Availabilities:
Unit Summary
Unit type
Hons Unit
Credit points
12
AQF level
Level of learning
Advanced
Former School/College
Unit aim
Provides a critical evaluation of data analysis techniques and current approaches to best practice evaluation techniques in the applied setting. Describes contemporary alternatives to null hypothesis testing in the context of their value for real-world professional conduct within a scientist-practitioner framework. Supports best practices in data management, programme evaluation and scientific and applied dissemination of outcomes and utilises a combination of problem based learning applied to a project and student reflective practice.
Unit content
- Designing an effective data analysis strategy: Issues and resolutions
- Introduction to the “New” statistics: effect size estimation
- Alternatives to NHT: Bayes and p-rep
- Research in the real world: single case designs
- Research in the real world: time series
- Real world applications: programme evaluation
- Real world applications: Big data
- Good data practices
Learning outcomes
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
| On completion of this unit, students should be able to: | GA1 | GA2 | GA3 | GA4 | GA5 | GA6 | GA7 | |
|---|---|---|---|---|---|---|---|---|
| 1 | explain and defend their strategy for analysis of data from their thesis research or another appropriate project | |||||||
| 2 | critically evaluate the use of NHT methods for data analysis and employ alternatives where more appropriate | |||||||
| 3 | select and apply appropriate research strategies and techniques for analysis of real-world (small sample) data | |||||||
| 4 | be cognisant of the impact of selected data-analytic techniques for solving real-world problems | |||||||
| 5 | identify and apply analytic techniques relevant to program evaluation and their importance in practical settings and application | |||||||
| 6 | utilise appropriate methods to manage data and to ensure the integrity of data. | |||||||
On completion of this unit, students should be able to:
-
explain and defend their strategy for analysis of data from their thesis research or another appropriate project
- GA4:
-
critically evaluate the use of NHT methods for data analysis and employ alternatives where more appropriate
- GA4:
-
select and apply appropriate research strategies and techniques for analysis of real-world (small sample) data
- GA2:
-
be cognisant of the impact of selected data-analytic techniques for solving real-world problems
- GA2:
-
identify and apply analytic techniques relevant to program evaluation and their importance in practical settings and application
- GA4:
-
utilise appropriate methods to manage data and to ensure the integrity of data.
- GA3:
Prescribed texts
- No prescribed texts.