ANU Micro-credentials

Data Analysis and Interpretation

Friday 12 May, 9am - 5pm

Become equipped 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, you 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

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Administrative and Big Data for Policy Analysis

Friday 19 May, 9am - 5pm

The aims of this micro-credential is to equip you with the skills and confidence to operate in the world of ‘big data.’ The micro-credential will take a social science perspective and to discuss the role of social science and theory in analysing and interpreting ‘big data.’ The micro-credential will not be technical, but rather use key examples of ‘big data’ being used to inform policy to help motivate and engage with the issues. Enrollees will become familiar with some of the technological options and constraints in the storage and analysis of ‘big data’.

Topics

1. Introduction to data linkage
2. Analysis of linked and transactional data
3. Combining linked and survey data

Learning outcomes

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

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

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Research and Data Design and Collection

Wednesday 21 June, 9am - 5pm

Be equipped with the skills and knowledge to engage with empirical research either directly as a researcher, or as a policy maker critically engaging with the most recent research.

Topics

1. Designing a quantitative research project
2. Incorporating qualitative insights
3. Principles of sampling
4. Principles of survey design
5. Using administrative and linked datasets

Learning outcomes

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

1. Specify a research question related to the policy process that is answerable using empirical methods
2. Communicate and critique existing research in a rigorous manner
3. Understand the assumptions, strengths and limitations of the main empirical techniques for policy design
4. Understand the different forms of sampling design and their strengths/limitations
5. Design or critique a survey or data collection methodology

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