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Unit Summary
Unit type
UG Coursework Unit
Credit points
12
AQF level
Level of learning
Advanced
Former School/College
Pre-requisites
Unit aim
Equips students with a range of advanced methodological and data-analysis techniques likely to be utilised in psychological research at honours level. Introduces students to the conceptual basis and practical use of the General Linear Model. Students will learn to use the SPSS computer program for the analysis of complex data using ANOVA, multiple regression, factor analysis and other routines. The treatment of potential threats to interpretation (outlying data points, failures to meet relevant assumptions) will be stressed.
Unit content
Topic 1 :Overview of basic statistical concepts, especially variance and parametric statistics
Topic 2: Introduction to the Analysis of Variance:
- How variance may be partitioned into between subjects and within subjects variance and
- The basic model
Topic 3: Assumptions of the analysis of variance
Topic 4: Performing multiple comparisons to follow up an ANOVA
Topic 5: Effect size and statistical power
Topic 6: Introduction to Factorial ANOVA
Topic 7: Following up a significant factorial ANOVA
Topic 8: Repeated measures ANOVA
Topic 9: Factorial designs involving repeated measures
Topic 10: Linear regression and introduction to multivariate statistics
Topic 11: Multiple regression
Topic 12: Factor analysis
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 | demonstrate an understanding of variance and how it is partitioned during analysis of variance | |||||||
| 2 | demonstrate an understanding of research methodology and appropriate methods of data analysis | |||||||
| 3 | demonstrate an awareness of problems with data interpretation, and know how to detect outliers and lack of conformity with statistical assumptions | |||||||
| 4 | demonstrate an understanding of the difference between statistical significance and practical significance (treatment effect) | |||||||
| 5 | use SPSS to perform statistical analyses | |||||||
| 6 | report the outcome of statistical analyses in the format required by the American Psychological Association. | |||||||
On completion of this unit, students should be able to:
-
demonstrate an understanding of variance and how it is partitioned during analysis of variance
- GA4:
-
demonstrate an understanding of research methodology and appropriate methods of data analysis
- GA1:
- GA4:
-
demonstrate an awareness of problems with data interpretation, and know how to detect outliers and lack of conformity with statistical assumptions
- GA1:
- GA4:
-
demonstrate an understanding of the difference between statistical significance and practical significance (treatment effect)
- GA4:
-
use SPSS to perform statistical analyses
- GA4:
-
report the outcome of statistical analyses in the format required by the American Psychological Association.
- GA4:
- GA6:
Prescribed texts
- Field, A, 2013, Discovering Statistics using IBM SPSS Statistics, 4th edn, Sage Publications, London. ISBN: 978-4462-4918-5.