1 Semester

36 hours

Hawthorn

Admission to the  Bachelor of Arts (Honours)- Psychology stream, the Postgraduate Diploma of Psychology or Bachelor of Science (Psychophysiology)(Honours), or equivalent as assessed by the program convenor.

Nil

Credit Points: 12.5 Credit Points

Related Course/s:

A unit of study in the Bachelor of Arts (Honours)- Psychology stream, the Postgraduate Diploma of Psychology and the Bachelor of Science (Psychophysiology)(Honours).

Aims & Objectives:

This unit of study provides students with a conceptual framework for understanding univariate and multivariate analyses and interpretations of psychological data. It also provides an opportunity for students to develop practical skills in a range of data analysis techniques. These may include analysis of variance and covariance, multivariate analysis of variance, discriminant function analysis, multiple regression analysis, factor analysis and structural equation modelling. At the end of this unit student will:

• Be competent with computers and SPSS
• Be able to conduct basic and advanced statistical techniques
• Be able to design an empirical research hypothesis and test it
• Understand the ethical issues in research with human participants
• Demonstrate advanced research reporting skills

Teaching Methods:

1 hour x 12 week lecture, 2 hour x 12 week computer laboratory classes

The subject utilises lectures, computer laboratory classes, individual study, enquiry based learning, blended learning materials, and online resources. Students are provided with feedback from their Tutors for six pieces of assessment that occur continuously throughout the semester. Tutors are also available for general feedback and after the completion of the main assessment tasks.

Assessment:

Examination 50%, Data analysis plan 30%, Four individual assignments each worth 5% (total of 20%).

Generic Skills Outcomes:

Graduates are adaptable and manage change

Content:

This unit of study covers basic and advanced multivariate statistical procedures such as ANOVA (and its variants), multiple regression, discriminant analysis, exploratory factor analysis and structural equation modelling. Students are expected to achieve a high level of competence in multivariate data analyses, which will allow them to analyse their own data independently for their 4th-year thesis projects. It also covers the preparation of data for multivariate techniques, the use of SPSS and AMOS software, and the reporting of results in line with APA style.

References: