Design and Measurement 2
Unit code: HMA278
| Credit points | 12.5 Credit Points |
| Duration | 1 Semester |
| Contact hours | 4 Hours per Week |
| Campus | Hawthorn |
| Prerequisites | HMA103 Statistics and Research Methods A |
| Corequisites | Nil |
Related course(s)
Aims and objectives
The aims are:
- To introduce you to the statistical package SPSS for Windows (SPSS stands for Statistical Package for the Social Sciences).
- To develop your capacity to carry out independent statistical investigations, together with an awareness of the assumptions and limitations involved with the generalisation of results of such investigations.
On completion of the unit students will be able to:
- Make a clear statement of the objectives of a study.
- Prepare the data for analysis by SPSS for Windows.
- Analyse the data using SPSS for Windows.
- Interpret the results and write a concise report.
Generic skills outcomes
This unit of study will contribute to helping students achieve some of the attributes expected of Swinburne graduates as set out in the Higher Education Learning and Teaching Strategic Development Plan. The material chosen for this unit of study reflects the quantitative and statistical knowledge and skills expected in your chosen profession, and will be linked as far as possible, through choices of examples and problems, with current professional practice.
The graduate attributes which relate to this unit of study help to produce students who:
- Are capable in their chosen professional areas: Students will attain statistical knowledge and skills that will support their professional careers. This will include abilities in critical enquiry and report writing, an awareness of the relationship between statistical theory and practice and an appreciation of the strengths and limitations of statistical models.
- Are adaptable and manage change: Statistical and research skills are an integral part of studies in the social sciences and other disciplines. Such skills enable students to investigate problems and issues of their own choosing as well as those covered in this unit of study.
- Are aware of environments: Using appropriate research designs and technology will be an important part of this unit of study and will assist students to develop a socially responsible awareness of the role of research in society. The development of statistical and research skills will contribute to students being able to critically evaluate the implications of research on economic, social or environmental planning and policy decisions.
Content
The content is divided into two modules and each module is further divided into topics.
Module 1: Using SPSS for Windows for Basic Data Analysis
- Review of basic statistics: providing a framework for the unit of study.
- Introduction to SPSS for Windows: exploring existing data sets, summarising the distribution of a categorical variable.
- Describing the distribution of a metric variable.
- Describing the relationship between two metric variables.
- Testing significance using Pearson's r.
- Comparing the relationship between two metric variables for two or more sub-groups.
- Describing the relationship between two categorical variables.
- Testing significance using the chi-square statistic.
- Comparing the relationship between two categorical variables for two or more sub-groups.
- Describing the relationship between a categorical variable and a metric variable.
- Testing significance using t-tests.
- Comparing the relationship between a categorical variable and a metric variable for two or more sub-groups.
- Entering your own data into SPSS.
Module 2: Analysis of Variance
- Review of variance and t-tests.
- Introduction to the analysis of variance: the single factor, independent groups design.
- Using SPSS to produce an analysis of variance.
- Effect size and power analysis for ANOVA.
- Reporting an analysis of variance. Analytical comparisons in the single factor independent groups design.
- Analysis of variance for the single factor within subjects design.
- Analysis of variance for the completely randomised factorial design.
- Analysis of variance for the two factor mixed design.
