes of people over time however they can be complex and difficult use to inform policy. The aim of this micro-credential is to equip enrollees with the applied skills and knowledge to access and use information from longitudinal studies to inform public policy as a researcher, or as a policy maker.

Topics 

  1. Types of longitudinal studies and how they can inform public policy
  2. Contemporary longitudinal studies in Australia
  3. Designing a research project using longitudinal data to answer a policy question
  4. Principles of longitudinal data analyses of surveys and administrative data

Learning outcomes 

Upon successful completion, enrollee's will have the knowledge and skills to:

  1. Specify a research question related to the policy process that is answerable using longitudinal studies
  2. Communicate and critique existing research in a rigorous manner
  3. Understand the assumptions, strengths and limitations of the main empirical techniques for longitudinal analysis
  4. Understand the different forms of longitudinal studies and their strengths/limitations
  5. Design or critique a secondary data analysis project

Indicative assessment 

Assignment 1 – Introductions and identification of research question that uses longitudinal data (500 words, 20% of final mark) LO: 1, 2

Assignment 2 – Research design (500 words, 80% of final mark) LO: 3, 4, 5

Assumed knowledge 

This micro-credential is taught at graduate level and assumes the generic skills of a Bachelors or equivalent.

Micro-credential stack information 

This micro-credential is undertaken as a stand-alone course.

Details 

Course Code: DATA21

Workload: 22 hours 

  • Contact hours: 7 hours
  • Individual study and assessment: 15 hours

ANU unit value: 1 unit

AQF Level: 8

Contact: Associate Professor Ben Edwards

 

This Micro-credential is taught at a graduate level.  This is not an AQF qualification.

Escape from Excel: Data Wrangling and Visualisation in the Health and Environmental Sciences using R

This micro-credential includes one full-day session on campus at ANU. Description  Producing attractive, informative data visualisations is critical to the effective communication of quantitative ...

View Details / Enrol

Graphical Data Analysis A

Details Session 1: Self-study Period (weeks 1-3)         Students will work through the textbook and practice problems, as well as watch videos. Session 2: In-person period (week 4)         Monday...

View Details / Enrol

Graphical Data Analysis B

Details Session 1: Self-study Period (weeks 1-3)         Students will work through the textbook and practice problems, as well as watch videos. Session 2: In-person period (week 4)         Monday...

View Details / Enrol