Overview

This unit provides theoretical and practical data analysis skills for psychology research and practice. The unit prepares students to use analytic techniques commonly required in fourth-year thesis and future employment. Content includes basic and advanced skills in data preparation, statistical analyses, interpretation of data, and reporting.

Requisites

Prerequisites
PSY80112 Advanced Quantitative Methods

Rule

Enrolment in GD-PSYADS Graduate Diploma of Psychology (Advanced)

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Teaching Period 2
Location
Online
Start and end dates
06-July-2026
04-October-2026
Last self-enrolment date
19-July-2026
Census date
04-August-2026
Last withdraw without fail date
25-August-2026
Results released date
27-October-2026

Unit learning outcomes

Students who successfully complete this unit will be able to:
 

  1. Select appropriate statistical analyses for a given research design and data set
  2. Appraise the integrity of data sets and suggest appropriate remedial steps for treating corrupt or missing data
  3. Conduct basic and advanced statistical analysis using a statistical software package
  4. Interpret and report the results of statistical tests to the standards of the American Psychological Association.

Teaching methods

Swinburne Online

Type Hours per week Number of weeks Total (number of hours)
Live Online
Class		1.00 12 weeks 12
Online
Directed Online Learning and Independent Learning		11.50 12 weeks 138
TOTAL150

Assessment

Type Task Weighting ULO's
AnalysisIndividual 20% 1 
ExaminationIndividual 50% 1,2,4 
Final WorkbookIndividual 30% 2,3,4 

Content

  • Data preparation: Checking data integrity and test assumptions
  • Multiple regression and advanced multiple regression techniques
  • Analysis of Variance (ANOVA): Between-groups ANOVA, repeated measures ANOVA, and mixed designs
  • Exploratory and confirmatory factor analysis
  • Structural Equation modelling (SEM)

Additional topics may be rotated to align with contemporary themes or content elsewhere in the course. Typical analyses include

  • Power analysis
  • Logistic regression
  • Analysis of Covariance (ANCOVA)
  • Discriminant function analysis
  • Cluster analysis

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.