ANU Micro-credentials

Mixed Methods Research Design

Tuesday 29th August, 9am-5pm online (via zoom)

Taught by Professor Naomi Priest & Ryan Perry

Delivery Format

• Micro-credential pre-course reading opens on 16 August
• Intensive Delivery 9 am - 5 pm, 29 August 2023

Description 

Many research questions can be answered by using either quantitative research methods or qualitative research methods. However, many other research questions are best approached by using a mixed methods design. This micro-credential will summarise the key aspects of quantitative and qualitative mixed methods, and use a number of example research projects to discuss how they can be combined to answer key social research questions.

Topics 

1. Parallel mixed methods - Qualitative or quantitative research done separately with results compared at the end
2. Sequential or iterative mixed methods – Interaction between qualitative and quantitative research/researchers throughout (e.g. Use qualitative research to obtain understanding of issues; construct a survey instrument that integrates understanding; derive hypotheses and test using survey data)
3. Quasi-mixed methods – Culturally informed quantitative research/Empirically informed qualitative research

Learning outcomes 

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

1. Evaluate the main strengths and weaknesses of quantitative research methods
2. Evaluate the main strengths and weaknesses of qualitative research methods
3. Critique existing empirical research based on the choice of methodology
4. Differentiate between research questions which rely on cross-sectional survey data, longitudinal survey data, and qualitative data
5. Design a mixed-methods research project on an important social research question

Indicative assessment 

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

Assignment 2 – 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 may be undertaken as a stand-alone course.

Details 

Course Code: DATA13

Workload: 22 hours 

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

ANU unit value: 1 unit

AQF Level: 8

Contact: ryan.perry@anu.edu.au

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

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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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